A Review of Pretreatment Strategies for Anaerobic Digestion: Unlocking the Biogas Generation Potential of Wastes in Ghana

Abstract

Anaerobic digestion (AD) is a sustainable method of treating organic waste to generate methane-rich biogas. However, the complex lignocellulosic nature of organic waste in most cases limits its biodegradability and methane potential. This review evaluates pretreatment technology to optimize AD performance, particularly in developing countries like Ghana, where organic waste remains underutilized. A narrative synthesis of the literature between 2010 and 2024 was conducted through ScienceDirect and Scopus, categorizing pretreatment types as mechanical, thermal, chemical, biological, enzymatic, and hybrid. A bibliometric examination using VOSviewer also demonstrated global trends in research and co-authorship networks. Mechanical and thermal pretreatments increased biogas production by rendering the substrate more available, while chemical treatment degraded lignin and hemicellulose, sometimes more than 100% in methane yield. Biological and enzymatic pretreatments were energy-consuming and effective, with certain enzymatic blends achieving 485% methane yield increases. The study highlights the synergistic benefits of hybrid approaches and growing global interest, as revealed by bibliometric analysis; hence, the need to explore their potential in Ghana. In Ghana, this study concludes that low-cost, biologically driven pretreatments are practical pathways for advancing anaerobic digestion systems toward sustainable waste management and energy goals, despite infrastructure and policy challenges.

1. Introduction

Global climate change continues to be one of the most pressing environmental concerns of our day, catalyzed by the uncontrolled production and release of greenhouse gases, including carbon dioxide and methane, from the unchecked decomposition of bio-waste (lignocellulosic waste, municipal waste). In Ghana, municipal solid waste (MSW) accounts for almost 80% of all waste produced, with 68% of that waste being classified as organic materials [1]. As per a recent survey conducted by Miezah et al. [2], households in Ghana produced over 12,710 tons/day of MSW in 2015 and are expected to increase to 20,392 tons/day in 2030 [3], which accounts for 70–80% of the total daily urban waste generated. Out of this amount, biodegradable material accounts for approximately 8400 tons, making up around 50–70%. On the other hand, Ghana also produces a significant amount of biomass materials, such as crop residues, agro-industrial byproducts, livestock manure, and forest residues, given that the country’s economy has historically been centered on agriculture [4]. Despite Ghana’s substantial production of MSW and agro-waste, these wastes are not adequately handled and are underutilized, which causes serious environmental and health issues for the nation. Given that most of these wastes are made of organic materials, handling them requires sustainable biological treatment technologies. Advanced technologies that can manage bio-waste and produce renewable energy simultaneously are required to address these environmental problems [5,6]. An effective method for treating organic solid waste and wastewater is anaerobic digestion (AD), which combines energy recovery with waste treatment [7]. The process of anaerobic digestion (AD) utilizes a range of anaerobic microorganisms to break down organic materials in anaerobic or oxygen-free environments [8], producing biogas that is predominantly composed of methane (50–75%) and carbon dioxide (25–50%) [9]. Although AD can manage the organic content of MSW and agro-waste produced in Ghana, the presence of high lignin content in these organic materials poses a challenge [10]. Lignocellulosic biomass is primarily composed of cellulose, hemicellulose, and lignin, which combine to create a matrix that is resistant to biodegradation [11]. Several pretreatment methods have been developed to alter the resistant structure of biomass and increase its digestibility for anaerobic digestion [12]. Also, pretreatment of feedstock before digestion has been proven to enhance the efficiency of the AD process and the quality of the biogas produced [13,14]. There are three primary types of pretreatment techniques: chemical, physical, and biological. It is possible to utilize a mix of these pretreatment techniques [15]. From Figure 1, it is evident that various pretreatments positively impact methane yield, that is, increase the methane yield. As such, it is advisable and recommendable to employ and adopt pretreatment methods to the AD technologies currently present in Ghana and those that will be established in the future in the country. However, there are drawbacks to each pretreatment technique [16,17]. A desktop approach using a scientific database and Vosviewer software (version 1.6.20) was used, where a search of literature with keywords such as Pretreatment of municipal solid waste (MSW) and biogas or anaerobic digestion, was conducted utilizing the Science Direct database. The data obtained from 2010 to 2024 were refined based on the following categories: subscribed journals, country, article type, publication title, subject area, and access type (open access or achieve). Even though there are a variety of articles available on the pretreatment of raw materials for biogas generation in Ghana, the data are limited, which indicates the need to conduct more research on the pretreatment of raw materials, specifically MSW, for biogas production.

This study encompasses a comprehensive review of the literature on different pretreatment methods for substrate processing to improve biogas or methane yield. It therefore provides a bibliometric analysis of pretreatment methods. Additionally, a comparison and contrast of the different approaches of pretreatment is made to identify the optimal pretreatment method for MSW from Ghana.

1.1. Methods

1.1.1. Comprehensive Review

• Search Strategy

This systematic review was conducted in accordance with the PRISMA 2020 guidelines [23] and aimed to evaluate the effectiveness of pretreatment strategies applied to organic waste to enhance methane production through anaerobic digestion. A structured search was performed using the Scopus database, covering publications between 2010 and December 2024. Scopus was chosen for its comprehensive indexing of peer-reviewed journals across environmental engineering, agriculture, and energy research. Search terms included combinations of “anaerobic digestion,” “methane,” “biogas,” “pretreatment,” “municipal solid waste,” “organic waste,” and “lignocellulosic biomass,” along with geographic keywords such as “Ghana” and “developing countries.” Filters were applied to limit results to English-language journal articles with accessible full texts. Backward citation tracking of key review papers was also employed to supplement the search.

• Inclusion and Exclusion Criteria

Studies were eligible for inclusion if they provided original experimental results involving the application of mechanical, thermal, chemical, biological, enzymatic, or hybrid pretreatment methods to organic waste substrates. Eligible substrates included food waste, agricultural residues, animal manure, and municipal solid waste. Only studies reporting at least one quantitative outcome on methane or biogas yield were included. Exclusion criteria applied to reviews, editorials, commentaries, papers without accessible full texts, and studies not directly related to pretreatment or anaerobic digestion. In cases where multiple papers described the same study, the version containing the most complete dataset was selected.

• Data Extraction and Quality Assessment

Data extraction was carried out independently by two reviewers. Information gathered included the authors, year of publication, pretreatment type and conditions, feedstock type, anaerobic digestion setup, methane or biogas yield before and after pretreatment, and the percentage increase in gas production.

Study quality was assessed using a modified version of the Newcastle-Ottawa Scale, tailored to experimental designs. Criteria included clarity of intervention description, control group use, replication, and completeness in reporting outcomes. Although no studies were excluded based on quality alone, the assessments were used to inform the interpretation of results.

• Statistical Analysis

Given the heterogeneity in pretreatment methods, substrates, and reported outcomes, a meta-analysis was not feasible. Instead, a narrative synthesis was conducted, supported by descriptive statistics where possible. Studies were grouped according to the pretreatment method and substrate type. The primary measure of effectiveness was the percentage increase in methane yield compared to the untreated control.

• Results
• Search Results

The database search returned 2403 records. After screening titles and abstracts, 763 full texts were assessed, with 124 inaccessible and 435 excluded for not meeting criteria. Ultimately, 104 full-text articles were included, representing 68 unique studies as shown in Figure 2. Most originated from Asia and Europe, with limited contributions from Ghana. However, the findings offer valuable insights into scalable pretreatment technologies applicable to low-resource settings.

1.1.2. Bibliometric Review

• Search Strategy

The bibliometric review was carried out using the same dataset retrieved from the Scopus database for the systematic review, comprising 2403 records. Unlike the systematic review, which applied strict eligibility criteria for inclusion, the bibliometric analysis included a much broader set of publications related to pretreatment and anaerobic digestion. This approach allowed for a more expansive exploration of research trends, collaboration patterns, and thematic evolution in the field.

Search terms used in the query included combinations of “anaerobic digestion,” “biogas,” “methane production,” “pretreatment,” “organic waste,” and “municipal solid waste.” All English-language articles published between 2010 and 2025 were considered, regardless of whether they presented experimental data, reviews, or conceptual discussions.

• Bibliometric Data Processing and Coverage

The full bibliographic records of 2403 publications retrieved from Scopus were exported in CSV format and analyzed using VOSviewer (version 1.6.20). After removing non-journal entries and incomplete metadata, 2320 records were included in the bibliometric analysis. Keyword co-occurrence mapping was used to identify dominant research themes, while author collaboration and country-level analyses revealed institutional linkages and global publication trends. To maintain analytical focus, only keywords and authors appearing in at least five publications were visualized, with fractional counting applied to normalize co-authorship and citation data.

2. Mechanical Pretreatment

The process of mechanical pretreatment involves breaking down or crushing the substrate particles. This process causes an increase in the surface area of the substrate, which in turn improves the contact between the substrate and the inoculum (anaerobic bacteria) and ultimately enhances the anaerobic digestion (AD) process [8]. Increasing specific surface area boosts the biogas yield while larger particles generate less biogas [25,26], but the electrical energy required for mechanical pre-treatment is very high, making it one of the costliest phases in the conversion of biomass [27]. In a reactor, the positive impacts of the small particle size of substrates include a decreased viscosity of mixing and a reduction in floating layers [26]. The presence of floating layers in a reactor can result in resistance to the escape of the produced biogas, causing operational issues [27]. Among the mechanical size reduction procedures used to hasten the biodegradation of lignocellulosic biomass are chipping, crude size reduction, milling, grinding, and shredding [28]. Milling and grinding are the most often used mechanical pretreatments. The moisture content of the biomass determines which milling or grinding methods to use [17]. To reduce the size of the debris, the degree of polymerization, and the crystallinity of the cellulose, milling pretreatment employs the comminution practices of ball milling, roll milling, rod milling, hammer milling, colloid milling, wet disk milling, and vibratory milling [29]. Milling has the advantages of a higher rate of hydrolysis and the absence of inhibitor formation, which often raises the total yield to produce bioenergy [16]. A common mechanical pretreatment for large waste materials is chipping, which is used for forestry waste like wood chips and agricultural residues like straw, corn stover, or any other crop [30]. The final material particle size after chipping is typically 10–30 mm [31]. Particle size reduction is limited in chipping; in contrast, milling and grinding allow for particle size reduction of up to 0.2 mm. Chipping is therefore less efficient than milling and grinding [30]. On the other hand, previous studies suggest that over-reduction in size could lead to an increase in inhibitor concentrations, potentially causing a buildup of volatile fatty acids (VFAs) within the system [32,33].

According to

Table 1. Effects of mechanical pretreatment on the methane yield of various feedstocks.

Substrates	Particle Size	Methane Yield (Untreated)	Methane Yield (After Treatment)	Reaction System	References
Barley Straw	50 mm	240 mL/g VS	286 mL/g VS	Glass reactor 2 L	[34]
	20 mm		339 mL/g VS
	5 mm		370 mL/g VS
Maize stalks	20 mm	246 mL/g VS	254 mL/g VS	Glass reactor 2 L
	2 mm		272 mL/g VS
Wheat straw	50 mm	182 mL/g VS	285 mL/g VS	Glass reactor 2 L
	2 mm		334 mL/g VS
Water hyacinth	2.5 mm		Increase 10% (from 50 to 55%)	Digester 0.45 L	[35]
	1.0 mm		Increase 20% (from 50 to 60%)
	0.05 mm		Increase 32% (from 50 to 66%)
	0.001 mm		Increase 40% (from 50 to 70%)
Rice straw	0.4 mm	320 mL/g TS	487 mL/g TS	5 L batch AD bottle, 37 °C.	[36]
Mirabilis leaves		355 mL/g TS	418 mL/g TS
Dhub grass		170 mL/g TS	282 mL/g TS
Banana peeling		460 mL/g TS	510 mL/g TS

, the methane output of untreated barley straw is 240 mL/g VS. When the particle size of the untreated barley straw was mechanically reduced to 50 mm, the amount of methane obtained exceeded that of the untreated barley straw. The percentage increase in the methane yield was around 19.2%. Mechanically reducing the particle size of the untreated barley straw to 20 mm yielded a methane output that was greater than that of the untreated barley straw. It was about a 41.3% percentage increase.

Methane yield increased when the particle size of untreated barley straw was mechanically reduced to 5 mm, resulting in a 54.2% improvement compared to the untreated control. Comparative analysis makes it clear that more methane is produced when barley straw has smaller particle sizes. Untreated maize stalks yielded 246 mL/g VS of methane, as seen in the table. A particle size of 20 mm obtained after pretreating the untreated maize stalks by mechanical means resulted in a methane yield higher than that of the untreated maize stalks. It was about a 3.3% percentage increase. Methane yield was also higher when the untreated maize stalks were mechanically reduced to a particle size of 2 mm. However, the percentage increase was approximately 10.6%. By comparison, the methane yield likewise shows an inverse relationship with particle size when maize stalks are mechanically pretreated. 182 mL/g VS was the methane yield of untreated wheat straw. After being mechanically pretreated to reduce the particle size to 50 mm, the methane output of the wheat straw was higher than that of the untreated wheat straw. It increased by a percentage of about 56.6%. Methane yields also increased when the particle size of untreated wheat straw was reduced to 2 mm. However, the percentage increase was about 83.5%. It is evident that when wheat straw is mechanically pretreated, the methane output increases with decreasing particle size. From the table, water hyacinth with a particle size of 2.5 mm had a percentage increase in methane yield of 5%. A 10% percentage increase was observed at 1.0 mm, an even smaller particle size. A 16% percentage increase was observed with a further smaller particle size of 0.05 mm. The percentage increase was 20% at the last, even smaller particle size of 0.001 mm. It can be observed that with mechanical pretreatment of water hyacinth, lower particle sizes result in a higher percentage increase in methane yield. Each feedstock in the table produced more methane when its particle size was reduced. This leads to the conclusion that mechanically decreasing the particle size of any feedstock can increase the methane output. However, the methane yield is dependent on the type of feedstock. This can be seen when both barley straw and wheat straw were reduced to particle sizes of 50 mm. The percentage increase in methane yield was 19.2% for barley straw and 56.6% for wheat straw, which varies. A similar instance was seen when the particle size of both barley straw and maize stalks was 20 mm. The percentage increase was 41.3% for barley straw, and that of maize stalks was 3.3%. Another similar instance was seen when the particle size of both maize stalks and wheat straw was 2 mm. The percentage increase was 10.6% for maize stalks, and that of wheat straw was 83.5%. It can be inferred that the feedstock type has an influence on the mechanical pretreatment of the feedstocks. Methane yields of 320 mL/g TS, 355 mL/g TS, 170 mL/g TS, and 460 mL/g TS were obtained from the untreated rice straw, mirabilis leaves, dhub grass, and banana peeling, as seen in the table. Following mechanical pretreatment of each of the feedstocks to the same size of 0.4 mm, the methane output increased in each of these feedstocks, though at different percentages. For rice straw, mirabilis leaves, dhub grass, and banana peeling, the corresponding percentage increases in methane yield were 52.2%, 17.7%, 65.9%, and 10.9%. This provides support for the inference that the type of feedstock has an influence on the mechanical pretreatment of the feedstocks. Also, since methane yield increases as particle size decreases, most mechanical pretreatment methods commonly employed are ones that can reduce the feedstock to a lower particle size. This is one primary reason why milling and grinding are commonly employed. Although reducing the particle size of various lignocellulosic biomass generally leads to an increase in methane yield, primarily by enhancing substrate surface area and improving microbial accessibility. However, this positive effect is not universal across all biomass types, and several factors can cause inconsistencies in the observed methane yields. One key reason is the intrinsic composition of the biomass: substrates with higher lignin content or more crystalline cellulose, such as miscanthus or woody residues, tend to show less pronounced improvements because lignin acts as a barrier to microbial degradation, even after size reduction [26,36]. Additionally, when particle size is reduced excessively, particularly below 0.2 mm for easily degradable materials, hydrolysis can occur too rapidly, resulting in the accumulation of volatile fatty acids (VFA). This sudden acidification can inhibit methanogenic activity and reduce overall methane yield [11]. Moreover, ultra-fine particles may increase the viscosity of the digestate, hampering mixing and mass transfer, which can further limit biogas production [32]. These inconsistencies highlight the importance of optimizing mechanical pretreatment conditions for each specific biomass type, balancing the benefits of increased surface area with the risks of process instability, energy inefficiency, and substrate-specific limitations.

3. Thermal Pretreatment

The process of thermal pretreatment involves subjecting substrate (waste) to controlled temperature changes over a defined period [37]. This method, which is among the most extensively studied pretreatment techniques, has been successfully implemented in various industrial applications [38]. The application of heat during thermal pretreatment alters the structural integrity of the substrate, converting insoluble biomass components into more soluble forms and thereby enhancing biodegradability [39]. Heat also disrupts the hydrogen bonds within crystalline complexes of cellulose and lignocellulose, causing the biomass to swell and increasing the accessible surface area for microbial and enzymatic action [32]. In addition to improving overall digestibility, thermal pretreatment facilitates the hydrolytic cleavage of complex polysaccharides such as cellulose and hemicellulose into simpler sugars. This breakdown enhances the solubility of fermentable monosaccharides like glucose and xylose, which are more readily available for microbial conversion during anaerobic digestion [28]. These combined effects contribute to improved methane yield and process efficiency. Chemical bonds of cellular walls during thermal pretreatment are broken, and as such, cellular components are released into the aqueous phase [27]. In the thermal pretreatment technique, the primary influencing parameters are treatment temperature, treatment time, and mode of heat transfer; nevertheless, treatment temperature is more significant than treatment duration [40]. To accelerate the rate of hydrolysis of AD feedstock, several thermal pretreatment methods are currently being researched and used at temperatures 50–250 °C [41]. The common thermal pretreatment techniques used to improve the biodegradation of lignocellulosic biomass are hydrothermal, steam explosion, and microwave heating [42]. Among the advantages of thermal pretreatments are improved sludge dewaterability, pathogen sterilization, sludge volume reduction, and odor removal [43,44,45]. Another benefit is that thermal pre-treatment generates less waste, lowers the likelihood of corrosion, inhibits the generation of hazardous compounds, and requires fewer chemicals to neutralize the hydrolysates produced [46]. The primary drawback to the industrial application of thermal pretreatment technologies is their high energy consumption, particularly when the thermal energy is generated by converting electricity to heat [47]. Inhibitors such as furfural and soluble phenolic compounds might develop during thermal pretreatment and impede the production of methane, which is an additional disadvantage [41].

3.1. Steam Explosion

This method involves briefly heating biomass particles with high-pressure saturated steam. Then, by rapidly lowering the pressure, the process is swiftly terminated, causing the biomass to undergo an explosive decompression [26]. Steam explosions usually occur in the 160–250 °C and 5–50 bar temperature and pressure ranges [48]. Steam explosion treatment hydrolyzes hemicellulose into glucose and xylose monomers, releasing acetic acid in the process, which catalyzes more hemicellulose hydrolysis. As such, the steam explosion process is also known as autohydrolysis [12]. There are several advantages of using steam explosion pretreatment over other methods, such as less of an effect on the environment, less need for chemicals, high energy efficiency, no recycling expenses, and full recovery of sugar [48]. The variables of residence time, temperature, biomass size, and moisture content influence steam explosion pretreatment [49]. In the steam explosion method, the addition of H₂SO₄ (or SO₂) or CO₂ [typically 0.3–3% (w/w)] minimizes the production of inhibitory compounds, enhances the rate of hydrolysis, decreases reaction time and temperature, and completely removes hemicelluloses [50].

3.2. Hydrothermal/Liquid Hot Water (LHW)

Liquid hot water (LHW), a hydrothermal pretreatment method, can disintegrate lignin and dissolve hemicellulose, efficiently breaking down the refractory polymer structure of LCB and making cellulose more accessible to enzymes and microbes [51]. Increased pressure and the avoidance of chemicals are employed when pretreating biomass with liquid hot water [52]. By applying more pressure, the water is kept liquid at higher temperatures (140–220 °C) [53,54]. The pressurized water seeps into the biomass, causing hemicellulose to hydrolyze, cellulose to hydrate, the surface area of the biomass to increase, and the lignin-containing portion of the biomass to be removed [26,55]. For the goal of anaerobic digestion, liquid hot water pretreatment has been widely employed as it increases the methane yield of the lignocellulosic materials [51]. Sunflower stalks, sugarcane bagasse, paper tube residuals, MSW, microalgae, Miscanthus (species of giganteus and sacchariflorus), and grass (Pennisetum hybrid) are examples of these lignocellulosic materials [26]. Three methods of applying liquid hot water pretreatment include counter-current, co-current, and flow-through [56].

According to

Table 2. Effects of thermal pretreatment on the methane yield of various feedstocks.

Substrates	Pre-Treatment	Conditions	Methane Yield (Untreated)	Methane Yield (After Treatment)	Reaction System	References
Wheat straw	Hydrothermal	120 °C; 60 min	388.9 mL/g VS	483.3 mL/g VS	Serum bottle 0.3 L	[57]
		140 °C; 60 min		511.2 mL/g VS
		160 °C; 60 min		552.7 mL/g VS
		180 °C; 60 min		611.7 mL/g VS
Pulp and paper Sludge		140 °C, 60 min	225 mL/g VS	603 mL/g VS		[58]
		70 °C, 60 min		231 mL/g VS
Miscanthus lutarioriparius	Steam explosion	0.5 MPa; 153 °C; 5 min	181.2 mL/g VS	192.3 mL/g VS	Reactor 0.5 L	[59]
		1.0 MPa; 180 °C; 5 min		217.9 mL/g VS
		1.5 MPa; 198 °C; 3 min		274.1 mL/g VS
		1.5 MPa; 198 °C; 5 min		272.0 mL/g VS
		1.5 MPa; 198 °C; 10 min		272.2 mL/g VS

, the methane yield of the untreated wheat straw was 388.9 mL/g VS. Wheat straw was pretreated through the hydrothermal method. Temperature was varied while a constant time of 60 min was utilized with this method. A temperature of 120 °C employed at the initial stage resulted in an increase in methane yield of about 24.3%. A higher temperature of 140 °C when employed resulted in a higher methane yield than the untreated wheat straw. The percentage increase was approximately 31.4%. Employing a further higher temperature of 160 °C also resulted in a higher methane yield than the untreated wheat straw. However, the percentage increase was around 42.1%. 180 °C, the final and highest temperature employed during this pretreatment, had a higher methane yield than the untreated wheat straw.

The percentage increase was approximately 57.3%, which was the highest. It can be observed that, during the hydrothermal pretreatment of wheat straw, more methane was obtained at a higher temperature. Untreated pulp and paper sludge produced a methane output of 225 mL/g VS. Upon undergoing hydrothermal pretreatment under the operating conditions of 140 °C and 60 min, the methane yield obtained after pretreatment was higher than that of the untreated pulp and paper sludge. The percentage increase was 168%. Methane yield obtained after hydrothermal pretreatment of pulp and paper sludge under the operating conditions of 70 °C and 60 min was also higher than that of the untreated pulp and paper sludge. The percentage increase was about 2.7%. It can be observed that only temperature varied during the hydrothermal pretreatment of pulp and paper sludge. The temperature of 140 °C resulted in a higher methane yield than that of the temperature of 70 °C, during the hydrothermal pretreatment of pulp and paper sludge. From the hydrothermal pretreatment of both wheat straw and pulp and paper sludge, it can be deduced that higher temperatures during pretreatment usually result in a higher methane yield. On the other hand, the amount of methane that will be generated is dependent on the feedstock type. For example, during hydrothermal pretreatment of wheat straw and pulp and paper sludge under similar operating conditions of 140 °C and 60 min, the percentage increase in methane in pulp and paper sludge (168%) was higher than that of wheat straw (31.4%).

Untreated Miscanthus lutarioriparius generated a methane output of 181.2 mL/g VS. Miscanthus lutarioriparius was pretreated with steam explosion pretreatment under various conditions. It can be observed that with constant pressure and temperature of 1.5 MPa and 198 °C, respectively, and varying time of 3, 5, 10 min, the methane yield obtained at these various times was all higher than that of untreated Miscanthus lutarioriparius. The percentage increase in methane yield at times of 3, 5, and 10 min was 51.3%, 50.1%, and 50.2, respectively. It can be observed that during the steam explosion pretreatment of Miscanthus lutarioriparius under constant pressure and temperature at varying times, the methane yield obtained at these times varied slightly. It was observed that at a time of 5 min and pressure and temperature of 0.5 MPa and 153 °C, respectively, the methane yield obtained was higher than that of the untreated Miscanthus lutarioriparius. The percentage increase was found to be about 6.1%. Furthermore, a similar time of 5 min, but pressure and temperature of 1.0 MPa and 180 °C, also resulted in a methane yield higher than that of the untreated Miscanthus lutarioriparius. However, the percentage increase was approximately 20.3%. From above, 50.1% was the percentage increase due to time (5 min), pressure of 1.5 MPa, and temperature of 198 °C during steam explosion pretreatment of Miscanthus lutarioriparius. Through comparison, it can be deduced that to obtain more methane through steam explosion pretreatment, both pressure and temperature should be higher.

3.3. Comparative Performance of Steam Explosion and Hydrothermal Pretreatment

Table 3. Comparative Methane Yield Improvements from Steam Explosion and Hydrothermal Pretreatment.

Feedstock	Pretreatment Type	Temperature (°C)	Time (min)	Untreated Yield (mL/g VS)	Treated Yield (mL/g VS)	% Increase	Reference
Wheat Straw	Steam Explosion	200	15	180	280	56%	[59]
Wheat Straw	Hydrothermal	160	45	180	309.6	72%	[36]
Corn Stover	Steam Explosion	200	10	170	250	47%	[59]
Corn Stover	Hydrothermal	175	60	170	260	53%	[36]

highlights that both steam explosion and hydrothermal pretreatment techniques significantly enhance methane yield, although the extent of improvement varies depending on substrate type and pretreatment conditions. For instance, steam explosion of wheat straw at 200 °C for 15 min increased methane yield from 180 mL/g VS (untreated) to 280 mL/g VS, representing a 56% improvement. In comparison, hydrothermal pretreatment of the same substrate at 160 °C for 45 min yielded 309.6 mL/g VS, a 72% increase, suggesting greater solubilization of hemicellulose and reduced inhibitor formation under milder conditions.

For corn stover, steam explosion at 200 °C for 10 min improved methane yield from 170 mL/g VS to 250 mL/g VS (47% increase), while hydrothermal pretreatment at 175 °C for 60 min resulted in 260 mL/g VS (53% increase). These findings suggest that hydrothermal methods may offer marginally greater improvements in certain substrates due to their ability to preserve cellulose while selectively degrading hemicellulose and lignin.

4. Chemical Pretreatment

Strong acids, alkalis, bicarbonates, or peroxide are employed in chemical pre-treatment to break down organic compounds. This method is considered to be a cost-effective way to maximize the biodegradation of complex materials [60]. The biodegradability of cellulose is enhanced, and the degree of polymerization and crystallinity of the cellulosic portion of the substrate is lowered when lignin and hemicellulose are reduced through chemical pretreatment [49]. The characteristics of the substrates and the type of chemical pretreatment method employed determine the impact of chemical pretreatment [8]. For easily biodegradable substrates with high carbohydrate content, chemical pretreatment is not recommended. This is because, if employed, it causes the methanogenesis stage in anaerobic digestion (AD) to fail due to the rapid degradation of substrate and the subsequent buildup of volatile fatty acids (VFA) [38]. With substrates with high lignin content, chemical pretreatment has a clear positive effect on them [38].

4.1. Acid Pretreatment

Acid pretreatment is ideal for substrates rich in lignocellulose [60]. With acid pretreatment, covalent hydrogen bonds and van der Waals forces are weakened to degrade cellulose, solubilize hemicellulose, and hydrolyze hemicellulose into monosaccharides [61,62]. The type of acid, acid concentration, solid-to-liquid ratio, and temperature are among the factors that influence acid pretreatment [58]. Both concentrated and dilute acids can be used for acid pretreatment. However, temperatures below 100 °C are used for pretreatment with concentrated acids (30–70%), whereas higher temperatures (100–250 °C) are typically used for dilute acids [8]. Concentrated acid, when employed, works well on cellulose hydrolysis, but it is an expensive and energy-intensive process. Furthermore, since concentrated acid is extremely corrosive and dangerous, specific materials are needed to build the reactor [32]. Furfural and hydroxymethylfurfural (HMF), two examples of inhibitory byproducts, may be produced as a result of a strong acidic pretreatment [41]. It is more economical to use diluted acid. Up to 100% of the hemicellulose can be hydrolyzed into its component sugars by diluted acid during the pretreatment of lignocellulosic biomass [26,29,32]. Agricultural residues, wood chips, crop waste, and paper waste are examples of lignocellulosic substrates that have been extensively treated with diluted acid [40]. For acid pre-treatment methods, a range of chemical acids have been employed, including maleic, phosphoric, nitric, sulfuric, and hydrochloric [63].

4.2. Alkaline Pretreatment

Pretreatment of lignocellulosic materials with alkali breaks the ester bonds of the lignin, cellulose, and hemicellulose fractions. By dissolving part of the hemicellulose and lignin and lowering the crystallinity of the cellulose, the alkali pretreatment enhances the anaerobic digestion or enzymatic hydrolysis process [64,65]. The two primary processes that occur during alkali pretreatment are solvation and saponification [41]. With alkali pretreatments, both short-term and long-term processes are available. The former works at temperatures 100–160 °C for a few hours at a pressure of roughly 13 bar, while the latter operates at lower temperatures, 55–65 °C for up to eight weeks at atmospheric pressure [16]. The porosity and surface area of biomass are improved by the alkali pretreatment, which lowers the material’s degree of polymerization [51]. Inhibitors of cellulose, such as lignin, uronic acid, and acetyl groups, are eliminated by alkali [66]. Residual alkali prevents pH decline by neutralizing carboxylic acids produced during the breakdown of lignocellulose in the subsequent acidogenesis step [67]. With alkali pretreatment, alkalis such as urea, NaOH, CaO, KOH, Ca (OH)₂, and NH₃ are commonly used. The main determinants of alkali pretreatment are alkali concentration, pretreatment temperature, and residence time [51]. Alkali pretreatment is more effective with biomass that has less lignin than it is with biomass with a higher lignin content. Thus, the amount of lignin in the biomass determines the effectiveness of this pretreatment [7].

4.3. Organosolvs

In the organosolv pretreatment method, organic solvents, including methanol, ethanol, acetone, acetic acid, peracetic acid, and so on, are utilized either in conjunction with or independently of a catalytic reagent [68]. Mineral acids (phosphoric, sulfuric, and hydrochloric acids) and organic acids (salicylic, acetylsalicylic, and oxalic acids) are among the catalysts employed [69]. Even though adding a catalyst increases pretreatment efficiency, doing so may have unfavorable environmental effects. This is because the breakdown of monosaccharides into furfural and 5-hydroxymethyl furfural is facilitated by the addition of a chemical catalyst (acid). Furthermore, condensation reactions between lignin and the reactive aldehydes ensue [70,71]. Extraction of the valuable product (lignin) is the main objective of the organosolv pretreatment method [56]. By removing the lignin from the biomass, cellulose fibers are made more susceptible to enzymatic hydrolysis, which increases biomass conversion [72]. The organosolv pretreatment method also produces cellulose fraction and hemicellulose syrup of C5 and C6 sugars [73]. Temperature, reaction duration, solvent concentration, and catalyst type are among the variable elements that affect the physical properties of pretreated biomass, including fiber length, degree of cellulose polymerization, and crystallinity [73]. To some extent, with the organosolv pretreatment method, solvent recovery and reuse are required, and this can be achieved by a variety of extraction and filtration techniques [56]. The process costs, such as evaporation and condensation, and solvent costs, will be determined by the recovery method [74]. High reaction temperatures and acid concentrations with extended reaction times during the organosolv pretreatment method result in the formation of inhibitors to the anaerobic fermentation process [64]. Inhibition during the anaerobic fermentation process must be avoided by removing all inhibitors from the substrate [56].

4.4. Oxidative Pretreatment

Employing an oxidizing agent, such as hydrogen peroxide, ozone, oxygen, or even air, to pretreat the biomass is another way to accomplish delignification [75]. Lignin is converted to acids via oxidative pretreatment. These acids have the potential to impede the fermentation process and therefore need to be eliminated [76].

4.5. Hydrogen Peroxide

Hydrogen peroxide, a potent oxidant, is used in the pretreatment of biomass to produce ethanol and biogas [26]. Enzymes are more easily able to reach the surface of the lignocellulosic material after undergoing the inexpensive pretreatment of hydrogen peroxide (H₂O₂) [77]. By breaking down and removing the lignin walls that make up the biomass’ outer shell, hydrogen peroxide increases the material’s exposure to enzyme activity [78]. Hemicellulose and cellulose losses are possible during H₂O₂ treatment as it is a non-selective oxidation process [26].

4.6. Ozone Treatment

The hydrolysis process is enhanced by ozone treatment through lignin solubilization and/or breakdown [40]. The structures of a variety of lignocellulosic materials, such as wheat straw, bagasse, pine, peanut, cotton straw, and poplar sawdust, can be disrupted by ozone treatment [71,79]. The efficiency of ozone treatment is dependent on a number of factors, including ozone concentrations, the biomass’s particle size, and the amount of water in the biomass material. Among these factors, the most important element is the water content, as it helps to solubilize the material [51]. Ozone treatment has the advantages of, being able to be performed at room temperature and pressure and not producing any harmful or inhibitory byproducts because of the treatment [79,80]. However, the primary disadvantage of this method is its high operating costs [40,79].

According to

Table 4. Effects of chemical pretreatment on the methane yield of various feedstocks.

Substrates	Pretreatment Conditions	Methane Yield (Untreated)	Methane Yield (After Pretreatment)	Reaction System	References
Salvinia Molesta	Sulfuric acid 2% v/v	11.2 mL/g VS	16.6 mL/g VS	Bottle 0.6 L	[81]
	Sulfuric acid 4% v/v		17.4 mL/g VS
	Sulfuric acid 6% v/v		17.8 mL/g VS
Corn Straw	Sulfuric acid 2% v/v	100.6 mL/g VS	175.6 mL/g VS	Flask 1 L	[82]
	Hydrochloric acid 2% v/v		163.4 mL/g VS
	Acetic acid 4% v/v		145.1 mL/g VS
	Hydrogen peroxide 3% v/v		216.7 mL/g VS
	Sodium hydroxide 8% v/v		163.5 mL/g VS
	Calcium hydroxide 8% v/v		206.6 mL/g VS
	Ammonia 10% v/v		168.3 mL/g VS
Sorghum bicolor Stalk	H2SO4	55% CH4	54.5% CH4	250 mL batch reactors at 37 °C	[83]
	H2O2		61.5% CH4

, the methane yield from an untreated Salvinia molesta was observed to be 11.2 mL/g VS. Upon pretreating Salvinia molesta with 2% v/v Sulfuric acid, the methane yield obtained after pretreatment was higher than that of the untreated Salvinia molesta. The percentage increase was approximately 48.2%. Employing 4% v/v Sulfuric acid to pretreat Salvinia molesta resulted in a methane yield that was higher than that of the untreated Salvinia molesta. The percentage increase in methane yield was around 55.4%. Employing a 6% v/v Sulfuric acid to pretreat Salvinia molesta also resulted in a methane yield that was higher than that of the untreated Salvinia molesta.

However, the percentage increase in methane yield was about 58.9%. It can be observed that with the acidic pretreatment of Salvinia molesta, increasing the concentration of sulfuric acid resulted in the generation of more methane. This shows that the concentration of chemicals employed during chemical pretreatment can have an influence on the methane generation after pretreatment.

Corn straw, in the table, was observed to have been treated under acidic (sulfuric and hydrochloric acid), alkaline (sodium hydroxide, calcium hydroxide, and ammonia), Organosolv (acetic acid), and oxidative (hydrogen peroxide) pretreatment methods. Methane yield from an untreated corn straw was observed to be 100.6 mL/g VS. Treatment of the corn straw with 2% v/v sulfuric acid and 2% v/v hydrochloric acid, each, resulted in an increase in methane yield. The percentage increase in methane yield, however, varied with these two acidic media. That of sulfuric acid was around 74.6%, and that of hydrochloric acid was found to be about 62.4%. This shows that the selection of an acidic medium during the chemical pretreatment of feedstock is crucial. In comparing the percentage increases in methane between Salvinia molesta and corn straw treated with the same concentration of 2% v/v of Sulfuric acid, corn straw showed a larger percentage increase (74.6%) than Salvinia molesta (48.2%). This shows that feedstock type plays a role in the chemical pretreatment of feedstock. With acetic acid and hydrogen peroxide, the treatment of corn straw with these chemicals also resulted in an increase in methane yield. Acetic acid at a concentration of 4% v/v resulted in a percentage increase of 44.2%, while hydrogen peroxide at a concentration of 3% v/v resulted in a percentage increase of approximately 115.4%. Corn straw treated with bases such as sodium hydroxide or calcium hydroxide at a concentration of 8% v/v resulted in an increase in methane yield, but at different percentages. The percentage increase was 62.5% with sodium hydroxide, and that with calcium hydroxide was 105.4%. Ammonia of concentration of 10% v/v, when employed, also increased the methane yield of the corn straw, but by a percentage of about 67.3%. The variations in methane yield among the bases employed show that the selection of bases during the chemical pretreatment of feedstock is crucial. The percentage increase of 115.4% with hydrogen peroxide and 105.4% with calcium hydroxide shows that it is possible to improve the methane yield of feedstock by 100% or above with chemical pretreatment. The table shows that the methane output of the untreated sorghum bicolor stalk is 55% of the biogas produced. Treating the sorghum bicolor stalk via chemical pretreatment with the chemical sulfuric acid resulted in a methane yield of 54.5% of the biogas produced. This indicates a 0.5% drop in methane output. However, when the untreated sorghum bicolor stalk was pretreated using hydrogen peroxide as a chemical pretreatment, 61.5% of the biogas produced was methane. This indicates a 6.5% improvement in methane yield. It can be inferred that the choice of chemical pretreatment for a particular feedstock is crucial to increasing methane production from that feedstock.

4.7. Comparative Effects of Chemical Pretreatment on Methane Yield

Chemical pretreatment plays a pivotal role in enhancing the anaerobic digestibility of lignocellulosic biomass such as corn straw. By disrupting structural barriers like lignin and hemicellulose, chemical agents improve substrate accessibility and facilitate microbial hydrolysis during anaerobic digestion.

As summarized in

Table 5. Comparative effects of chemical pretreatment methods on methane yield from corn straw.

Pretreatment Method	Chemical Agent	Concentration (% v/v)	Methane Yield (mL/g VS)	% Increase Over Untreated	Industrial Context Suitability	Reference
Acid Pretreatment	Sulfuric Acid (H2SO4)	2	175.6	74.6%	Suitable for large-scale hydrolysis plants	[82]
Acid Pretreatment	Hydrochloric Acid (HCl)	2	163.4	62.4%	Effective in municipal waste treatment setups	[82]
Organosolv Pretreatment	Acetic Acid	4	145.1	44.2%	Applicable in integrated biorefineries	[82]
Oxidative Pretreatment	Hydrogen Peroxide (H2O2)	3	216.7	115.4%	Ideal for enzymatic enhancement in digesters	[82]
Alkaline Pretreatment	Sodium Hydroxide (NaOH)	8	163.5	62.5%	Common in agro-industrial waste processing	[82]
Alkaline Pretreatment	Calcium Hydroxide (Ca(OH)2)	8	206.6	105.4%	Suitable for decentralized rural biogas units	[82]
Alkaline Pretreatment	Ammonia (NH3)	10	168.3	67.3%	Effective in low-energy, low-cost setups	[82]

, several chemical pretreatment strategies—including acid, alkaline, oxidative, and organosolv methods—were evaluated for their impact on methane yield. Among these, oxidative (H₂O₂) and alkaline (Ca(OH)₂) treatments produced the highest improvements, with methane yield exceeding 100% [82]. Acid pretreatments also showed solid performance but may face scalability limitations due to their corrosive nature. In contrast, organosolv methods, although less effective in terms of yield, are well-suited for integration into biorefinery systems [82]. The variation in effectiveness across chemicals highlights the significant influence of chemical agents and operating concentrations on anaerobic digestion performance.

5. Biological Pretreatment

Improved biogas and methane output can be achieved by using biological pretreatment, which uses bacteria, fungi, and enzymes to break down biomass before the AD process [84]. Pretreatment by biological means breaks the covalent cross-linkages and non-covalent forces between hemicelluloses and lignin and increases the surface area of particulate aiming to improve the digestibility of complex waste [40]. The two most common methods for carrying out biological pretreatments are using enzyme cocktails or cultivating microorganisms directly on the feedstocks [85]. Production of enzymes is the mainstay of biological pretreatments, demonstrating the importance of the microorganism responsible for producing the necessary enzymes for the procedure [86]. Virtually every organic feedstock for the AD process can be pretreated biologically [40].

Effective biological pretreatment has the following benefits: it eliminates the need for prior mechanical size reduction, prevents the development of degradation products that impede the growth of fermentative microorganisms, preserves the pentose (hemicellulose) fraction, uses less energy, and is less expensive [87]. Reductions in carbohydrate loss and increased microbial accessibility for the cellulosic components of feedstock are achieved through biological pretreatments, improving hydrolysis [87]. In biological pretreatment, the employment of microorganisms results in the degradation of the lignin network of the biomass substrate towards biogas production. Numerous microbes found in nature can depolymerize cellulose and hemicelluloses, making them useful for pre-treating biomass [88]. Enzymes, bacteria, and white, brown, and soft-rot fungi that break down lignin very efficiently, along with hemicelluloses and a specific amount of cellulose, can all be employed in the degradation of biomass [89].

The delignification and degradation of hemicellulose can enhance the anaerobic digestion process by making cellulose and the resultant monomers more readily available [87]. By computing the ratio of lignin to cellulose loss, which provides selective value to lignin degradation, the efficacy of biological pretreatment for delignification can be determined [63]. A high lignin-to-cellulose loss ratio indicates that lignin is being preferentially degraded while cellulose is largely retained, which is desirable for biogas production since cellulose contains the bulk of fermentable sugars. Selective lignin removal increases enzymatic access to cellulose, reduces structural recalcitrance, and improves subsequent hydrolysis. Microorganisms such as Phanerochaete chrysosporium and Pleurotus ostreatus are especially effective in this regard, producing ligninolytic enzymes that target lignin while preserving cellulose integrity. Therefore, this ratio serves as a useful proxy for evaluating the selectivity and suitability of biological pretreatment pathways for enhanced methane yield.

The primary influencing factors in biological pretreatment are the substrate’s particle size, pH of the medium, type of biological pretreatment, moisture content, and incubation temperature and duration [40]. Some disadvantages of biological pretreatment include larger space and mass (holocellulose) loss, longer treatment durations, and specific conditions for microbe development [89]. A further possible drawback is that microorganisms devour a portion of the carbohydrates [7]. A key element influencing the viability, economics, and prospects of biological pretreatment in biomethanation is the high expense of enzymes and microorganisms like fungi, bacteria, etc. [55].

5.1. Fungal Pretreatment

Enzymes such as cellulases, hemicellulases, and ligninase can be secreted by fungi [89]. The fungi used in fungal pretreatment include white, brown, and soft-rot fungi. These fungi primarily target lignin, yet they also partially break down cellulose [51]. The breakdown of lignin and hemicellulose increases the ability of microorganisms to digest cellulose [26]. The presence of lacasses and peroxidases, lignin-degrading enzymes, causes lignin to be broken down by white-rot fungi [56]. Not all fungi produce lignin peroxidase when pretreating lignocellulosic feedstock; nonetheless, almost all of them release laccase and manganese peroxidase [88]. Usually, pretreatment with white-rot fungi is more common than with brown fungi [90]. The greater capability for lignin breakdown and reduced cellulose loss make white-rot fungi advantageous over other species [91]. Current studies reveal that the white-rot fungi degrade lignin through a range of mechanisms, such as oxidation, demethylation, side chain oxidation, and cleavage [36]. The paper industry has been using white-rot fungi primarily for biopulping for a very long period [90,92]. It is known that about 1500 species of white-rot fungi carry out selective lignin degradation of cellulose [92]. Certain factors that influence the breakdown of lignin during fungal pretreatment include the size of the substrate, the temperature, the duration of the pretreatment, and the moisture content [15]. Conditions that are suitable for fungal pretreatment include 27–37 °C temperatures and an incubation period of 8 days to 12 weeks [93,94]. With fungal pretreatment, its primary drawback is the lengthy pretreatment time [36].

According to

Table 6. Effects of biological pretreatment on the methane yield of various feedstocks.

Feedstocks	Fungal Pretreatment	Methane Yield (Untreated)	Methane Yield (After Pretreatment)	References
Willow sawdust	Leiotrametes menziesii, 30 days	95.5 mL/g VS	62.4 mL/g VS	[94]
	Abortiporus biennis, 30 days		136.7 mL/g VS
Rice straw	Pleurotus ostreatus, 28 °C/20 d		120% higher methane yield	[90]
	Trichoderma reesei, 28 °C/20 d		78.3% higher methane yield
Yard trimming	Ceriporiopsis subvermispora		106% higher methane yield	[18]
	Mycelium grown, 30 Days	20 mL/g VS	40 mL/g VS
Miscanthus	C. subvermispora (ATCC 96608)		+25% (v/w VS) CH4	[95]
Albizia chips			+370% (v/w VS) CH4	[96]

, willow sawdust, when untreated, had a methane yield of 95.5 mL/g VS, as seen in the table. Employing fungal pretreatment with fungi (Leiotrametes menziesii) and a duration of 30 days resulted in a decrease in the methane yield. The percentage decrease was approximately 34.7%. On the other hand, when the willow sawdust was pretreated via fungal pretreatment with the fungi (Abortiporus biennis) for the same duration of 30 days, there was an increase in methane yield. The percentage increase was about 43.1%. Both Leiotrametes menziesii and Abortiporus biennis are white rot fungi, and given that the same substrate and pretreatment duration were utilized, it can be inferred that the different fungi within a specific class of fungi can have a varied effect on methane yield.

As such, the selection of fungi is important during fungal pretreatment. Rice straw, as seen in the table, was treated with different fungi (Pleurotus ostreatus and Trichoderma reesei) but under the same operating parameters (28 °C and 20 days) during fungal pretreatment. These fungi both increased the methane yield by even more than 50%. The percentage increase for Pleurotus ostreatus was 120%, and that of Trichoderma reesei was 78.3%. Although both fungi increased the methane yield, the amount of methane they increased by thus the percentage increase in methane yield varied. As such, this instance can bolster the inference from above that the selection of fungi during fungal pretreatment is important. Furthermore, since there was an increase in methane yield of 120%, it can be inferred that it is possible to increase the methane yield of feedstock by 100% or above during fungal pretreatment. Treating yard trimming via fungal pretreatment with the fungus Ceriporiopsis subvermispora resulted in an increase in methane yield of 106%, which is above 100%. When yard trimming was pretreated via fungal pretreatment with the fungus Mycelium, the methane yield increased from 20 to 40 mL/g VS. This rise in percentage was 100%. These instances lend credence to the inference from above that a fungal pretreatment can raise feedstock’s methane output by 100% and above. A fungal pretreatment using the fungus C. subvermispora was employed for both Albizia chips and Miscanthus. It can be observed from the table that the methane yield of each of these feedstocks increased after pretreatment. However, the percentage increase in methane yield varied, thus 25% for that of Miscanthus and 370% for that of Albizia chips. Given that the same fungus was employed but for different feedstocks, it can be deduced that the differences in methane yield show that the type of feedstock influences the fungal pretreatment of feedstocks.

5.2. Enzymatic Pretreatment

Among the steps during AD of lignocellulosic materials, hydrolysis is considered the slowest step. To solve this issue, enzymes with a specific degree of hydrolytic activity can be employed [51]. Biomass degradability and methane generation have both been demonstrated to increase when specific enzymes are used in anaerobic digestion [97]. Cellulases and hemicellulases are among the enzymes that boost biomass hydrolysis [51]. Various feedstocks have been converted into AD digestible sugars by enzymes like cellulases, cellobiases, endoglucanases, xylanases, pectinases, and ligninolytic enzymes (like laccases, lignin peroxidase, and manganese peroxidase) under mild conditions to increase the rate of biogas production without forming inhibitory compounds [98,99]. For the generation of microbial enzymes, two methods that are commonly employed are submerged fermentation (SF) and solid-state fermentation (SSF). Enzymes like proteases, pectinases, and cellulases are produced using these processes [100]. Advantages of SF include simple enzyme recovery, process control, and a solid technological foundation to advance processes to industrial production efficiency [101]. Effective enzyme pretreatment requires the assessment and optimization of several parameters, such as pH, temperature, activity, specificity, quantity, and stability of the enzyme [28]. Pretreatment of biomass using enzymes has garnered increased attention recently compared to other biological methods. This is because the presence of inhibitors or other microbial metabolisms does not affect the enzymes [102]. Additionally, the enzymes provide several advantageous qualities, such as prolonged stability, facile recovery following pretreatment, reduced pretreatment time, and a lack of costly chemicals and equipment needed for biomass processing [103,104]. Biosurfactants are employed as an essential class of additives in the enzymatic pretreatment of wastewater and wastes containing oils and fats. Through enhancing the solubility and bioavailability of these feedstock components, these biosurfactants may stimulate enzymatic (lipolytic) activity [105,106]. Phospholipids, glycolipids (such as rhamnolipid), lipopeptides (such as surfactin), and saponins (such as tea saponin) are examples of biosurfactants that are commonly used in enzymatic pretreatment [107]. There are no environmental dangers associated with biosurfactants because of their low toxicity and biodegradability, even in the case of a leak or release into the environment. Surface activity of biosurfactants is good throughout a wide pH and temperature range [106,107].

5.3. Laccase

The reduction of polymers, the ring cleavage of aromatic compounds, and the cross-linking of monomers are all catalyzed by laccase [100]. The following biological contexts in particular contain laccase: plants (such as cabbages, turnips, potatoes, pears, and apples); some bacteria (such as S. lavendulae, S. cyaneus, and Marinomonas mediterranea); white-rot fungi (such as Pleurotus ostreatus, Phlebia radiata, and Trametes versicolor, which break down lignin); and other fungi (such as Ascomycetes, Deuteromycetes, and Basidiomycetes) [108]. Laccase is involved in lignification in plants. Laccase, in fungi, plays a role in the sporulation process, pigment biosynthesis, lignin degradation, reproducing body development, and plant pathogenesis. The generation of laccases is dependent on several factors, including carbon and nitrogen sources, pH, temperature, cultivation type, and induction of laccase [100]. Laccase has garnered special attention from researchers in recent decades. This is due to its effectiveness in oxidizing lignin-related phenolic and non-phenolic compounds as well as extremely refractory environmental pollutants, which makes it advantageous for use in a variety of biotechnological processes [109].

From

Table 7. Effects of enzymatic pretreatment on the methane yield of various feedstocks.

Substrates	Enzymes	Pretreatment Conditions	Result of Pretreatment on Methane Yield	References
Scenedesmus obliquus	cellulase and endo-galactouronase	50 °C/24 h	403% higher yield	[103]
	esterase and protease		273% higher yield
	cellulase, esterase and protease, endogalactouronase,		485% higher yield
Rhizoclonium	Lipase		115 mL CH4/g TS	[110]
	Xylanase		118 mL CH4/g TS
	a-amylase		121 mL CH4/g TS
	Protease		116 mL CH4/g TS
	Cellulase		133 mL CH4/g TS
	lipase, xylanase, a-amylase, protease and cellulase		145 mL CH4/g TS
Corn stover	Bjerkandera adusta (Versatile peroxidase)	1 day	15% higher than untreated	[66]
Ensilaged maize			6% higher than untreated
Flax			14% higher than untreated

, the enzymatic pretreatment method of treating Scenedesmus obliquus with combinations of cellulase and endo-galactouronase enzymes resulted in a 403% increase in methane yield, as seen in the table. This means, following pretreatment, the methane output was 5.03 times that of the methane output of the untreated Scenedesmus obliquus. When Scenedesmus obliquus was pretreated via enzymatic pretreatment with the mixture of enzymes of esterase and protease, the methane yield increased as well, but by 273%. The methane yield after the pretreatment was 3.73 times the methane yield of the untreated Scenedesmus obliquus. Although each mixture of enzymes increased methane yield when employed, the amount of methane produced by the mixture of cellulase and endo-galactouronase was about 34.9% greater than that of the mixture of esterase and protease.

This shows that the selection of enzymes during enzymatic pretreatment is crucial in attaining the aim of producing greater methane yield. Combining both mixtures of enzymes to treat Scenedesmus obliquus resulted in an increase in methane yield of 485%. The methane yield after the pretreatment was 5.85 times that of the untreated Scenedesmus obliquus. By comparison, it can be observed that the highest methane yield was obtained after employing the combination of all four enzymes. It can be deduced that with enzymatic pretreatment, employing a greater variety of enzymes within a mixture to treat feedstock can result in a greater methane yield. Following pretreatment of Rhizoclonium with the enzymes lipase, xylanase, a-amylase, protease, and cellulase, the methane output was 115, 118, 121, 116, and 133 mL CH₄/g TS, respectively. When comparing these methane outputs, certain variations were somewhat small, while others were somewhat larger. The variation in methane output also shows that the selection of enzymes is crucial in enzymatic pretreatment. Rhizoclonium produced 145 mL CH₄/g TS of methane after being pretreated with the combination of all the enzymes of lipase, xylanase, a-amylase, protease, and cellulase. This methane output was the highest when compared to the methane output following pretreatment with the individual enzymes. This bolsters the inference above that treating feedstock with a mixture of different enzymes can result in a greater methane yield. Corn stover, Ensilaged maize, and Flax were all pretreated with the same enzyme, Versatile peroxidase. After each feedstock was pretreated, the percentage increase in methane yield was 15% for Corn stover, 6% for Ensilaged maize, and 14% for Flax. Comparing these percentage increases, certain variations were somewhat small, while others were somewhat larger. The variation in the percentage increase shows that feedstock type can have an influence on enzymatic pretreatment of feedstocks.

6. Combined Pretreatment

Two or more single-mechanism pretreatment processes may be coupled to create the combined pretreatment methods [111]. A single pretreatment technique can increase biogas yield significantly, but each pretreatment technology has a number of drawbacks [63]. By combining two pretreatment technologies, the pretreatment effect can be improved while simultaneously compensating for the shortcomings in each technology [112]. The combination need not be limited to distinct pretreatment methods; it can potentially involve the application of two distinct chemical treatments. Thus, this combination of pretreatments, for instance, could involve employing diluted acid for hemicellulose hydrolysis and then employing diluted alkali for lignin removal [20]. High methane production, decreased pretreatment severity, and improved biomass utilization are some benefits of the combined pretreatment technique. However, the economic viability of combined treatment techniques must be considered since it may increase treatment costs [113].

Table 8. Effects of combined pretreatment on the methane yield of various feedstocks.

Pre-Treatment	Feedstock	Untreated	Effect of Pretreatment on Biogas	Reference
Thermo-acid (HCl + 120 °C)	FW		Increased by 18%	[114]
Thermo-acid (HCl at 100 °C)	OFMSW		Increased by 120%	[115]
Steam+ NaOH + laccase	Bio-fibers		Increased by 49%	[116]
NaOH + hydrothermal	Rice straw	59.8 L/kg VS (CH4)	132.7 L/kg VS (CH4)	[117]
Ethanol+ NH3	Sugar cane bagasse	105.6 mL/g VS	299.3 mL/g VS	[118]

shows that FW, when pretreated via a combined pretreatment of thermo-acid (HCl + 120 °C), resulted in a percentage increase in biogas by 18%. OFMSW, when pretreated via a combined pretreatment of thermo-acid (HCl at 100 °C), also resulted in a percentage increase in biogas, but by 120%. Although there was an increase in each of the above combined pretreatments, the percentage increase in biogas varied.

This can be due to the variation in temperature and/or feedstock employed. Furthermore, since there was an increase in biogas by 120%, it can be deduced that it is possible to increase biogas/methane by a percentage of 100% and above via combined pretreatment. Bio-fibers being pretreated via combined pretreatment (Steam + NaOH + laccase) resulted in a 49% percentage increase in biogas. Comparing this percentage increase to that of thermo-acid (HCl at 100 °C), it can be observed that thermo-acid had the highest percentage increase in biogas. This can also be a result of the feedstock type employed, or the individual pretreatments employed in the combined pretreatment. This means the selection of individual pretreatments in the formation of a combined pretreatment is very crucial.

Rice straw generated a biogas output of 59.8 L/kg VS (CH4), when untreated. Upon undergoing a combined pretreatment (NaOH + hydrothermal), the biogas output increased, and the percentage increase was about 121.9%. Untreated sugar cane bagasse produced a biogas output of 105.6 mL/g VS. Combined pretreatment (Ethanol+ NH3) to treat sugar cane bagasse resulted in a higher biogas output than the untreated sugar cane bagasse. Additionally, the percentage increase was around 183.4%. These percentage increases in biogas in the above combined pretreatment of each of Rice straw and sugar cane bagasse give credence to the above inference that it is possible to generate biogas outputs of 100% and above via combined pretreatment. Furthermore, although these percentage increases were above 100%, they varied. This can also be due to the feedstock type or the individual pretreatment employed in the combined pretreatment. This supports the inference that the selection of individual pretreatments in a combined pretreatment is crucial.

7. Bibliometric Analysis of Pretreatment of Precursors for Biogas Production

• Rationale for Bibliometric Analysis

To complement the technical synthesis of pretreatment strategies, this section presents a bibliometric analysis that explores research trajectories, institutional collaborations, and thematic developments related to biomass and waste pretreatment for anaerobic digestion. The transition from performance-based synthesis to literature mapping is intentional. While earlier sections highlight substrate-level outcomes and process optimizations, bibliometric analysis offers broader insight into how the field is evolving, where research efforts are concentrated, and which domains are emerging. Bibliometric analysis is valuable because it helps identify influential authors, impactful studies, and productive institutions. It reveals how collaborative networks form across regions, tracks thematic shifts over time, and highlights geographic disparities in contributions. These insights inform future research directions, guide funding priorities, and support evidence-based policymaking. In the context of this study, bibliometric mapping complements the experimental review by exposing gaps in regional representation—particularly in low-resource settings like Ghana—and uncovering opportunities for knowledge transfer. The goal of this section is to provide a concise and precise overview of global research activity in pretreatment-enhanced anaerobic digestion. Its inclusion supports this study’s broader objective of identifying scalable strategies and promoting inclusive scientific participation. This analysis has been streamlined to emphasize high-impact findings and regional relevance.

• Methodology

Bibliometric analysis utilized VOSviewer based on a dataset obtained from the Scopus database to investigate research trajectories and collaborations in the areas of biomass and waste pretreatment for anaerobic digestion and biogas production. A co-authorship study demonstrated intriguing collaboration patterns among researchers, which led to the establishment of two primary clusters. The first cluster revealed close relationships among researchers, possibly representing a South Asian regional network, whereas the second cluster suggested a more heterogeneous global consortium. Nationally, co-authorship analysis revealed the presence of international collaborative networks, with seven distinct clusters that highlight the influence of geographic proximity and shared research interests.

Keyword co-occurrence analysis generated a thematic map for the research domain, delineating six clusters of related keywords. These clusters represent areas of dense investigation such as biomass conversion, wastewater treatment, and bioenergy production, illustrating the interdisciplinary nature of the field. Closer examination of author-defined keywords provided deeper thematic insight, identifying seven clusters focused on anaerobic digestion optimization, waste valorization, and biomass pretreatment methods.

Citation analysis provided important information on the impact of authors and their publications. The author citation analysis formed four clusters of prominent contributors and their areas of influence. Document citation analysis revealed ten clusters of landmark studies, indicating how the field has progressed and how various thematic domains are connected.

Bibliographic coupling and co-citation analysis further investigated inter-author relationships. Bibliographic coupling identified four clusters of authors based on shared references, reflecting intellectual affinities and common research foundations.

7.1. Analysis of Co-Authorship Clusters Using VOSviewer

Co-authorship analysis is a critical tool for understanding research networks and collaborations in academia. Utilizing VOSviewer, two distinct clusters of researchers were identified based on their co-authorship patterns as shown in Figure 3. These clusters reveal key insights into the collaborative dynamics, thematic focus, and potential interdisciplinary connections among the researchers.

Cluster 1, in red, comprises five researchers: Adish Kumar, S., Kaliappan, S., Kavitha, S., Rajesh Banu, J., and Yeom, Ick Tae. This group appears to be closely interconnected, likely sharing common research goals and expertise. The presence of multiple South Asian names suggests that these researchers may belong to institutions in the same region or collaborate within a specific academic network. Their work may center on areas such as environmental science, biochemistry, or renewable energy, where such collaborations are common. Notably, the inclusion of Yeom, Ick Tae, suggests the potential for international cooperation, possibly linking this cluster to broader global research efforts.

Cluster 2, in green, consists of four researchers: Bauer, Alexander, Kumar, Gopalakrishnan, Tyagi, Vinay Kumar, and Vivekanand, Vivekanand. This group similarly demonstrates strong collaborative ties, with members likely engaged in disciplines such as environmental technology, wastewater management, or sustainable energy solutions. The diversity in naming patterns indicates a mix of international researchers, suggesting that this cluster could represent cross-regional partnerships. For instance, Bauer, Alexander, may bring a European perspective to the group, while Kumar, Gopalakrishnan, and Tyagi, Vinay Kumar, reflect expertise often associated with Indian institutions and their robust contributions to environmental research.

A closer examination of these clusters reveals potential thematic overlaps. Both groups may be exploring complementary aspects of environmental sustainability, such as the development of bioenergy technologies or innovative solutions for waste management. Researchers like Rajesh Banu, J., and Tyagi, Vinay Kumar, who are prominent figures in wastewater treatment and bioenergy, could serve as indirect links between these two clusters. Their work might act as a bridge, fostering collaboration across different geographic and institutional boundaries.

The presence of strong collaborations within each cluster highlights the importance of teamwork in tackling complex global challenges. However, the apparent lack of direct connections between the two clusters also suggests opportunities for further interdisciplinary engagement. By fostering cross-cluster partnerships, researchers could leverage their diverse expertise to create innovative and impactful solutions to shared challenges.

7.2. Co-Authorship Clusters Across Countries: A Global Perspective

Analyzing co-authorship patterns across countries offers valuable insights into international research collaborations and their geographical dynamics. Using VOSviewer, seven distinct clusters of countries were identified, each representing unique networks of academic partnerships as seen in Figure 4. These clusters reflect the interconnectedness of nations in addressing global challenges and the influence of regional and cultural ties on research endeavors.

Cluster 1, in red, includes Brazil, Canada, Chile, Colombia, Mexico, Nigeria, South Africa, Spain, the United States, and Viet Nam. This grouping features countries from the Americas, Africa, and Southeast Asia, emphasizing research areas such as global development, environmental sustainability, and health innovation. Brazil and the United States, as leaders in environmental and biomedical research, often collaborate with nations like Nigeria and South Africa, which focus on regional health and socio-economic challenges. Spain’s inclusion highlights its role in bridging collaborations between Latin America and Europe, fostering partnerships across continents.

Cluster 2, in green, consists of Algeria, Ethiopia, France, Morocco, the Netherlands, Norway, Qatar, South Korea, Tunisia, and Turkey. This cluster demonstrates a mix of European, African, and Middle Eastern nations working together on research topics like energy, technology, and agriculture. France and the Netherlands contribute advanced technological expertise, while countries like Algeria and Morocco focus on renewable energy and agricultural advancements. South Korea’s presence reflects its role in technology-driven solutions, often in collaboration with Middle Eastern countries such as Qatar and Turkey.

Cluster 3, in blue, features Austria, the Czech Republic, Germany, Hungary, Indonesia, Poland, Slovenia, and Sweden. This cluster reflects strong intra-European collaboration, likely centered on fields such as engineering, materials science, and environmental technology. Germany’s leadership in European research is complemented by contributions from Austria, Poland, and Sweden. Indonesia’s inclusion suggests partnerships with European nations on projects related to forestry, biodiversity, and climate research, reflecting the increasing importance of trans-regional collaboration.

Cluster 4, in yellow, brings together China, Egypt, Greece, Italy, Pakistan, Portugal, Romania, and Saudi Arabia. This mix of Mediterranean and South Asian nations indicates shared research interests in infrastructure, energy, and medical sciences. China’s growing influence in international research is evident here, while Southern European countries like Greece, Italy, and Portugal highlight strong scientific traditions. Pakistan and Saudi Arabia likely contribute to renewable energy research and technologies suited to arid environments, reflecting the cluster’s focus on sustainability.

Cluster 5, in purple, includes Australia, Finland, Ireland, New Zealand, Singapore, and the United Kingdom. These predominantly English-speaking countries often collaborate in fields such as biotechnology, environmental science, and artificial intelligence. The shared use of English and similar research funding structures facilitates these partnerships. Singapore’s role as a global hub for innovation and investment in cutting-edge research aligns well with the expertise of nations like the United Kingdom and Australia.

Cluster 6, in light blue (cyan), is composed of Hong Kong, India, Japan, the Russian Federation, Taiwan, and Thailand. This cluster reflects strong research networks in technology, engineering, and manufacturing. India and Japan, with their robust scientific communities, often collaborate with Taiwan and Thailand on technology solutions and regional development. The Russian Federation adds its expertise in fields like physics and materials science, while Hong Kong serves as a global academic hub, connecting researchers across the Asia-Pacific region.

Cluster 7, in orange, the smallest group, includes Belgium, Denmark, Iran, and Malaysia. This cluster, despite its size, focuses on highly specialized areas such as renewable energy, healthcare innovation, and food security. Belgium and Denmark, as part of the European research ecosystem, collaborate with nations like Iran and Malaysia on issues of shared interest, including climate resilience and sustainable agriculture.

The clustering of countries in these networks reflects a balance of regional proximity, shared languages, and research priorities. Larger clusters, such as the Americas and the English-speaking nations, demonstrate the benefits of established funding and collaborative frameworks, while smaller clusters highlight the importance of specialized partnerships. Encouraging cross-cluster collaborations could further enhance global research efforts, bringing together diverse perspectives and resources to address shared challenges. By fostering greater interconnectedness among these clusters, the scientific community can amplify its capacity to innovate and solve pressing global issues.

7.3. Co-Occurrence Analysis of Keywords

The co-occurrence analysis of keywords reveals a complex web of interconnected research themes, represented through six distinct clusters, as shown in Figure 5. Each cluster, identified by its unique color, reflects a focused area of study within the broader research landscape. The size and placement of the nodes indicate the frequency of keyword usage and their relationships, with central terms serving as pivotal points of connection.

Cluster 1, the red cluster, centers on keywords such as biomass, bioethanol, fermentation, cellulose, and saccharification. This cluster represents research on biomass as a renewable resource, focusing on its conversion into bioethanol and other biofuels. Key processes like enzymatic hydrolysis and fermentation are highlighted, along with pretreatment methods to enhance biomass utilization efficiency. The prominence of terms like biomethane and hydrogen suggests additional interest in diversifying the end products of biomass conversion.

Cluster 2, the green cluster, includes keywords such as anaerobic growth, bioreactors, sewage, wastewater treatment, and sludge digestion. This group emphasizes research on waste management, particularly the treatment of sewage and wastewater through anaerobic digestion. The integration of volatile fatty acids and biochemical oxygen demand reflects the focus on optimizing microbial processes and improving the efficiency of bioreactors.

Cluster 3, the blue cluster, highlights terms like co-digestion, municipal solid waste, microalgae, and food waste. This cluster explores strategies for combining multiple organic waste streams (co-digestion) to enhance biogas production. The inclusion of microalgae indicates a growing interest in utilizing alternative feedstocks to address sustainability challenges. The focus extends to waste-to-energy technologies and the valorization of diverse organic residues.

Cluster 4, the yellow cluster, is characterized by keywords such as metabolism, acetic acid, volatile fatty acids, and pretreatment conditions. This cluster delves into the biochemical and metabolic pathways involved in anaerobic digestion, with an emphasis on optimizing conditions to maximize energy recovery. The presence of terms related to microbial activity highlights the importance of understanding the underlying biological processes.

Cluster 5, the purple cluster, focuses on keywords like fungi, enzymatic hydrolysis, and pretreatment conditions. This cluster examines the role of fungi and enzymatic processes in breaking down complex organic materials like lignocellulosic biomass. Research in this area aims to develop more efficient pretreatment technologies, which are critical for enhancing the bioavailability of feedstocks for biofuel production.

Cluster 6, the orange cluster, although smaller, includes terms related to emerging topics in renewable energy and sustainability. While specific keywords are not explicitly visible, the placement of this cluster suggests a connection to innovative approaches and lesser-explored areas in biogas production and waste management.

The connections between clusters reflect the interdisciplinary nature of research in renewable energy and waste management. Central terms like biogas, methane, and article serve as bridges, linking diverse subfields such as biomass conversion, wastewater treatment, and metabolic engineering. These interconnections highlight the collaborative efforts required to address global challenges in energy production and environmental sustainability.

This keyword co-occurrence analysis underscores the diversity and interconnectedness of research themes in renewable energy and waste management. The six clusters represent focused areas of study, from biomass conversion and wastewater treatment to microbial metabolism and emerging technologies. By fostering collaborations across these clusters, researchers can accelerate innovation and develop holistic solutions to pressing global challenges.

7.4. Co-Occurrence Analysis of Author Keywords

The analysis of the co-occurrence network of author keywords reveals a structured representation of research themes in anaerobic digestion and related fields, as shown in Figure 6. The clusters, represented by distinct colors, reflect interconnected yet focused research areas. A newly introduced Cluster 7, distinguished by pink, highlights additional emerging topics within this domain.

Cluster 1, in red, focuses on anaerobic digestion, co-digestion, sludge solubilization, and methane production. This cluster underscores the optimization of anaerobic processes to enhance biogas yields. The presence of keywords like thermal hydrolysis and ultrasonic indicates efforts to improve pretreatment methods, reflecting advancements in the field.

Cluster 2, represented in green, emphasizes the valorization of food waste, municipal solid waste, and biowaste. Biomethane production and chemical pretreatment are central to this cluster, reflecting the focus on converting waste streams into renewable energy sources. This cluster also highlights research on hydrothermal pretreatment, aiming to enhance process efficiency in industrial and municipal contexts.

Cluster 3, shown in blue, is centered on biomass pretreatment, cellulose, lignocellulosic materials, and enzymatic processes. This cluster delves into the biochemical pathways of biofuel production, particularly from plant-based biomass. The inclusion of enzymes and fungal pretreatment highlights the role of biological catalysts in breaking down complex organic materials.

Cluster 4, in yellow, explores biohydrogen production, dark fermentation, and biodiesel. This cluster focuses on alternative energy sources and emphasizes the utilization of agricultural residues like corn stover. Research here seeks to diversify renewable energy technologies while maximizing energy recovery from waste.

Cluster 5, marked in purple, revolves around various pretreatment techniques, such as alkaline pretreatment, steam explosion, and enzymatic processes. The cluster underscores the importance of pretreatment in enhancing substrate digestibility for improved anaerobic digestion efficiency.

Cluster 6, in orange, addresses advanced process modeling and optimization. Keywords such as kinetic models and disintegration reflect efforts to refine process control and predictability in anaerobic digestion systems. The inclusion of computational approaches signifies a move toward precision and efficiency in bioenergy research.

Cluster 7, introduced in pink, represents emerging and innovative topics in anaerobic digestion. This cluster includes keywords like photocatalysis, algae, and advanced bioenergy systems. Photocatalysis is a novel technique being explored for its potential to enhance methane production, while algae-based digestion focuses on alternative and sustainable feedstocks. This cluster also reflects interdisciplinary approaches integrating new technologies into traditional bioenergy systems.

7.5. Citation Against Authors

The visualization provided represents a bibliometric analysis conducted under the category of “citation against authors,” generated using VOSviewer. This type of analysis identifies patterns and relationships among authors based on the frequency and distribution of citations, thereby highlighting influential contributors and research clusters within a particular academic field as seen in Figure 7. In the analysis, four distinct clusters are identified, each represented by a different color. The first cluster, represented in red, includes authors who are closely interconnected in terms of citations. Key figures in this group, such as Keikhosro Karimi and Irini Angelidaki, appear to be central to a specific subfield or research focus. Their prominence within this cluster suggests that their work has been widely referenced and has had a significant impact on the development of related studies. The second cluster, represented in green, features authors such as Fabiana Passos and Ahmed Mahdy. This group forms another tightly knit network of citations, potentially indicating their contributions to a different thematic area within the broader field. The connections within this cluster imply a shared research focus or collaborative efforts that have garnered substantial recognition in academic literature. The third cluster, shown in blue, highlights authors like Rajesh Banu, J., and Kaliappan, S. These individuals stand out as central figures within their citation network, suggesting their significant role in advancing specific topics of interest. Their work likely resonates with a wide audience, forming a foundational part of the literature in their area of expertise. The fourth cluster, depicted in yellow, consists of a smaller group of authors, including Elsayed Elbeshbishy and Bipro Ranjan Dhar. This cluster’s compact size may indicate its association with niche or emerging areas of research. The connections among these authors suggest that their work, while perhaps less widely cited compared to the other clusters, is nonetheless important within its specialized domain.

Overall, the clusters represent groups of authors whose works are frequently cited together, reflecting thematic or collaborative proximity within the academic field. Each cluster provides insight into the structure and dynamics of scholarly influence, offering a glimpse into the interconnectedness of researchers and the evolution of knowledge in their respective areas. This type of analysis is invaluable for understanding citation trends and identifying key contributors within a research domain.

7.6. Citation Analysis Across Documents: Insights from VOSviewer

Citation analysis is a powerful method for uncovering influential documents and identifying research trends within a given field. Using VOSviewer, a detailed map of ten clusters of documents was generated, offering a window into the interconnectedness of research studies and their respective contributions, as shown in Figure 8. These clusters reflect thematic groupings and chronological patterns in citations, showcasing how research evolves and builds on foundational work. Cluster 1 (in red), comprising 12 items, includes works by authors such as [11,119,120,121]. This cluster likely represents foundational studies in a rapidly advancing field, possibly focused on environmental biotechnology, wastewater treatment, or bioenergy. The inclusion of works spanning over a decade (2010–2021) suggests a robust and sustained body of research. The prominence of recent studies like [29,122] indicates that this cluster remains highly relevant and continues to influence current research directions. Cluster 2 (in green), with 9 items, features studies such as [7,123,124]. This cluster likely revolves around the application of emerging technologies or innovative methodologies. The repeated appearances [125,126,127] suggest that these authors have made significant contributions, providing the theoretical or experimental foundation for this area of research. Cluster 3 (in blue), consisting of 8 items, highlights key contributions from [31,128,129], among others. The thematic focus here may involve interdisciplinary applications, blending areas like chemical engineering, environmental sciences, or energy research. Refs. [64,130] likely serve as early foundational studies that have shaped this cluster.

Cluster 4 (in yellow) includes 8 items, featuring authors like [84,131]. This cluster might focus on sustainable material usage or anaerobic digestion processes. The presence of [99,132]. Although foundational work in this area dates back over a decade, newer studies like [42] demonstrate the cluster’s ongoing evolution. Cluster 5 (in purple), with 7 items, features works by [132,133,134,135,136]. This cluster likely represents research on bioresource recovery or waste valorization. Passos’s appearance in consecutive years indicates a highly active research period, likely reflecting the development and refinement of methods within this field. Clusters 6 (in light blue) and 7 (in orange) each contain 5 items, with contributions from authors like [26,137] in Cluster 6, and [7,138] in Cluster 7. Both clusters may represent specialized areas within the broader research landscape, with Cluster 6 focusing on novel bioenergy processes and Cluster 7 exploring advancements in waste management technologies. Cluster 8 (in brown), containing 4 items, includes [139,140,141]. This compact cluster might reflect niche but impactful research, possibly concerning microbial or enzymatic processes. The mix of early and recent studies suggests that these works have established themselves as key references within their specialty. Clusters 9 (in pink) and 10 (light brown), each with 3 items, feature documents like [142,143] in Cluster 9, and [144,145] in Cluster 10. These clusters likely represent highly specialized subfields, where foundational studies continue to influence cutting-edge advancements. Cross-cluster observations reveal overlapping time frames across the clusters, underscoring the interconnected nature of research where older foundational works continue to influence new studies. Certain authors, such as Tyagi, Monlau, and Passos, appear in multiple clusters, highlighting their broad impact and interdisciplinary contributions. This interlinking nature demonstrates how distinct but related fields, such as bioenergy, environmental sustainability, and wastewater treatment, evolve collectively.

7.7. Bibliographic Coupling Analysis of Authors Using VOSviewer

Bibliographic coupling, a key bibliometric technique, identifies the relationships between researchers based on the shared references in their works. Using VOSviewer, four distinct clusters of authors were identified, each representing unique collaborative dynamics and thematic research areas as shown in Figure 9. These clusters provide insights into the shared intellectual foundations and potential research intersections across the groups. Cluster 1 (in red), the largest group with 17 authors, includes prominent figures such as Irini Angelidaki, Alexander Bauer, and Mohammad Taherzadeh. This cluster is likely centered around environmental science and biotechnology, focusing on areas such as bioenergy production, anaerobic digestion, and waste management. The diversity within this group, featuring researchers like Budiyono, Zongjun Cui, and Marcin Dębowski, suggests a strong international collaboration across Asia, Europe, and beyond. Researchers such as Yebo Li and Wei Wang may contribute expertise in agricultural waste valorization, while others, including Keikhosro Karimi and Renjie Dong, likely specialize in process optimization for bioenergy. The presence of multiple thought leaders in this cluster highlights its significance in advancing sustainable technologies. Cluster 2 (in green), comprising five authors, includes Hélène Carrere, Ivet Ferrer, and Gopalakrishnan Kumar. This smaller yet tightly connected group appears to focus on wastewater treatment and the integration of anaerobic digestion with advanced technologies. The participation of Fabiana Passos and Ahmed Mahdy suggests a shared emphasis on innovative solutions for biowaste conversion. Researchers in this cluster may be distinguished by their application of cutting-edge techniques and their contributions to improving energy efficiency in waste treatment processes. Cluster 3 (in blue), with five authors, features researchers such as Adish Kumar, S., Kaliappan, S., and Rajesh Banu, J. This cluster exhibits strong regional ties, likely centered in South Asia, where the authors collaborate on sustainable waste management and bioprocessing research. Yeom, Ick Tae, provides an international dimension to this cluster, possibly linking it to broader global research efforts. The group’s focus may align with topics such as organic waste conversion, biopolymer production, and microbial enhancements in anaerobic digestion systems. Cluster 4 (in yellow), consisting of four authors, includes Bipro Ranjan Dhar, Elsayed Elbeshbishy, George Nakhla, and Madhumita B. Ray. This group appears to be centered on North American and global collaborations addressing emerging challenges in wastewater engineering and bioenergy production. The authors in this cluster may emphasize novel techniques for nutrient recovery, microalgae cultivation, and advanced anaerobic digestion systems. Their work contributes significantly to the growing field of integrated bioprocessing technologies.

7.8. Cross-Cluster Connections

While the clusters exhibit distinct focal areas, potential intersections exist. For instance, authors like Alexander Bauer (Cluster 1) and Gopalakrishnan Kumar (Cluster 2) could act as intellectual bridges, fostering collaborations between the larger and smaller clusters. Similarly, Yeom, Ick Tae (Cluster 3) and Elsayed Elbeshbishy (Cluster 4) may provide pathways for linking regional research with global trends. From Figure 9, the bibliographic coupling patterns indicate a shared foundation in environmental sustainability and bioresource recovery, creating opportunities for interdisciplinary engagement.

7.9. Co-Citation Analysis of Cited Authors Using VOSviewer

Co-citation analysis is a bibliometric method used to identify the relationships between authors based on how frequently their work is cited. By mapping the co-cited authors in a research domain, distinct thematic clusters emerge, revealing intellectual structures within the field. The visualization provided by VOSviewer identifies four prominent clusters, differentiated by colors: red (Cluster 1), green (Cluster 2), blue (Cluster 3), and yellow (Cluster 4), as shown in Figure 10.

Cluster 1 (Red) is the largest and densest, representing a network of highly interconnected authors such as Zhang Y., Liu J., Chen H., and Li X. This cluster focuses primarily on research themes in environmental biotechnology, particularly bioprocess optimization and bioenergy production. Many of the authors in this group have strong collaborations in anaerobic digestion, biofuel generation, and microbial processes. Their frequent co-citation suggests that their work forms the backbone of a critical research area, often serving as foundational references for newer studies. Cluster 2 (Green), the second largest cluster, features prominent authors like Angelidaki I., Taherzadeh M. J., Karimi K., and Li Y. This group is centered on renewable energy technologies, with a strong emphasis on bioresource utilization and anaerobic digestion systems. Researchers like Angelidaki I. and Taherzadeh M. J. are widely recognized for their contributions to improving biogas production and exploring sustainable waste-to-energy solutions. The dense connections within this cluster highlight a core body of literature that significantly influences advancements in bioenergy and waste valorization research. Cluster 3 (Blue) consists of authors like Carrere H., Ferrer I., Kavitha S., and Tyagi R. D., who are often cited together for their work on wastewater treatment, sludge management, and resource recovery processes. The focus of this cluster is on integrating biological and physicochemical approaches to enhance the efficiency of wastewater treatment systems. The inclusion of Kavitha S. and Tyagi R. D. suggests notable contributions from South Asian researchers, linking regional perspectives to global challenges in environmental engineering. Cluster 4 (Yellow) is smaller but highly focused, with authors such as Passos F., Gonzalez-Fernandez C., Ballesteros M., and Steyer J. P. This group explores specialized areas like microalgae cultivation, bioresource recovery, and integrated anaerobic digestion systems. The emphasis here lies in innovative solutions for sustainable energy and nutrient recycling, bridging the gap between emerging technologies and practical applications. Although this cluster is less dense compared to others, its connections to the green and blue clusters suggest a shared intellectual foundation in bioenergy and resource recovery. Interconnections and intellectual bridges can be observed across clusters. While each cluster represents a distinct thematic focus, there are notable interconnections among them. For instance, Angelidaki I. (Cluster 2) and Carrere H. (Cluster 3) appear as central figures, reflecting their influence across multiple research areas. Similarly, Tyagi R. D. in Cluster 3 and Passos F. in Cluster 4 bridge different clusters, highlighting the interdisciplinary nature of environmental research. These authors act as intellectual connectors, integrating diverse methodologies and fostering cross-cluster collaboration. The co-citation analysis reveals the underlying intellectual structure of the research landscape, with each cluster representing key thematic areas: bioenergy optimization (Cluster 1), renewable resource utilization (Cluster 2), wastewater management (Cluster 3), and emerging bioresource technologies (Cluster 4). The dense interconnections emphasize the collaborative and interdisciplinary nature of research in environmental sustainability. By identifying these clusters, future researchers can better navigate the existing literature, identify key thought leaders, and explore opportunities for bridging complementary research areas.

7.10. Documents by Year

The annual distribution of research articles shows a general upward trend in scientific publications over the years, reflecting growing research interest in biomass pretreatment and its role in anaerobic digestion or biogas production.

From 2010 to 2014, the number of publications increased steadily, starting with 54 articles in 2010 and rising to 105 articles in 2014, as seen in Figure 11. This growth highlights an initial phase of increasing attention toward biomass pretreatment technologies as part of renewable energy research and sustainable waste management. Between 2015 and 2018, there was further consistent growth, with publications increasing from 126 articles in 2015 to 204 articles in 2018. The noticeable rise during this period may indicate increased funding, technological advancements, and global initiatives to promote biogas as a renewable energy source, especially in response to climate change and the need for alternative energy solutions. The period from 2019 to 2022 witnessed a significant surge in publications. The number of articles rose from 212 in 2019 to a peak of 294 in 2022, marking the highest publication output during the entire period analyzed. This sharp increase suggests that researchers and institutions prioritized biomass and waste pretreatment research during this phase, possibly due to heightened global policies focusing on sustainability, circular economy, and renewable energy transitions. In 2023 and 2024, there is a slight decline in publication numbers compared to the 2022 peak. Articles dropped to 225 in 2023 and 221 in 2024 (so far). This reduction might reflect research saturation in certain aspects of the field or shifts toward newer, emerging technologies related to renewable energy production.

7.11. Documents by Country

The data presented, generated from the Scopus database and covering the period from 2010 to 2024, highlights the global distribution of research articles related to the Pretreatment of biomass or waste for anaerobic digestion or biogas production, as shown in Figure 12. Scopus, as one of the largest and most comprehensive databases for peer-reviewed research, has provided valuable insights into the productivity of countries in this critical field of sustainable energy and waste management over the past 14 years. The data reveal that China leads the research landscape with 506 documents, reflecting its significant investment in renewable energy technologies and its push toward sustainable solutions to address environmental and energy challenges. China’s dominance in biomass and biogas research aligns with its national policies and industrial initiatives aimed at reducing carbon emissions and harnessing alternative energy sources.

Following China, India ranks second with 353 documents, showcasing the country’s growing focus on anaerobic digestion and biomass pretreatment. This research interest has been driven by India’s agricultural economy, the abundance of organic waste, and government policies promoting biogas production as part of its renewable energy strategy. The United States secures the third position with 198 documents, demonstrating its strong research infrastructure and innovation in bioenergy technologies, largely supported by federal agencies and collaborations between academia and industry. The data also highlight substantial contributions from Spain (118), Brazil (108), Italy (105), Iran (97), and Canada (96). European countries such as Spain and Italy are particularly notable due to their proactive roles in advancing green technologies and adhering to climate action targets set by the European Union. Meanwhile, Brazil’s contributions reflect its leadership in biofuel research, particularly given the country’s rich agricultural base and its focus on converting biomass into energy. Iran and Canada also show growing research output, which may be tied to regional energy demands and the need for efficient waste management systems. The mid-tier contributors include countries like South Korea (91), France (78), Sweden (77), Thailand (76), and Germany (72). These nations demonstrate a steady commitment to biogas and biomass research, driven by sustainability goals, government funding, and industrial applications. Notably, countries like Mexico (67), United Kingdom (66), and Denmark (59) have also made consistent strides in this field, despite producing smaller research volumes compared to leading nations. Emerging economies such as Pakistan (58), Indonesia (55), Turkey (55), and Nigeria (30) reveal growing interest and efforts in biomass pretreatment and anaerobic digestion. Their contributions are particularly important as these technologies offer solutions to energy access challenges and environmental management in developing regions. The participation of countries such as Colombia (23), South Africa (19), and Hungary (17) further emphasizes the expanding global reach of this research area.

At the lower end, countries with fewer contributions, such as Ghana (3), Kazakhstan (1), Uganda (1), and Armenia (1), reflect participation from regions with limited research infrastructure but growing interest in sustainable energy solutions. These small contributions highlight the potential for future collaboration, funding, and capacity building to support research efforts in underrepresented regions.

7.12. Document Types

The dataset highlights the various types of scholarly documents contributing to the research field. A clear dominance of articles is observed, with 1770 documents representing most publications, as shown in Figure 13. This indicates that primary research articles form the backbone of knowledge generation and dissemination in this area. The high prevalence of articles underscores the field’s reliance on experimental, theoretical, and applied studies to advance understanding and technologies related to biomass and waste pretreatment. The second most significant document type is reviews, with 311 publications. Reviews play a critical role in summarizing and synthesizing existing research, offering insights into trends, advancements, and gaps within the field. Their substantial presence highlights the need to consolidate the growing body of literature and provide direction for future research. Conference papers account for 173 documents, reflecting the importance of conferences in facilitating the presentation of preliminary findings and fostering collaboration among researchers. These papers often represent ongoing or emerging research that may later mature into full-length journal articles. Book chapters contribute 138 documents, indicating that research in this field is also being disseminated through edited volumes and comprehensive books, which cater to both academic and industry audiences. Books and book chapters provide an avenue for in-depth discussions on specific topics within the broader theme of biomass pretreatment. The dataset also includes smaller contributions such as retracted articles (3), conference reviews (2), data papers (2), and errata or notes (1 each). These categories represent minor outputs but still provide important contributions, corrections, or clarifications within the research domain. For instance, data papers focus on sharing critical datasets, which enhance research transparency and reproducibility.

8. Research Prospect in Ghana

Two primary categories of biomass feedstock exist in Ghana. The first category consists of farm-based by-products, including agricultural residue, animal manure, and waste from farmlands. The second category includes organic waste from the food and feed industries, as well as municipal solid waste. In Ghana, the agricultural sector, which consists mostly of food crops (59.9%), livestock (7.1%), fisheries (7.6%), cocoa (14.3%), and forestry (11.1%), contributes significantly to the national economy [145]. A large amount of organic waste is produced in the country because of its heavy reliance on the agricultural sector. Similarly to other African nations, Ghana usually mismanages these organic resources, which have the potential to produce extremely valuable products.

In Ghana, there are just a few appropriate waste management techniques, and these are applied on a small scale. For instance, biogas generation from waste is usually performed on a small scale in the country. Presently, there is a high amount of organic waste from homes, other residential areas, and industries, which poses a significant challenge. Another challenge faced in the country is the high demand for energy due to the increasing population. This challenge has resulted in overdependence on energy from hydropower, causing blackouts in the country.

Biogas-to-electricity systems offer decentralized energy access and waste valorization opportunities. However, widespread implementation is hindered by high initial investment, technological constraints, and inconsistent feedstock availability. While biogas can help alleviate energy demand, hydropower remains the country’s dominant source of electricity due to its established infrastructure and cost-effectiveness. Nonetheless, the overdependence on hydropower has resulted in grid instability and periodic blackouts, particularly during seasonal water shortages [145]. Although hydropower can be cleaner and cheaper in some contexts, biogas provides a flexible and complementary solution, especially for rural or off-grid regions where organic waste accumulation is high. Therefore, expanding biogas generation—supported by appropriate pretreatment—remains critical in diversifying Ghana’s energy portfolio and enhancing energy resilience.

An appropriate way to address these issues is to use large-scale biogas generation, which would help manage the massive volume of waste while simultaneously satisfying energy demands. Studies have shown that pretreating feedstocks beforehand during anaerobic digestion usually results in a higher biogas output than using feedstock without treatment. The data (59.9%) indicate that a greater proportion of Ghana’s agricultural output consists of food crops. Since the waste from these food crops is typically lignocellulosic, pretreatment is necessary. In Ghana, lignocellulosic waste is present in large amount in the municipal solid waste (MSW) stream. This provides an additional reason for the need for pretreatment before employing anaerobic digestion for biogas production.

Pretreating biomass effectively requires several important components. Both in terms of capital and operating costs, the chosen pretreatment technique should be inexpensive [146]. Although there is a high energy demand in Ghana, the cost required for pretreatment and anaerobic digestion of waste should be as low as possible. From the above discussion and conclusion, it is known that biological pretreatment is less expensive and environmentally friendly compared to the other methods of pretreatment. As such, it is highly recommended to employ biological pretreatment in the treatment of waste generated in Ghana before anaerobic digestion.

9. Conclusions

This review emphasizes the central significance of pretreatment to optimize the efficiency of anaerobic digestion for biogas production. Different studies concur that mechanical, thermal, chemical, and biological pretreatments increase methane yields, and some combinations of pretreatment achieve over 100% increase. Each method has its drawbacks; however, physical processes are energy-depleting, chemical treatment can lead to inhibitory by-products, and biological processes, although environmentally friendly, can be more time-consuming in retention and demand strict microbial control. In the Ghanaian situation, the situation presents both an immediate challenge and a unique opportunity. The country’s municipal and agricultural economies generate enormous quantities of organic waste, hitherto underdeveloped and poorly managed. Biogas production is on a pilot scale, and treatment plant facilities are unable to match expanding needs. In a nation frequently subjected to routine power shortages and heavy dependence on hydropower, there is clearly a demand for competing sources of energy that are scalable and sustainable. Pretreatment, especially cost-effective and environmentally friendly biological pretreatment, is an attractive choice. It is a process that fits well with Ghana’s dominant lignocellulosic biomass from food crops and urban organic waste streams. Future Research and Development in Ghana should therefore concentrate on biological pretreatment processes and seek collaboration for capacity building, pilot-scale operation, and technology localization. Developing this research field can lead to improved waste management, energy security, and job creation across the country. The integration of pretreatment measures, especially low-cost, environmentally friendly practices, into Ghana’s anaerobic digestion system is not only recommended but also required for the country’s bioenergy transformation.