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

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.

A complete review on the oxygen-containing functional groups of biochar: Formation mechanisms, detection methods, engineering, and applications

Biochar is a porous carbon material generated by the thermal treatment of biomass under anaerobic or anoxic conditions with wealthy Oxygen-containing functional groups (OCFGs). To date, OCFGs of biochar have been extensively studied for their significant utility in pollutant removal, catalysis, capacitive applications, etc. This review adopted a whole system philosophy and systematically summarizes up-to-date knowledge of formation, detection methods, engineering, and application for OCFGs. The formation mechanisms and detection methods of OCFGs, as well as the relationships between OCFGs and pyrolysis conditions (such as feedstocks, temperature, atmosphere, and heating rate), were discussed in detail. The review also summarized strategies and mechanisms for the oxidation of biochar to afford OCFGs, with the performances and mechanisms of OCFGs in the various application fields (environmental remediation, catalytic biorefinery, and electrode material) being highlighted. In the end, the future research direction of biochar OCFGs was put forward.

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.

Machine learning application for predicting key properties of activated carbon produced from lignocellulosic biomass waste with chemical activation

The successful application of gradient boosting regression (GBR) in machine learning to forecast surface area, pore volume, and yield in biomass-derived activated carbon (AC) production underscores its potential for enhancing manufacturing processes. The GBR model, collecting 17 independent variables for two-step activation (2-SA) and 14 for one-step activation (1-SA), demonstrates effectiveness across three datasets-1-SA, 2-SA, and a combined dataset. Notably, in 1-SA, the GBR model yields R2 values of 0.76, 0.90, and 0.83 for TPV, yield, and SSA respectively, and records R2 of 0.90 and 0.91 for yield in 2-SA and combined datasets. The model highlights the significance of the soaking procedure alongside activation temperature in shaping AC properties with 1-SA or 2-SA, illustrating machine learning's potential in optimizing AC production processes.

Machine learning models for predicting biochar properties from lignocellulosic biomass torrefaction

This study developed six machine learning models to predict the biochar properties from the dry torrefaction of lignocellulosic biomass by using biomass characteristics and torrefaction conditions as input variables. After optimization, gradient boosting machines were the optimal model, with the highest coefficient of determination ranging from 0.89 to 0.94. Torrefaction conditions exhibited a higher relative contribution to the yield and higher heating value (HHV) of biochar than biomass characteristics. Temperature was the dominant contributor to the elemental and proximate composition and the yield and HHV of biochar. Feature importance and SHapley Additive exPlanations revealed the effect of each influential factor on the target variables and the interactions between these factors in torrefaction. Software that can accurately predict the element, yield, and HHV of biochar was developed. These findings provide a comprehensive understanding of the key factors and their interactions influencing the torrefaction process and biochar properties.

Active Learning-Based Guided Synthesis of Engineered Biochar for CO2 Capture

Biomass waste-derived engineered biochar for CO2 capture presents a viable route for climate change mitigation and sustainable waste management. However, optimally synthesizing them for enhanced performance is time- and labor-intensive. To address these issues, we devise an active learning strategy to guide and expedite their synthesis with improved CO2 adsorption capacities. Our framework learns from experimental data and recommends optimal synthesis parameters, aiming to maximize the narrow micropore volume of engineered biochar, which exhibits a linear correlation with its CO2 adsorption capacity. We experimentally validate the active learning predictions, and these data are iteratively leveraged for subsequent model training and revalidation, thereby establishing a closed loop. Over three active learning cycles, we synthesized 16 property-specific engineered biochar samples such that the CO2 uptake nearly doubled by the final round. We demonstrate a data-driven workflow to accelerate the development of high-performance engineered biochar with enhanced CO2 uptake and broader applications as a functional material.

Feature engineering for improved machine-learning-aided studying heavy metal adsorption on biochar

Due to the broad interest in using biochar from biomass pyrolysis for the adsorption of heavy metals (HMs) in wastewater, machine learning (ML) has recently been adopted by many researchers to predict the adsorption capacity (η) of HMs on biochar. However, previous studies focused mainly on developing different ML algorithms to increase predictive performance, and no study shed light on engineering features to enhance predictive performance and improve model interpretability and generalizability. Here, based on a dataset widely used in previous ML studies, features of biochar were engineered-elemental compositions of biochar were calculated on mole basis-to improve predictive performance, achieving test R2 of 0.997 for the gradient boosting regression (GBR) model. The elemental ratio feature (H-O-2N)/C, representing the H site links to C (non-active site to HMs), was proposed for the first time to help interpret the GBR model. The (H-O-2N)/C and pH of biochar played essential roles in replacing cation exchange capacity (CEC) for predicting η. Moreover, expanding the coverages of variables by adding cases from references improved the generalizability of the model, and further validation using cases without CEC and specific surface area (R2 0.78) and adsorption experimental results (R2 0.72) proved the ML model desirable. Future studies in this area may take into account algorithm innovation, better description of variables, and higher coverage of variables to further increase the model's generalizability.

Machine learning prediction of higher heating value of biochar based on biomass characteristics and pyrolysis conditions

The higher heating value of biochar is an important parameter for the utilization of biomass energy. In this work, extreme gradient boosting regression and artificial neural network were used to predict it based on the characteristics of biomass and pyrolysis conditions. Besides, empirical correlations were developed for comparison. Results showed that the extreme gradient boosting regression models showed better performance (R2 = 0.83-0.94). The shapley additive explanations and partial dependence plot indicated that lignin content and higher heating value of raw material were highly positively correlated with higher heating value of biochar, and found the better conditions such as pyrolysis temperature (>550 °C), lignin content (>40 wt%) for high-higher heating value biochar preparation. What's more, a program that predicted higher heating value of biochar was developed through PySimpleGUI library. It offered a new optimization idea for the directional preparation process of biochar.

Biochar as Alternative Material for Heavy Metal Adsorption from Groundwaters: Lab-Scale (Column) Experiment Review

The purpose of this manuscript is to present a review of laboratory experiments (including methodology and results) that use biochar, a specific carbon obtained by a pyrolysis process from different feedstocks, as an alternative material for heavy metal adsorption from groundwater. In recent years, many studies have been conducted regarding the application of innovative materials to water decontamination to develop a more sustainable approach to remediation processes. The use of biochar for groundwater remediation has particularly attracted the interest of researchers because it permits the reuse of materials that would be otherwise disposed of, in accordance with circular economy, and reduces the generation of greenhouse gases if compared to the use of virgin materials. A review of the different approaches and results reported in the current literature could be useful because when applying remediation technologies at the field scale, a preliminary phase in which the suitability of the adsorbent is evaluated at the lab scale is often necessary. This paper is therefore organised with a short description of the involved metals and of the biochar production and composition. A comprehensive analysis of the current knowledge related to the use of biochar in groundwater remediation at the laboratory scale to obtain the characteristic parameters of the process that are necessary for the upscaling of the technology at the field scale is also presented. An overview of the results achieved using different experimental conditions, such as the chemical properties and dosage of biochar as well as heavy metal concentrations with their different values of pH, is reported. At the end, numerical studies useful for the interpretation of the experiment results are introduced.

Machine learning applications for biochar studies: A mini-review

Biochar is a promising carbon sink whose application can assist in reducing carbon emissions. Development of this technology currently relies on experimental trials, which are time-consuming and labor-intensive. Machine learning (ML) technology presents a potential solution for streamlining this process. This review summarizes the current research on ML's applications in biochar production, characterization, and applications. It briefly explains commonly used machine learning algorithms and discusses prospects and challenges. A hybrid model that combines ML with mechanism-based analysis could be a future trend, addressing the ML's black-box nature. While biochar studies have adopted ML technology, current works mostly use lab-scale data for model training. Further work is needed to develop ML models based on pilot or industrial-scale data to realize the use of ML techniques for the field application of biochar.

Machine learning-aided hydrothermal carbonization of biomass for coal-like hydrochar production: Parameters optimization and experimental verification

Biomass to coal-like hydrochar via hydrothermal carbonization (HTC) is a promising route for sustainability development. Yet conventional experimental method is time-consuming and costly to optimize HTC conditions and characterize hydrochar. Herein, machine learning was employed to predict the fuel properties of hydrochar. Random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) models were developed, presenting acceptable prediction performance with R2 at 0.825---0.985 and root mean square error (RMSE) at 1.119---5.426, and XGB outperformed RF and SVM. The model interpretation indicated feedstock ash content, reaction temperature, and solid to liquid ratio were the three decisive factors. The optimized XGB multi-task model via feature re-examination illustrated improved generalization ability with R2 at 0.927 and RMSE at 3.279. Besides, the parameters optimization and experimental verification with wheat straw as feedstock further demonstrated the huge application potential of machine learning in hydrochar engineering.

Biomass based biochar production approaches and its applications in wastewater treatment, machine learning and microbial sensors

Biochar is a stable carbonaceous material derived from various biomass and can be utilized as adsorbents, catalysts and precursors in various environmental applications. This review discusses various feedstock materials and methods of biochar production via traditional as well as modern approaches. Additionally, the biochar characteristics, HTC process, and its modification by employing steam and gas purging, acidic, basic / alkaline and organo-solvent, electro- and magnetic fields have been discussed. The recent biochar applications for real water, wastewater and industrial wastewater for the abstraction of environmental contaminants also reviewed. Moreover, applications in machine learning and microbial sensors were discussed. In the meantime, analyses on commercial and environmental profit, current ecological concerns and the future directions of biochar application have been well presented.

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.

Recent advancements and challenges in emerging applications of biochar-based catalysts

The sustainable utilization of biochar produced from biomass waste could substantially promote the development of carbon neutrality and a circular economy. Due to their cost-effectiveness, multiple functionalities, tailorable porous structure, and thermal stability, biochar-based catalysts play a vital role in sustainable biorefineries and environmental protection, contributing to a positive, planet-level impact. This review provides an overview of emerging synthesis routes for multifunctional biochar-based catalysts. It discusses recent advances in biorefinery and pollutant degradation in air, soil, and water, providing deeper and more comprehensive information of the catalysts, such as physicochemical properties and surface chemistry. The catalytic performance and deactivation mechanisms under different catalytic systems were critically reviewed, providing new insights into developing efficient and practical biochar-based catalysts for large-scale use in various applications. Machine learning (ML)-based predictions and inverse design have addressed the innovation of biochar-based catalysts with high-performance applications, as ML efficiently predicts the properties and performance of biochar, interprets the underlying mechanisms and complicated relationships, and guides biochar synthesis. Finally, environmental benefit and economic feasibility assessments are proposed for science-based guidelines for industries and policymakers. With concerted effort, upgrading biomass waste into high-performance catalysts for biorefinery and environmental protection could reduce environmental pollution, increase energy safety, and achieve sustainable biomass management, all of which are beneficial for attaining several of the United Nations Sustainable Development Goals (UN SDGs) and Environmental, Social and Governance (ESG).

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-accelerated screening of single metal atoms anchored on MnPS3 monolayers as promising bifunctional oxygen electrocatalysts

Searching for bifunctional oxygen electrocatalysts with good catalytic performance to promote the oxygen evolution/reduction reactions (OER/ORR) is of great significance to the development of sustainable and renewable clean energy. Herein, we performed density functional theory (DFT) and machine-learning (DFT-ML) hybrid computations to investigate the potential of a series of single transition metal atoms anchored on the experimentally available MnPS₃ monolayer (TM/MnPS₃) as the bifunctional electrocatalysts for the ORR/OER. The results revealed that the interactions of these metal atoms with MnPS₃ are rather strong, thus guaranteeing their high stability for practical applications. Remarkably, the highly efficient ORR/OER can be achieved on Rh/MnPS₃ and Ni/MnPS₃ with lower overpotentials than those of metal benchmarks, which can be further rationalized by establishing the volcano and contour plots. Furthermore, the ML results showed that the bond length of TM atoms with the adsorbed O species (d_TM-O), the number of d electrons (N_e), the d-center (ε_d), the radius (r_TM) and the first ionization energy (I_m) of the TM atoms are the primary descriptors featuring the adsorption behavior. Our findings not only suggest novel highly efficient bifunctional oxygen electrocatalysts, but also provide cost-effective opportunities for the design of single-atom catalysts using the DFT-ML hybrid method.

Prediction of heavy metals adsorption by hydrochars and identification of critical factors using machine learning algorithms

Hydrochar has become a popular product for immobilizing heavy metals in water bodies. However, the relationships between the preparation conditions, hydrochar properties, adsorption conditions, heavy metal types, and the maximum adsorption capacity (Q_m) of hydrochar are not adequately explored. Four artificial intelligence models were used in this study to predict the Q_m of hydrochar and identify the key influencing factors. The gradient boosting decision tree (GBDT) showed excellent predictive capability for this study (R²=0.93, RMSE=25.65). Hydrochar properties (37%) controlled heavy metal adsorption. Meanwhile, the optimal hydrochar properties were revealed, including the C, H, N, and O contents of 57.28-78.31%, 3.56-5.61%, 2.01-6.42%, and 20.78-25.37%. Higher hydrothermal temperatures (>220 °C) and longer hydrothermal time (>10 h) lead to the optimal type and density of surface functional groups for heavy metal adsorption, which increased the Q_m values. This study has great potential for instructing industrial applications of hydrochar in treating heavy metal pollution.

Unveiling the migration of Cr and Cd to biochar from pyrolysis of manure and sludge using machine learning

Heavy metal (HM) in biochar derived from pyrolysis of sludge or manure is the main issue for its large-scale application in soils for carbon sequestration. However, there is a paucity of efficient approaches to predict and comprehend the HM migration during pyrolysis for preparing low HM-contained biochar. Herein, the data on the feedstock information (FI), additive, total concentration of feedstock (FTC) of HM Cr and Cd, and pyrolysis condition, were extracted from the literature, to predict total concentration (TC) and retention rate (RR) of Cr and Cd in sludge/manure biochar using ML for mapping their migration during pyrolysis. Two datasets for Cr and Cd were compiled with 388 and 292 data points from 48 and 37 peer-review papers. The results indicated that the TC and RR of Cr and Cd could be predicted by the Random Forest model with test R² of 0.74-0.98. Their TC and RR in biochar were dominated by the FTC and FI, respectively; while pyrolysis temperature was the most important to Cd RR. Moreover, potassium-based inorganic additives decreased the TC and RR of Cr while increased those of Cd. The predictive models and insights provided by this work could aid the understanding of HM migration during manure and sludge pyrolysis and guide the preparation of low HM-contained biochar.

Artificial intelligence and machine learning for smart bioprocesses

In recent years, the digital transformation of bioprocesses, which focuses on interconnectivity, online monitoring, process automation, artificial intelligence (AI) and machine learning (ML), and real-time data acquisition, has gained considerable attention. AI can systematically analyze and forecast high-dimensional data obtained from the operating dynamics of bioprocess, allowing for precise control and synchronization of the process to improve performance and efficiency. Data-driven bioprocessing is a promising technology for tackling emerging challenges in bioprocesses, such as resource availability, parameter dimensionality, nonlinearity, risk mitigation, and complex metabolisms. This special issue entitled "Machine Learning for Smart Bioprocesses (MLSB-2022)" was conceptualized to incorporate some of the recent advances in applications of emerging tools such as ML and AI in bioprocesses. This VSI: MLSB-2022 contains 23 manuscripts, and summarizes the major findings that can serve as a valuable resource for researchers to learn major advances in applications of ML and AI in bioprocesses.

Machine learning for hydrothermal treatment of biomass: A review

Hydrothermal treatment (HTT) (i.e., hydrothermal carbonization, liquefaction, and gasification) is a promising technology for biomass valorization. However, diverse variables, including biomass compositions and hydrothermal processes parameters, have impeded in-depth mechanistic understanding on the reaction and engineering in HTT. Recently, machine learning (ML) has been widely employed to predict and optimize the production of biofuels, chemicals, and materials from HTT by feeding experimental data. This review comprehensively analyzed the application of ML for HTT of biomass and systematically illustrated basic ML procedure and descriptors for inputs and outputs of ML models (e.g., biomass compositions, operation conditions, yield and physicochemical properties of derived products) that could be applied in HTT. Moreover, this review summarized ML-aided HTT prediction of yield, compositions, and physicochemical properties of HTT hydrochar or biochar, bio-oil, syngas, and aqueous phase. Ultimately, future prospects were proposed to enhance predictive performance, mechanistic interpretation, process optimization, data sharing, and model application during ML-aided HTT.