A Review on Biohydrogen Production Through Dark Fermentation, Process Parameters and Simulation

Abstract

This study explores biohydrogen production through dark fermentation, an alternative supporting sustainable hydrogen generation. Dark fermentation uses organic waste under anaerobic conditions to produce hydrogen in the absence of light. Key process parameters affecting hydrogen yield, including substrate type, microorganism selection, and fermentation conditions, were examined. Various substrates, such as organic wastes and carbohydrates, were tested, and the role of anaerobic and facultative anaerobic microorganisms in optimizing the process was analyzed. The research also focused on factors such as pH, temperature, and hydraulic retention time to enhance yields and scalability. Additionally, the study modelled the process using ASPEN Plus software 14. This simulation identifies the bottle necks of this process. Due to the lack of available data, modelling and simulation of the described processes in ASPEN Plus required certain approximations. The simulation provides insight into the key challenges that need to be addressed for hydrogen production. Future research should indeed explore current limitations, such as substrate efficiency, process scalability, and cost-effectiveness, as well as potential advancements like the genetic engineering of microbial strains and improved bioreactor designs.

1. Introduction

Burning fossil fuels has greatly increased greenhouse gases and polluted the environment. Since fossil fuels are non-renewable and will eventually be depleted, it is crucial to find alternative energy sources that are both renewable and environmentally friendly. Hydrogen is a promising alternative because it only produces water as a byproduct when combusted. Additionally, hydrogen can be produced through various methods, making it a renewable energy source. In recent years, hydrogen production has garnered increasing attention worldwide due to its potential role in advancing sustainable energy solutions. Hydrogen is poised to play a crucial role in the future of energy production, offering significant benefits over traditional fossil fuels. The energy released from hydrogen (122 kJ/g) is approximately 2.75 times higher than that produced by commonly used fossil fuels, highlighting its efficiency and potential as a cleaner energy source [1,2]. The literature review indicates that the majority of global hydrogen production is derived from fossil fuel combustion and steam reforming, with less than 5 percent coming from water electrolysis and less than 1 percent from biomass [3].

The production of biological hydrogen (bio-H2) is becoming more important due to the utilization of energy resources and its utility at ambient pressures and temperatures [3]. This process is less energy-intensive and more eco-friendly compared with conventional chemical methods. It can also use various agricultural waste products, which facilitates waste recycling [4].

Biohydrogen production can be achieved through four main technologies: photo fermentation, dark fermentation (DF), bio photolysis, and microbial electrolysis cells (MECs). Among these, DF stands out due to its higher hydrogen production rate and its adaptability to a wide range of organic substrates. This versatility not only reduces operational costs but also offers a sustainable method for revalorizing waste materials [5].

Photo fermentation converts organic acids into hydrogen and carbon dioxide through photosynthesis, facilitated by anaerobic bacterial strains. Hydrogen is produced when nitrogenase reduces molecular nitrogen, while also reducing protons to generate molecular hydrogen [6].

Bio photolysis is a process that harnesses light energy—whether from solar or artificial sources—to produce biohydrogen from water molecules through photosynthesis by photoautotrophic organisms. This process primarily occurs in green algae and cyanobacteria, with nitrogenase or hydrogenase being the key enzymes involved in biohydrogen production [7,8]. For optimal biohydrogen production via bio photolysis, the optimum light intensity, pH, and temperature should be applied, respectively [9].

MECs (microbial electrolysis cells) typically use two-chambered cells and operate on the principle of organic oxidation at the anode and reduction reactions at the cathode. This process, also known as electro-fermentation, involves bio-catalyzed electrolysis where microorganisms at the anode catalyzed the oxidation of an organic substrate, resulting in the generation of electrons, protons, and carbon dioxide [10].

Dark fermentation primarily utilizes anaerobic bacteria to produce biohydrogen. This process involves the use of anaerobic microorganisms in the absence of light, operating at temperatures ranging from mesophilic (25–40 °C) to thermophilic (50–65 °C) conditions, with some processes occurring at extreme thermophilic temperatures exceeding 80 °C [11]. It is one of the most extensively researched and promising technologies for biohydrogen production due to its higher production rates and its effectiveness in treating organic wastes. Various carbohydrate-rich substrates can be utilized in this process, including first-generation fuel crops like sugarcane, wheat, corn, and sugar beets, as well as second-generation biomass sources such as agricultural residues, industrial waste, and wastewater [12]. gasses such as CO₂, carbon monoxide (CO), and hydrogen sulphide (H₂S) are also released in this process.

Dark fermentation is a preferred biohydrogen production method due to its high hydrogen production rate, operational simplicity, and adaptability to various feedstocks, including organic waste. While other technologies such as photo fermentation, bio photolysis, and microbial electrolysis cells (MECs) offer advantages in hydrogen yield and efficiency under specific conditions, DF stands out for its practicality. Unlike photo fermentation and bio photolysis, DF produces a higher concentration of H₂ and supports microorganisms with a shorter doubling time, enhancing overall process efficiency. DF is particularly scalable and cost-effective, requiring low energy inputs and allowing for waste valorization, making it an attractive option for sustainable hydrogen production. Although other technologies may achieve higher yields under controlled laboratory conditions, DF remains the most viable for real-world applications when considering energy efficiency, operational feasibility, and total lifecycle environmental impact. DF outperforms other biohydrogen production methods in waste-rich, low-energy-input, and high-rate production environments, making it ideal for industrial biorefineries, wastewater treatment plants, and decentralized energy systems. While photo fermentation and MECs have advantages in specific setups, DF is the most practical and scalable solution for large-scale, sustainable biohydrogen production [1,13,14].

Lignocellulosic materials and agricultural wastes are promising feedstocks for biohydrogen production. However, these feedstocks require the addition of enzymes to undergo fermentation [15]. For large-scale profitability, DF requires low-cost substrates, as sugar-based feedstocks might be more economically viable for other uses. Consequently, researchers have explored various organic residues as alternative feedstocks. Among these, agricultural, industrial, and food wastes are the most extensively studied, likely due to their carbohydrate-rich compositions [16,17]. Biohydrogen production through dark fermentation (DF) is currently one of the most established biological methods, demonstrating practical readiness and potential for scale-up [18].

Although fermentation technologies have improved in recent years, biohydrogen production through fermentation remains limited to the lab scale [19]. In recent years, research activities in dark fermentation (DF) have increased, as reflected by the growing number of reviewed articles. Ghimire et al. [14] reviewed improvements in hydrogen yield and production rates achieved through various seed inoculum enrichment methods, bioreactor design modifications, and optimization of operational conditions within DF bioreactors. They describe how hydrogen yield can be improved by optimizing the design and operation of DF bioreactors [20]. The cost of hydrogen production can be reduced by using low-cost renewable feeds, such as waste biomass, as feedstocks [21,22]. Additionally, inoculum enrichment methods [23,24,25,26,27] enhance hydrogen yield, while the pretreatment of feed can further boost productivity [28].

Lukajtis et al. [6] reviewed research on bacterial strains used for hydrogen production from agro-industrial waste and lignocellulosic biomass. They investigated the effects of various parameters, including the type of raw material, processing methods, temperature, pH, substrate concentration, hydrogen partial pressure, hydraulic retention time, inoculum preparation, and the type and operating conditions of the reactor, on the yield of DF.

Ahmad et al. conducted a literature review on biohydrogen production through dark fermentation. They examined the metabolic pathways involved in biohydrogen production and identified inhibitory substances that negatively impact dark fermentation. Additionally, they provided a brief review of pilot-scale biohydrogen production using various substrates through dark fermentation [13].

The mixed cultures composed of diverse bacteria are efficient for biohydrogen production and were compared with pure bacteria by some researchers [29,30,31,32,33]. They found that mixed cultures are more appropriate to overcome operational difficulties such as a metabolic shift in response to environmental stress, and the need for a sterile environment. Mohanakrishna and Pengadeth [34] reviewed the use of mixed-culture bacteria for biohydrogen production and found that mixed cultures are more suitable than pure cultures, particularly for scaling up DF.

According to the literature, most DF studies have been conducted at the lab scale, with no reports found on industrial or full-scale implementations. Only a limited number of studies have explored the pilot-scale application of DF processes [35,36,37]. Process modelling and simulation are appropriate tools for the identification of technical and fundamental challenges that exist in biohydrogen production. No study was found in the literature on process simulation for biohydrogen production. Furthermore, there is a lack of literature on the process modelling of biohydrogen production using the DF process.

The aim of this research is to develop a comprehensive process simulation for biohydrogen production through fermentation. Initially, the DF process is thoroughly explained, with a focus on identifying key process parameters crucial to optimizing hydrogen production. This includes an in-depth analysis of the type of microorganisms involved and the specific fermentation parameters that affect their activity. Based on these insights, a detailed process design for hydrogen production is created, followed by the modelling and simulation of the system to evaluate its performance. The goal is to use these simulations to better understand the process dynamics and optimize conditions for a maximum hydrogen yield. The process simulation is performed based on simple assumptions to highlight the bottlenecks of this process.

2. Dark Fermentation Process

DF uses anaerobic bacteria to convert organic compounds into hydrogen without the need for light. This allows DF to produce hydrogen continuously, independent of light availability, which contributes to its higher biohydrogen production rate and hydrogen content compared to other biohydrogen methods. Additionally, DF is a simple process with stable operation, requiring less investment, thus making it an attractive option for biohydrogen production [38]. It has the potential to replace of fossil fuel for biohydrogen production as a renewable energy source.

Figure 1 shows the pathways for biological hydrogen production through DF. In this process, the biomass is broken down into sugar molecules, such as glucose. Hydrogen-producing microorganism then convert glucose into pyruvate via glycolytic pathways, generating adenosine triphosphate (ATP) from adenosine diphosphate (ADP) and reduced nicotinamide adenine dinucleotide (NADH). The subsequent conversion of pyruvate depends on the type of microorganism involved. Facultative anaerobes typically use the pyruvate formate lyase (PFL) pathway, while strict anaerobes generally employ the pyruvate ferredoxin oxidoreductase (PFOR) pathway [39]. Acetyl coenzyme A (acetyl-CoA) is produced in both pathways and can be further converted into various compounds, including acetate, butyrate, butanol, and ethanol, during the DF of complex carbohydrates by mixed anaerobic microbiota. The resulting intermediates and byproducts can vary widely depending on operational parameters such as substrate type, substrate loading rate, pH, temperature, and other environmental and operating conditions. These factors also significantly influence the microbial community structure within bioreactors [14].

The differences in metabolic pathways between facultative and strict anaerobes significantly impact hydrogen production efficiency and yield. Facultative anaerobes, capable of switching between aerobic and anaerobic metabolism, often consume oxygen rapidly, creating favourable anaerobic conditions for strict anaerobes that specialize in fermentative hydrogen production. Strict anaerobes, such as Clostridium spp., primarily rely on fermentative pathways, leading to higher hydrogen yields under optimal conditions. However, their efficiency is influenced by factors such as substrate utilization patterns and byproduct formation [40,41].

Variations in operational parameters directly affect the metabolic distribution of products in dark fermentation (DF) processes. Substrate type dictates the dominant metabolic pathways; for instance, carbohydrate-rich substrates typically favour acetate and butyrate pathways, promoting higher hydrogen yields, whereas protein- or lipid-rich substrates may lead to increased solvent production (e.g., ethanol and butanol), potentially reducing hydrogen output. Organic loading rate (OLR) influences microbial community dynamics; excessively high OLR can lead to substrate inhibition and the increased accumulation of reduced end-products like lactate and ethanol, which are less favourable for hydrogen production. pH plays a crucial role in regulating metabolic shifts; acidic conditions (<5.5) tend to favour solventogenic pathways (ethanol, butanol), whereas neutral to slightly acidic conditions (5.5–6.5) enhance acetate and butyrate production, optimizing hydrogen yields [39,41,42,43].

A stable DF process requires a balanced microbial consortium, where changes in substrate composition, OLR, and pH can cause shifts in community structure, favouring either hydrogen-producing bacteria or competing microbial groups (e.g., methanogens, lactic acid bacteria). Adaptation mechanisms such as selective pressure, microbial succession, and syntrophic interactions play key roles in maintaining system performance. Operational instability, such as pH fluctuations or substrate shocks, can lead to process failure due to microbial washout or the dominance of less efficient pathways. Thus, optimizing these parameters is essential for enhancing process efficiency and scalability in hydrogen production bioreactors [41,44].

The biochemical reactions involved in DF by facultative anaerobes can be illustrated by reactions (1) and (2), as shown.

$${\mathrm{C}}_6{\mathrm{H}}_{12}{\mathrm{O}}_6+2{\mathrm{H}}_2\mathrm{O}\to 2{\mathrm{C}\mathrm{H}}_3\mathrm{C}\mathrm{O}\mathrm{O}\mathrm{H}+2{\mathrm{C}\mathrm{O}}_2+4{\mathrm{H}}_2$$

(1)

$${\mathrm{C}}_6{\mathrm{H}}_{12}{\mathrm{O}}_6\to {\mathrm{C}\mathrm{H}}_3(\mathrm{C}{\mathrm{H}}_2{)}_2\mathrm{C}\mathrm{O}\mathrm{O}\mathrm{H}+2{\mathrm{C}\mathrm{O}}_2+2{\mathrm{H}}_2$$

(2)

Acetate and butyrate are the primary products of this process. As these reactions demonstrate, glucose can be converted into 4 moles of hydrogen per mole of substrate when acetic acid is produced as a co-product (Equations (1) and (2)) moles of hydrogen per mole of substrate when butyric acid is the co-product (Equation (2)). Therefore, the acetate pathway is preferred because it yields more hydrogen than the butyrate pathway [45]. However, the actual hydrogen yield is lower than the theoretical yield because a portion of the substrate is used for biomass production, and the degradation of substrates can follow alternative biochemical pathways that do not produce hydrogen [46,47]. Under certain conditions, metabolic pathways shift toward ethanol and acetate production, reducing the stoichiometric hydrogen yield to 2 moles of hydrogen per mole of glucose [23].

$${\mathrm{C}}_6{\mathrm{H}}_{12}{\mathrm{O}}_6+2{\mathrm{H}}_2\mathrm{O}\to {\mathrm{C}\mathrm{H}}_3\mathrm{C}\mathrm{O}\mathrm{O}\mathrm{H}+{\mathrm{C}}_2{\mathrm{H}}_5\mathrm{O}\mathrm{H}+2{\mathrm{C}\mathrm{O}}_2+2{\mathrm{H}}_2$$

(3)

DF primarily utilizes anaerobic bacteria to ferment organic compounds into hydrogen without the need for light. As a result, DF can continuously generate hydrogen without relying on light availability. This advantage contributes to its high biohydrogen production rate and hydrogen content [38].

3. Process Parameters

To design an optimal process for hydrogen production via DF, it is essential to identify the key parameters that influence the process. Hydrogen production under DF is complex, with several factors affecting its efficiency. The type of substrate and microorganism are the primary factors influencing hydrogen production. Additionally, operating parameters for the bioreactor, such as temperature, pH, and hydrogen partial pressure, play crucial roles [14]. These parameters are reviewed below.

3.1. Types of Substrate

Selecting a suitable substrate has a significant effect on any fermentation process. In the DF process, raw materials containing organic compounds serve as substrates for hydrogen production. Monosaccharides and disaccharides such as glucose or lactose are the desired carbon sources for the conversion of microorganisms in DF. Renewable sugar sources, including starch, cellulose, and hemicellulose, are primarily found in plants as polymers. Using raw materials rich in starch, which can be easily hydrolyzed into simple carbohydrates, offers particular advantages [48].

Sugars like glucose, xylose, and sucrose are biodegradable substrates that have been applied for DF since the 1980s [49]. These substrates do not require complicated processing and produce the highest yields [50]. However, they are not suitable for large-scale applications because their costs are too high [6].

Renewable resources like lignocellulose or starch waste can be used for continuous large-scale hydrogen production. These materials, known as second-generation substrates, require pretreatment before use in the DF process due to their initially low hydrogen yield. To enhance hydrogen production, these substrates should be processed to change long polymer chains into simple or short oligomers [6].

The organic fraction of readily biodegradable municipal waste is suitable for the DF process. These materials are primarily from households, restaurants, and food processing plant residues, which contain materials rich in polysaccharides, proteins, lipids, and simple carbohydrates. Simple sugars like glucose and sucrose offer high bioavailability and efficient hydrogen production, but their cost can be prohibitive for large-scale applications. Conversely, complex substrates, such as lignocellulosic biomass, municipal waste, and industrial effluents, are more affordable but require extensive pretreatment to break down polymers into fermentable monomers [51]. For managing economic and efficiency Trade-offs, the pretreatment optimization of substrates is performed. Cost-effective pretreatment methods (e.g., enzymatic hydrolysis, mild acid hydrolysis, or anaerobic digestion) can enhance hydrolysis efficiency while minimizing operational costs and inhibitory byproducts. Furthermore, the co-digestion of mixed substrates—blending simple and complex substrates (e.g., glucose with agricultural residues)—balances bioavailability and cost, improving microbial adaptability and hydrogen yield. The utilization of industrial byproducts and readily available industrial effluents (e.g., dairy wastewater, food processing waste) as feedstock reduces raw material costs while providing valuable organic content [52,53]. The optimal substrate concentration can be determined by conducting laboratory-scale batch fermentations. Applying kinetic models (Monod or substrate-inhibition models) enables predictive control over substrate loading rates to prevent inhibitory effects. Furthermore, using online sensors for volatile fatty acids (VFAs), pH, and hydrogen production rates ensures real-time adjustments in feed rates to maintain optimal fermentation conditions [54].

Sewage sludge contains a high concentration of simple carbohydrates, disaccharides, and peptides. To generate hydrogen from sewage sludge, it is essential to eliminate bacteria that consume hydrogen during their metabolic processes, particularly methanogenic bacteria. This can be achieved through pretreatment methods such as heat shock, ultrasonication, acidic or alkaline treatment, or by supplementing with organic materials [55,56].

The composition of these wastes fluctuates and pretreatment methods such as filtration, dilution, or nutrient supplementation can help to standardize feedstock composition. Enriching microbial communities with diverse metabolic capabilities enhances resilience to substrate variability. Furthermore, implementing feedback loops for pH, OLR, and VFA concentrations enables real-time process optimization to counteract variations in feedstock composition [57,58].

Table 1. Pros and cons of different types of substrates for dark fermentation.

Substrate	Example	Advantage	Disadvantage	Hydrogen Yield (Mole H2/Mole Substrate)	Reference
Monosaccharides	Glucose, D-Xylose	Biodegradable substrate, high yield	High cost for industrial level	4, 0.95	[59]
Disaccharides	Sucrose, lactose	Biodegradable substrate, high yield	High cost for industrial level	6, 3	[50]
Organic municipal waste	Kitchen waste, kitchen garbage	Low cost and availability	Pretreatment is needed	72 cm3 H2/g VS, 66 cm3 H2/g VS	[37] [60]
Manure	Swine manure, dairy manure	Reduce the gas pollution, low cost	Wastewater treatment is needed	1.63, 31.5 cm3 H2/g TVS	[61] [62]
Agriculture waste	Cornstalk, bagasse, wheat straw	Availability, low cost	Lignin present in waste resists on biodegradation	6.38 mole/kg substrate 2.3, 3.8	[63] [64] [65]
Industrial waste	Cheese whey, glycerol waste, brewery wastewater	Reduce the cost of waste treatment, low cost	Presence unwanted component inhibit the process	0.78, 0.33, 2	[66] [67] [68]

TVS: total volatile solid; VS: volatile solid.

provides a brief classification of suitable substrates for the DF process, highlighting their advantages and disadvantages. According to the table, renewable resource substrates rich in lignocellulose or starch, such as organic municipal waste, manure, agricultural waste, industrial waste, and effluent, are identified as suitable sources. However, these renewable sources need pretreatment and hydrolyzation into simple molecules.

The substrate concentration is a critical factor in the DF process. High concentrations can lead to reduced hydrogen production, as is common in many biological processes. Optimal hydrogen yields are generally achieved with dilute substrate concentrations, highlighting the importance of diluting the substrate to an ideal range [48]. Furthermore, substrate recycling and wastewater treatment are vital components of the DF process and should consider for the scale-up process.

3.2. Microorganism Type

In designing an efficient biohydrogen production process, selecting an appropriate microorganism is the most important step. Various types of microorganisms capable of producing hydrogen are found in environments, such as soil, wastewater, sludge, and compost. These materials can therefore serve as potential sources of inoculum for fermentative hydrogen production. Hydrogen-producing microorganisms exist in environments either as single species or as mixtures of various species. The choice of strain largely depends on the type of substrate used. Bacteria involved in DF process are anaerobic and can be classified into two groups. The first group is based on oxygen sensitivity, divided them into strict anaerobes and facultative anaerobes. The second group is determined by the temperature ranges at which bacteria exhibit the highest growth rate and activity [6].

Strict anaerobic microorganisms require completely oxygen-free conditions. These include anaerobes such as Clostridia, Methylotrophs, Methanogenic bacteria, Archaea, and Rumen bacteria [69]. The most used obligate anaerobes belong to the genus Clostridium, producing hydrogen in the logarithmic growth phase. As these microorganisms enter the stationary phase, their metabolism shifts toward producing liquid organic compounds, particularly volatile fatty acids [22]. Facultative anaerobes are organisms that primarily generate ATP through aerobic respiration when oxygen is available, but can switch to fermentation when oxygen is absent. Their ability to tolerate oxygen makes them easier to use in dark fermentation processes. Furthermore, a high oxygen partial pressure in the reactor does not negatively affect the fermentation yield. This group includes Enterobacteriaceae, a large family of Gram-negative, non-spore-forming bacteria [6]. The metabolic pathway of these microorganisms involves glucose metabolism, leading to hydrogen production. The end-products of this process include butyric, lactic, and formic acids, along with small amounts of ethanol.

Mixed or pure cultures of these strains can be applied for hydrogen production. Mixed cultures are more practical than pure cultures because the production of hydrogen is simpler to operate and control [3]. However, some undesirable microorganisms that might be present in mixed cultures reduce the total hydrogen yield, either by consuming the hydrogen produced or by altering the biochemical pathways of the hydrogen [23]. Mixed bacterial cultures that can produce hydrogen abundantly are commonly found in municipal sewage, composts, and organic waste, from which they can be isolated.

The metabolic pathways for pure cultures are understood well, but they require a sterile environment and are sensitive to changes in operational parameters. In contrast, mixed cultures can operate under non-sterile conditions and are easier to manage on a large scale. However, due to the presence of hydrogen-consuming strains, pretreatment is necessary [34]. Pretreatment is necessary because mixed cultures often contain a variety of microbial species, some of which may compete with or inhibit the desired hydrogen production process. The pretreatment method is summarized in

Table 2. Pretreatment method for mixed cultural dark fermentation.

Pretreatment Method	Operating Condition	Advantage	Disadvantage	Comment	Reference
Heat shock	80–121 °C	Increase cell permeability Simple and cost-effective	Cell damage Energy consumption	Treating the culture with a high temperature kills non-spore-forming bacteria.	[70,71]
Freezing and thawing	−25 to −10 °C freezing Thawing Incubating 20–30 °C	Cell disruption Low cost	Time-consuming Limited scalability	Inoculum is frozen and this is maintained, followed by thawing and the incubating	[72]
Aeration	Time: 2 hours–14 days	Enhance microbial growth Low cost Scaleable	Energy-intensive	use of the air to eliminate the bacteria sensitive to oxygen.	[73,74]
Acid and alkaline treatment	Acid: pH 2–4 Alkaline:pH 10–12	Efficient biomass breakdown Widely used	Corrosive chemical disposal	Adjustment of the pH of the inoculum to a value that microorganisms cannot survive.	[75,76]
Chemical treatment	2-bromoethane sulfonate (2-BES) 2-bromoethane sulfonic acid (2-BESA)	Selective breakdown High efficiency	Toxic byproduct High cost	Chemical compounds can block metabolic pathways of methanogenic bacteria selectively.	[77,78]
Microwave	Frequencies ranging from 300 MHz to 300 GHz	Rapid and efficient Scalable	Uneven heating Energy consumption	This applies for the pretreatment of lignocellulosic biomass for enhanced hydrolysis	[79]
Ultraviolet	Wavelengths varying from 10 nm to 400 nm	Effective for sterilization Environmental friendly	Limited penetration Cost	Ultraviolet lies between visible light and x-rays in the electromagnetic spectrum It applies for inactivation of bacterial population	[79]

. The choice of pretreatment method depends on the specific characteristics of the mixed culture, the types of microorganisms present, and the operational conditions of the fermentation process.

3.3. Fermentation Process Parameters

Anaerobic microorganisms see major application during hydrogen production in DF. The growth of microorganisms is highly dependent on the condition of the fermentation process. Temperature has a significant effect on the growth rate of microorganisms. The optimal temperature varies depending on the types of microorganism: mesophiles (25–40 °C), thermophiles (45–65 °C), extreme thermophiles (65–80 °C), and hyperthermophiles (above 80 °C) [80]. Literature studies have shown that the temperature can affect the metabolic pathways and alter the composition of the byproducts of DF [14]. Valdez-Vazquez et al. [81] reported that thermophilic fermentation yields higher H₂ production compared to mesophilic conditions. Additionally, acetic acid was the dominant byproduct in thermophilic digestion, while butyrate was more prevalent in mesophilic digestion. In the DF of food waste, acetate was the primary end-product in mesophilic cultures, whereas thermophilic cultures produced higher levels of butyrate and hydrogen [82]. DF is limited at high temperatures due to economic considerations [83]. While thermophilic conditions can be cost-effective, offering higher hydrogen yields and the ability to process more complex substrates, extreme thermophilic and hyper thermophilic conditions require substantial energy inputs, which diminishes the profitability of the process [84].

Operational pH determines the optimal metabolic pathways for hydrogen production and helps to inhibit hydrogen-consuming processes that may occur simultaneously [85]. The pH value has a large effect on the activity of various microorganism in a mixed culture. It changes the metabolic pathways of microorganisms, as well as their morphology and cell structure [86]. All enzymes involved in bacterial metabolic processes are active within a specific pH range, with peak activity occurring at an optimal pH. Maintaining and controlling the pH at this optimal level is crucial during fermentation. This is because hydrogen production is complemented by the formation of acids such as acetic, lactic, butyric, and propionic acid. These acids lower the medium’s pH, inhibiting the enzymes responsible for hydrogen production [76]. A low pH (below 5) also impairs the bacteria’s ability to maintain appropriate intracellular pH levels [87]. According to the literature, the optimal initial and operational pH values depend on the specific bacterial strain or source of mixed cultures. Additionally, some studies suggest that the type of substrate in the medium influences the optimal pH [4]. Generally, the optimal pH for fermentative hydrogen production ranges from 5.0 to 7.0, which aligns with the pH range favourable for bacterial growth [23]. pH has a significant impact on hydrogen production yield and the concentration of other metabolic byproducts [88,89]. Studies show that at low pH values, acetic and butyric acids are the primary byproducts, while increasing the pH leads to higher concentrations of ethanol, propionic acid, and lactic acid [90]. The election of the operational pH is dependent on the substrate type and organic loading rate. The optimum pH for organic food waste varies from 4.5 to 7 and for lignocellulosic waste it varies from 6.5 to 7, whereas a neutral pH is optimal for animal manure [91].

The hydraulic retention time (HRT) influences hydrogen yield in DF processes. It measures the average time a substrate stays in the fermentation chamber for. HRT significantly impacts hydrogen productivity in continuous or semi-continuous DF operations. The rate of hydrogen production increases within a certain HRT range, but exceeding the optimal HRT value results in a decline in production [92]. The value of HRT is highly dependent on the substrate type. HRT can influence substrate hydrolysis, impacting the production of intermediates and products, thereby affecting fermentative H₂ production. In addition to hydrolysis, HRT can also serve as a control parameter for methanogenic activity [14]. Several studies were conducted to determine the optimal HRT for various substrate types. They reported that HRT values vary within the range of 2 to 24 h [92,93,94]. During continuous culture growth, HRT is typically reduced gradually from longer to shorter intervals. This allows microorganisms to adapt to new conditions and prevents the target bacteria from being washed out. As HRT shifts, the microbial population undergoes dynamic changes, causing some species to disappear while others emerge [95].

The partial pressure of hydrogen is another factor influencing hydrogen production. During hydrogen production, the enzyme hydrogenase catalyzes the oxidation and reduction of ferredoxin in a reversible process. From a thermodynamic perspective, a rise in hydrogen partial pressure can promote ferredoxin reduction, which inhibits further substrate conversion into hydrogen. As hydrogen partial pressure increases, hydrogen production decreases, while the concentrations of other metabolic byproducts, such as lactic acid, ethanol, acetone, and butanol, increase [14,48]. Lowering the partial pressure in the reactor’s headspace enhances the mass transfer of hydrogen from the liquid to the gas phase [96]. Therefore, it is crucial to remove hydrogen as it forms to maintain a high and consistent production rate. Currently, several methods exist to reduce the pressure of hydrogen in a reactor, and thereby the dissolved hydrogen concentration in the liquid medium. Stirring the media is the easiest method for the removal of the gas phase from the fermenter [6].

Hydrogen yields in DF depend on the mode of fermentation, which can be batch, semi-continuous, or continuous. Batch reactors are mostly used in laboratory settings for preliminary research to determine the optimal parameters of dark fermentation [48]. These reactors have a straightforward and cost-effective design, allowing for the convenient control of fermentation parameters, such as temperature and pH. However, industrial-scale hydrogen production requires the use of continuous reactors, as they offer higher process efficiency. Continuous reactors are usually initiated in batch mode to allow for proper inoculum preparation and pretreatment. The success of the transition to a continuous mode depends significantly on the start-up strategy [95]. The most popular bioreactor configurations include continuous stirred tank reactors (CSTRs), upflow anaerobic sludge blanket reactors (UASBs), anaerobic fluidized bed reactors (AFBR), and membrane bioreactors (MBR) [6]. The advantages and disadvantages of various bioreactor configurations are presented in Table 3.

Recently, the Dark Fermentation Moving-Bed Biofilm Reactor (DF-MBBR) process was proposed for the first time as a coupled system integrating the DF-MBBR treatment of organic residues with the bioleaching of concrete debris. This innovative approach enables the simultaneous production of biohydrogen, aggregates, and calcium-based compounds for reuse in the construction industry while facilitating the disposal of both biodegradable and construction and demolition waste. A key advantage of this configuration is the elimination of physical or mechanical treatment for biomass separation and recirculation, making it highly compatible with bioleaching processes [33].

CSTRs are normally used for continuous fermentation. These reactors have a simple design and allow for the easy modification of operating conditions. Stirring ensures there is a homogeneous environment within the media and promotes effective contact between microorganisms and substrates [97,98]. The MBR is a commonly used reactor for fermentation processes; however, it includes a membrane or membrane system. Membranes can be positioned externally (side-stream MBRs) or immersed within the reactor. The immersed design offers advantages, including lower operating costs and more compact membranes. By retaining microorganisms inside the reactor, the membrane helps maintain a consistently high biomass concentration [23].

The membrane enables the selection of an optimal HRT, independent of the activated sludge retention time, allowing for better control of process parameters [59].

Hydrogen production rates were compared between UASBs and CSTRs. The results indicate that the UASB achieved higher production rates than the CSTR at low retention times, with 19.05 mmol H₂/h/L versus 8.42 mmol H₂/h/L at a 2 h HRT. However, the CSTR consistently produced higher hydrogen yields (mmol H₂/mol glucose) across all tested HRTs. These findings suggest that a balance must be struck between technical efficiency (based on hydrogen yield) and economic efficiency (based on production rate) when selecting between these two systems [99].

Table 3. The advantages and disadvantages of different bioreactor configurations.

Bioreactor	Advantage	Disadvantage	Reference
CSTR	Simple design. Easy adjustment of operating conditions. Homogeneous conditions with stirring. Precise control of pH and temperature.	Limited biomass concentration. Biomass removal and inactivation of microorganisms.	[23,48,97,98]
MBR	Lower operating costs. Small membranes. The membrane retains microorganisms inside the reactor. Optimal HRT. Control of process parameters.	Membrane fouling. High operating cost.	[23,59]
UASB	High effectiveness of hydrogen. Production. Short HRT. Stable operating conditions. Good substrate conversion.	Sensitivity to channelling effects. Long. Initiation time.	[100,101]
AFBR	Good. Mass and heat transfer. Uniform particle mixing. Can be operated continuously.	High energy consumption. Difficulties in scale-up.	[102,103]

Table 3. The advantages and disadvantages of different bioreactor configurations.

Bioreactor	Advantage	Disadvantage	Reference
CSTR	Simple design. Easy adjustment of operating conditions. Homogeneous conditions with stirring. Precise control of pH and temperature.	Limited biomass concentration. Biomass removal and inactivation of microorganisms.	[23,48,97,98]
MBR	Lower operating costs. Small membranes. The membrane retains microorganisms inside the reactor. Optimal HRT. Control of process parameters.	Membrane fouling. High operating cost.	[23,59]
UASB	High effectiveness of hydrogen. Production. Short HRT. Stable operating conditions. Good substrate conversion.	Sensitivity to channelling effects. Long. Initiation time.	[100,101]
AFBR	Good. Mass and heat transfer. Uniform particle mixing. Can be operated continuously.	High energy consumption. Difficulties in scale-up.	[102,103]

4. Modelling and Simulation

Most studies on hydrogen production using the DF process have been conducted at the laboratory scale. To the best of our knowledge, there are no studies in the literature regarding the industrial-scale application of this process. Only a few studies have explored pilot-scale applications of DF processes [35,36,37,104,105]. Process modelling and simulation methods facilitated the scale-up of this process, as laboratory-scale experiments alone were insufficient to fully understand the underlying science of the fermentation process. Therefore, there is a need to develop suitable models to comprehend the complex operation of biohydrogen production. In this regard, the process modelling of biohydrogen can help evaluate the design, scale-up, optimization, and control of the system. To achieve these objectives, a reliable process model is essential.

Process modelling allows us to create detailed representations of the fermentation process, which can be used to simulate different operating conditions and configurations. This approach helps to identify optimal parameters for maximize hydrogen yield and production rates. Furthermore, modelling and simulation enable a deeper understanding of the interactions between different process variables, providing insights into the underlying mechanisms of biohydrogen production.

When it comes to scaling up from laboratory or pilot-scale systems to industrial-scale operations, modelling and simulation become even more valuable. They provide a cost-effective way to predict the performance of large-scale reactors, minimizing the risks associated with scaling up. By using reliable models, it is possible to evaluate the effects of scale-up on process stability, efficiency, and product yield without the need for extensive experimental trials. Additionally, these models can assist in designing control strategies and optimizing reactor configurations, further enhancing the economic viability and technical efficiency of biohydrogen production at an industrial scale.

4.1. Process Description

A general block diagram for the hydrogen production is designed and depicted in Figure 2.

Biomass is mixed with water in a mixing tank. Straw, a common agricultural residue, is considered a valuable biomass. Wheat, maize, and rice straw are among the largest agricultural residues worldwide. Consequently, utilizing this abundant biomass as a feedstock for dark fermentative hydrogen production could have significant potential for future feedstock supply [14].

Straw is a lignocellulosic residue composed of cellulose, hemicellulose, and lignin. Due to its complex structure, the bioconversion of straw is challenging, making pretreatment essential for effective utilization [106]. Acid and alkali pretreatment methods are commonly used to degrade straw. These methods alter the cellulose structure and enhance sugar yield in a relatively short time [107]. Acid or base treatment takes place in the mixing tank, after which the slurry is pumped into the hydrolyser reactor. In this reactor, hydrolytic enzymes are added to convert carbohydrates into monomeric sugar molecules through a batch process.

The effluent from the hydrolysis reactor passes through a filter to separate solids from the liquid stream. The resulting sugar solution is then fed into a fermenter, where microorganisms convert the sugars into hydrogen according to their metabolic pathways. The produced gases are separated from the liquid phase and sent to a gas separation unit for hydrogen purification. A gas separation unit is used to separate hydrogen from carbon dioxide and water. This unit can be designed as a membrane or an adsorption column, utilizing a suitable adsorbent. The liquid waste from the fermenter is directed to the wastewater treatment section, where the water is treated and recycled back into the process.

4.2. Process Simulation

ASPEN Plus V14.0 is used for simulation of hydrogen production. The software has a large data bank that enables us to calculate the materials’ properties. The model was employed to simultaneously solve the steady-state governing equations of mass and energy for different compositions of solid streams utilizing the integrated “SOLIDS” property package. A steady state model is developed for this process. gasses are modelled as conventional components, and the biomass and ash are modelled as non-conventional components. Also, the “Mixed”, and “NCPSD” packages were chosen as the sub-stream class in the simulations. The biomass, as a non-conventional component, is modelled in two stages. In the first stage, biomass is decomposed into its ultimate and proximate component in a RYield reactor and, in the second stage, the component is converted into sugar molecules in a Rstoich reactor. The biomass section was simulated and validated with the literature results. Due to the lack of experimental data for the DF process, the model’s reliability has only been partially confirmed. The models were examined to ensure that the calculations were conducted without any ‘error’ or ‘warning’ messages, indicating that the energy and mass balances were within acceptable limits. The benefit of this method is that it allows for a preliminary validation of the model, even in the absence of experimental data for the DF. By ensuring that the calculations are free of ‘error’ or ‘warning’ messages, the model demonstrates that the energy and mass balances are within acceptable limits. This suggests the model is functioning correctly in terms of basic conservation laws, which provides a foundation for its reliability, even though its full accuracy has not yet been confirmed through direct experimental comparison.

Figure 3 represent the process simulation model, which comprises four units: hydrolyzation and filtration, the fermenter, water treatment and recycling, and gas treatment and recycling. In this figure, straw is crushed and mixed with water in a mixer (CH-101) and then fed into the biomass reactors (BIOMA-R1 and R2), where it is converted into glucose. The resulting mixture of glucose, water, and unconverted straw is then separated using a solid–liquid separation unit (SP-101). DF focuses more on substrate breakdown and gas production in anaerobic conditions. The model simulates different operational parameters (temperature, pH, substrate concentration) and their influence on hydrogen production rates, allowing for the optimization of process conditions.

Straw is considered as a biomass and the proximate and ultimate analyses are reported in

Table 4. Proximate and ultimate analysis of straw [108].

Ultimate Analysis (wt%)		Proximate Analysis (wt%)
C	42.5	Moisture	8.3
H	6.3	Volatiles	70.3
N	0.8	Fixed carbon	18.7
O	38.7	Ash	11
S	0.2
Cl	0.5
LHV (MJ/kg)		16.32

[108]. After the hydrolysis of straw (BIOMA-R), the resulting glucose solution is fed into the fermenter, where it is converted into hydrogen via reaction (1) (BC-101). The acetate pathway is chosen for this step because it yields a higher amount of hydrogen, producing 4 moles of hydrogen per mole of glucose. Phosphate-buffered solution (PBS) is added to the fermenter to prevent pH fluctuation.

Due to the high complexity of metabolic pathways, formal kinetic studies are rarely reported in the literature, although some kinetic data are available for specific microorganisms. For simplicity, in this research, hydrogen production is modelled as first-order kinetics [109].

$$R_{H2}=k·S$$

(4)

where R_H2 is the rate of hydrogen production (mol/L·h), k is the first-order rate constant (h⁻¹), and S is substrate concentration (glucose, mole/L). The rate constant is expressed via the Arrhenius equation, as follows:

$$k=k_0{e}^{{\displaystyle \frac{-E}{RT}}}$$

(5)

where k₀ is the Arrhenius constant (pre-exponential factor), E is the activation energy, R is the universal gas constant (8.314 J/mol·K), and T is the absolute temperature. The activation energy and Arrhenius constant of the DF process for glucose were extracted from the literature [110,111] and set to 67.3 kJ/mol, the value which was derived for E. aerogenes (NCIMB 10102) [110].

It is worth noting that hydrogen production increases up to 40 °C, after which it decreases linearly. The activation energy calculated for this range, associated with thermal inactivation, is approximately 118 kJ/mol—which is significantly lower than the values reported for microbial death, which range between 290 and 380 kJ/mol [112].

The output from the fermenter is fed into a separator where the gas and liquid are separated (S-101). The gas stream passes through a condenser unit to reduce its water content (C-102), and then it is directed to a gas separation unit to separate carbon dioxide from hydrogen (GS-01). The resulting hydrogen is then sent to a compressor to be pressurized (CO-102). Meanwhile, the liquid stream from the first separator is directed to another separator to separate acetic acid from water and other products (GS-02). The remaining liquid is sent to a water treatment and recycling unit, where water is separated from the residual contents of the fermenter and reused in the process.

4.3. Simulation Results

The simulation was conducted based on a hydrogen production rate of 5 tons per day. Due to the lack of available data, the modelling and simulation of the described processes in ASPEN Plus required certain approximations. It was assumed that the straw was directly converted into glucose molecules, with the pretreatment process being ignored. Straw was used as the biomass source and mixed with water to form a slurry, which was stored in a holding tank and treated with either acid or base. The slurry is then pumped into an acid hydrolysis reactor, where enzymes break down carbohydrates into monomeric sugar molecules, primarily glucose. The concentration of glucose was set to fall in the range reported by the literature for glucose (1–10 g glucose/L) [4]. The solids are removed using a filter press, and the liquid phase is sent to a makeup tank where nutrients and a phosphate-buffered solution are added to regulate pH. The nitrogen source plays a crucial role in the fermentation process and is provided by the addition of ammonium chloride.

The prepared feed is then pumped into a fermenter, where the glucose solution is converted into hydrogen and acetic acid under a first-order reaction until all glucose is consumed. The temperature of the fermentation process is set at 25 °C. During fermentation, the gas is separated from the liquid. The gas phase, containing hydrogen, carbon dioxide, and water vapor, is sent to a condenser to reduce humidity. The gas mixture is then processed through an adsorption or membrane separator to remove carbon dioxide from the hydrogen. It was assumed that 98% of carbon dioxide was separated from hydrogen. Furthermore, the hydrogen product passes through a compressor to increase its pressure to 4 bar.

The liquid phase from the fermenter, containing acetic acid, is directed to a separation column, where the acetic acid is separated. The remaining solution is sent to a wastewater treatment system, where water is separated from other materials and recycled back into the process. The simulation results are presented in

Table 5. ASPEN Plus simulation results for temperature, pressure, and composition of various streams in kg/h for hydrogen production from straw in dark fermentation process with consideration of acetate pathway.

Streams No.	1	2	3	4	5	6	7	8	9	10	11	12	13
Temp. (°C)	25	25	25	50	50	34	20	20	20	25	25	25	25
P (Bar)	1	1	1	1	1	2	4	4	4	1	1	1	3
F (kg/h)	10,000	105,144	115,144	115,144	35,267	79,877	2733	275	2458	77,144	3984	73,160	65,144
Phase	Liquid	Liquid	Liquid	Liquid	Liquid	Liquid	Vapor	Vapor	Vapor	Liquid	Liquid	Liquid	Liquid
mBiomass	10,000	0	10,000	0	0	0	0	0	0	0	0	0	0
mash	0	0	0	1016	1016	0	0	0	0	0	0	0	0
mCO2	0	0	0	0	0	0	2504	50	2454	0	0	0	0
mH2O	0	105,144	105,144	105,912	31,773	74,139	0	0	0	73,114	731	72,381	65,144
mH2	0	0	0	0	0	0	229	225	4	0	0	0	0
msolid	0	0	0	2433	2433	0	0	0	0	0	0	0	0
mCH3COOH	0	0	0	0	0	0	0	0	0	3417	3246	171	0
mglucose	0	0	0	5694	0	5694	0	0	0	569	5	564	0
mNH4Cl	0	0	0	68	34	34	0	0	0	34	1	34	0
msulphur	0	0	0	21	11	10	0	0	0	10	1	10	0

. In this Table, the stream number are indicated based on the numbers in Figure 2.

The simulation results indicate that 10 tons per hour of straw can produce approximately 5694 kg per hour of glucose. This amount of glucose is reasonable and aligns with the typical composition of straw. As previously mentioned, straw is composed of cellulose, hemicellulose, and lignin, which typically constitute 40–60%, 20–40%, and 10–25% of straw, respectively. Cellulose is a linear polymer made up of repeating glucose units, and hemicellulose also contains glucose [113]. Therefore, the amount of glucose obtained after treatment is consistent with these compositional ranges. The hydrogen production amount is 212 kg/h and 3412 kg/h acetic acid as a subsidiary product. The results indicated that for one mole of glucose, 3.5 moles of hydrogen were produced, which aligns with the experimental data [19].

Since acetate and butyrate are the primary products in DF, the second scenario included both the butyrate and acetate pathways (Equations (1) and (2)), and the simulation was conducted accordingly. The simulation results are presented in

Table 6. ASPEN Plus simulation results for temperature, pressure and composition of various streams in kg/h for hydrogen production from straw in dark fermentation process with consideration of acetate and butyrate pathway.

Streams No.	1	2	3	4	5	6	7	8	9	10	11	12	13
Temp. (°C)	25	25	25	50	50	34	20	20	20	25	25	25	25
P (Bar)	1	1	1	1	1	2	4	4	4	1	1	1	3
F (kg/h)	10,000	106,358	116,358	116,358	35,632	80,727	2676	219	2457	78,051	3565	74,486	66,358
Phase	Liquid	Liquid	Liquid	Liquid	Liquid	Liquid	Vapor	Vapor	Vapor	Liquid	Liquid	Liquid	Liquid
mBiomass	10,000	0	10,000	0	0	0	0	0	0	0	0	0	0
mash	0	0	0	1016	1016	0	0	0	0	0	0	0	0
mCO2	0	0	0	0	0	0	2504	50	2454	0	0	0	0
mH2O	0	106,358	106,358	107,126	32,138	74,988	0	0	0	74,476	745	73,731	66,358
mH2	0	0	0	0	0	0	172	169	3	0	0	0	0
msolid	0	0	0	2433	2433	0	0	0	0	0	0	0	0
mCH3COOH	0	0	0	0	0	0	0	0	0	1708	1623	85	0
mbutyrate	0	0	0	0	0	0	0	0	0	1253	1190	63	0
mglucose	0	0	0	5694	0	5694	0	0	0	569	5	564	0
mNH4Cl	0	0	0	68	34	34	0	0	0	34	1	34	0
msulphur	0	0	0	21	11	10	0	0	0	10	1	10	0

.

The results indicate a significant shift in product distribution during the process. Hydrogen production decreases to 169 kg/h, showing a reduction compared to previous levels. Similarly, acetic acid output drops to 1792 kg/h. However, an increase in butyric acid production is observed, reaching 1190 kg/h. This suggest that the metabolic pathway favours the production of butyric acid. The stoichiometric yield of hydrogen is 2 moles per mole of glucose, as outlined in Equation (2), when butyric acid is the final product. However, the actual hydrogen yield is lower than this theoretical value. This discrepancy arises because a portion of the substrate is diverted for biomass production, and some of the substrate may follow alternative biochemical pathways that do not generate hydrogen. Consequently, the overall hydrogen output is reduced compared to the expected theoretical yield [14]. Based on these results, 1.8 moles of hydrogen are produced per mole of glucose, which is consistent with the literature data for this metabolic pathway [4].

To mitigate efficiency losses and optimize process conditions, pH control and process optimization significantly influence metabolic pathway selection. Maintaining an optimal pH range (typically 5.5–6.0) can favour acetate production over butyrate, thereby enhancing hydrogen yield. Selecting or genetically modifying microbial strains with higher hydrogen-producing efficiency can help direct metabolic flux towards the acetate pathway while minimizing the butyrate shift. Adjusting nutrient composition, C/N ratio, and supplementing trace metals (e.g., iron, nickel) can enhance enzymatic activity in the hydrogenase pathway, promoting acetate fermentation.

5. Possible Future Directions

Future research in dark fermentation for hydrogen production is a promising area that seeks to overcome several key challenges. Biological processes such as fermentation still face application problems because of the existing bottlenecks. The main problem is cost, which is higher because of lower hydrogen yields and the instability of the process. Addressing these limitations requires innovative approaches to enhance both efficiency and stability.

One major focus of future research is the optimization of microbial strains involved in dark fermentation. By employing genetic engineering and synthetic biology techniques, researchers aim to develop more robust strains with higher hydrogen yields and better resistance to fluctuations in environmental conditions. Furthermore, identifying novel microorganisms or consortia capable of operating under more diverse substrates or conditions can significantly improve the overall sustainability of the process. It is necessary to consider the ethical and regulatory rules. All genetically engineered strains are developed following biosafety guidelines established by organizations such as the EPA, FDA, and EFSA, ensuring their controlled use in research and industrial applications. Comprehensive risk assessments evaluate potential ecological impacts before deployment, and continuous monitoring programmes are implemented to track microbial behaviour in operational settings.

Another critical area is improving reactor design and operation strategies. Current bioreactors often struggle with maintaining stable fermentation conditions, which directly impacts hydrogen production efficiency. Research efforts are directed toward advanced reactor technologies, such as immobilized cell systems and continuous-flow reactors, to stabilize the process and enhance yields. Integrating fermentation with other processes, such as bio-electrochemical systems, could also help address inefficiencies and improve energy output.

Substrate utilization and pretreatment are also important research areas. By investigating the specific relationships between substrate concentration, microbial activity, and hydrogen production rates, the optimal conditions for maximizing hydrogen yield are identified. This includes studying the kinetics of different substrates, such as complex lignocellulosic biomass, and how their degradation rates influence hydrogen evolution. Developing cost-effective methods to utilize a wide variety of waste materials and biomass can reduce feedstock costs and contribute to a more sustainable hydrogen economy. Innovations in pretreatment technologies, including enzymatic and thermal approaches, can make substrates more accessible for microbial degradation, improving overall hydrogen yield.

Finally, integrating dark fermentation with other renewable hydrogen production methods, like photo-fermentation or water-splitting techniques, offers a hybrid approach that may overcome current bottlenecks. Combining different methods can increase overall efficiency and lower costs, pushing dark fermentation closer to large-scale, commercial viability.

In summary, while dark fermentation faces challenges related to cost, yield, and stability, future research focused on microbial strain improvement, reactor design, and process integration holds great potential to overcome these barriers and unlock its potential as a sustainable hydrogen production method.

6. Conclusions

Hydrogen production through dark fermentation (DF) depends on substrate type, microorganism selection, and key bioreactor conditions such as temperature, pH, and hydrogen partial pressure. Easily degradable carbohydrates generally yield higher hydrogen levels, while different bacterial strains influence production efficiency through varied metabolic pathways. Operational conditions like temperature, pH, and hydrogen partial pressure significantly impact process efficiency, with mesophilic (30–40 °C) and thermophilic (50–60 °C) conditions being optimal for microbial activity. Maintaining pH within the range of 5.0 to 6.5 supports bacterial growth and enzyme function, while controlling hydrogen partial pressure prevents inhibitory feedback effects.

This study focuses on hydrogen production from straw via dark fermentation, analyzing the metabolic pathways involved. A simple process model and simulation were conducted for the first time to better understand and optimize hydrogen production in this context. The simulation framework allows researchers to examine interactions between variables, such as substrate concentration, hydrogen production rate, and operating conditions, providing insights into optimizing bioreactor performance. By predicting how changes in one parameter affect overall system efficiency, the model enables precise control over the fermentation process to enhance hydrogen yield.

The results confirm that straw is a viable biomass source for hydrogen production due to its abundance and high carbohydrate content. However, the efficiency of hydrogen yield depends on factors like microbial consortia, pH control, and temperature optimization.

One of the key findings of the simulation is the identification of bottlenecks that can hinder large-scale hydrogen production, such as substrate availability, microbial activity, and operational inefficiencies related to temperature and pH fluctuations. Addressing these challenges is critical for successfully scaling up dark fermentation from laboratory to industrial applications.

Additionally, the findings indicate that byproducts from the fermentation process can significantly impact the overall cost of hydrogen production. Byproducts, such as acetic or butyric acids, may require additional processing, which increases the complexity and expense of the system. Reducing or managing these byproducts is essential for making hydrogen production more economically viable.