Biochar is a highly efficient carbon-rich product with enhanced properties obtained by pyrolysis of residual biomass. The versatile utilization of biochar allows its application in the energy recovery and the environmental remediation [41,46,74]. Tables 1 and 2 lists what reason contribute its superior physicochemical properties, including the N element and O-containing functional groups, etc. However, the further modification of virgin biochar by a variety of physical, mechanical, and chemical processes to produce engineered biochar for energy and environmental applications is a topical issue requiring an up-to-date review [4].

The modification of biochar with non-metallic elements is predominantly achieved through the modification of functional groups, organic polymer modification, and other techniques. The process of modification induces changes in the physicochemical attributes of biochar, including but not limited to polarity, ion exchange capacity, surface charge, and specific surface area. These modifications have a consequential impact on the interactions between biochar and the surrounding environment. These adjustments present advantages with respect to the adsorption of pollutants and the augmentation of biological mechanisms. The functional groups that are frequently utilized comprise of O-containing functional groups, N-containing functional groups, P-containing functional groups and S-containing functional groups [31]. The O-containing functional groups (e.g., hydroxyl groups, phenolic groups, lactonic groups) has an important effect on the physicochemical properties of biochar. These functional groups have a pronounced impact on the surface behavior, hydrophilicity, electrical and catalytic of biochar [116]. The incorporation of functional groups increases the adsorption capacity of biochar for various wastewater heavy metals [52]. Biochar from lignocellulosic materials has many O-containing functional groups, which boosts methane production in AD. This is due to microbial activity and electron transfer processes [89,99,122]. Jiang et al. [34] modified the blue algal based biochar with H₂O₂ to improve the content of phenolic and lactonic groups. The result demonstrated that the redox capacity of biochar, especially the electron-donating capacity, increased by 64.9%, which is beneficial for the electron transfer and methanogenesis.

The implementation of N-containing functional groups modification, specifically pyridinic-N and oxidized-N, on biochar has been identified as a highly efficacious approach. It can be introduced into the biochar surface via hydrothermal carbonization and high-temperature carbonization [51]. By this way, the electrical conductivity of biochar could be improved [86]. The biochar surface modified with pyrroline-N, pyridine-N and oxide-N showed basicity, which could promote the interaction of BC with acidic CO₂, enhance the adsorption of CO₂ and utilization by hydrogenophilic methanogenic [18]. However, it should be noted that the pH of the system should be maintained at a stable level when N-containing functional groups of biochar are input because the released nitrate or nitro groups in system decrease the pH of system (lower than 6), which causing a inhibit effect on the methanogenesis [34]. The incorporation of P into biochar has been identified as a potential strategy to enhance biochar performance. This addition can reduce a charge transfer resistance from microbes to the carbon surface, which is beneficial for the electron transfer [69]. Li et al. [51] investigated the effects of dual-heteroatom doping, specifically N-P and N-S, on AD systems. The findings indicated that the presence of P-OH facilitated the diffusion of H during AD system, which improved hydrogen consumption by hydrogenotrophic methanogenesis [32,69]. N-containing functional groups has been observed to enhance the adsorption of CO₂ and promote the charge transfer between acetogens and methanogens in the digestion system [51].

The main types of organic polymer modification include surface active agents, chitosan, amino acids, etc. Yu et al. [119] found that chitosan-modified biochar could increase the Cr (VI) adsorption and bio-reduction. The chitosan could induce Cr (VI) migration from an aqueous to a solid phase and accelerate the EET by Mtr respiratory pathway in Shewanella oneidensis MR-1. Also, previous research has demonstrated that arginine modification can alter the zeta potentials of biochar. The biochar capture ability of microorganisms can also be improved [127].

The utilization of metal elements or metal oxides as modifiers for biochar represents a promising approach to augment the energy recovery efficiency through AD. Common metals include Fe, Cu, and Mg, among others. Fe or Fe oxides have been the most prevalent modification materials due to their low cost, abundant source availability, and high efficiency [31,46,62]. Applying iron-based biochar derived from biogas residue and ferric chloride could show higher specific surface area and more abundant functional groups, which increased 22.50% than the control group for methane production from food waste [56]. The relative abundance of functional genes involved in DIET can be promoted. Also, the biochar based on zero-valent iron (ZVI) derived by corn straw pyrolysis could showed an increased performance for methane production potential [128]. However, acetotrophic methanogens can be inhibited by ZVI-based biochar, which showed that this element could only enrich the hydrogenotrophic methanogens and improve the DIET between bacteria and methanogens [118,128]. In addition, iron-based biochar could also achieve higher energy recovery efficiency from industrial organic wastewater, like sulfamethoxazole wastewater [78] and salty organic wastewater [9]. Using iron-based biochar exhibits diverse potential applications within environmental science. Su et al. [96] found that the use of magnesium (Mg) and calcium (Ca) as a means of modifying biochar improved its physicochemical properties, thus favoring the AD process. However, despite the numerous advantages of metal-modified biochar in enhancing methane production, it is crucial to exercise caution in the use of metallic-containing reagents during the modification process. It is imperative to explore low-carbon footprint synthesis methods to enhance the efficiency of modification [36].

The yield of biochar plays a crucial role in its preparation, as it requires a careful balance between energy-consuming processes and energy recovery. To assist in predicting the biochar yield, the use of ML models can be explored, taking into account factors such as feedstock characteristics and temperature. However, predicting the biochar yield can be challenging due to the diverse characteristics of feedstocks and the complex conditions involved in the preparation process. ML models designed for biochar yield prediction must consider various input parameters, including feedstock characteristics, pyrolysis conditions, and process parameters. Different algorithms perform differently with different data types, and the accuracy of model predictions can be impacted by several variables [2]. ML-assisted biochar yield prediction has been extensively studied. Zhu et al. [134] proposed that the RF algorithm could effectively predict biochar yield, achieving a high R² value of 0.85, given appropriate input variables. They discovered that structural information, such as the relative proportions of lignin, cellulose, and hemicellulose, produced more accurate results than constituent compositions. Similarly, Hai et al. [26] demonstrated that RF outperformed other algorithms, including Multiple Linear Regression (MLR), Decision Tree (DT), SVM, and K-Nearest Neighbor (KNN), with an R² value of 0.855 in biochar yield prediction. Pathy et al. [83] suggested that Extreme Gradient Boosting (XGBoost) could achieve comparable performance with an R² value of 0.84 in biochar prediction.

In contrast, Li et al. [59] showcased the impressive accuracy of Multi-Layer Perceptron Neural Network (MLP-NN) in biochar yield prediction, achieving an R² value of 0.964. However, selecting appropriate hyperparameters for the model is crucial to avoid underfitting or overfitting the data, which can lead to inaccurate predictions. In a comparative study that explored integrated methods (ML models integrated with Genetic Algorithm and Particle Swarm Optimization). Haq et al. [28] found that the Ensembled Learning Tree (ELT-PSO) model outperformed others, achieving an R² value of 0.99. Even though various studies have identified various parameters for feature importance analysis, the pyrolysis temperature has emerged as the most significant variable in all of these investigations.

In general, simple algorithms (e.g., MLR) might exhibit less acceptable outcomes compared with RF, XGBoost, SVM, and ANN. Combined methods have demonstrated exceptional performance, although one must consider the trade-off among computational complexity, cost-effectiveness and convergence speed when applying these models [123].

Predicting the carbon content of biochar is a vital aspect of evaluating its potential. The carbon content of biochar is potentially linked to its carbon dioxide sequestration, stability, and adsorption capacity for various applications [81]. Accurate prediction of carbon content is essential for determining the effectiveness of biochar in mitigating carbon emissions and enhancing digestate quality. ML has been successfully applied to predict the carbon content of biochar with a high degree of accuracy. Interestingly, while the inclusion of structure information (such as lignin, cellulose, and hemicellulose contents) significantly improved the accuracy of predicting biochar yield, the elemental compositions (i.e. C-H-O-N) played a more critical role in predicting the carbon content [134].

Aromaticity in biochar plays a vital role in interactions with organic matter, adsorption capacity for contaminants, stability, and resistance to degradation [8]. Measuring aromaticity in biochar is often complicated, expensive, and time-consuming, making it less accessible in many research organizations. To address this challenge, researchers have explored more straightforward approaches using basic characteristic properties to predict biochar aromaticity [80]. Cao et al. [7] compared simple polynomial models with ML algorithms (Genetic Algorithms) and found that ML methods provided more accurate predictions, capturing the underlying mapping relationship between elemental compositions and biochar aromaticity. Similarly, Pan et al. [80] investigated the ANFIS and least square support vector machine (LSSVM) algorithms. The results indicated that the LSSVM method exhibited greater reliability (R² = 0.986), with carbon percentage being the most influential input parameter. These results demonstrate the feasibility and dependability of using element composition-based input parameters for predicting biochar aromaticity with ML methods, offering a plausible solution to the dearth of support for aromaticity measurement in many academic institutions. Besides, the electron capacitance of biochar is a crucial characteristic for enriching microorganisms. Yang et al. [117] explored four regression algorithms (DT, ANN, XGBoost, and RF) to predict the specific capacitance of biochar. Among these algorithms, XGBoost demonstrated superior performance with an R² of 0.983, with the activator ratio being the most influential factor. Additionally, accurate prediction of specific capacitance in heteroatoms-rich activated carbon was achieved using MLP-NN, where micropore surface area and volumes had the most significant impact on the prediction performance [88].

The cellular and metabolic activity of anaerobic microorganisms is the key to the energy conversion of organic waste. Biochar plays a crucial role in facilitating the colonization and growth of microorganisms, enabling close interspecific material and energy exchange, and influencing pH, alkalinity, and oxidation-reduction (REDOX) potential. Additionally, the bioavailability of elements, despite being a neglected factor, is of utmost importance in terms of biological regulation. The bioavailability of elements determines the biological activity within anaerobic digesters, as trace elements serve as non-substitutable coenzymes and cofactors in the enzyme systems of anaerobic microorganism [120]. Notably, biochar contains significant amounts of trace elements, making it worthwhile to investigate the bioavailability of these elements when biochar or elementally modified biochar is introduced into AD systems. Previous studies have demonstrated that biochar can enhance the bioavailability of trace elements such as Fe, Co, and Ni [85].

Fe is the most crucial element involved in the synthesis of most enzymes, and is a necessary trace element for hydrogenase, methane monooxygenase and nitrogenase, which plays an essential role in the growth and metabolism of anaerobic bacteria [97]. However, most of the Fe in AD is generally found in carbonates, sulfides, hydroxides, and residual fractions, which are in a biologically unavailable state. In the previous research, lignocellulose-based biochar can increase Fe bioavailability by 5 times [85]. For instance, this phenomenon can contribute to the removal of sulfides and their derived anions, the enhancement of chemisorbed capacity by oxygen-rich functional groups and the release of bound Fe by other specific functional groups such as hydroxyl, carboxyl and carbonyl groups. Zhang et al. [131] found that the increase of iron bioavailability benefited the proportion of viable microorganisms and the increase of methane production. As a former preparation material for biochar, food waste comes from a variety of sources and carries more iron. Shin’s research demonstrates that food waste-based biochar leads to a 33-fold increase in micronutrient Fe in digestion. Iron elutes from food waste-based biochar lead to the growth and continuous increase in methanogenic microorganisms, and methane production increases by 4% [95]. The absolute concentration of iron is needed for bioavailability, but dosing concentration does not increase bioavailability [131]. Many factors affect the actual bioavailability of iron by microorganisms, such as its availability, interspecific competition and physical distance. Although the iron content of waste wood-based biochar is slightly lower than that of food waste-based biochar, the chemical adsorption capacity of cellulosic biochar for Fe is 15 mg/g (the highest relative to other trace elements). This contributes to the availability of Fe to electroactive microorganisms due to the increment of its electrical conductivity and accumulates a large number of electroactive microorganisms [6]. By increasing the proportion of exchangeable parts of iron, the bioavailability of Fe in the biochar treatment was nearly tripled to 0.33. The content of methane in biogas is stable in the range of 55-62% [6]. In addition to enhancing the methane production effect, under the same lignocellulosic biochar enhancement, Fe increased the H₂ production by 63.27%. This strong promoting effect is related to the requirement of Fe by the H₂-producing bacteria for hydrogenase protein redox [97].

In addition to Fe, many other trace elements have good bioavailable functions and are indispensable growth factors for microorganisms [85]. The addition of biochar significantly increases the contents of total Co, Mo, and Ni in digestion, and since Ni is also required for hydrogenase, the metabolic pathways involved in methanogenesis have increased [85]. Biochar increases the bioavailability of Co, Ni, Mo, Se, Mn, Cu, and Zn, which are essential cofactors of key enzymes [124] involved in methane production and are generally used to improve methane production and substrate utilization [6]. The methane yield increased to 134.7 mL/(g VS) during the treatment of nickel-loaded shrimp shell biochar by promoted the number and activity of Methanosarcina and Methanosaeta [58]. Food waste-based biochar contains a wider range of trace elements (Ti, Cu, Cr and Zn). Notably, Ca is required for the growth and aggregation of certain methanogenic microorganisms [95]. The contents (wt) of total nitrogen, phosphate and potassium in microalgal biochar were 6.34%, 2.44% and 3.64wt %, respectively, which were 2.31%, 2.19% and 3.0% higher than the corresponding values in the control sample. These results suggest that biochar amendments increase the nutrient content of the digestives and enrich Genus Proteiniphilum and genus Defluviitoga, thereby increasing average methane production by 12-54% [125].

Maintaining the stability of the microbial energy conversion process is dependent upon a reasonable level of element control. Excessive element concentration may be fatal to cell activity. Ca content in kitchen waste-based biochar is the highest, about 60% [6,95], it was found that 47.4-237 mg/L of Ca can inhibit microbial growth [97]. However, it is important to note that the elemental composition of biochar may differ from the concentration of elements that are released from the biochar into the AD system. Therefore, it is necessary to conduct further evaluations to determine the extent to which these elements contribute to the performance of the AD system. In addition, excessive Fe input will cause excessive cell oxidation and reduce microbial activity [131]. The removal of heavy metals is also a crucial part of organic waste treatment, which not only inactivates methanogenic microorganisms but also damages the stability of anaerobic systems and eventually causes further harm when it flows into the environment. Biochar is a good metal passivator with a strong adsorption capacity for the exchangeable part of heavy metals. Adding lignocellulosic-based biochar promotes the transformation of Cr, Cu, Pb and Zn into bio-unavailable states, which is conducive to reducing the toxicity of Cr, Cu, Pb and Zn to cells [115]. It can improve the AD efficiency of pigment sludge containing heavy metals, reduce the bioavailability volatility of Cd and Pb, and increase the solid removal rate and cumulative methane production by 37.8% and 56.3% [90]. Previous studies have proposed to adsorb Hg (II) to biochar by complexation and precipitation to reduce the bioavailability of Hg (II). Another possibility is that biochar induces S and Fe to stabilize Hg (II) by solid-state precipitation of mercury sulfide minerals and FeS (s) minerals [110]. Paper mill sludge biochar has a high pH value, rich ash content and large pores, which can reduce the ecotoxicity of Cu, Zn and As in biogas. Additionally, it has been observed that As can be immobilized in a non-bioavailable state through the adsorption process of paper mill sludge biochar. Complex pollution of heavy metals can be reduced while higher methane production can be achieved [33]. Biochar prepared by mixing algae whit other biomass residues showed a maximum adsorption capacity of 50.71 mg/g for Cu²⁺, which was more than three times that of the original biochar [37].

Microbial electron transfer is fundamental to redox reactions and serves as the foundation for converting organic waste into energy. It is important to note that this electron transfer is not solely limited to the formation of polymers or the transfer of hydrogen and formic acid. Interspecific formic acid transfer and interspecific hydrogen transfer (IHT) are more common cooperative modes in AD, which methanogens use as electron donors for CO₂ methanation and solve the thermodynamic adverse conditions caused by high hydrogen reaction for bacteria [129]. In a previous study, it was reported that biochar could serve as an ideal additive to promote IHT [12], sawdust biochar is believed to be a temporary electron acceptor that promotes butyrate oxidation, and methanogens accept these electrons from the reduced biochar to reduce carbon dioxide to methane. In this way, the inhibition of butyrate oxidation by high hydrogen partial pressure is alleviated [126]. At the same time, methanogens (such as Methanothermobacte and Methanosarcina) exhibit a synergistic effect of hydrogenotrophic and acetoclastic methanogenesis pathways [126].

The concept of DIET has garnered significant attention in recent years. Unlike IET, the transfer efficiency of DIET is not limited by the mass transfer rate and typically occurs between electroactive bacteria and methanogens. There was clear evidence that Geobacter, a type of electroactive bacteria, showed the highest abundance in the lignocellulose-based biochar treatment, increasing 34-fold. This phenomenon can potentially be attributed to an enhancement in the biochar's conductivity following the adsorption of trace elements, resulting in an approximate ten-fold increase in conductivity and improved DIET performance. Furthermore, biochar has the ability to enhance the growth of hydrogenophilic methanogenic archaea, a functional microorganism associated with DIET, and the presence of trace elements showed an enhanced performance on this effect. Trace element stimulated the abundance of Methanobacillus and Spirospira [6], and the effect of cyanobacteria biochar in Taihu Lake on methane production in the AD process of sludge. The study found that lower vaccination rates (4% and 1%, v/v) increased methane output. Algae-based biochar at lower vaccination rates enriched methane sarcoma better than higher vaccination rates. According to prior study, focused DIET may enable methane synthesis [35]. The algal biochar, owing to its porous structure and distinct functional groups, exhibits remarkable electron transport capabilities with superior storage capacity. This property expedites the reduction of Fe (III) to Fe (II) [104], and the cytochrome and ferredoxin involved in the synthesis of Fe (II) are key factors in electron transport [104]. It is worth mentioning that most microorganisms participating in DIET, such as Geobacter, Sphaerochaeta and Sporanaerobacter species, rely on exchangeable (bioavailable) iron and sulfur to complete the process of extracellular respiration [61]. Surface functional groups may trigger the DIET mechanism between anaerobic microorganisms. The addition of rice straw biochar has no significant effect on the function of bacterial flora, but biochar material with rich specific surface area and hydrophobicity can provide better electronic conductivity as an electronic conduit. Thus, enhancing electron transport capacity between microorganisms [108]. Besides, it is worth noting that the augmentation of distinct surface functional groups, namely phenolic and lactic acid groups, is the underlying cause of the alteration in redox characteristics of algal biochar. This phenomenon is also responsible for the rise in electron-donating potential. The coenzyme F₄₂₀ activity of methanogenic was enhanced, and the methane yield was increased by 58.7%. Thus, algal biochar acts as an effective electron conduit for DIET during anaerobic methane generation [34]. In thermophilic digesters modified from waste wood pellet biochar, beneficial bacteria (e.g., Thermotogae and Defluviitoga) play a leading role in the fermentation of food waste. Due to the good electrical conductivity of biochar, the DIET process between methanogens and their syntrophic partners is promoted. The addition of sawdust biochar effectively shortened the lag period by 27.5-64.4%, and increased the maximum methanogenesis rate by 22.4%-40.3%, due to the enrichment of Anaerolineaceae and Methanosaeta, typical microorganisms for DIET [12]. Agricultural waste corn straw biochar also promoted the abundance of two electron-synergistic microorganisms, Clostridia and Methanosarcina [93]. Draff-based biochar has abundant active surface functional groups (such as -CO, pyridine-N, and graphite-N), and it has been shown that increased methane production is associated with DIET through the charge-discharge cycle containing C, N, and O functional groups on the surface [17]. Furthermore, previous reported that the presence of elements also exerts a significant influence on the development of biochar functional groups, which affect the DIET indirectly [133]. Accordingly, trace or common elements with bioavailability can enhance DIET through two paths: (1) Enhancing the DIET by abundant functional groups on biochar surface; (2) Enriching the electroactive microorganisms related to DIET and strengthening the cooperative relationship of DIET between electroactive microorganisms and methanogens.

In addition to biofuels, biochar can also facilitate the recovery of value-added chemicals, such as volatile fatty acids (VFA). The surface functional groups on biochar surface, such as OH groups and C-O groups, can affine to the cell surface, which could facilitate biofilm development and accelerate the hydrolysis process of large molecular organic substance [20]. Also, the DIET efficiency could be accelerated by high biochar bulk crystallinity [71]. Nonetheless, considering the syntrophic growth of fermentative bacteria and homoacetogens in the biochar could lead to a change in the H₂ partial pressure in the system, the main product of fermentation would vary with different types of biochar. For instance, Lu et al. [71] found that the enrichments of acetate over butyrate from glucose follow: boconut > longan shell > bamboo > borncob > pinecone. In addition, the algae-derived biochar has been proven to enhance the algae anaerobic fermentation for short-chain fatty acids production. And the acetate concentration varied with biochar content [20]. The algae-based biochar could destroy the algae cells and release more macromolecular organic substrates, which can be used for short-chain fatty acids production. Currently, the abundant hydrophilic functional groups permit the attachment and growth of microorganisms on its surface, while the conductive properties of biochar promote electron transport in the system. The economy analysis also showed that the algae-based biochar couple AD could have a 1.08 Yuan/kg profits more than that in solo-algae AD [20]. Although AD has widely used biochar in VFA production, VFA yield can be further improved by modifying the biochar. Du et al. [19] explored the effect of activated persulfate corn straw-based biochar on VFA production from animal wastewater. According to the research, persulfate modification could lengthen the acidification period and inactivate. The rate of released dissolved organic matter with low molecular weight (0-10 kDa) increased nearly 2.1 times that of the control group, which led to higher VFAs yield [19]. The research has given a novel strategy for enhancing the VFA recovery by synergistic chemical and microbial effects of biochar.

Alcohol production from biomass is a meaningful modern adjunct to fossil fuels. However, during the production process, some pretreatment technology could introduce toxic substances, i.e., furfural and acetic acid [29]. Biochar has been proven to enhance alcohol production. The biochar could act as an effective adsorbent to remove this toxic inhibition. The research by Sun et al. [98] improves ethanol production by switchgrass, forage sorghum, redcedar and poultry litter-based biochar addition. In their research, the biochar could adsorb the lignocellulose-derived microbial inhibitory compounds. Also, Klasson et al. [44] used dew-retted flax shive-based biochar to adsorb the released by-product furfural and hydroxymethylfurfural during pretreatment of ethanol fermentation. The added 2.5% (w/v) biochar in fermentation broth could significantly reduce the fermentation lag phase [44]. However, Wang et al. [109] found that the increase in ethanol production from lignocellulosic biomass by biochar was mainly due to the biochar extracts in the fermentation broth and cell immobilization on biochar. Adsorption detoxification is not a factor in controlling ethanol production. Further studies are needed to elucidate the formation process of cell immobilization on biochar and the effect of biochar on alcohol production. Moreover, the biochar could couple with the bio-electrochemical system to enhance alcohol production [13]. The bio-electrochemical system coupled with biochar showed a high alcohol/acid ratio of 0.179 mol/mol, which increases nearly threefold of unmediated mixotrophy at an open circuit. The research indicated that the biochar could be coupled with other technology to enhance the biochemical recovery efficiency.

The recovery of other biochemical products can also be improved by biochar. Medium-chain carboxylates acids (MCCAs) such as hexanoate (C6) and caprylate (C8) are valuable commercial chemicals with relatively higher energy density and a wide range of applications. It can be the precursor of spices, renewable fuels, fungicides and food additives [87,106]. The addition of biochar has been reported to enhance the MCCAs production and product selectivity [106]. The detailed mechanism can be divided into follows points: (1) reinforcement effect in chain elongation. The biochar addition could be inducing more extracellular polymers substance by its high conductivity, which is beneficial for creating a more demanding system with a more stable microorganism community structure on the biochar for chain elongation [68]. Moreover, it should be noted that the biochar smaller than 5 μm may be more efficient for enhancing chain elongation due to its higher K content in an aqueous solution, electrical conductivity and surface area[67]. (2) shorten the lag phase in the chain elongation reactions. The biochar could create some microlocations or ‘hotspots’ with higher local pH around the biochar, which would decrease the toxicity of undissociated carboxylates [68,102]. (3) increasing the selectivity of MCCAs. Ghysels et al. [23] concluded that the biochar could enhance the ethanol conversion and selectivity to caproic acid by Clostridium kluyveri. The biochar could serve as a pH buffer and enrich the functional microorganism [68]. Recent research by Cheng et al. [13] found that butyrate and hexanoate production from glucose increased significantly by biochar. Beside the MCCAs, the production of acetone-butanol-ethanol (ABE) fermentation could also be enhanced by biochar. The biochar derived from switchgrass, forage sorghum, and red cedar could act as buffer and provide mineral nutrients to functional microorganism, i.e., Na、Ca、Mg、K、S、P、Fe、Zn and Mn, which are crucial for metabolic processes and the growth of microbes [103].

Recent importance analysis has highlighted a significant correlation between cumulative methane production (CMP) and the physicochemical properties of biochar [42]. This discovery implies that it may be possible to forecast CMP under unexamined conditions by accurately predicting these properties. This finding opens up new opportunities for comprehending and optimizing biochar-enhanced AD systems. And it is essential to consider the conditions and operational parameters of AD systems that can be optimized to favor either methane or hydrogen production, the latter of which is known as “dark fermentation” [111]. The prediction of energy production is an attractive application for industries, as it directly impacts profitability. Many methane prediction models have been applied in techno-economic analysis (TEA) to assess the economic feasibility of new biogas plants or evaluate optimization strategies for future scenarios [40,60,76]. Extensive work has been done in this field; however, the models vary in complexity and reliability.

Notably, many kinetic-based models are derived from the well-known Anaerobic Digestion Model No. 1 (ADM1), which requires multidisciplinary knowledge. In contrast, more straightforward techniques based on approximate stoichiometry equations have been more commonly used in TEA models without considering the kinetics of the reactions [76]. These prediction methods have complex expressions and may not be adaptable to novel feedstocks (e.g., lignocellulosic biomass), different reactor configurations, reaction temperatures, and reaction types (e.g., dry AD). Consequently, the prediction results can exhibit significant variations, leading to unreliable economic assessments. Moreover, conventional models often overlook factors affecting methane yields, such as inhibition factors, trace elements, and enhancement strategies [2,64]. The conventional models are either too simple to adequately depict the overall performance or too complex to be applied at the commercial and full-scale industrial levels. Some studies have attempted to overcome these limitations by developing statistically based, multi-level regression optimal models [66]. However, these models often require a large amount of full-scale data and may struggle to accurately capture the complex nonlinear responses that occur in biogas or biohydrogen processes [47].

In contrast, ML methods offer a quick and effective way to predict methane production and identify the key parameters influencing the prediction. It requires no knowledge of the biological, physical, or chemical sciences [16,114,132]. Simple parameter inputs can lead to highly reliable predictions. For instance, Ghatak and Ghatak [22] utilized feedstock composition, HRT, and temperature to predict biogas production, achieving an R² value of 0.99. Similarly, Cheon et al. [14] employed OLR, pH, alkalinity, VFA, and COD removal efficiency to predict methane yield, resulting in an impressive R² value of 0.97. Similarly, Wang and Wan [105] demonstrated the usefulness of neural networks in optimizing multiple responses in fermentative hydrogen production processes. In a review by Kumar Sharma et al. [47], various ML algorithms were critically discussed, and it was concluded that ML methods hold promise in predicting biohydrogen yield. The authors encouraged using advanced ML techniques, such as deep learning, and integrating theoretical models to enhance interpretation and prediction capabilities. Pandey et al. [82] also discussed the applications of ML models for predicting and optimizing biohydrogen production. The study emphasized the need for robust ML techniques that can explore the target system and aid in decision-making processes.

Several cases have confirmed the outstanding performance of biochar-enhanced anaerobic systems. However, most ML models in this field primarily focus on conventional parameters and are limited to case-to-case studies. Only a few studies have investigated the correlation between biochar properties and energy production performance [42]. The feature importance of biochar concerning CMP remains unclear, and the impact of biochar on hydrogen production performance in this context is still unknown. These studies demonstrate the flexibility of ML in prediction, as it is not limited to specific input parameters. Such findings suggest the potential for developing highly efficient prediction models for industrial applications.

Bioplastics have gained significant attention recently due to their environmental benefits and sustainable characteristics. Among the fully degradable bioplastics, PHA can be produced by a wide range of bacteria and archaea [54].

To optimize the selection process for PHA production, Xu et al. [113] integrated mechanistic and deep learning models to accurately predict the dynamic enrichment of PHA accumulating bacteria (PAB) in mixed microbial cultures. Luna et al. [72] developed a hybrid model to describe the continuous production of PHA at a laboratory culture scale. It is worth noting that the use of ML for PHA production prediction is an emerging field, and further research is needed to fully explore the capabilities and potential applications of ML models in this context. However, the eco-friendliness, biosynthesis capability, and modifiable properties of PHA-based polymers make them promising alternatives to petroleum-based plastics, fueling the interest in developing efficient production strategies [84].

Biochar has been shown to enhance the performance of AD, a strategy known as the additive approach. Similar to carbon-rich materials such as activated carbon and graphene, biochar demonstrates immobilization and support for microbial growth, as well as mitigation of acid stress and ammonia inhibition [130]. Moreover, there is a growing interest in exploring the role of biochar in facilitating DIET among microbes [10].

ML methods provide a new approach to understanding these interactions with microorganisms, mitigating labor-intensive, costly, and time-consuming work. For instance, Treloar et al. [101] applied reinforcement learning combined with neural networks to control multiple interacting species in a bioreactor, showing promising results for co-culture control. Feature importance analysis conducted by Xu et al. [114] demonstrated that certain microbes play crucial roles despite having relatively low abundances. ML-assisted identification of these features can guide the proactive management of microbial communities. However, Li et al. [53] pointed out that most ML-assisted applications in the AD process have been limited to simple feature importance analysis, and there is a need for a more in-depth interpretation of microbial interactions.