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Article

Malachite Green Dye Removal in Water by Using Biochar Produced from Pinus patula Pellet Gasification in a Reverse Downdraft Reactor

by
Hillary Henao-Toro
1,
Juan F. Pérez
2 and
Ainhoa Rubio-Clemente
1,3,*
1
Grupo de Investigación Energía Alternativa—GEA, Faculta de Ingeniería, Universidad de Antioquia UdeA, Calle 67 No. 53–108, Medellín 1226, Colombia
2
Grupo de Manejo Eficiente de la Energía (GIMEL), Departamento de Ingeniería Mecánica, Facultad de Ingeniería, Universidad de Antioquia UdeA, Calle 67 No. 53–108, Medellín 1226, Colombia
3
Escuela Ambiental, Facultad de Ingeniería, Universidad de Antioquia UdeA, Calle 67 No. 53–108, Medellín 1226, Colombia
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(24), 11043; https://doi.org/10.3390/su162411043
Submission received: 18 July 2024 / Revised: 6 December 2024 / Accepted: 9 December 2024 / Published: 17 December 2024
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Abstract

:
The efficiency of the elimination of malachite green dye (MG) in water was investigated using biochar (BC) obtained from Pinus patula wood pellets (BC-WP). The biomass was gasified, reaching a temperature of 391.07 °C near the reactor wall. During the adsorption tests, three independent factors were considered: the solution pH, BC concentration, and the BC particle size, which were optimized using different study ranges (4–10, 6–12 g/L, and 150–600 μm, respectively) at 30 min of contact time. The response surface methodology was used through a face-centered central composite design for this purpose. The experimental results were analyzed to develop a quadratic regression model that fitted the experimental data achieved. The highest removal percentage of MG by BC-WP (94.25%) was attained under a solution pH of 10, a BC concentration of 12 g/L, and an average BC particle size of 225 μm. Furthermore, the validated regression model was found to explain 94.72% of the obtained results, demonstrating the ability of BC-WP to remove the target dye. Thus, a new and sustainable alternative to conventional systems for treating dye-polluted water is proposed, utilizing the solid by-product of the thermochemical process, contributing to the circular economy.

1. Introduction

Fresh water is a vital resource, whose quality and availability are increasingly threatened [1,2]. The pressure exerted by different sectors of the industry on water as a resource has a detrimental effect on water, significantly affecting the quality of life for humanity and all aquatic ecosystems [3,4,5]. Water sources are often contaminated by discharges generated by various anthropogenic activities [6], such as agriculture [7] and the pharmaceutical and textile industries [8,9].
The textile industry is responsible for multiple negative impacts on various environmental matrices due to the emission of dust, volatile organic compounds, and sulfur and nitrogen oxides during the dyeing process and the large amount of water required. Additionally, the sludge produced in this industry carries high loads of organic matter, pathogenic microorganisms, and heavy metals, as well as dye molecules that were not fixed to the fiber [10], some of them being non-biodegradable substances [6]. In this regard, the removal of dyes, especially synthetic ones, by conventional methods used in water treatment facilities presents a challenge [11,12]. The presence of dyes in water results in a decrease in solar radiation penetration, compromising the photosynthetic reactions that occur in the ecosystem and inhibiting the growth of plants and phytoplankton [9], significantly altering the entire aquatic food chain. Furthermore, dyes have bioaccumulation capabilities. In aquaculture, the use of certain dyes like malachite green (MG) is gaining attention due to their low cost, easy acquisition, and antiparasitic, antifungal, and antibacterial potential in fish [11]. However, MG can also be carcinogenic; therefore, its use by various industries should be suppressed to reduce the associated risks.
There are multiple conventional treatments that can be applied to solve the problem of water pollution by MG. Among these treatments, pre-oxidation, coagulation–flocculation, filtration, and disinfection can be named [13]. On the other hand, biological processes also play a crucial role in water treatment [14]. Nevertheless, their efficiency is limited regarding dye degradation. Therefore, new and efficient alternatives are continuously being researched for the treatment of natural resources with MG, such as the use of biochar (BC) as an adsorptive material.
BC is a carbonaceous material that is rich in organic carbon and is derived from biomass thermal decomposition [15]. This thermochemical transformation can occur through different processes (e.g., pyrolysis, gasification, torrefaction, and hydrothermal carbonization), which generate liquid, gaseous, and solid by-products, including bio-oil, syngas, and BC, respectively [16,17]. BC, as a solid waste product from these processes, acquires specific physicochemical characteristics, which are mainly related to the raw material used and the generation process itself [15], positioning it as a good alternative for water decontamination [18,19,20,21]. In relation to the raw material used, lignocellulosic biomasses from forest and agricultural waste are commonly used, since the carbon amount contained in the BC is directly proportional to the lignin content of the biomass utilized [15]. Additionally, concerning the influence of the BC production process, the operational temperature is one of the parameters that most affects BC properties, since it positively influences the pore size distribution, carbon content, and the surface area of the BC, as well as the heating rate, which is reflected in the generation of a more chemically and thermally stable BC [13]. Furthermore, the high porosity of BC is the result of the release of volatile matter. In fact, when lower temperatures are used, BC presents low hydrophobicity and aromatic character, along with a higher polarity and surface acidity [16]. The adsorption process that occurs in the BC is simple, efficient, economical, and based on ionic or hydrophobic interactions between the adsorbent surface and the pollutant so that adsorption is improved thanks to the BC surface area and the presence of functional groups [13]. Likewise, the importance of the water pH is highlighted as certain compounds have a greater affinity with the BC surface at basic, neutral, or acid pH [15]. Moreover, the BC concentration and the particle size used must be considered as factors that influence the BC adsorption capacity.
Currently, the use of BC is highly investigated in the field of organic and inorganic remediation of different environmental matrices [22]. Although most of the results reported are based on the use of BC generated through pyrolytic processes, the research aimed at evaluating the adsorption efficiency of BC derived from the gasification process is scarce. As a matter of fact, no studies related to the implementation of BC from the reverse downdraft gasification of Pinus patula pellets for water decontamination with MG have been informed so far from the authors’ knowledge.
In addition, it is important to note that optimization methods of adsorption systems have frequently employed the one-factor-at-a-time (OFAT) strategy, where each operating parameter is adjusted separately to analyze its individual impact on the system considered. This method, nevertheless, often neglects the interactions and synergies among multiple factors affecting the adsorption system. An alternative to overcome this issue is using response surface methodology (RSM), which adopts a comprehensive approach by assessing the relationships between various factors simultaneously [23]. Utilizing RSM allows for the discovery of complex interactions and the identification of the optimal operation that may not be apparent when OFAT approaches are used. The effectiveness of RSM hinges on the careful selection of independent variables that influence the system’s performance. In this context, these variables could include factors such as the solution pH and the dose and particle size of the BC under study. The interdependence of these factors and their effects on the response variable to be evaluated require a thorough optimization approach. Consequently, experiments focusing on single factors should not be performed, as they would overlook interaction effects. Therefore, RSM has been preferred over OFAT techniques [24,25,26].
Furthermore, the development model based on a circular economy is gaining more strength worldwide every day, since it arises from the need to find ways to satisfy the demand for resources by human beings and their different actions, with the goal of curbing or reducing the negative environmental impact resulting from the traditional development model based on a linear economy [27]. The latter has an unfriendly relationship with the environment, since during the production process, materials are usually extracted, combined, and processed for their consumption afterward and final disposal [28]. There are common strategies within the circular economy such as recovery, recycling, reuse, remanufacture, restore, repair, reduce, rethink, and reject, aiming at minimizing waste generation and reducing primary resource utilization, leading towards sustainable development [29]. For this reason, the application of BC for water treatment is constituted as an attractive treatment technology that can be used within the action framework of the circular economy [17].
Under this scenario, this work is focused on the efficiency evaluation of the BC derived from patula pine (Pinus patula) pellet gasification in a reverse downdraft reactor as a sustainable alternative for the elimination of MG in water. For this purpose, RSM is used to optimize the operational conditions that maximize the elimination of the dye in water, including the BC particle size and concentration, and the solution pH. This work is intended to provide a new and sustainable solution to a current problem that affects public health and living beings, while contributing to the achievement of the principles by which the circular economy is governed.

2. Materials and Methods

2.1. Production and Properties of the BC

Biomass was chosen considering its availability and use, with pellets being a commonly used raw material for energy generation, given their abundance and properties as solid biofuel [30]. In this work, the raw material used to obtain the BC was Pinus patula pellets (WPs). This biomass was obtained from a sawmill located in the city of Medellín. The BC was produced from the gasification process in a reverse-downdraft-type reactor. This process was carried out with the objective of generating a gas composed mostly of hydrogen (H2), methane (CH4), and carbon monoxide (CO), among other substances, to finally have fuel gas and BC as the main product and by-product, respectively. It should be noted that the reverse downdraft reactor used air as a gasifying agent, which was provided by a reciprocating compressor (300 rpm, up to 254 L/min, and 2.60 kW). The air supply also featured a rotameter and a manometer to regulate the airflow and pressure, respectively. The gasification air flux supplied was set at 0.12 kg/m2/s ± 3% and kept fixed. The initial biomass mass used for the process was 1300 g of WPs.
Different physicochemical characteristics are highlighted to influence the BC efficiency in the pollutant adsorption processes. These characteristics are clearly affected by the kind of raw material and the conditions used for obtaining the BC; i.e., the gasification process and the conditions under which it was carried out [31]. Therefore, to study the behavior of BC, a physicochemical characterization of the WP properties as well as its derived BC (BC-WP) was carried out. The contents of basic elements such as oxygen (O), hydrogen (H), nitrogen (N), carbon (C), and sulfur (S) present in the biomass were determined using the ASTM D5373-08 standard [30] thanks to the use of a Truspec micro (Leco®, Saint Joseph, MI, USA). For the analysis of the hemicellulose, cellulose, and lignin fibers from the biomass and BC, FiberCap Foss-2022 equipment (Foss, Hilleroed, Denmark) and the method described in Van Soest [32] were utilized. Another important chemical aspect is the CEC (cation exchange capacity), a characteristic that was analyzed using the Colombian Technical Standard 5167 [30]. FTIR (Fourier Transform Infrared) spectroscopy was employed for discerning the functional groups in the BC-WP surface [33]. Furthermore, many solids used in the adsorption processes are highly porous and irregular, which makes it difficult to measure some of their characteristics, like the specific surface area and the shape, size, and distribution of the pores. For this purpose, the pore volume was determined by using the BJH (Barrett, Joyner, and Halenda) method, and an IRAffinity-1 (Shimadzu, Kyoto, Japan) equipped with a detector and operated in the wave number range of 4000–400 cm–1 was utilized. In turn, the BET (Brunauer–Emmett–Teller) nitrogen adsorption technique and an ASAP 2020 kit were used and purchased from Micrometrics Instrument Corp. (Norcross, GA, USA) for measuring the specific surface area of the adsorbing material. Proximate analysis, ash content, fixed carbon content, and volatile material were determined according to Mui et al. [34] and using a TGA Q50 thermogravimetric analyzer, which was provided by TA Instruments (New Castle, DE, USA). The higher calorific value was determined by using a bomb calorimeter and following the ASTM (1996) standard [30]. The bulk density was calculated as described by Gutiérrez et al. [30].

2.2. Reactants and Chemicals

For the study carried out with BC-WP as an adsorbing material, MG synthetic organic dye (99%) was used (Carlo Erba, Cornaredo, Italy). The solution pH was achieved using nitric acid (65%, HNO3) and sodium hydroxide (≥98%, NaOH), which were purchased from Merck (Darmstadt, Germany) and Macron Fine Chemicals (Culiacán Sinaloa, Mexico), respectively.

2.3. Adsorption Experiments

The process of adsorption was carried out in batches, using a solution containing 300 mL of water and a specific amount of BC. Afterward, a stock solution of 1000 mg/L of MG was used to reach 50 mg/L of the dye of interest. BC adsorption capacity was analyzed at three different levels (6, 9, and 12 g/L). In addition, the BC particle size was evaluated in the ranges of 450–600, 150–300, and 300–450 µm with central points of 525, 225, and 375 µm (average particle size), respectively. Subsequently, the solution pH was adjusted up to the desired value, considering a pH variation in a range from 4 to 10. Finally, the solution was constantly stirred using a stir bar and a magnetic stirrer for 60 min.
During the preliminary tests, and to determine the contact time for the optimization of the operational conditions, the samples were taken at different time intervals throughout the treatment. A 0.45 µm nylon syringe filter was used for filtering the samples before storage in amber containers at room temperature until their respective analysis by the spectrophotometer.

2.4. Analytical Method

The amount of MG contained in the aqueous solution was analyzed by visible spectrophotometry at a fixed wavelength of 622 nm. For this, a spectrophotometer purchased from Thermo Scientific (Waltham, MA, USA), specifically a Spectronic 200 visible spectrophotometer, was used. Subsequently, with the prepared calibration curve, the MG removal percentage was calculated by means of Equation (1), where Ct (mg/L) and C0 (mg/L) are the final and initial levels of MG, respectively.
M G   r e m o v a l   % = C 0 − C t C 0 × 100

2.5. Design of Experiments

In order to determine the MG removal percentage obtained during experimentation, three factors as independent variables were evaluated. These variables were the BC concentration, BC particle size, and solution pH.
A face-centered central composite design (CCD) was used; it consisted of 17 experimental runs, including three replicates at the central point. The tests were executed randomly to obtain a second-order regression model Equation (2) [23], which allows the determination of the relationships between the response variable and the selected factors and the positive or negative relationships influencing the MG removal percentage. Likewise, the regression model built allows the determination of the optimal operational conditions that maximize the elimination of MG in water. For the construction of this model, an ANOVA (analysis of variance) was performed [35].
y = β 0 + ∑ i = 1 k β i x i + ∑ i = 1 k β i i x i 2 + ∑ 1 ≤ i ≤ j k β i j x i x j + ε
where y is the MG removal percentage; β0 is a constant; βij, βii, and βi are the regression parameters for the effects of the interaction, quadratic, and linear coefficients, respectively; ε is the experimental error; xi corresponds to each of the factors considered; and k stands for the number of independent factors selected. The independent variables were evaluated at 3 levels (Table 1). The experimental matrix and the randomized experimental trials that were conducted are identified in Table 2.
Subsequently, the obtained experimental data were processed using Statgraphics Centurion XVII software V17.2.07 (Statpoint, Warrenton, VA, USA) with a significance level (α) of 5%.

3. Results and Discussion

The results found are mainly linked to the selected biomass from which the BC was produced and the operational conditions used during the BC generation process [36]. The gasification process was carried out at high temperatures [37], which represents a significant improvement in the surface area, porosity, and type of functional groups contained in the BC matrix [16], leading to an increase in the efficiency of the adsorption process.

3.1. BC Characterization

In Table 3, the BC-WP physicochemical properties are presented. Comparing these characteristics with those reported in Gutiérrez et al. [30], significant differences between the raw material (WPs) used to obtain the BC and the BC-WP produced after the gasification process are observed. The raw biomass used had a fibrous and solid structure without a high degree of porosity unlike BC-WP, influencing the surface area, pore structure, and the distribution and charge density of BC [38]. Forest waste is usually rich in hemicellulose, lignin, and cellulose; so, when the biomass is gasified to obtain the BC, the devolatilization of these constituents is promoted. Additionally, the thermal stability of lignin preserves the porous structure of the material [39,40], resulting in a BC-WP with a porous structure. This characteristic positively influences the efficiency of the adsorption process [15]. The temperature reached in the gasification process for the generation of BC-WP was 391.07 °C (in the reactor wall). This temperature level was linked to the bulk density and the calorific value (19.03 MJ/kg) of the WPs used as a raw material [30]. In turn, the BET surface area was 367.33 m2/g. This property, as well as the porosity, allows BC-WP to be considered as an efficient adsorbent of contaminants in water [16].
Regarding the functional groups, it was observed that BC is a product obtained by the loss of functional groups through the -OH and -CH bands of the raw material [30]. In the raw material, there is evidence of a greater presence of hydroxyl groups (-OH), phenols, and water molecules, but once the biomass gasification process is carried out, there is a decrease in these properties. This can be explained thanks to the release of volatile matter [15], which in turn increases the porosity of the BC, thus improving its performance as an adsorbent. The aliphatic compounds, such as CH2 and CH3, present in WPs are associated with materials that are rich in cellulose and hemicellulose. Therefore, once the BC is obtained, a decrease in the aliphatic groups is evidenced, which explains the predominance of lignin in this adsorbing material.

3.2. Preliminary Adsorption Tests

To determine the time that BC-WP takes to reach equilibrium in the MG adsorption process, several experimental runs were carried out in a pH range between 4 and 10 (Figure 1), keeping the values of BC concentration and particle size fixed at 6 g/L and 150–300 µm, respectively. In the first 5 min of treatment, elimination percentages of 33.90% at pH 4 and 66.90% at pH 10 were reached. After 10 min, a significant increase was evidenced at pH 10, indicating a removal of 81.60%. Equilibrium was reached after 30 min, with an MG removal percentage close to 85%. The behavior in the adsorption process increased within the first minutes of treatment until equilibrium was reached; i.e., in the first minutes of the experiment, the BC surface had large amounts of active sites available to host the contaminant molecules. As time went by, the sites available for MG adsorption were reduced and the BC began to be saturated; the equilibrium point was reached, and a significant increase in the adsorption was no longer found. This similar behavior has been evidenced by Salem and coworkers for ibuprofen, who observed that equilibrium was found after 40 min of treatment [20]. The adsorption peak and equilibrium are not always found in small time ranges, since they depend on the contaminant and, in turn, on the physicochemical characteristics of the BC. As a matter of fact, an adsorption peak for cadmium (Cd) was found within the first 6 h of the total 7 h observed to ensure equilibrium [41]. Research conducted for more than 10 days has even been reported [34].
Once the equilibrium point was observed, the experimental runs (described in Table 2) were carried out to know the influence of each of the variables considered (pH and BC concentration and particle size) within the experimental domain, as explained below. The goal was to find the optimal conditions that magnify BC efficiency for MG adsorption.

3.3. Influence of the Experimental Factors During the Adsorption Process

To determine the influence of the BC concentration, BC particle size, and the solution pH on the capacity of BC-WP as an adsorbent for the MG dye and optimize the system, a face-centered CCD was used. The results obtained for the treatments are listed in Table 4, as well as those estimated by the quadratic regression model built.
Table 5 presents the ANOVA for the experimental design and the matrix domain studied. Through the p-values, the relevance of each of the factors or their interactions on the response variable can be determined. The solution pH is the most influential factor, contributing 62.84% to the model, followed by the BC particle size (18.48%), and the BC concentration (12.48%). All main factors are highly significant in the removal of MG in water, while interactions between factors do not exceed 3% influence. Particularly, pH is a critical factor, highlighted by its significance with an associated p-value of 1.50 × 10−3. Moreover, the associated error in the model is low, with an F-value of 7.01 for the lack-of-fit test, indicating that the regression model significantly fits the experimental data. This is supported by the p-value associated with this test.
A Pareto diagram (Figure 2) graphically shows the degree of significance of the factors studied, considering that the highlighted parameters are those that exceed the vertical line. As observed, each one of these factors considerably influences the adsorption process of MG on the BC-WP. In the figure, it is observed that the solution pH and the BC concentration positively affect the dye retention as they go from the lowest to the highest level, with the solution pH being the most significant factor in this study, as mentioned above. In contrast, the pH quadratic factor and BC concentration quadratic factor do not exhibit a high level of relevance within the experimental domain considered. On the other hand, the BC particle size has a high degree of importance and influences the pollutant adsorption efficiency as it goes from the highest to the lowest levels. The other effects did not affect the adsorption system from a statistical point of view under the experimental domain studied here.
The negative or positive influence of the main factors in the adsorption of MG on BC-WP is evidenced in Figure 3. For the BC concentration, an increase in the removal was evidenced as the concentration value increased. Likewise, the solution pH influenced the response variable, but the influence was more significant when going from 4 to 10. Regarding the BC particle size, it was observed that there was an inversely proportional relationship since better performance was presented as this factor was reduced; thus, BC particle size was considered to have a negative effect, unlike the other two factors mentioned above, whose effects were positive.

3.3.1. Effect of the Solution pH

The solution pH was found to be one of the most influential parameters that affect the MG removal by BC-WP and, with this, its efficiency. This study was conducted in a pH range from 4 to 10, with 4, 7, and 10 as reference points. The results obtained showed an increase in the dye elimination as the pH increased, starting with an adsorption of 43.13% at an acid pH. Once the solution became basic, the percentage of MG adsorption rose, ending with 94.25% at a pH of 10, when a BC concentration and a particle size of 12 g/L and 225 µm, respectively, were used.
The solution pH influence on the adsorptive process is mainly due to the protons and hydroxide ions (H+ and OH−, respectively) contained within the solution at different pH ranges, which interact with the cationic species of the MG dye and the BC-WP surface. During the BC characterization process, the BC pHpzc (pH at the point of zero charge) value was 6 [42]. Under the presence of H+, the BC surface was protonated, and repulsive electrostatic forces were generated with other cations within the bulk at acid pH, such as the cations of the dye under study [43,44]. Under these conditions, the adsorption of MG was limited. Additionally, with the high presence of H+, the active adsorption sites on the BC surface could be reduced, hindering their interactions with the dye, which was in the cationic form when protons were abundant due to the values of pKa1 (6.90) and pKa2 (10.30) [45]. As the pH of the solution increased, there was a rise in OH−, intervening on the BC surface and improving the attraction between the cationic dye and the adsorbing material [46].
The results obtained agree with those found in other reported studies where a better MG removal behavior was attributed to a basic pH. Lin et al. [47] analyzed the adsorption of MG on BC derived from microalgae under a pyrolysis process at a variable temperature between 400 and 800 °C. In the referred study, pH values were taken in a range from 2 to 10, evidencing an increase in the adsorption of the dye with a rise in pH from 2 to 6 (an adsorption rate of up to 99.90% was evidenced). As the pH increased from 6 to 10, the adsorption rate did not present a significant rise [47]. Furthermore, Singh [46] observed a similar phenomenon. In that research, a pH range between 3 and 9 was studied; the pollutant elimination increased from 24 to 85% as the pH also increased. This behavior was attributed to the content of H+ or OH− and its relationships with the cations of the dye and the BC surface [46].

3.3.2. Influence of the BC Particle Size

The optimization of the BC particle size is essential when determining the efficiency of an adsorption system. This factor is represented with a high degree of significance, as evidenced in Figure 2 and Figure 3, as well as in the results obtained from the ANOVA (p-value equal to 4.90 × 10–3). Here, three ranges of BC particle sizes were used (150–300, 300–450, and 450–600 μm). Unlike the linear increasing behavior reflected with other factors, the effect of the BC particle size was equally linear but in a decreasing way. Figure 3 shows a decrease in the percentage of dye removal as the BC particle size increased from 150 to 600 μm; i.e., a small BC particle size presented a greater surface area, increasing the adsorption efficiency [31]. Obtaining important results is highly dependent on the surface area and the pore volume in an absorbing material. These characteristics are attributed to smaller particles, thus enhancing the efficiency of BC for different compounds [48]. Likewise, by reducing the BC particle size, the surface area was increased, so the access by the MG molecules to the empty sites available on the BC surface was favored. This phenomenon, combined with a high pore volume, has been associated with the presence of a quality adsorbent to treat polluted water [49]. The porous surface of the BC has been linked to the temperature used during its generation process. In the literature, there are multiple studies that support this behavior, since by raising the operational temperature, a BC with a higher porous structure and a significant surface area can be produced, which considerably influences the availability of active sites in the BC matrix. Thus, the adsorption of different contaminants is improved [44].
In studies carried out by Pradhan and coworkers [50], the absorption of microalgae biomass for the elimination of Cr (VI) (hexavalent chromium) was studied, and the influence of the BC particle size was analyzed between various ranges with averages of 45, 60, 87.50, and 100 μm. As a result, a greater efficiency was obtained when using finer particle sizes, since this led to an increase in the exposure of potential active sites. In this regard, the efficiency and bioadsorption capacity decreased as the BC particle size increased. The percentage of removal exceeded 90% with particle sizes between 40 and 60 μm; at 100 μm, it dropped below 50% [50].

3.3.3. Influence of the BC Concentration

As illustrated in Figure 3, a linear increase in the adsorption of MG with respect to the BC concentration stands out. There was a noticeable increase in the percentage of dye removal as the BC concentration increased from 6 to 12 g/L. This behavior was mainly due to the number of spaces available in the BC matrix for the adsorption of the MG dye; characteristics such as the high porosity and irregular structure of BC positively influence the adsorption of contaminants in water [49]. The porous structure and the surface area of the BC facilitate physical adsorption, and on the other hand, the functional groups and electrostatic interactions govern the chemical adsorption within the process [51]. As the dose of the adsorbent is increased, the efficiency of the contaminant removal increases mainly due to the fact that both variables are positively related. Nevertheless, once the optimum point is reached, the adsorption becomes constant without significant changes; even with an increase in the BC dose, the scenario where the adsorption decreases rapidly with the addition of BC can also occur. This phenomenon might be related to the agglomeration of particles of the adsorbent and/or the saturation of the binding sites [31]. Using a residence time of 30 min, the adsorption peak was reached. Subsequently, there was no evidence of a significant increase, probably due to the BC saturation, resulting in the reduction of the number of available sites onto which the dye can adsorb.
The results obtained here are similar to other studies reported in the literature where different contaminants are treated. Puglla and collaborators found an increase in the lead (Pb) adsorption capacity, going from a removal of approximately 60% with a BC concentration of 8 g/L to ~90% for a BC level of 14 g/L [52]. A maximum MG adsorption of 0.10 g was observed to have an efficiency of 30.18 mg/g MG uptake by Vigneshwaran [49]. Both studies attributed this behavior to the number of sites available on the porous surface for pollutant adsorption. Although it is common to find this increasing and linear behavior in the elimination of contaminants by raising the BC dose, this fact does not always occur; i.e., increasing the BC concentration is not synonymous with greater efficiency. Indeed, in a study carried out with the purpose of adsorbing Pb, the dose of BC was kept constant at 4 g/L, while the concentration of the contaminant was increased. It was observed that without the need to increase the BC dose, better adsorption results were obtained because Pb ions were housed both on the external surface and in the internal structure of the adsorbing material [53]. It is worth clarifying that these behaviors are mainly linked to the process and operational conditions, as well as the raw material from which the adsorbent used is obtained, as mentioned above.
The effect concerning the interaction between the BC concentration and the solution pH is illustrated in Figure 4. The elimination efficiency was favored as the BC concentration increased from 6 to 12 g/L, being more efficient for a solution pH of 10. At acid pH, even with a 12 g/L dose of adsorbent, the dye removal did not exceed 60%. On the contrary, an MG removal close to 100% was obtained, increasing the pH to a basic range. In fact, regardless of the BC concentration, basic pH values provided a removal between 75 and 95%, reaching the highest value when a higher dose of adsorbent was used. The increase observed with respect to pH was also ascribed to electrostatic interactions and ion exchanges between the BC matrix and the dye in water, being higher when the solution to be treated was at pH 10.

3.4. Regression Model Validation and Adsorption System Optimization

The regression model obtained after the ANOVA (Table 5) was performed, based on Equation (3) [23], which represents the behavior of the experimental data in the adsorption of MG on the BC-WP within the experimental domain, is presented in Equation (3).
M G   r e m o v a l   % = 51.41 − 0.19 × B C   c o n c e n t r a t i o n − 0.05 × B C   p a r t i c l e   s i z e + 2.15 × p H + 0.17 × B C   c o n c e n t r a t i o n 2 − 0.11 × B C   c o n c e n t r a t i o n × p H + 0.25 × p H 2
The model had a high degree of correlation with an R2 of 94.72% and an R2adj of 91.55%. These values indicate that the model explains the behavior of the results obtained with great accuracy. The degree of fit of the regression model was supported by the lack-of-fit test, which determines how adequate the model is to describe the observed results. Table 5 shows a lack-of-fit with an associated p-value of 0.13 (>0.05); therefore, the reliability of the model was confirmed for a confidence level of 95% [44]. Additionally, the p-value of the regression model was 0.11 × 10−1, which reflects the significance of the constructed quadratic regression model at an α of 5% for the considered experimental domain.
For the regression model, the assumption of normality was evaluated. Figure 5 depicts the normal probability for the percentage removal of MG. A distribution along the straight line is evident, with exceptions in three points, the first point is observed on the left side of the graph, between 5 and 20 on the Y axis, and on the −6 value of the standardized effects axis. The other two points are on the right side of the graph, above 4 on the standardized effects axis and between 60 and 95 on the Y axis. Due to these variations, the Shapiro–Wilk test was performed, obtaining a p-value of 8.97 × 10−1 (>0.05). Therefore, the hypothesis is reaffirmed. The data related to the MG elimination come from a normal distribution with a confidence interval of 95%. Additionally, the assumption of homoscedasticity of the residuals was validated (Figure 6). A constant variance of the model stands out, which provides a greater reliability to the model. In order to determine a possible significant autocorrelation, to check whether the values are dependent on the order in which they were obtained, the Durbin–Watson statistical test was performed, and a p-value of 3.48 × 10−1 (>0.05) was achieved; thus, it can be inferred that there is not a serial autocorrelation of the residuals [45].
Finally, the estimated response surface can be observed in Figure 7. The experimental combinations resulting in the highest efficiency for the adsorption system and, therefore, the optimal operational conditions are depicted. MG adsorption results greater than 90% were obtained using a BC concentration of 12 g/L, a solution pH of 10, and the lowest BC particle size (150–300 μm). As previously discussed, the influences of factors such as pH and BC particle size in the studied adsorption system were evident.
In Table 6, a compilation of various studies on the adsorption of MG on BC is presented. The data show that for comparable dye concentrations, BC produced from agro-industrial and agroforestry wastes achieved similar removal rates within a shorter contact time. However, when comparing the current study results with those obtained using BC modified with aluminum (Al) and magnesium (Mg), a higher removal value was observed. Notably, BC produced from the gasification of Pinus patula wood pellets exhibited a high efficiency in removing MG in water, without requiring prior activation or chemical modification.

4. Conclusions

The BC produced from Pinus patula through the gasification process presented a high performance in the elimination of MG in water, reaching a maximum experimental efficiency of 94.25%, under a solution pH, a BC particle size, and a BC concentration of 10, 150–300 μm, and 12 g/L, respectively. Additionally, the quadratic regression model was adjusted to the experimental results with an R2 of 94.72%, providing a better understanding of the behavior of the capacity of the BC under study in the removal of MG.
Therefore, the BC as a by-product obtained from the generation of energy through the gasification process of Pinus patula wood pellets turned out to be an efficient and economical alternative that allows the mitigation of the environmental damage that may occur in aqueous media as a result of MG.

Author Contributions

H.H.-T.: investigation, conceptualization, writing—original draft preparation, methodology, and writing—review and editing. J.F.P.: conceptualization, writing—original draft preparation, writing—review and editing, and supervision. A.R.-C.: conceptualization, writing—original draft preparation, methodology, writing—review and editing, formal analysis, and supervision. All authors have read and agreed to the published version of the manuscript.

Funding

The financial support provided by Universidad de Antioquia (Estrategia de Sostenibilidad 2023. ES84230042) is acknowledged. Additionally, the author J.F. Pérez acknowledges the financial support of Universidad de Antioquia through the research project PRG2019-31090 (ES25190102).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. MG removal (%) on BC-WP vs. treatment time (min). [Pollutant]0 = 50 mg/L; pH = 4 and 10; BC particle size = 150–300 μm; BC concentration = 6 g/L.
Figure 1. MG removal (%) on BC-WP vs. treatment time (min). [Pollutant]0 = 50 mg/L; pH = 4 and 10; BC particle size = 150–300 μm; BC concentration = 6 g/L.
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Figure 2. Pareto diagram representing the adsorption capacity of MG dye on BC-WP. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 6–12 g/L; time = 30 min. Legend: A: BC concentration, B: BC particle size, C: solution pH, AA: quadratic effect of the BC concentration, CC: quadratic effect of the solution pH, AC: interaction effect between the BC concentration and the solution pH.
Figure 2. Pareto diagram representing the adsorption capacity of MG dye on BC-WP. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 6–12 g/L; time = 30 min. Legend: A: BC concentration, B: BC particle size, C: solution pH, AA: quadratic effect of the BC concentration, CC: quadratic effect of the solution pH, AC: interaction effect between the BC concentration and the solution pH.
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Figure 3. Main effects related to the adsorption capacity of MG dye on BC-WP. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 6–12 g/L; time = 30 min.
Figure 3. Main effects related to the adsorption capacity of MG dye on BC-WP. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 6–12 g/L; time = 30 min.
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Figure 4. Effect of the interaction between pH and BC in the MG removal process on BC-WP. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 6–12 g/L; time = 30 min.
Figure 4. Effect of the interaction between pH and BC in the MG removal process on BC-WP. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 6–12 g/L; time = 30 min.
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Figure 5. Relative normal probability related to MG removal on BC-WP. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 6–12 g/L; time = 30 min.
Figure 5. Relative normal probability related to MG removal on BC-WP. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 6–12 g/L; time = 30 min.
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Figure 6. Residuals vs. predicted values related to MG removal on BC-WP. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 6–12 g/L; time = 30 min.
Figure 6. Residuals vs. predicted values related to MG removal on BC-WP. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 6–12 g/L; time = 30 min.
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Figure 7. Response surface related to MG removal on BC-WP. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 12 g/L; time = 30 min.
Figure 7. Response surface related to MG removal on BC-WP. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 12 g/L; time = 30 min.
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Table 1. Selected experimental factors and levels.
Table 1. Selected experimental factors and levels.
Independent VariableValues of the LevelFactor Level
BC Concentration (g/L)BC Particle Size (μm)Solution pH
6150–3004−1Low
9300–45070Medium
12450–600101High
Table 2. Matrix of the experimental design. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 6–12 g/L.
Table 2. Matrix of the experimental design. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 6–12 g/L.
Test CodeBC Concentration (g/L)BC Particle Size (μm)pH
193757
2122254
3622510
493757
5125254
6937510
765254
893754
995257
1093757
1163757
121252510
131222510
14123757
1592257
16652510
1762254
Table 3. Physiochemical characterization of the BC-WP.
Table 3. Physiochemical characterization of the BC-WP.
CharacteristicStandard [30]BC-WP
Ultimate Analysis (wt%)
HASTM D 5373-080.97
NASTM D 5373-080.19
CASTM D 5373-0897.94
OBy difference0.90
Biomass Constituents (wt%)
Hemicellulosevan Soest method1.04
Lignin86
Cellulose1.69
Proximate Analysis Dry Base (wt%)
Volatile matter ASTM D 5142-0420.59
Content of ashASTM D 5142-041.92
Fixed carbonBy difference77.49
Other Physicochemical Properties
BET superficial area (m2/g)BET theory367.33
pHpzc
CEC (meq/100 g)		NTC5167 standard6.00
21.70
Density (kg/m3)-236.28
Higher calorific power (MJ/kg)ASTM D240-1929.25
Table 4. Experimental and estimated results regarding the MG removal. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 6–12 g/L.
Table 4. Experimental and estimated results regarding the MG removal. [Pollutant]0 = 50 mg/L; pH = 4–10; BC particle size = 225–525 μm; BC concentration = 6–12 g/L.
RunExperimental Removal of MG (%)Estimated Removal of MG (%)
164.7064.92
267.8869.50
384.6385.20
467.8064.92
554.8854.18
682.6381.36
743.1339.62
849.0053.04
952.2557.26
1067.5064.92
1152.8860.14
1278.6380.56
1394.2595.89
1477.2572.76
1575.1372.59
1672.7569.88
1756.3854.94
Table 5. ANOVA of the quadratic regression model.
Table 5. ANOVA of the quadratic regression model.
SourceDegrees of Freedom (d.f)Sum of SquaresMean SquareF-Ratiop-Value
A: BC concentration1398.41398.41136.297.30 × 10−3
B: BC particle size1587.22587.27200.874.90 × 10−3
C: pH12005.622005.62686.071.50 × 10−3
AA17.037.032.4126.11 × 10−2
AC17.517.512.5725.02 × 10−2
CC115.6615.665.3614.67 × 10−2
Lack of fit8163.9520.497.0113.08 × 10−2
Error25.852.92
Total (corr.)163214.08
Table 6. Results of MG removal on BC reported in the literature.
Table 6. Results of MG removal on BC reported in the literature.
Biomass SourceGenerating MethodInitial MG Concentration (mg/L)Efficiency Residence TimeReference
Mg-Al biochar CompositeCo-Precipitation Method at pH 10 2566.73%60 min[54]
Pinus patula Wood ChipsGasification Under an Air Flow5099.70%60 min[45]
Spent Coffee Ground Pyrolysis5099.20%43 min[55]
Fe-Wheat Husk BiocharCo-Precipitation Process6.56 97.10%22 min[56]
Pinus patula Wood PelletsGasification Under an Air Flow5094.25%30 minThis study
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Henao-Toro, H.; Pérez, J.F.; Rubio-Clemente, A. Malachite Green Dye Removal in Water by Using Biochar Produced from Pinus patula Pellet Gasification in a Reverse Downdraft Reactor. Sustainability 2024, 16, 11043. https://doi.org/10.3390/su162411043

AMA Style

Henao-Toro H, Pérez JF, Rubio-Clemente A. Malachite Green Dye Removal in Water by Using Biochar Produced from Pinus patula Pellet Gasification in a Reverse Downdraft Reactor. Sustainability. 2024; 16(24):11043. https://doi.org/10.3390/su162411043

Chicago/Turabian Style

Henao-Toro, Hillary, Juan F. Pérez, and Ainhoa Rubio-Clemente. 2024. "Malachite Green Dye Removal in Water by Using Biochar Produced from Pinus patula Pellet Gasification in a Reverse Downdraft Reactor" Sustainability 16, no. 24: 11043. https://doi.org/10.3390/su162411043

APA Style

Henao-Toro, H., Pérez, J. F., & Rubio-Clemente, A. (2024). Malachite Green Dye Removal in Water by Using Biochar Produced from Pinus patula Pellet Gasification in a Reverse Downdraft Reactor. Sustainability, 16(24), 11043. https://doi.org/10.3390/su162411043

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