Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach

Machine learning unveils the role of biochar application in enhancing tea yield by mitigating soil acidification in tea plantations

Biochar, a widely utilized soil amendment in environmental applications, has been employed to enhance tea cultivation. This study utilized three machine learning models to investigate the effects of biochar on tea growth and yield, with the random forest (RF) model demonstrating superior performance (R2 = 0.8768, Root Mean Square Error = 6.1537). Feature importance analysis revealed that biochar characteristics and experimental conditions constitute critical factors exerting an impact on the output, accounting for 39.2 % and 38.6 %, respectively. Specifically, the Ca content of biochar (weight 0.274), the quantity of biochar applied (weight 0.206), and the calcium (Ca) content of soil (weight 0.120) emerged as the three most significant factors affecting tea yield. In conclusion, the machine learning models developed in this study elucidate the multifactorial impact of biochar application on tea yield, providing theoretical and methodological support for practical biochar application strategies in tea production.

Critical insights into ensemble learning with decision trees for the prediction of biochar yield and higher heating value from pyrolysis of biomass

Pyrolysis is an efficient thermochemical conversion process, but accurate prediction of yield and properties of biochar presents a significant challenge. Three prominent ensemble learning methods, viz. Random Forest (RF), eXtreme Gradient Boosting (XGB), and Adaptive Boosting (AdaBoost) were utilized to develop models to predict yield and higher heating value (HHV) of biochar. Dataset comprising 423 observations from 44 different biomasses was curated from peer-reviewed journals for predicting biochar yield. RF regressor achieved a test R2 of 0.86 for biochar yield, while XGB regressor achieved a test R2 of 0.87 for biochar HHV prediction. The SHapley Additive exPlanations (SHAP) analysis was conducted to assess influence of each feature on the model's output. Pyrolysis temperature and ash content of biomass were identified as the most influential features for the prediction of both yield and HHV of biochar. The partial dependence plots (PDPs) revealed nonlinear relationships, interpreting how the model formulates its predictions.

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.

Uptake of zinc from the soil to the wheat grain: Nonlinear process prediction based on artificial neural network and geochemical data

Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils absorbed by plants is a nonlinear process, traditional multiple linear regression (MLR) models failed to provide accurate predictions. Zinc (Zn) was chosen as the objective element in this case. Using soil geochemical data, artificial neural networks (ANN) were utilized to develop predictive models that accurately estimated Zn content within wheat grains. A total of 4036 topsoil samples and 73 paired rhizosphere soil-wheat samples were collected for the simulation study. Through Pearson correlation analysis, the total content of elements (TCEs) of Fe, Mn, Zn, and P, as well as the available content of elements (ACEs) of B, Mo, N, and Fe, were significantly correlated with the Zn bioaccumulation factor (BAF). Upon comparison, ANN models outperformed MLR models in terms of prediction accuracy. Notably, the predictive performance using ACEs as input factors was better than that using TCEs. To improve the accuracy, a two-step model was established through multiple testing. Firstly, ACEs in the soil were predicted using TCEs and properties of the rhizosphere soil as input factors. Secondly, the Zn BAF in grains was predicted using ACE as input factors. Consequently, the content of Zn in wheat grains corresponding to 4036 topsoil samples was predicted. Results showed that 85.69 % of the land was suitable for cultivating Zn-rich wheat. This finding offers a more accurate method to predict the uptake of trace elements from soils to grains, which helps to warn about abnormal levels in grains and prevent potential health risks.

Thermogravimetric experiments based prediction of biomass pyrolysis behavior: A comparison of typical machine learning regression models in Scikit-learn

A variety of machine learning (ML) models have been extensively utilized in predicting biomass pyrolysis owing to their prowess in deciphering complex non-linear relationships between inputs and outputs, but there is still a lack of consensus on the optimal methods. This study elaborates on the development, optimization, and evaluation of three ML methodologies, namely, artificial neural networks, random forest (RF), and support vector machines, aimed to determine the optimal model for accurate prediction of biomass pyrolysis behavior using thermogravimetric data. This work assesses the utility of thermal data derived from these models in the computation of kinetic and thermodynamic parameters, alongside an analysis of their statistical performance. Eventually, the RF model exhibits superior physical interpretability and the least discrepancy in predicting kinetic and thermodynamic parameters. Furthermore, a feature importance analysis conducted within the RF model framework quantitatively reveals that temperature and heating rate account for 98.5 % and 1.5 %, respectively.

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.

Enhancing lead adsorption capacity prediction in biochar: a comparative study of machine learning models and parameter optimization

Machine learning models for predicting lead adsorption in biochar, based on preparation features, are currently lacking in the environmental field. Existing conventional models suffer from accuracy limitations. This study addresses these challenges by developing back-propagation neural network (BPNN) and random forest (RF) models using selected features: preparation temperature (T), specific surface area (BET), relative carbon content (C), molar ratios of hydrogen to carbon (H/C), oxygen to carbon (O/C), nitrogen to carbon (N/C), and cation exchange capacity (CEC). The RF model outperforms BPNN, improving R2 by 10%. Additional features and particle swarm optimization enhance the RF model's accuracy, resulting in an 8.3% improvement in R2, a decrease in RMSE by up to 56.1%, and a 55.7% reduction in MAE. The importance ranking of features places CEC > C > BET > O/C > H/C > N/C > T, highlighting the significance of CEC in lead adsorption. Strengthening the complexation effect may improve lead removal in biochar. This study contributes valuable insights for predicting and optimizing lead adsorption in biochar, addressing the accuracy gap in existing models. It lays the foundation for future investigations and the development of effective biochar-based solutions for sustainable lead removal in water remediation.

Prediction of cadmium contents in rice grains from Quaternary sediment-distributed farmland using field investigations and machine learning

The Quaternary sediment-distributed regions of South China are suitable for rice cultivation, which is crucial for ensuring food security. Spatial correlations between soil cadmium (Cd) and rice Cd contents are generally poor, making the evaluation of rice quality and associated health risks challenging. In this study, we developed machine learning algorithms for predicting rice Cd contents using 654 data pairs of soil-rice samples collected in Guangxi province, China. After a comprehensive comparison, our results showed that the random forest (RF) had the better performance than artificial neural network (ANN) based on all the data (RMSEtesting 0.066 vs. 0.099 and R2testing 0.860 vs. 0.688). The feature importance analysis showed that soil CaO, Cd, elevation, and rainfall were the four most important features affecting the rice Cd contents in the study area. Using the established RF-predicated model, the rice Cd contents were predicted at the provincial level with an additional dataset of 1176 paddy soil samples. The prediction result revealed about 23 % of farmland cultivated rice with Cd content over 0.2 mg kg-1 in the study area. Therefore, it is recommended to implement strict measures by local agricultural departments to reduce rice Cd contents and ensure food safety in these areas. Our study provides valuable insights into the prediction of rice Cd contents, thus contributing to ensuring food safety and preventing Cd exposure-associated health risks.

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.

Machine-learning analysis to predict the fluorescence quantum yield of carbon quantum dots in biochar

Biochar nanoparticles have recently attracted attention, owing to their environmental behavior and ecological effects. However, biochar has not been shown to contain carbon quantum dots (< 10 nm) with unique photovoltaic properties. Therefore, this study utilized several characterization techniques to demonstrate the generation of carbon quantum dots in biochar produced from 10 types of farm waste. The generated carbon quantum dots had a quasi-spherical morphology and high-resolution lattice stripes with lattice spacings of 0.20-0.23 nm. Moreover, they contained functional groups with good hydrophilic properties, such as amino and hydroxyl groups, and elemental O, C, and N on the surface. A crucial determinant of the photoluminescence properties of carbon quantum dots is their fluorescence quantum yield. Therefore, the relationship between the biochar preparation parameters and the fluorescence quantum yield was investigated using six machine learning analytical models based on 480 samples. Among the models, the gradient-boosting decision-tree regression model exhibited the best predictive performance (R2 > 0.9, RMSE <0.02, and MAPE <3), and was used for the analysis of feature importance; compared to the properties of the raw material, the production parameters had a greater effect on the fluorescence quantum yield. Additionally, four key features were identified: pyrolysis temperature, residence time, N content, and C/N ratio, which were independent of farm waste type. These features can be used to accurately predict the fluorescence quantum yield of carbon quantum dots in biochar. The relative error range between the predicted and the experimental value of fluorescence quantum yield is 0.00-4.60 %. Thus, the prediction model has the potential to predict the fluorescence quantum yield of carbon quantum dots in other types of farm waste biochar, and provides fundamental information for the study of biochar nanoparticles.

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002-2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to cost-effectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.

Artificial intelligence for waste management in smart cities: a review

The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

Utilization of current pyrolysis technology to convert biomass and manure waste into biochar for soil remediation: A review

Traditional disposal of animal manures and lignocellulosic biomass is restricted by its inefficiency and sluggishness. To advance the carbon management and greenhouse gas mitigation, this review scrutinizes the effect of pyrolysis in promoting the sustainable biomass and manure disposal as well as stimulating the biochar industry development. This review has examined the advancement of pyrolysis of animal manure (AM) and lignocellulosic biomass (LB) in terms of efficiency, cost-effectiveness, and operability. In particular, the applicability of pyrolysis biochar in enhancing the crops yields via soil remediation is highlighted. Through pyrolysis, the heavy metals of animal manures are fixated in the biochar, thereby both soil contamination via leaching and heavy metal uptake by crops are minimized. Pyrolysis biochar is potentially use in soil remediation for agronomic and environmental co-benefits. Fast pyrolysis assures high bio-oil yield and revenue with better return on investment whereas slow pyrolysis has low revenue despite its minimum investment cost because of relatively low selling price of biochar. For future commercialization, both continuous reactors and catalysis can be integrated to pyrolysis to ameliorate the efficiency and economic value of pyrolysis biochar.

Prediction of nano, fine, and medium colloidal phosphorus in agricultural soils with machine learning

Soil colloids have been shown to play a critical role in soil phosphorus (P) mobility and transport. However, identifying the potential mechanisms behind colloidal P (P_coll) release and the key influencing factors remains a blind spot. Herein, a machine learning approach (random forest (RF) coupled with partial dependence plot analyses) was applied to determine the effects of different soil physicochemical parameters on P_coll content in three colloidal subfractions (i.e., nano- (NC): 1-20 nm, fine- (FC): 20-220 nm and medium-sized colloids (MC): 220-450 nm) based on a regional dataset of 12 farmlands in Zhejiang Province, China. RF successfully predicted P_coll content (R² = 0.98). Results showed that colloidal- organic carbon (OC_coll) and minerals were the major determinants of total P_coll content (1-450 nm); their critical values for increasing P_coll release were 87.0 mg L^-1 for OC_coll, 11.0 mg L^-1 for iron (Fe_coll) or aluminium (Al_coll), 2.6 mg L^-1 for calcium (Ca_coll), 9.0 mg L^-1 for magnesium (Mg_coll), 2.5 mg L^-1 for silicon (Si_coll), and 1.4 mg L^-1 for manganese (Mn_coll). Among three colloidal subfractions, the major factors determining P_coll were soil Olsen-P (P_Olsen; 125.0 mg kg^-1), Ca_coll (2.5 mg L^-1), and colloidal P saturation (21.0%) in NC; Mn_coll (1.5 mg L^-1), Mg_coll (6.8 mg L^-1), and P_Olsen (135.0 mg kg^-1) in FC; while Mn_coll (1.5 mg L^-1), Al_coll (2.5 mg L^-1), and Fe_coll (3.8 mg L^-1) in MC, respectively. OC_coll had a considerable effect in the three fractions, with critical values of 80.0 mg L^-1 in NC or FC, and 50.0 mg L^-1 in MC. Our study concluded that the information gleaned using the RF model can be used as crucial evidence to identify the key determinants of different size fractionated P_coll contents. However, we still need to discover one or more easy-to-measure parameters that can help us better predict P_coll.

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.

Biorefining waste into nanobiotechnologies can revolutionize sustainable agriculture

Modern agriculture has evolved technological innovations to sustain crop productivity. Recent advances in biorefinery technology use crop residue as feedstock, but this raises carbon sequestration concerns as biorefining utilizes carbon that would otherwise be returned to the soil, thus causing a decline in crop productivity. Furthermore, biorefining generates abundant lignin waste that significantly impedes the efficiency of biorefineries. Valorizing lignin into advanced nanobiotechnologies for agriculture provides a unique opportunity to balance bioeconomy and soil carbon sequestration. Integration of agricultural practices such as utilization of agrochemicals, fertilizers, soil modifiers, and mulching with lignin nanobiotechnologies promotes crop productivity and also enables advanced manufacturing of high-value bioproducts from lignin. Lignin nanobiotechnologies thus represent state-of-the-art innovations to transform both the bioeconomy and sustainable agriculture.

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 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.

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

In this study, an integrated framework of artificial neural networks (ANNs) and metaheuristic algorithms have been developed for the prediction of biochar yield using biomass characteristics and pyrolysis process conditions. Comparative analysis of six different metaheuristic algorithms was performed to optimize the ANN architecture and select important features. The results suggested that the ANN model coupled with the Rao-2 algorithm outperformed (R² ∼ 0.93, RMSE ∼ 1.74%) all other models. Furthermore, the detailed information behind the models was acquired, identifying the most influencing factors as follows: pyrolysis temperature (56%), residence time (23%), and heating rate (8%). The partial dependence plot analysis revealed how each influencing factor affected the target variable. Finally, an easy-to-use software tool for predicting biochar yield was built using the ANN-Rao-2 model. This study demonstrates huge potential that machine learning presents in predictive modelling of complex pyrolysis processes, and reduces the time-consuming and expensive experimental work for estimating the biochar yield.

The state of art on the prediction of efficiency and modeling of the processes of pollutants removal based on machine learning

During the last few years, important advances have been made in big data exploration, complex pattern recognition and prediction of complex variables. Machine learning (ML) algorithms can efficiently analyze voluminous data, identify complex patterns and extract conclusions. In chemical engineering, the application of machine learning approaches has become highly attractive due to the growing complexity of this field. Machine learning allows computers to solve problems by learning from large data sets and provides researchers with an excellent opportunity to enhance the quality of predictions for the output variables of a chemical process. Its performance has been increasingly exploited to overcome a wide range of challenges in chemistry and chemical engineering, including improving computational chemistry, planning materials synthesis and modeling pollutant removal processes. In this review, we introduce this discipline in terms of its accessible to chemistry and highlight studies that illustrate in-depth the exploitation of machine learning. The main aim of the review paper is to answer these questions by analyzing physicochemical processes that exploit machine learning in organic and inorganic pollutants removal. In general, the purpose of this review is both to provide a summary of research related to the removal of various contaminants performed by ML models and to present future research needs in ML for contaminant removal.

Yield prediction of "Thermal-dissolution based carbon enrichment" treatment on biomass wastes through coupled model of artificial neural network and AdaBoost

The "Thermal-dissolution based carbon enrichment" was proven as an efficient and homogenizing treatment method in converting biomass wastes into similar high-quality carbon materials. However, their yields varied significantly with respect to the different experimental parameters employed. It is therefore imperative to establish the correlation between product yield and experimental parameters for material selection and condition optimization. In this study, Adaboost was coupled with an artificial neural network algorithm to precisely describe the abovementioned correlation. The results demonstrated the effectiveness of this model through its outstanding predicting performance for all the products, especially, the coefficient of determination in predicting the yield of Residue was as high as 0.97. Additionally, the coupling effect of temperature and time was observed. This study not only validates a close correlation between selected experimental parameters and product yields, but also provides a quick and reliable way for material selection and condition optimization.

The role of machine learning to boost the bioenergy and biofuels conversion

The development and application of bioenergy and biofuels conversion technology can play a significant role for the production of renewable and sustainable energy sources in the future. However, the complexity of bioenergy systems and the limitations of human understanding make it difficult to build models based on experience or theory for accurate predictions. Recent developments in data science and machine learning (ML), can provide new opportunities. Accordingly, this critical review provides a deep insight into the application of ML in the bioenergy context. The latest advances in ML assisted bioenergy technology, including energy utilization of lignocellulosic biomass, microalgae cultivation, biofuels conversion and application, are reviewed in detail. The strengths and limitations of ML in bioenergy systems are comprehensively analysed. Moreover, we highlight the capabilities and potential of advanced ML methods when encountering multifarious tasks in the future prospects to advance a new generation of bioenergy and biofuels conversion technologies.

Artificial intelligence (AI) applications in adsorption of heavy metals using modified biochar

The process of removal of heavy metals is important due to their toxic effects on living organisms and undesirable anthropogenic effects. Conventional methods possess many irreconcilable disadvantages pertaining to cost and efficiency. As a result, the usage of biochar, which is produced as a by-product of biomass pyrolysis, has gained sizable traction in recent times for the removal of heavy metals. This review elucidates some widely recognized harmful heavy metals and their removal using biochar. It also highlights and compares the variety of feedstock available for preparation of biochar, pyrolysis variables involved and efficiency of biochar. Various adsorption kinetics and isotherms are also discussed along with the process of desorption to recycle biochar for reuse as adsorbent. Furthermore, this review elucidates the advancements in remediation of heavy metals using biochar by emphasizing the importance and advantages in the usage of machine learning (ML) and artificial intelligence (AI) for the optimization of adsorption variables and biochar feedstock properties. The usage of AI and ML is cost and time-effective and allows an interdisciplinary approach to remove heavy metals by biochar.

Improved Estimation of Bio-Oil Yield Based on Pyrolysis Conditions and Biomass Compositions Using GA- and PSO-ANFIS Models

This paper incorporates the adaptive neurofuzzy inference system (ANFIS) technique to model the yield of bio-oil. The estimation of this parameter was performed according to pyrolysis conditions and biomass compositions of feedstock. For this purpose, this paper innovates two optimization methods including a genetic algorithm (GA) and particle swarm optimization (PSO). Primary data were gathered from previous studies and included 244 data of biodiesel oils. The findings showed a coefficient determination (R²) of 0.937 and RMSE of 2.1053 for the GA-ANFIS model, and a coefficient determination (R²) of 0.968 and RMSE of 1.4443 for PSO-ANFIS. This study indicates the capability of the PSO-ANFIS algorithm in the estimation of the bio-oil yield. According to the performed analysis, this model shows a higher ability than the previously presented models in predicting the target values and can be a suitable alternative to time-consuming and difficult experimental tests.

Machine learning prediction of pyrolytic gas yield and compositions with feature reduction methods: Effects of pyrolysis conditions and biomass characteristics

This study aimed to utilize machine learning algorithems combined with feature reduction for predicting pyrolytic gas yield and compositions based on pyrolysis conditions and biomass characteristics. To this end, random forest (RF) and support vector machine (SVM) was introduced and compared. The results suggested that six features were adequate to accurately forecast (R² > 0.85, RMSE < 5.7%) the yield while the compositions only required three. Moreover, the profound information behind the models was extracted. The relative contribution of pyrolysis conditions was higher than that of biomass characteristics for yield (55%), CO₂ (73%), and H₂ (81%), which was inverse for CO (12%) and CH₄ (38%). Furthermore, partial dependence analysis quantified the effects of both reduced features and their interactions exerted on pyrolysis process. This study provided references for pyrolytic gas production and upgrading in a more convenient manner with fewer features and extended the knowledge into the biomass pyrolysis process.

Enhancement of hydrothermal carbonization of chitin by combined pretreatment of mechanical activation and FeCl3

In this work, a combined mechanical activation and FeCl₃ (MA + FeCl₃) method was applied to pretreat chitin to enhance the degree of hydrothermal carbonization. MA + FeCl₃ pretreatment significantly disrupt the crystalline region of chitin and Fe³⁺ entered into the molecular chain, resulting in the destruction of the stable structure of chitin. The chemical and structural properties of hydrochars were characterized by EA, SEM, FTIR, XRD, XPS, ¹³C solid state NMR, and N₂ adsorption-desorption analyses. The results showed that the H/C and O/C atomic ratios of HC-MAFCT/230 (the hydrochar derived from MA + FeCl₃ pretreated chitin with hydrothermal reaction temperature of 230 °C) were 0.96 and 0.34, respectively. Van Krevelen diagram indicated that the hydrothermal carbonization of chitin underwent a series of reactions such as dehydration, decarboxylation, and aromatization. HC-MAFCT/230 had abundant oxygen- and nitrogen-containing functional groups. HC-MAFCT/230 exhibited a porous structure, with the specific surface area of 128 m² g^-1, which was a promising carbon material.

Artificial Neural Networks for Pyrolysis, Thermal Analysis, and Thermokinetic Studies: The Status Quo

Artificial neural networks (ANNs) are a method of machine learning (ML) that is now widely used in physics, chemistry, and material science. ANN can learn from data to identify nonlinear trends and give accurate predictions. ML methods, and ANNs in particular, have already demonstrated their worth in solving various chemical engineering problems, but applications in pyrolysis, thermal analysis, and, especially, thermokinetic studies are still in an initiatory stage. The present article gives a critical overview and summary of the available literature on applying ANNs in the field of pyrolysis, thermal analysis, and thermokinetic studies. More than 100 papers from these research areas are surveyed. Some approaches from the broad field of chemical engineering are discussed as the venues for possible transfer to the field of pyrolysis and thermal analysis studies in general. It is stressed that the current thermokinetic applications of ANNs are yet to evolve significantly to reach the capabilities of the existing isoconversional and model-fitting methods.

Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review

Conventional treatment and recycling methods of organic solid waste contain inherent flaws, such as low efficiency, low accuracy, high cost, and potential environmental risks. In the past decade, machine learning has gradually attracted increasing attention in solving the complex problems of organic solid waste treatment. Although significant research has been carried out, there is a lack of a systematic review of the research findings in this field. This study sorts the research studies published between 2003 and 2020, summarizes the specific application fields, characteristics, and suitability of different machine learning models, and discusses the relevant application limitations and future prospects. It can be concluded that studies mostly focused on municipal solid waste management, followed by anaerobic digestion, thermal treatment, composting, and landfill. The most widely used model is the artificial neural network, which has been successfully applied to various complicated non-linear organic solid waste related problems.

Characteristics and mechanisms of phosphorous adsorption by rape straw-derived biochar functionalized with calcium from eggshell

Biochar modified with calcium source is acted as an effective adsorbent for phosphorous recovery. In this research, eggshell is used as a low-cost and environmentally friendly calcium source to replace chemical reagents such as CaCO₃, Ca(OH)₂ and CaCl₂ used in the modified biochar production. Biochar derived from rape straw and modified with eggshell shows prominent phosphorous adsorption performance (e.g., equilibrium adsorption amount, 109.7 mg/g). The kinetic and isotherm analysis demonstrate that chemical adsorption process is performed as the main controlled step for the modified biochar adsorption, and the phosphate adsorption process is composed of both monolayer adsorption and multi-layer adsorption. Moreover, it is found from the physicochemical structures comparison before and after phosphate adsorption that Ca-P precipitation, hydrogen bonding and electrostatic attraction are identified as main adsorption mechanisms. In addition, the adsorbed phosphates are mainly distributed inside the space with pore sizes of 15-50 nm.

Improved Adaptive Neuro-Fuzzy Inference System Using Gray Wolf Optimization: A Case Study in Predicting Biochar Yield

This paper presents an alternative method for predicting biochar yields from biomass thermochemical processes. As biochar is considered a renewable and sustainable energy source, it has received more attention. Several methods have been presented to predict biochar, such as neural network (NN) and least square support vector machine (LS-SVM). However, each of them has its own drawbacks, such as getting stuck in a local optimum, which occurs in NN, and lack of uncertainty and time complexity, as in LS-SVM. Therefore, this paper avoids this limitation by using a hybrid method between the adaptive neuro-fuzzy inference system (ANFIS) and gray wolf optimization (GWO) algorithm. The proposed method is called ANFIS-GWO, which consists of two stages. In the first stage, GWO is used to learn the parameters of ANFIS using the training set. Meanwhile, in the second stage, the testing set is used to evaluate the performance of the proposed ANFIS-GWO method. Three experiments were performed to assess the performance of the proposed method. The first experiment used a set of UCI (University of California, Irvine) benchmark datasets to evaluate the effectiveness of ANFIS-GWO. The aim of the second experiment was to evaluate the performance of the proposed ANFIS-GWO method to predict biochar yield from manure pyrolysis. The third experiment aimed to estimate the values of input parameters of pyrolysis that maximize biochar production. The obtained results were compared to those of other methods, such as ANFIS using gradient descent, practical swarm optimization, genetic algorithm, whale optimization algorithm, sine-cosine algorithm, and LS-SVM. The results of the ANFIS-GWO method were >35% of the standard ANFIS and also better than those of other methods.

The application of machine learning methods for prediction of metal sorption onto biochars

The adsorption of six heavy metals (lead, cadmium, nickel, arsenic, copper, and zinc) on 44 biochars were modeled using artificial neural network (ANN) and random forest (RF) based on 353 dataset of adsorption experiments from literatures. The regression models were trained and optimized to predict the adsorption capacity according to biochar characteristics, metal sources, environmental conditions (e.g. temperature and pH), and the initial concentration ratio of metals to biochars. The RF model showed better accuracy and predictive performance for adsorption efficiency (R² = 0.973) than ANN model (R² = 0.948). The biochar characteristics were most significant for adsorption efficiency, in which the contribution of cation exchange capacity (CEC) and pH_H2O of biochars accounted for 66% in the biochar characteristics. However, surface area of the biochars provided only 2% of adsorption efficiency. Meanwhile, the models developed by RF had better generalization ability than ANN model. The accurate predicted ability of developed models could significantly reduce experiment workload such as predicting the removal efficiency of biochars for target metal according to biochar characteristics, so as to select more efficient biochar without increasing experimental times. The relative importance of variables could provide a right direction for better treatments of heavy metals in the real water and wastewater.

Reusing Cow Manure for the Production of Activated Carbon Using Potassium Hydroxide (KOH) Activation Process and Its Liquid-Phase Adsorption Performance

In this work, cow manure (CM) was reused as a potential precursor in the production of activated carbon (AC) using a potassium hydroxide activation process at different temperatures (i.e., 500, 600 and 700 °C). The optimal activated carbon from cow manure (CM-AC) with high specific surface area (ca. 950 m2/g) was further investigated for its adsorption performance in the removal of a model compound (i.e., methylene blue) from aqueous solution with various initial concentrations and adsorbent dosages at 25 °C. It was found that the resulting AC could be an effective adsorbent for removal of cationic dye from aqueous solution in comparison with a commercial coal-based AC. Based on the observations of the energy dispersive X-ray spectroscopy and Fourier transform infrared spectroscopy (FTIR), the CM-AC adsorbent has a stronger interaction with the cationic compound due to its more oxygen-containing complex on the surface. Furthermore, the adsorption kinetic parameters fitted using the pseudo-second order model with high correlations were in accordance with their pore properties.

A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting

Oil is the primary source of energy, therefore, oil consumption forecasting is essential for the necessary economic and social plans. This paper presents an alternative time series prediction method for oil consumption based on a modified Adaptive Neuro-Fuzzy Inference System (ANFIS) model using the Multi-verse Optimizer algorithm (MVO). MVO is applied to find the optimal parameters of the ANFIS. Then, the hybrid method, namely MVO-ANFIS, is employed to forecast oil consumption. To evaluate the performance of the MVO-ANFIS model, a dataset of two different countries was used and compared with several forecasting models. The evaluation results show the superiority of the MVO-ANFIS model over other models. Moreover, the proposed method constitutes an accurate tool that effectively improved the solution of time series prediction problems.

Cow manure-derived biochar: Its catalytic properties and influential factors

The conversion of waste biomass into biochar is considered as a waste disposal alternative, especially because biochar is a low-cost adsorbent for soil contaminants. However, a risk of desorption of contaminants from biochar may lead to secondary pollution. This study investigated the degradation behavior of soil fumigant, 1,3-dichloropropne (1,3-D), on cow manure-derived biochar (CMB) pyrolyzed at five different temperatures from 300 to 700 °C (termed as C-300 to C-700). Results showed that 1,3-D degradation rate was U-shape related to biochar pyrolysis temperature. Four degradation byproducts (NH₂CH₂CH₂CH₃OH, CH₃CH₂NH₂, NH₂COCONH₂, OHCH₂COOH) were identified by headspace GC-MS. When biochar humidity improved from 0 to 50% or incubation temperature increased from 20 to 40 °C, the degradation of cis-1,3-D on C-300 improved 24.26% and 35.48%, respectively. The OH concentrations, detected by the terephthalic acid method, were considerably higher for C-300 than that for C-700. Pyrolysis temperature (300-700 ° C) governed biochar physicochemical properties and further affected 1,3-D degradation mechanisms (pH-controlled substitution or OH-restricted oxidation reaction). All these findings showed that CMB can adsorb and degrade 1,3-D, thereby reduce its desorption risk, indicative of the conversion of cow manure into biochar as an effective waste management practice.

Methods for the Treatment of Cattle Manure—A Review

Environmental concerns, caused by greenhouse gases released to the atmosphere and overrunning of nutrients and pathogens to water bodies, have led to reducing direct spread onto the land of cattle manure. In addition, this practice can be a source of water and air pollution and toxicity to life by the release of undesirable heavy metals. Looking at the current practices, it is evident that most farms separate solids for recycling purposes, store slurries in large lagoons or use anaerobic digestion to produce biogas. The review explores the potential for cattle manure as an energy source due to its relatively large calorific value (HHV of 8.7–18.7 MJ/kg dry basis). This property is beneficial for thermochemical conversion processes, such as gasification and pyrolysis. This study also reviews the potential for upgrading biogas for transportation and heating use. This review discusses current cattle manure management technologies—biological treatment and thermochemical conversion processes—and the diverse physical and chemical properties due to the differences in farm practices.

Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques

Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. The analytical method considers the protein as the only source of ammonia production in AD after degradation. Total ammonia nitrogen (TAN), total solids (TS), chemical oxygen demand (COD), and total volatile solids (TVS) were measured in the influent and effluent of the process. The TAN concentration in the effluent was predicted, this being the most inhibiting and polluting compound in AD. Despite the limited data available, the SVM-based model outperformed the analytical method for the TAN prediction, achieving a relative average error of 15.2% against 43% for the analytical method. Moreover, SVM showed higher prediction accuracy in comparison with Artificial Neural Networks. This result reveals the future promise of SVM for prediction in non-linear and dynamic AD processes. Graphical abstract ᅟ.

Component optimization of dairy manure vermicompost, straw, and peat in seedling compressed substrates using simplex-centroid design

Vermicomposting is a promising method to disposal dairy manures, and the dairy manure vermicompost (DMV) to replace expensive peat is of high value in the application of seedling compressed substrates. In this research, three main components: DMV, straw, and peat, are conducted in the compressed substrates, and the effect of individual components and the corresponding optimal ratio for the seedling production are significant. To address these issues, the simplex-centroid experimental mixture design is employed, and the cucumber seedling experiment is conducted to evaluate the compressed substrates. Results demonstrated that the mechanical strength and physicochemical properties of compressed substrates for cucumber seedling can be well satisfied with suitable mixture ratio of the components. Moreover, DMV, straw, and peat) could be determined at 0.5917:0.1608:0.2475 when the weight coefficients of the three parameters (shoot length, root dry weight, and aboveground dry weight) were 1:1:1. For different purpose, the optimum ratio can be little changed on the basis of different weight coefficients.

IMPLICATIONS

Compressed substrate is lump and has certain mechanical strength, produced by application of mechanical pressure to the seedling substrates. It will not harm seedlings when bedding out the seedlings, since the compressed substrate and seedling are bedded out together. However, there is no one using the vermicompost and agricultural waste components of compressed substrate for vegetable seedling production before. Thus, it is important to understand the effect of individual components to seedling production, and to determine the optimal ratio of components.

Hydrothermal carbonization for the preparation of hydrochars from glucose, cellulose, chitin, chitosan and wood chips via low-temperature and their characterization

In this work, the hydrothermal carbonization of glucose, cellulose, chitin, chitosan and wood chips at 200°C at processing times between 6 and 48h was studied. The carbonization degree of wood chips, cellulose and chitosan obviously increases as function of time. The heating value of glucose increases to 88% upon carbonization for 48h, while it is only 5% for chitin. It is calculated to be between 44 and 73% for wood chips, chitosan and cellulose. Glucose yielded complete formation of spherical hydrochar structures at a shorter processing time, as low as 12h. However, carbon spheres with narrow size (∼560nm) distribution were obtained upon 48h of residence time. Cellulose and wood chips yielded a similar morphology with an irregular size distribution. Chitin seemed not to undergo hydrothermal carbonization, whereas densely aggregated spheres of a uniform size around 42nm were obtained from chitosan after 18h.