Machine learning for hydrothermal treatment of biomass: A review

Targeted conversion of cellulose and hemicellulose macromolecules in the phosphoric acid/acetone/water system: An exploration of machine learning evaluation and product prediction

The simultaneous hydrolysis of cellulose and hemicellulose involves trade-offs, making precise control of hydrolysis products crucial for sustainable development. This study employed three machine learning (ML) models-Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machines (SVM)-to simulate and predict the yields of xylose (Xyl), furfural (FF), glucose (Glu), 5-hydroxymethylfurfural (5-HMF), and levulinic acid (LA) in a phosphoric acid/acetone/water system. The RF model demonstrated the highest accuracy, with R2 values between 0.782 and 0.887, and RMSE from 1.740 to 3.370. Key factors affecting the targeted conversion of macromolecules were identified as the solid-liquid ratio, reaction temperature, and acid dosage, with 160 °C recognized as a critical threshold for converting sugars derived from cellulose and hemicellulose into aldehydes and acids. The presence of metal chlorides, particularly AlCl3, significantly enhanced the selectivity of reactions and affected the distribution of products. It was found that corncobs are more efficient than bagasse in producing Glu. This study supports precise control over a multivariate system for producing multiple hydrolysis products from hemicellulose and cellulose, paving the way for data-driven optimization of lignocellulosic biomass conversion to high-value chemicals.

Machine Learning-Assisted Multi-Property Prediction and Sintering Mechanism Exploration of Mullite-Corundum Ceramics

Mullite-corundum ceramics are pivotal in heat transfer pipelines and thermal energy storage systems due to their excellent mechanical properties, thermal stability, and chemical resistance. Establishing relationships and mechanisms through traditional experiments is time-consuming and labor-intensive. In this study, gradient boosting regression (GBR), random forest (RF), and artificial neural network (ANN) models were developed to predict essential properties such as apparent porosity, bulk density, water absorption, and flexural strength of mullite-corundum ceramics. The GBR model (R2 0.91-0.95) outperformed the RF and ANN models (R2 0.83-0.89 and 0.88-0.91, respectively) in accuracy. Feature importance and partial dependence analyses revealed that sintering temperature and K2O (~0.25%) positively affected bulk density while negatively influencing apparent porosity and water absorption. Additionally, sintering temperature, additives, and Fe2O3 (optimal content ~5% and 1%, respectively) were positively related to flexural strength. This approach provided new insight into the relationships between feedstock compositions and sintering process parameters and ceramic properties, and it explored the possible mechanisms involved.

Cellulose Acetates in Hydrothermal Carbonization: A Green Pathway to Valorize Residual Bioplastics

Bioplastics possess the potential to foster a sustainable circular plastic economy, but their end-of-life is still challenging. To sustainably overcome this problem, this work proposes the hydrothermal carbonization (HTC) of residual bioplastics as an alternative green path. The focus is on cellulose acetate - a bioplastic used for eyewear, cigarette filters and other applications - showing the proof of concept and the chemistry behind the conversion, including a reaction kinetics model. HTC of pure and commercial cellulose acetates was assessed under various operating conditions (180-250 °C and 0-6 h), with analyses on the solid and liquid products. Results show the peculiar behavior of these substrates under HTC. At 190-210 °C, the materials almost completely dissolve into the liquid phase, forming 5-hydroxymethylfurfural and organic acids. Above 220 °C, intermediates repolymerize into carbon-rich microspheres (secondary char), achieving solid yields up to 23 %, while itaconic and citric acid form. A comparison with pure substrates and additives demonstrates that the amounts of acetyl groups and derivatives of the plasticizers are crucial in catalyzing HTC reactions, creating a unique environment capable of leading to a total rearrangement of cellulose acetates. HTC can thus represent a cornerstone in establishing a biorefinery for residual cellulose acetate.

Graph-learning-based machine learning improves prediction and cultivation of commercial-grade marine microalgae Porphyridium

A graph learning [Binarized Attributed Network Embedding (BANE)] model enhances the single-target and multi-target prediction performances of random forest and eXtreme Gradient Boosting (XGBoost) by learning complex interrelationships between cultivation parameters of Porphyridium. The BANE-XGBoost has the best prediction performance (train R2 > 0.96 and test R2 > 0.87). Based on Shapley Additive Explanation (SHAP) model, illumination intensity, culture time, and KH2PO4 are the most critical factors for Porphyridium growth. The combined facilitating roles of cultivation parameters are found using the SHAP value-based heat map and group. To reach high biomass and daily production rate concurrently, one-way and two-way partial dependent plots models find the optimal conditions. The top 2 critical parameters (illumination intensity and KH2PO4) were selected to verify using the graphical user interface website based on the optimized model and lab experiments, respectively. This study shows thegraph-learning-based model can improve prediction performance and optimize intricate low-carbon microalgal cultivation.

Machine learning prediction and exploration of phosphorus migration and transformation during hydrothermal treatment of biomass waste

Hydrothermal treatment (HTT) held promise for phosphorus (P) recovery from high-moisture biomass. However, traditional experimental studies of P hydrothermal conversion were time-consuming and labor-intensive. Thus, based on biomass characteristics and HTT parameters, Random Forest (RF) and Gradient Boosting Regression machine learning (ML) models were constructed to predict HTT P migration between total P in hydrochar (TP_HC) and process water (TP_PW) and hydrochar P transformation among inorganic P (IP_HC), organic P (OP_HC), non-apatite inorganic P (NAIP_HC), and apatite P (AP_HC). Results demonstrated that the RF models (test R2 > 0.86) exhibited excellent performance in both single-target and multi-target predictions. Feature importance analysis identified TP_feed, O, C, and N as critical features influencing P distribution in hydrothermal products. TP_feed, NAIP_feed, temperature, and IP_feed were crucial factors affecting P form transformation in HC. This study provided valuable insights into understanding the migration and transformation of P and further guided experimental research.

Method for Valorization of Coffee Cherry Waste via Hydrothermal Valorization Using Organic and Inorganic Acids as Catalysts

The valorization of coffee cherry waste through hydrothermal carbonization (HTC) was investigated using various organic and inorganic acid catalysts to produce platform chemicals. This study aimed to evaluate the effectiveness of these catalysts for enhancing reaction rates, improving yields, and promoting selectivity. The results showed that sulfuric acid and adipic acid were the most effective, each resulting in a 20% increase in the total yield, demonstrating the potential of organic acids as efficient catalysts in HTC. Other catalysts, such as benzoic acid and phenylacetic acid, also showed promising results, while butyric acid significantly decreased the total yield. The most abundantly produced platform chemicals were sugars, followed by formic acid, levulinic acid, HMF, and furfural. These findings highlight the potential of coffee cherry waste as a valuable resource for producing key chemicals, and the feasibility of hydrothermal carbonization as a sustainable approach for biomass valorization. This study emphasizes the importance of selecting the appropriate catalysts to optimize the conversion process and maximize the extraction of valuable chemicals. The environmental and economic implications of these findings are significant, as they can contribute to the development of sustainable and efficient biomass utilization technologies that could transform agricultural waste into high-value products while reducing waste and promoting a circular economy.

Automated machine learning-aided prediction and interpretation of gaseous by-products from the hydrothermal liquefaction of biomass

Hydrothermal liquefaction (HTL) is a thermochemical conversion technology that produces bio-oil from wet biomass without drying. However, by-product gases will inevitably be produced, and their formation is unclear. Therefore, an automated machine learning (AutoML) approach, automatically training without human intervention, was used to aid in predicting gaseous production and interpreting the formation mechanisms of four gases (CO2, CH4, CO, and H2). Specifically, four accurate optimal single-target models based on AutoML were developed with elemental compositions and HTL conditions as inputs for four gases. Herein, the gradient boosting machine (GBM) performed excellently with train R2 ≥ 0.99 and test R2 ≥ 0.80. Then, the screened GBM algorithm-based ML multi-target models (maximum average test R2 = 0.89 and RMSE = 0.39) were built to predict four gases simultaneously. Results indicated that biomass carbon, solid content, pressure, and biomass hydrogen were the top four factors for gas production from HTL of biomass. This study proposed an AutoML-aided prediction and interpretation framework, which could provide new insight for rapid prediction and revelation of gaseous compositions from the HTL process.

Elevating clean energy through sludge: A comprehensive study of hydrothermal carbonization and co-gasification technologies

This study explores the recycling challenges of industrial sludge, owing to its non-recyclable properties and associated environmental problems. To promote sustainable energy utilization, a novel approach combining hydrothermal carbonization and co-gasification was employed to facilitate the conversion from waste to energy. The industrial sludge was pretreated in the batch-type hydrothermal treatment unit at 180-220 °C, followed by co-gasification. The experimental results indicate that pretreating the sludge at the hydrothermal temperature of 200 °C maximized its thermal decomposition, leading to a rougher structure with obvious cracks, eventually transforming into numerous fragmented small particles. At 1100 °C with a blending mass ratio of 1:1, the sludge hydrochar at 200 °C significantly enhanced the reactivity of coal char, exhibiting the gasification reactivity index R0.9 of 1.57 times higher than that of untreated char. Using the in-situ technique with the heating stage microscope, it was first observed that the addition of pretreated sludge coal chars underwent gasification in the shrinking core mode, displaying a significant ash melt flow phenomenon. Based on the in-situ X-ray diffraction, it was discovered that more amorphous structures were formed by the reaction of Fe with other minerals in the sludge-coal blended char after hydrothermal carbonization at 200 °C. With pretreatment at the hydrothermal temperature of 200 °C, the sludge can increase the specific surface area of the blended char and facilitate the cracking of carbon crystals during co-gasification. Its specific surface area and the Raman spectroscopic ratio ID1/IG were 1.76 and 1.17 times that of coal char, respectively. Collectively, this study highlights the potential for energy recovery from industrial sludge, contributing to sustainable waste management in the chemical industry.

Application of triple-branch artificial neural network system for catalytic pellets combustion

On the international level, it is common to act on reducing emissions from energy systems. However, in addition to industrial emissions, low-stack emissions also make a significant contribution. A good step in reducing its environmental impact, is to move to biofuels, including biomass. This paper examines the impact of placing a catalytic system in a retort boiler to minimize emissions of greenhouse gases, dust and other pollutants when burning pellets. The effect of platinum, and oxides of selected metals placed on the deflector as a solid catalyst was studied. Based on the experimental data, a branched artificial neural network was constructed and trained. The routing of three parallel topologies made it possible to achieve high accuracy while keeping the input data relatively simple. The system showed an average error of 3.54% against arbitrary test data. On the basis of experimental data as well as predictions returned by the artificial neural network, recommendations were shown for the catalysts used and their amounts. Depending on the biomass from which the pellet was produced, the experiment suggested the use of titanium or copper oxides. In the case of the neural network, it was able to select a better system, based on platinum, improving emission reductions by up to more than 19%, depending on the type of pellet used.

Machine learning-based exploration of biochar for environmental management and remediation

Biochar has a wide range of applications, including environmental management, such as preventing soil and water pollution, removing heavy metals from water sources, and reducing air pollution. However, there are several challenges associated with the usage of biochar for these purposes, resulting in an abundance of experimental data in the literature. Accordingly, the purpose of this study is to examine the use of machine learning in biochar processes with an eye toward the potential of biochar in environmental remediation. First, recent developments in biochar utilization for the environment are summarized. Then, a bibliometric analysis is carried out to illustrate the major trends (demonstrating that the top three keywords are heavy metal, wastewater, and adsorption) and construct a comprehensive perspective for future studies. This is followed by a detailed review of machine learning applications, which reveals that adsorption efficiency and capacity are the primary utilization targets in biochar utilization. Finally, a comprehensive perspective is provided for the future. It is then concluded that machine learning can help to detect hidden patterns and make accurate predictions for determining the combination of variables that results in the desired properties which can be later used for decision-making, resource allocation, and environmental management.

Machine-learning intervention progress in the field of organic waste composting: Simulation, prediction, optimization, and challenges

Aerobic composting stands as a widely-adopted method for treating organic solid waste (OSW), simultaneously producing organic fertilizers and soil amendments. This biologically-driven biochemical reaction process, however, presents challenges due to its complex non-linear metabolism and the heterogeneous nature of the solid medium. These characteristics inherently limit the simulation accuracy and efficiency optimization in aerobic composting. Recently, significant efforts have been made to simulate and control composting process parameters, as well as predicting and optimizing composting product quality. Notably, the integration of machine learning (ML) in aerobic composting of organic waste has garnered considerable attention for its applicability and predictive capability in exploring the complex non-linear relationships of organic waste composting parameters. Despite numerous studies on ML applications in OSW composting, a systematic review of research findings in this field is lacking. This study offers a systematic overview of the application level, current status, and versatility of ML in OSW composting. It spans various aspects, such as compost maturity, environmental pollutants, nutrients, moisture, heat loss, and microbial metabolism. The survey reveals that ML-intervention predominantly focuses on compost maturity and environmental pollutants, followed by nutrients, moisture, heat loss, and microbial activity. The most commonly employed predictive models and optimization algorithms are artificial neural networks (47%) and genetic algorithms (10%). These demonstrate high prediction accuracy and maximize composting efficiency in the simulation and prediction of organic waste composting, alongside regulation of key parameters. Deep neural networks and ensemble learning models prove effective in achieving superior predictive performance by selecting feature variables in compost maturity and pollutant residue prediction of organic waste composting in a simpler and more objective manner.

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 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 for municipal sludge recycling by thermochemical conversion towards sustainability

The sustainable disposal of high-moisture municipal sludge (MS) has received increasing attention. Thermochemical conversion technologies can be used to recycle MS into liquid/gas bio-fuel and value-added solid products. In this review, we compared energy recovery potential of common thermochemical technologies (i.e., incineration, pyrolysis, hydrothermal conversion) for MS disposal via statistical methods, which indicated that hydrothermal conversion had a great potential in achieving energy recovery from MS. The application of machine learning (ML) in MS recycling was discussed to decipher complex relationships among MS components, process parameters and physicochemical reactions. Comprehensive ML models should be developed considering successive reaction processes of thermochemical conversion in future studies. Furthermore, challenges and prospects were proposed to improve effectiveness of ML for energizing thermochemical conversion of MS regarding data collection and preprocessing, model optimization and interpretability. This review sheds light on mechanism exploration of MS thermochemical recycling by ML, and provide practical guidance for MS recycling.

Advanced dual-artificial neural network system for biomass combustion analysis and emission minimization

Management of agricultural waste is an important part of plantation operations. Not all wastes are suitable for composting or the process is simply inefficient and time-consuming. In their case, thermal treatment is acceptable, but it is necessary to optimize the process to minimize greenhouse gas emissions. This article investigates the feasibility of constructing artificial neural networks (ANNs) to predict feedstock and emission parameters from the combustion of vineyard biomass. In order to maximize accuracy while avoiding overfitting of the ANN, a novel dual-ANN system was proposed. It consisted of two cascade-forward ANNs trained on independent data, each with three hidden layers. A benchmark showed that the final networks had a relative error in the range of 0.81-2.83%, and the resulting dual-ANN up to a maximum of 2.09%. Based on the ANN, it was possible to make recommendations on the parameters of the feedstock that would be suitable for obtaining bioenergy. It was noted that the best calorific values are shown by waste from plants with an intermediate amount, distribution, and mass of leaves, with relatively low average leaf area. Emissivity reduction, however, requires significantly different conditions. Preference is given to waste from plants that have high amounts of leaves but are spread over many stems - that is, plants that are highly shrubby during the growing season. This proves that it is not possible to formulate universal recommendations that are both energy- and carbon-beneficial, but outlines a clear direction where consensus should be sought, depending on the goals adopted.

Trithiocyanurate-functionalized hydrochar for effectively removing methylene blue and Pb (II) cationic pollutants

Functionalization can change the physicochemical properties of hydrochar and improve its ability to adsorb pollutants. Herein, a trithiocyanurate-functionalized hydrochar (TTHC) was obtained from acylation of chloroacetyl chloride and hydrochar and modification with trithiocyanuric acid in alkaline conditions. TTHC can efficiently remove cationic methylene blue (MB) and Pb(II) from wastewater. The removal can be expressed with pseudo-second-order kinetic and Langmuir models. The MB and Pb(II) removed uptakes by TTHC at 298 K exceeded 909.9 and 182.8 mg g-1 respectively, and the removal rates reached 90% and 98% within 120 min respectively. Characterizations show TTHC is functionalized with trithiocyanurate, and rich in thiolate and aromaticity, and tends to adsorb MB/Pb(II) via multiple adsorption mechanisms. After five sorption-desorption regeneration cycles, TTHC maintained 80% and 99% adsorption capacities for MB and Pb(II) respectively. Therefore, TTHC is a promising efficient sorbent for removing MB and Pb(II) from effluents.

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.

Gasification of biomass for syngas production: Research update and stoichiometry diagram presentation

Gasification is a thermal process that converts organic materials into syngas, bio-oil, and solid residues. This mini-review provides an update on current research on producing high-quality syngas from biomass via gasification. Specifically, the review highlights the effective valorization of feedstocks, the development of novel catalysts for reforming reactions, the configuration of novel integrated gasification processes with an assisted field, and the proposal of advanced modeling tools, including the use of machine learning strategies for process design and optimization. The review also includes examples of using a stoichiometry diagram to describe biomass gasification. The research efforts in this area are constantly evolving, and this review provides an up-to-date overview of the most recent advances and prospects for future research. The proposed advancements in gasification technology have the potential to significantly contribute to sustainable energy production and reduce greenhouse gas emissions.

Mechanisms, methods and applications of machine learning in bio-alcohol production and utilization: A review

Bio-alcohols have been proven promising alternatives to fossil fuels. Machine learning (ML), as an analytical tool for uncovering intrinsic correlations and mining data connotations, is also becoming widely used in the field of bio-alcohols. This article reviews the mechanisms, methods, and applications of ML in the bio-alcohols field. In terms of mechanisms, we describe the workflow of ML applications, emphasizing the importance of a well-defined research problem and complete feature engineering for a robust model. Prediction and optimization are the main application scenarios. In terms of methods, we illustrate the characteristics of different ML models and analyze their applicability in the bio-alcohol field. The role of ML in the production of bio-methanol by pyrolysis and gasification, as well as in the three stages of fermentation for bioethanol production are highlighted. In terms of utilization, ML is used to optimize engine performance and reduce emissions. This review provides guidance on how to use novel ML methods in the bio-alcohol field, showing the potential of ML to streamline work in the whole biofuel field.

Optimizing directional recovery of high-bioavailable phosphorus from human manure: Molecular-level understanding and assessment of application potential

Phosphorus (P) recovery from human manure (HM) is critical for food production security. For the first time, a one-step hydrothermal carbonation (HTC) treatment of HM was proposed in this study for the targeted high-bioavailable P recovery from P-rich hydrochars (PHCs) for direct soil application. Furthermore, the mechanism for the transformation of P speciation in the derived PHCs was also studied at the molecular level. A high portion of P (80.1∼89.3%) was retained in the solid phase after HTC treatment (120∼240°C) due to high metal contents. The decomposition of organophosphorus (OP) into high-bioavailable orthophosphate (Ortho-P) was accelerated when the HTC temperature was increased, reaching ∼97.1% at 210°C. In addition, due to the high content of Ca (40.45±2.37 g/kg) in HM, the HTC process promoted the conversion of low-bioavailable non-apatite inorganic (NAIP) into high-bioavailable apatite inorganic P (AP). In pot experiments with pea seedling growth, the application of newly obtained PHCs significantly promoted plant growth, including average wet/dry weight and plant height. Producing 1 ton of PHCs (210°C) with the same effective P content as agricultural-type calcium superphosphate could result in a net return of $58.69. More importantly, this pathway for P recovery is predicted to meet ∼38% of the current agricultural demand.

Machine Learning Model Insights into Base-Catalyzed Hydrothermal Lignin Depolymerization

Lignin, an abundant component of plant matter, can be depolymerized into renewable aromatic chemicals and biofuels but remains underutilized. Homogeneously catalyzed depolymerization in water has gained attention due to its economic feasibility and performance but suffers from inconsistently reported yields of bio-oil and solid residues. In this study, machine learning methods were used to develop predictive models for bio-oil and solid residue yields by using a few reaction variables and were subsequently validated by doing experimental work and comparing the predictions to the results. The models achieved a coefficient of determination (R2) score of 0.83 and 0.76, respectively, for bio-oil yield and solid residue. Variable importance for each model was calculated by two different methodologies and was tied to existing studies to explain the model predictive behavior. Based on the outcome of the study, the creation of concrete guidelines for reporting in lignin depolymerization studies was recommended. Shapley additive explanation value analysis reveals that temperature and reaction time are generally the strongest predictors of experimental outcomes compared to the rest.

Carbon Sequestration Strategies in Soil Using Biochar: Advances, Challenges, and Opportunities

Biochar, a carbon (C)-rich material obtained from the thermochemical conversion of biomass under oxygen-limited environments, has been proposed as one of the most promising materials for C sequestration and climate mitigation in soil. The C sequestration contribution of biochar hinges not only on its fused aromatic structure but also on its abiotic and biotic reactions with soil components across its entire life cycle in the environment. For instance, minerals and microorganisms can deeply participate in the mineralization or complexation of the labile (soluble and easily decomposable) and even recalcitrant fractions of biochar, thereby profoundly affecting C cycling and sequestration in soil. Here we identify five key issues closely related to the application of biochar for C sequestration in soil and review its outstanding advances. Specifically, the terms use of biochar, pyrochar, and hydrochar, the stability of biochar in soil, the effect of biochar on the flux and speciation changes of C in soil, the emission of nitrogen-containing greenhouse gases induced by biochar production and soil application, and the application barriers of biochar in soil are expounded. By elaborating on these critical issues, we discuss the challenges and knowledge gaps that hinder our understanding and application of biochar for C sequestration in soil and provide outlooks for future research directions. We suggest that combining the mechanistic understanding of biochar-to-soil interactions and long-term field studies, while considering the influence of multiple factors and processes, is essential to bridge these knowledge gaps. Further, the standards for biochar production and soil application should be widely implemented, and the threshold values of biochar application in soil should be urgently developed. Also needed are comprehensive and prospective life cycle assessments that are not restricted to soil C sequestration and account for the contributions of contamination remediation, soil quality improvement, and vegetation C sequestration to accurately reflect the total benefits of biochar on C sequestration in soil.

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.