Artificial neural networks for the prediction of biochar yield: A comparative study of metaheuristic algorithms

Machine learning approach for photocatalysis: An experimentally validated case study of photocatalytic dye degradation

In this study, machine learning (ML) models coupled with genetic algorithm (GA) and particle swarm optimization (PSO) were applied to predict the relative influence of experimental parameters of photocatalytic dye removal. Specifically, the impact of bandgap, dye concentration, photocatalyst dosage, solution volume, specific surface area, and time duration on photocatalytic degradation rate constant of cationic dyes was discerned using selected ML models, i.e., ensembled learning tree (ELT), gaussian process regression (GPR), support vector machine (SVM), and decision tree (DT). Thus, the data points were sourced from literature studies recently published in 2024 and 2023 on materials related to working on fundamental principles of photocatalysis. The ELT-PSO hybrid model outperformed all models with R2 = 0.992 and RMSE = 2.6408e-04, followed by DT, GPR, and SVM. The partial dependence plots and Shapley's analysis demonstrate that the type of dye, bandgap, dye initial concentration, and time duration are essential parameters for photocatalytic degradation, while sensitivity analysis further displayed solution volume and time duration to be the most influential parameters for rate constant determination. The optimized ML model's prediction was also experimentally validated using as-synthesized different compositions of Cu2O/WO3 heterostructures and ZnO nanoparticles. The results suggest that an ML-optimized study can be used in designing photocatalysts with optimum properties desired for the removal of cationic dyes at high rates from wastewater, thus saving energy and cost for a sustainable environment.

Advancements in Biochar as a Sustainable Adsorbent for Water Pollution Mitigation

Biochar, a carbon-rich material produced from the partial combustion of biomass wastes is often termed "black gold" for its potential in water pollution mitigation and carbon sequestration. By customizing biomass feedstock and optimizing preparation strategies, biochar can be engineered with specific physicochemical properties to enhance its effectiveness in removing contaminants from wastewater. Recent studies demonstrate that biochar can achieve > 90% removal efficiency for heavy metals such as lead and cadmium, > 85% adsorption capacity for organic pollutants such as dyes and phenols, and > 80% reduction in microplastics and nanoplastics. This review explores recent advancements in biochar preparation technologies, such as pyrolysis, carbonization, gasification, torrefaction, and rectification, along with physical, chemical, and biological modifications that are crucial for efficient pollutant removal. The core of this review focuses on biochar's applications in removing a wide range of pollutants from wastewater, detailing mechanisms for organic pollutants, inorganic salts, pharmaceutical contaminants, microplastics, nanoplastics, and volatile organic compounds. In addition, the review introduces machine learning as a key technique for optimizing biochar production and functionality, showcasing its potential in advancing biochar technology. The conclusion provides a comprehensive outlook on biochar's future, emphasizing ongoing research and its role in sustainable environmental management.

Precision biochar yield forecasting employing random forest and XGBoost with Taylor diagram visualization

Waste-to-energy conversion via pyrolysis has attracted increasing attention recently owing to its multiple uses. Among the products of this process, biochar stands out for its versatility, with its yield influenced by various factors. Extensive and labor-intensive experimental testing is sometimes necessary to properly grasp the output distribution from various feedstocks. Nonetheless, data-driven predictive models using large-scale historical experiment records can provide insightful analysis of projected yields from a variety of biomass materials, hence overcoming the challenges of empirical modeling. As such, five modern approaches available in modern machine learning are employed in this study to develop the biochar yield prediction models. The Lasso regression, Tweedie regression, random forest, XGBoost, and Gradient boosting regression were employed. Out of these five XGBoost was superior with a training mean squared error (MSE) of 1.17 and a test MSE of 2.94. The XGBoost-based biochar yield model shows excellent performance with a strong predictive accuracy of the R2 values as 0.9739 (training) and 0.8875 (test). The mean absolute percentage error value was only 2.14% in the training phase and 3.8% in the testing phase. Precision prognostic technologies have broad effects on sectors including biomass logistics, conversion technologies, and effective biomass utilization as renewable energy. Leveraging SHAP based on cooperative game theory, the study shows that while ash and moisture lower biochar yield, FPT, nitrogen, and carbon content significantly boost it. Small variables like heating rate and volatile matter have a secondary impact on production efficiency.

Simulation, prediction and optimization for synthesis and heavy metals adsorption of schwertmannite by machine learning

Due to its sea urchin-like structure, Schwertmannite is commonly applied for heavy metals (HMs) pollutant adsorption. The adsorption influence parameters of Schwertmannite are numerous, the traditional experimental enumeration is powerless. In recent years, machine learning (ML) has been gradually employed for adsorbent materials, but there is no comprehensive research on the Schwertmannite adsorbent. In this paper, 27 features and 814 groups of experimental data were used to systematically analyze the adsorption modeling of Schwertmannite first time. The results indicated that the adsorption capacity of Schwertmannite was better predicted by the Random Forest (RF) model (the R2 was 0.874). Then, the RF model was used to analyze the features importance that affects the adsorption of HMs by Schwertmannite. And the importance of Schwertmannite synthesis conditions, Schwertmannite characteristics, adsorption environment, and HMs properties were 11.88%, 30.01%, 48.26%, and 8.19% respectively. Moreover, the synthesis and adsorption conditions of Schwertmannite were predicted and optimized based on RF model, it was predicted that the better synthesis method of Schwertmannite was biological oxidation > Fe2+ oxidation > Fe3+ hydrolysis. Finally, a predictive Graphical User Interface Web Page for Schwertmannite-HMs was developed. We hope that this paper can promote the integration of machine learning and Schwertmannite.

Machine learning prediction of fundamental sewage sludge biochar properties based on sludge characteristics and pyrolysis conditions

Sewage sludge biochar (SSBC) has significant potential for resource recovery from sewage sludge (SS) and has been widely studied and applied across various fields. However, the variability in SSBC properties, resulting from the diverse nature of SS and its intricate interaction with pyrolysis conditions, presents notable challenges to its practical use. This research employed machine learning techniques to predict fundamental SSBC properties, including elemental content, proximate compositions, surface area, and yield, which are essential for assessing the applicability of SSBC. The models achieved robust predictive accuracy (test R2 = 0.82-0.95), except for surface area. Notably, analysis of the optimal models revealed SS characteristics had a significant impact on SSBC properties, particularly total and fixed carbon content (combined importance exceeding 80%). This underscores the needs of source analysis and preparation optimization in targeted SS recovery or SSBC applications. To facilitate this, a graphical user interface was developed for strategic analyzation of sludge sources and pyrolysis settings.

Multi-output neural network model for predicting biochar yield and composition

In biomass pyrolysis for biochar production, existing prediction models face computational challenges and limited accuracy. This study curated a comprehensive dataset, revealing pyrolysis parameters' dominance in biochar yield (54.8 % importance). Pyrolysis temperature emerged as pivotal (PCC = -0.75), influencing yield significantly. Artificial Neural Network (ANN) outperformed Random Forest (RF) in testing set predictions (R2 = 0.95, RMSE = 3.6), making it apt for complex multi-output predictions and software development. The trained ANN model, employed in Partial Dependence Analysis, uncovered nonlinear relationships between biomass characteristics and biochar yield. Findings indicated optimization opportunities, correlating low pyrolysis temperatures, elevated nitrogen content, high fixed carbon, and brief residence times with increased biochar yields. A multi-output ANN model demonstrated optimal fit for biochar yield. A user-friendly Graphical User Interface (GUI) for biochar synthesis prediction was developed, exhibiting robust performance with a mere 0.52 % prediction error for biochar yield. This study showcases practical machine learning application in biochar synthesis, offering valuable insights and predictive tools for optimizing biochar production processes.

Prediction of Biochar Adsorption of Uranium in Wastewater and Inversion of Key Influencing Parameters Based on Ensemble Learning

With the rapid development of industrialization, the problem of heavy metal wastewater treatment has become increasingly serious, posing a serious threat to the environment and human health. Biochar shows great potential for application in the field of wastewater treatment; however, biochars prepared from different biomass sources and experimental conditions have different physicochemical properties, resulting in differences in their adsorption capacity for uranium, which limits their wide application in wastewater treatment. Therefore, there is an urgent need to deeply explore and optimize the key parameter settings of biochar to significantly improve its adsorption capacity. This paper combines the nonlinear mapping capability of SCN and the ensemble learning advantage of the Adaboost algorithm based on existing experimental data on wastewater treatment. The accuracy of the model is evaluated by metrics such as coefficient of determination (R2) and error rate. It was found that the Adaboost-SCN model showed significant advantages in terms of prediction accuracy, precision, model stability and generalization ability compared to the SCN model alone. In order to further improve the performance of the model, this paper combined Adaboost-SCN with maximum information coefficient (MIC), random forest (RF) and energy valley optimizer (EVO) feature selection methods to construct three models, namely, MIC-Adaboost-SCN, RF-Adaboost-SCN and EVO-Adaboost-SCN. The results show that the prediction model with added feature selection is significantly better than the Adaboost-SCN model without feature selection in each evaluation index, and EVO has the most significant effect on feature selection. Finally, the correlation between biochar adsorption properties and production parameters was discussed through the inversion study of key parameters, and optimal parameter intervals were proposed to improve the adsorption properties. Providing strong support for the wide application of biochar in the field of wastewater treatment helps to solve the urgent environmental problem of heavy metal wastewater treatment.

Activated biochar production from young coconut waste (Cocos nucifera) as bioadsorbent: a pathway through Artificial Neural Network (ANN) optimization

This pioneering work explores the immense potential of young coconut waste, a continuously marginalized residue of the food and beverage industry, to serve as an indispensable feedstock in the production of biochar. Through an examination of the key carbonization factors that include time, temperature, and concentrations of the activating agent, KOH, the outcomes offer relevant insights that could be leveraged to maximize biochar production for tailored applications. This study stands out for its innovative use of Artificial Neural Network (ANN) approaches for predictive modeling. Fifty datasets, supplemented with secondary data obtained from the literature and experiments, were utilized for the purposes of training, testing, and validating the neural network model. Here, the datasets were processed utilizing the Deep Neural Network (DNN) framework, which was designed and implemented with the minimal loss function framework feasible. The architectural configuration comprises the following; an input layer, four hidden layers (128-neuron dense layer, batch normalization, and 64-neuron dense layer, batch normalization), a dropout layer, and an output layer. With an R2 of 0.8238 for biochar yield and 0.7324 for iodine number, the trained DNN model showed a relatively high degree of accuracy in making predictions.

Machine learning screening of biomass precursors to prepare biomass carbon for organic wastewater purification: A review

In the past decades, the amount of biomass waste has continuously increased in human living environments, and it has attracted more and more attention. Biomass is regarded as the most high-quality and cost-effective precursor material for the preparation carbon of adsorbents and catalysts. The application of biomass carbon has extensively explored. The efficient application of biomass carbon in organic wastewater purification were reviewed. With briefly introducing biomass types, the latest progress of Machine learning in guiding the preparation and application of biomass carbon was emphasized. The key factors in constructing efficient biomass carbon for adsorption and catalytic applications were discussed. Based on the functional groups, rich pore structure and active site of biomass carbon, it exhibits high efficiency in water purification performance in the fields of adsorption and catalysis. In addition, out of a firm belief in the enormous potential of biomass carbon, the remaining challenges and future research directions were discussed.

Prediction model for biochar energy potential based on biomass properties and pyrolysis conditions derived from rough set machine learning

Biochar is a high-carbon-content organic compound that has potential applications in the field of energy storage and conversion. It can be produced from a variety of biomass feedstocks such as plant-based, animal-based, and municipal waste at different pyrolysis conditions. However, it is difficult to produce biochar on a large scale if the relationship between the type of biomass, operating conditions, and biochar properties is not understood well. Hence, the use of machine learning-based data analysis is necessary to find the relationship between biochar production parameters and feedstock properties with biochar energy properties. In this work, a rough set-based machine learning (RSML) approach has been applied to generate decision rules and classify biochar properties. The conditional attributes were biomass properties (volatile matter, fixed carbon, ash content, carbon, hydrogen, nitrogen, and oxygen) and pyrolysis conditions (operating temperature, heating rate residence time), while the decision attributes considered were yield, carbon content, and higher heating values. The rules generated were tested against a set of validation data and evaluated for their scientific coherency. Based on the decision rules generated, biomass with ash content of 11-14 wt%, volatile matter of 60-62 wt% and carbon content of 42-45.3 wt% can generate biochar with promising yield, carbon content and higher heating value via a pyrolysis process at an operating temperature of 425°C-475°C. This work provided the optimal biomass feedstock properties and pyrolysis conditions for biochar production with high mass and energy yield.

A new approach to explore and assess the sustainable remediation of chromium-contaminated wastewater by biochar based on 3E model

As a cost-effective material, biochar, known as 'black gold', has been widely used for environmental applications (EA), including chromium-contaminated wastewater remediation. However, limited reports focused on the multiple impacts of biochar, including energy consumption (EC) and environmental risk (ER). Hence, to recommend biochar as a green material for sustainable development, the three critical units were explored and quantitatively assessed based on an adapted 3E model (EA-EC-ER). The tested biochar was produced by limited-oxygen pyrolysis at 400-700 °C by using three typical biomasses (Ulva prolifera, phoenix tree, and municipal sludge), and the optimal operational modulus of the 3E model was identified using six key indicators. The findings revealed a significant positive correlation between EC and biochar yield (p < 0.05). The biochar produced by phoenix tree consumed more energy due to having higher content of unstable carbon fractions. Further, high-temperature and low-temperature biochar demonstrated different chromium removal mechanisms. Notably, the biochar produced at low temperature (400 °C) achieved better EA due to having high removal capacity and stability. Regarding ER, pyrolysis temperature of 500 °C could effectively stabilize the ecological risk in all biochar and the biochar produced by Ulva prolifera depicted the greatest reduction (37-fold). However, the increase in pyrolysis temperature would lead to an increase in global warming potential by nearly 22 times. Finally, the 3E model disclosed that the biochar produced by Ulva prolifera at 500 °C with low EC, high EA, and low ER had the most positive recommendation index (+78%). Importantly, a rapid assessment methodology was established by extracting parameters from the correlation. Based on this methodology, about eight percent of biochar can be the highest recommended from more than 100 collected peer-related data. Overall, the obtained findings highlighted that the multiple impacts of biochar should be considered to efficiently advance global sustainable development goals.

Nanomedicine facilitated cell signaling blockade: difficulties and strategies to overcome glioblastoma

Glioblastoma (GBM) is a highly aggressive and lethal type of brain tumor with complex and diverse molecular signaling pathways involved that are in its development and progression. Despite numerous attempts to develop effective treatments, the survival rate remains low. Therefore, understanding the molecular mechanisms of these pathways can aid in the development of targeted therapies for the treatment of glioblastoma. Nanomedicines have shown potential in targeting and blocking signaling pathways involved in glioblastoma. Nanomedicines can be engineered to specifically target tumor sites, bypass the blood-brain barrier (BBB), and release drugs over an extended period. However, current nanomedicine strategies also face limitations, including poor stability, toxicity, and low therapeutic efficacy. Therefore, novel and advanced nanomedicine-based strategies must be developed for enhanced drug delivery. In this review, we highlight risk factors and chemotherapeutics for the treatment of glioblastoma. Further, we discuss different nanoformulations fabricated using synthetic and natural materials for treatment and diagnosis to selectively target signaling pathways involved in GBM. Furthermore, we discuss current clinical strategies and the role of artificial intelligence in the field of nanomedicine for targeting GBM.

Machine learning prediction of biochar yield based on biomass characteristics

Slow pyrolysis is a widely used thermochemical pathway that can convert organic waste into biochar. We employed six machine learning models to predictively model 13 selected variables using pearson feature selection. Additionally, partial dependence analysis is used to reveal the deep relationship between feature variables. Both the gradient boosting decision tree and the Levenberg-Marquardt backpropagation neural network achieved training set R2 > 0.9 and testing set R2 > 0.8. But the other models displayed lower performance on the testing set, with R2 < 0.8. The partial dependence plot demonstrates that pyrolysis conditions have greater impact on biochar yield than biomass composition. Furthermore, the highest treatment temperature, being the sole consistently changing feature, can serve as a guiding factor for regulating biochar yield. This study highlights the immense potential of machine learning in experimental prediction, providing a scientific reference for reducing time and economic costs in pyrolysis experiments and process development.

Role of modeling and artificial intelligence in process parameter optimization of biochar: A review

Enhancement of crop yield, conservation and quality upgradation of soil, and efficient water management are the main objectives of sustainable agriculture and mitigating climate change's impact on agriculture. In recent days, biochar, obtained via thermochemical alteration of biomass is becoming a powerful agent for soil and water quality improvement, carbon sequestration, greenhouse gas emission reduction, and heavy metal adsorption. The present study predominantly focuses on various process parameters related to biochar preparation through pyrolysis, their impact on biochar production as well as physicochemical characteristics, and the optimization of such process parameters. Different designs of the experiment (DOE) and optimization techniques including traditional and non-traditional optimizations are discussed in the current review, along with their applicability and shortcomings. Since the biochar preparation process is tedious and energy-consuming, the present review will help to understand the importance of optimization in preparing biochar, thereby leading to a better way to prepare biochar.

Biochar production and its environmental applications: Recent developments and machine learning insights

Biochar production through thermochemical processing is a sustainable biomass conversion and waste management approach. However, commercializing biochar faces challenges requiring further research and development to maximize its potential for addressing environmental concerns and promoting sustainable resource management. This comprehensive review presents the state-of-the-art in biochar production, emphasizing quantitative yield and qualitative properties with varying feedstocks. It discusses the technology readiness level and commercialization status of different production strategies, highlighting their environmental and economic impacts. The review focuses on integrating machine learning algorithms for process control and optimization in biochar production, improving efficiency. Additionally, it explores biochar's environmental applications, including soil amendment, carbon sequestration, and wastewater treatment, showcasing recent advancements and case studies. Advances in biochar technologies and their environmental benefits in various sectors are discussed herein.

A review on modelling of thermochemical processing of biomass for biofuels and prospects of artificial intelligence-enhanced approaches

Biofuels from lignocellulosic biomass converted via thermochemical technologies can be renewable and sustainable, which makes them promising as alternatives to conventional fossil fuels. Prior to building industrial-scale thermochemical conversion plants, computational models are used to simulate process flows and conditions, conduct feasibility studies, and analyse process and business risk. This paper aims to provide an overview of the current state of the art in modelling thermochemical conversion of lignocellulosic biomass. Emphasis is given to the recent advances in artificial intelligence (AI)-based modelling that plays an increasingly important role in enhancing the performance of the models. This review shows that AI-based models offer prominent accuracy compared to thermodynamic equilibrium modelling implemented in some models. It is also evident that gasification and pyrolysis models are more matured than thermal liquefaction for lignocelluloses. Additionally, the knowledge gained and future directions in the applications of simulation and AI in process modelling are explored.

Machine learning assisted predicting and engineering specific surface area and total pore volume of biochar

Biochar produced from pyrolysis of biomass is a platform porous carbon material that have been widely used in many areas. Specific surface area (SSA) and total pore volume (TPV) are decisive to biochar application in hydrogen uptake, CO₂ adsorption, and organic pollutant removal, etc. Engineering biochar by traditional experimental methods is time-consuming and laborious. Machine learning (ML) was used to effectively aid the prediction and engineering of biochar properties. The prediction of biochar yield, SSA, and TPV was achieved via random forest (RF) and gradient boosting regression (GBR) with test R² of 0.89-0.94. ML model interpretation indicates pyrolysis temperature, biomass ash, and volatile matter were the most important features to the three targets. Pyrolysis parameters and biomass mixing ratios for biochar production were optimized via three-target GBR model, and the optimum schemes to obtain high SSA and TPV were experimentally verified, indicating the great potential of ML for biochar engineering.

Review of biochar production via crop residue pyrolysis: Development and perspectives

Worldwide surge in crop residue generation has necessitated developing strategies for their sustainable disposal. Pyrolysis has been widely adopted to convert crop residue into biochar with bio-oil and gas being two co-products. The review adopts a whole system philosophy and systematically summarises up-to-date knowledge of crop residue pyrolysis processes, influential factors, and biochar applications. Essential process design tools for biochar production e.g., cost-benefit analysis, life cycle assessment, and machine learning methods are also reviewed, which has often been overlooked in prior reviews. Important aspects include (a) correlating techno-economics of biochar production with crop residue compositions, (b) process operating conditions and management strategies, (c) biochar applications including soil amendment, fuel displacement, catalytic usage, etc., (d) data-driven modelling techniques, (e) properties of biochar, and (f) climate change mitigation. Overall, the review will support the development of application-oriented process pipelines for crop residue-based biochar.

Artificial intelligence technologies in bioprocess: Opportunities and challenges

Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, and which have made great progress in the past decades. Combination of the current control and optimization technologies with the latest computer-based strategies will be a worth expecting way to improve bioprocess further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the complex interactions used in mathematical models and has been gradually applied in bioprocess. In this review, firstly, AI-guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical process parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess are summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. Lastly, current research gaps and future perspectives on AI-guided optimization and control technologies are discussed. This review provides theoretical guidance for developing AI-guided bioprocess optimization and control technologies.

A review on properties, challenges and commercial aspects of eco-friendly biolubricants productions

Lubricants operate as antifriction media, preserving machine reliability, facilitating smooth operation, and reducing the likelihood of frequent breakdowns. The petroleum-based reserves are decreasing globally, leading to price increases and raising concerns about environmental degradation. The researchers are concentrating their efforts on developing and commercializing an environmentally friendly lubricant produced from renewable resources. Biolubricants derived from nonedible vegetable oils are environmentally favorable because of their non-toxicity, biodegradability, and close to net zero greenhouse gas emissions. The demand for bio lubricants in industry and other sectors is increasing due to their non-toxic, renewable, and environmentally friendly nature. Good lubrication, anti-corrosion, and high flammability are characteristic properties of vegetable oils due to their unique structure. This study presents several key properties of nonedible oils that are used to produce lubricants via the transesterification process. Bibliometric analysis is also performed, which provides us with a better understanding of previous studies related to the production of bio lubricants from the transesterification process. Only 371 published documents in the Scopus database were found to relate to the production of bio lubricants using the transesterification process. The published work was mostly dominated by research articles (286; 77.088%). Significant development can be seen in recent years, with the highest occurrence in 2021, reaching 68 publications accounting for 18.38% of the total documents. In the second step, (i) the authors with the most number of publications; (ii) journals with the most productions; (iii) most productive countries; and (iv) the authors' most frequently used keywords were evaluated. These results will provide a pathway for researchers interested in this field. Lastly, recommendation is made on research gaps to device possible strategies for its commercialization.

Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction

In this study, Machine learning (ML) models integrated with genetic algorithm (GA) and particle swarm optimization (PSO) have been developed to predict, evaluate, and analyze biochar yield using biomass properties and process operating conditions. Comparative study of different ML algorithms integrated with GA and PSO were performed to improve the ML models architecture and parameters selection. The results proposed that Ensembled Learning Tree (ELT-PSO) model outperformed all other models and is favored for biochar yield prediction (R² = 0.99, RMSE = 2.33). The partial dependence plots (PDPs) analysis shows the potential effects of each influencing parameter impact on the biochar yield and as well as shows that how these factors will interact during the pyrolysis process. A user-friendly software was developed based on the ELT-PSO model to avoid extensive and expensive experimentations without requiring considerable ML understanding. Difference recorded by GUI was less than 2% with experimental yield.

Machine learning prediction of nitrogen heterocycles in bio-oil produced from hydrothermal liquefaction of biomass

Hydrothermal liquefaction (HTL) of high-moisture biomass or biowaste to produce bio-oil is a promising technology. However, nitrogen-heterocycles (NH) presence in bio-oil is a bottleneck to the upgrading and utilization of bio-oil. The present study applied the machine learning (ML) method (random forest) to predict and help control the bio-oil NH, bio-oil yield, and N content of bio-oil (N_oil). The results indicated that the predictive performance of the yield and N_oil were better than previous studies, achieving test R² of 0.92 and 0.95, respectively. Acceptable predictive performance (test R² of 0.82 and RMSE of 7.60) for the prediction of NH was also achieved. The feature importance analysis, partial dependence, and Shapely value were used to interpret the prediction models and study the NH formation mechanisms and behavior. Then, forward optimization of NH was implemented based on optimal predictive models, indicating the high potential of ML-aided bio-oil production and engineering.

Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass

Biochar production via pyrolysis of various organic waste has potential to reduce dependence on conventional energy sources and mitigate global warming potential. Existing models for predicting biochar yield and compositions are computationally-demanding, complex, and have low accuracy for extrapolative scenarios. Here, two data-driven machine learning models based on Multi-Layer Perceptron Neural Network and Artificial Neuro-Fuzzy Inference System are developed. The data-driven models predict biochar yield and compositions for a variety of input feedstock compositions and pyrolysis process conditions. Feature importance assessment of the input dataset revealed their competitive significance for predicting biochar yield and compositions. Overall, the predictive accuracy of the models was up to 12.7% better than the Random Forest and eXtreme Gradient Boosting machine learning algorithms reported in the literature. The models developed are valuable for environmental footprint assessment of biochar production and rapid system optimization.