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

Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest

The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy metal pollution, posing grave ecological and human health risks. Developing an accurate predictive model for heavy metal content in this area has been challenging. In this study, we used machine learning techniques to model sediment pollution by heavy metals in this vital ecosystem. We collected 199 standardized sediment samples to predict the accumulation of eleven heavy metals using ten different machine learning algorithms. Among them, the extremely randomized tree model exhibited the best performance in predicting Fe (0.87), Cr (0.89), Zn (0.85), Ni (0.83), Cu (0.87), Co (0.62), As (0.68), and V (0.90), achieving notable R2 values. On the other hand, the random forest outperformed for predicting Cd (0.72) and Mn (0.91), whereas the decision tree model showed the best performance for Pb (0.73). The feature attribute analysis identified FeV, CrV, CuZn, CoMn, PbCd, and AsCd relationships resembled with correlation coefficients among them. Based on the established models, the prediction of the contamination factor of metals in sediments showed very high Cd contamination (CF ≥ 6). The Moran's I index for Cd, Cr, Pb, and As were 0.71, 0.81, 0.71, and 0.67, respectively, indicating strong positive spatial autocorrelation and suggesting clustering of similar contamination levels. Conclusively, this research provides a comprehensive framework for predicting heavy metal sediment pollution in the Sundarbans, identifying key areas needing urgent conservation. Our findings support the adoption of integrated management strategies and targeted remedial actions to mitigate the harmful effects of heavy metal contamination in this vital ecosystem.

Development and standardization of spectrophotometric assay for quantification of thermal hydrolysis-origin melanoidins and its implication in antioxidant activity evaluation

Melanoidins are brown recalcitrant polymers originating from the thermal hydrolysis pretreatment (THP) of organic solid waste (OSW). Owing to their various formation pathways and complex structures, there is currently no reliable method to quantify melanoidins. In this study, a spectrophotometric method was developed to determine melanoidins concentration in different OSW. Three typical model Maillard reaction systems (glucose-glycine, glucose/fructose-20 amino acids, and dextran-bovine serum albumin) were used to acquire the characteristic peaks and establish standard curves. The results showed that a standard curve using glucose/fructose-20 amino acids model melanoidins at 280 nm was the optimal quantification method, because it had the best correlation with the physicochemical indicators of melanoidins and semi-quantification results calculated by excitation-emission matrix fluorescence. In addition, the applicability of the proposed method was evaluated using multiple real melanoidins samples extracted from thermally pretreated OSW under different THP conditions and food-derived melanoidins as well, demonstrating its validity and advantages. This study is the first to provide a simple, effective, and accurate method for quantifying THP-origin melanoidins from different sources. Remarkably, as a specific and important application scenario, the proposed quantification method was employed to investigate the concentration dependence of melanoidins antioxidation in thermally pretreated OSW.

The role of black soldier fly (BSF) in eliminating the putrid odor of organic waste and its product application - A comprehensive review

Organic waste including food garbage (FG) forms a major part of man-made problems that are highly associated with global pollution. This includes emission of greenhouse gases (GHGs) and foul odor which negatively affect human health. Interestingly, bioconversion of FG by black soldier fly larvae (BSFL) has been reported to reduce foul odors released from decaying FG. This paper will give overview on the potential of BSFL in lowering putrid odors from FGs. Thus, various bioconversion treatment methods of managing FG including were compared and discussed. The life cycle and role of BSF in reducing putrid odors from biowastes were also discussed in detail. Lastly, the potential utilization of BSFL in controlling odors and GHGs as well as the economic value of products derived from BSFL bioconversion were also discussed. BSFL inoculation slightly reduces odor compounds by modifying odor-producing compounds and microbes in FG. However, BSFL effectiveness is highly influenced by FG decomposition rate.

Application of microbial agents in organic solid waste composting: a review

The rapid growth of organic solid waste has recently exacerbated environmental pollution problems, and its improper treatment has led to the loss of a large number of biomass resources. Here, we expound the advantages of microbial agents composting compared with conventional organic solid waste treatment technology, and review the important role of microbial agents composting in organic solid waste composting from the aspects of screening and identification, optimization of conditions, mechanism of action, combination with other technologies and ultra-high-temperature and ultra-low-temperature microbial composting. We discuss the value of microorganisms with different growth conditions in organic solid waste composting, and put forward a seasonal multi-temperature composite microbial composting technology. Provide new ideas for the all-round treatment of microbial agents in organic solid waste in the future. © 2024 Society of Chemical Industry.

Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques

The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.

Predicting bioavailability of potentially toxic elements (PTEs) in sediment using various machine learning (ML) models: A case study in Mahabad Dam and River-Iran

Considering the significant impact of potentially toxic elements (PTEs) on the ecosystem and human health, this paper, investigated the contamination level of four PTEs (Zn, Cu, Mo and Pb) and their mobility in sediments of Mahabad dam and river. Choosing the most effective machine learning algorithms is very important in accurately predicting bioavailability of PTEs. Therefore, four machine learning (ML) models including decision tree regression (DTR), random forest regression (RFR), multi-layer perceptron regression (MLPR) and support vector regression (SVR), were used and compared for estimating the selected PTEs bioavailability. For these models, 9 variables (total concentration, pH, EC, OM and five chemical forms F1 to F5 obtained by sequential extraction) in 100 sediment samples were considered. The results showed that contamination level decreases from Zn and Cu to Pb and Mo, but the order of the mobility coefficient of the elements in the sediment follows the trend of zinc > copper > molybdenum > lead, and variation coefficient indicated more variability of spatial distribution for Zn and Cu. Among the four tested models, DTR and RFR performed the best for predicting PTEs bioavailability variations (with roc_auc>0.9, R2 > 0.8 and MSE>0.5), followed by MLPR and SVR. Furthermore, the relevance of the factors controlling the metals availability, evaluated using the RFR-based feature importance method and Pearson correlation, revealed that the most important physicochemical property for Zn, Cu and Mo bioavailability was pH, whereas for Pb, EC was the determinant factor. In the case of chemical speciation, F5 had an inverse correlation with the target, while F1 and F2 had a direct correlation. These fractions contributed significantly to the prediction results. This study represents the potential successful application of ML to PTEs risk control in sediments and early warning for the surrounding water PTEs contamination.

Versatile Applications of Brewer's Spent Grain: Solid-State Fermentation and Nutritional Added Value

Brewer's spent grain (BSG) is a major by-product in the beer-brewing process which contributes to 85% of the entire generated by-product in the brewing process. BSG is rich in proteins, and most of the malt proteins (74-78%) remain insoluble in BSG after the mashing process. Solid-state fermentation (SSF) is a promising bioprocess that enables microorganisms to survive in environments with minimal water and has shown to enhance the nutritional composition of BSG. In this review, the potential application of protein, amino acids (proline, threonine, and serine), phenolic contents, and soluble sugars (glucose, fructose, xylose, arabinose, and cellobiose) extracted from BSG by various microorganisms using SSF is explored. Incorporation of BSG into animal feed, human diets, and as a substrate for microorganisms are the prospects that could be implemented in the industrial scale. This review also discussed various advances to improve the fermentation yield such as symbiotic fermentation, the addition of nitrogen supplements, and an optimal mixture of the agro-industrial waste substrate. Future perspectives on SSF are also addressed to provide important ideas for immediate and future studies. However, challenges include optimizing SSF conditions and design of bioreactors, and operational costs must be addressed in the future to overcome current obstacles. Overall, this mini review highlights the potential benefits of BSG utilization and SSF in a sustainable way.

Potential prediction and coupling relationship revealing for recovery of platinum group metals from spent auto-exhaust catalysts based on machine learning

As hazardous waste, the massive generation of spent auto-exhaust catalysts (SACs) puts enormous pressure on environmental management, but provides a rare opportunity for platinum group metals (PGMs) recycling. In this study, machine learning (ML) method was firstly applied to accurately predict regional SACs generation in China for 2025-2050 under five shared socio-economic pathways (SSPs) scenarios, based on which economic and carbon emission reduction potential of PGMs recycling were estimated. Population-GDP-GDPII-GDPIII and Random Forest were determined as key variables and the predictive model. Results indicate that SACs will reach 28.15 million sets (1.7 times that of 2020) and PGMs have economic potential of $890 million by 2050 (SSP1). Furthermore, based on environmental impact assessment, the capture enrichment-electrodeposition purification process is proposed as the best low-carbon recycling solution for SACs. And the integrated recovery process based on copper capture can realize 1.51 million tons of carbon emission reduction in China in 2050 (SSP1). This study can provide decision-making guidance for PGMs recovery and environmental management, as well as technical references for SACs recovery program selection.

Integrating advanced techniques and machine learning for landfill leachate treatment: Addressing limitations and environmental concerns

This review article explores the challenges associated with landfill leachate resulting from the increasing disposal of municipal solid waste in landfills and open areas. The composition of landfill leachate includes antibiotics (0.001-100 μg), heavy metals (0.001-1.4 g/L), dissolved organic and inorganic components, and xenobiotics including polyaromatic hydrocarbons (10-25 μg/L). Conventional treatment methods, such as biological (microbial and phytoremediation) and physicochemical (electrochemical and membrane-based) techniques, are available but face limitations in terms of cost, accuracy, and environmental risks. To surmount these challenges, this study advocates for the integration of artificial intelligence (AI) and machine learning (ML) to strengthen treatment efficacy through predictive analytics and optimized operational parameters. It critically evaluates the risks posed by recalcitrant leachate components and appraises the performance of various treatment modalities, both independently and in tandem with biological and physicochemical processes. Notably, physicochemical treatments have demonstrated pollutant removal rates of up to 90% for various contaminants, while integrated biological approaches have achieved over 95% removal efficiency. However, the heterogeneous nature of solid waste composition further complicates treatment methodologies. Consequently, the integration of advanced ML algorithms such as Support Vector Regression, Artificial Neural Networks, and Genetic Algorithms is proposed to refine leachate treatment processes. This review provides valuable insights for different stakeholders specifically researchers, policymakers and practitioners, seeking to fortify waste disposal infrastructure and foster sustainable landfill leachate management practices. By leveraging AI and ML tools in conjunction with a nuanced understanding of leachate complexities, a promising pathway emerges towards effectively addressing this environmental challenge while mitigating potential adverse impacts.

Machine learning-based prediction of construction and demolition waste generation in developing countries: a case study

Data is needed for making informed decisions regarding managing waste in the time of construction and demolition phases of buildings. However, data availability is very limited in most developing countries in the area of waste generation. The objective of this study is to employ an artificial intelligence (AI)-based approach to develop a reliable model for forecasting monthly construction and demolition waste (C&DW) generation in the case study of Tehran, Iran. We have trained different prediction models using various AI algorithms, including multilayer perceptron neural network, radial basis function neural network, support vector machines, and adaptive neuro-fuzzy inference system (ANFIS). According to the findings, all employed AI algorithms demonstrated high prediction performance for C&DW forecasting models. The ANFIS model, with R2 = 0.96 and RMSE = 0.04209, was identified as the model that better represented the observed values of C&DW generation. The better efficiency of the ANFIS model could be due to its effective enhancement of neural networks to model subjective variables based on fuzzy logic capabilities. The developed prediction model can be employed as an efficient tool for policy and decision-making for C&DW management by predicting waste quantities in the future.

Prediction models for bioavailability of Cu and Zn during composting: Insights into machine learning

Bioavailability assessment of heavy metals in compost products is crucial for evaluating associated environmental risks. However, existing experimental methods are time-consuming and inefficient. The machine learning (ML) method has demonstrated excellent performance in predicting heavy metal fractions. In this study, based on the conventional physicochemical properties of 260 compost samples, including compost time, temperature, electrical conductivity (EC), pH, organic matter (OM), total phosphorus (TP), total nitrogen, and total heavy metal contents, back propagation neural network, gradient boosting regression, and random forest (RF) models were used to predict the dynamic changes in bioavailable fractions of Cu and Zn during composting. All three models could be used for effective prediction of the variation trend in bioavailable fractions of Cu and Zn; the RF model showed the best prediction performance, with the prediction level higher than that reported in related studies. Although the key factors affecting changes among fractions were different, OM, EC, and TP were important for the accurate prediction of bioavailable fractions of Cu and Zn. This study provides simple and efficient ML models for predicting bioavailable fractions of Cu and Zn during composting, and offers a rapid evaluation method for the safe application of compost products.

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.

A data-driven approach for revealing the linkages between differences in electrochemical properties of biochar during anaerobic digestion using automated machine learning

Biochar is commonly used to enhance the anaerobic digestion of organic waste solids and wastewater, due to its electrochemical properties, which intensify the electron transfer of microorganisms attached to its large surface area. However, it is difficult to create biochar with both high conductivity and high capacitance, which makes selecting the right biochar for engineering applications challenging. To address this issue, two Auto algorithms (TPOT and H2O) were applied to model the effects of different biochar properties on anaerobic digestion processes. The results showed that the gradient boosting machine had the highest predictive accuracy (R2 = 0.96). Feature importance analysis showed that feedstock concentration, digestion time, capacitance, and conductivity of biochar were the main factors affecting methane yield. According to the two-dimensional (2D) partial dependence plots, high-capacitance biochar (0.27-0.29 V·mA) is favorable for substrates with low-solid content (< 19.6 TS%), while the high-conductivity biochar (80.82-170.58 mS/cm) is suitable for high-solids substrates (> 20.1 TS%). The software, based on the optimal model, can be used to obtain the ideal range of biochar for AD trials, aiding researchers in practical applications prior to implementation.

Machine learning for high solid anaerobic digestion: Performance prediction and optimization

Biogas production through anaerobic digestion (AD) is one of the complex non-linear biological processes, wherein understanding its dynamics plays a crucial role towards process control and optimization. In this work, a machine learning based biogas predictive model was developed for high solid systems using algorithms, including SVM, ET, DT, GPR, and KNN and two different datasets (Dataset-1:10, Dataset-2:5 inputs). Support Vector Machine had the highest accuracy (R2) of all the algorithms at 91 % (Dataset-1) and 87 % (Dataset-2), respectively. The statistical analysis showed that there was no significant difference (p = 0.377) across the datasets, wherein with less inputs, accurate results could be predicted. In case of biogas yield, the critical factors which affect the model predictions include loading rate and retention time. The developed high solid machine learning model shows the possibility of integrating Artificial Intelligence to optimize and control AD process, thus contributing to a generic model for enhancing the overall performance of the biogas plant.

Prophet time series modeling of waste disposal rates in four North American cities

In this study, three different univariate municipal solid waste (MSW) disposal rate forecast models (SARIMA, Holt-Winters, Prophet) were examined using different testing periods in four North American cities with different socioeconomic conditions. A review of the literature suggests that the selected models are able to handle seasonality in a time series; however, their ability to handle outliers is not well understood. The Prophet model generally outperformed the Holt-Winters model and the SARIMA model. The MAPE and R2 of the Prophet model during pre-COVID-19 were 4.3-22.2% and 0.71-0.93, respectively. All three models showed satisfactory predictive results, especially during the pre-COVID-19 testing period. COVID-19 lockdowns and the associated regulatory measures appear to have affected MSW disposal behaviors, and all the univariate models failed to fully capture the abrupt changes in waste disposal behaviors. Modeling errors were largely attributed to data noise in seasonality and the unprecedented event of COVID-19 lockdowns. Overall, the modeling errors of the Prophet model were evenly distributed, with minimum modeling biases. The Prophet model also appeared to be versatile and successfully captured MSW disposal rates from 3000 to 39,000 tons/month. The study highlights the potential benefits of the use of univariate models in waste forecast.

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.

Aspergillus oryzae as a Cell Factory: Research and Applications in Industrial Production

Aspergillus oryzae, a biosafe strain widely utilized in bioproduction and fermentation technology, exhibits a robust hydrolytic enzyme secretion system. Therefore, it is frequently employed as a cell factory for industrial enzyme production. Moreover, A. oryzae has the ability to synthesize various secondary metabolites, such as kojic acid and L-malic acid. Nevertheless, the complex secretion system and protein expression regulation mechanism of A. oryzae pose challenges for expressing numerous heterologous products. By leveraging synthetic biology and novel genetic engineering techniques, A. oryzae has emerged as an ideal candidate for constructing cell factories. In this review, we provide an overview of the latest advancements in the application of A. oryzae-based cell factories in industrial production. These studies suggest that metabolic engineering and optimization of protein expression regulation are key elements in realizing the widespread industrial application of A. oryzae cell factories. It is anticipated that this review will pave the way for more effective approaches and research avenues in the future implementation of A. oryzae cell factories in industrial production.

Tackling municipal solid waste crisis in India: Insights into cutting-edge technologies and risk assessment

Municipal Solid Waste (MSW) management is a pressing global concern, with increasing interest in Waste-to-Energy Technologies (WTE-T) to divert waste from landfills. However, WTE-T adoption is hindered by financial uncertainties. The economic benefits of MSW treatment and energy generation must be balanced against environmental impact. Integrating cutting-edge technologies like Artificial Intelligence (AI) can enhance MSW management strategies and facilitate WTE-T adoption. This review paper explores waste classification, generation, and disposal methods, emphasizing public awareness to reduce waste. It discusses AI's role in waste management, including route optimization, waste composition forecasting, and process parameter optimization for energy generation. Various energy production techniques from MSW, such as high-solids anaerobic digestion, torrefaction, plasma pyrolysis, incineration, gasification, biodegradation, and hydrothermal carbonization, are examined for their advantages and challenges. The paper emphasizes risk assessment in MSW management, covering chemical, mechanical, biological, and health-related risks, aiming to identify and mitigate potential adverse effects. Electronic waste (E-waste) impact on human health and the environment is thoroughly discussed, highlighting the release of hazardous substances and their contribution to air, soil, and water pollution. The paper advocates for circular economy (CE) principles and waste-to-energy solutions to achieve sustainable waste management. It also addresses complexities and constraints faced by developing nations and proposes strategies to overcome them. In conclusion, this comprehensive review underscores the importance of risk assessment, the potential of AI and waste-to-energy solutions, and the need for sustainable waste management to safeguard public health and the environment.

Applying machine learning to anaerobic fermentation of waste sludge using two targeted modeling strategies

Anaerobic fermentation is an effective method to harvest volatile fatty acids (VFAs) from waste activated sludge (WAS). Accurately predicting and optimizing VFAs production is crucial for anaerobic fermentation engineering. In this study, we developed machine learning models using two innovative strategies to precisely predict the daily yield of VFAs in a laboratory anaerobic fermenter. Strategy-1 focuses on model interpretability to comprehend the influence of variables of interest on VFAs production, while Strategy-2 takes into account the cost of variable acquisition, making it more suitable for practical applications in prediction and optimization. The results showed that Support Vector Regression emerged as the most effective model in this study, with testing R2 values of 0.949 and 0.939 for the two strategies, respectively. We conducted feature importance analysis to identify the critical factors that influence VFAs production. Detailed explanations were provided using partial dependence plots and Shepley Additive Explanations analyses. To optimize VFAs production, we integrated the developed model with optimization algorithms, resulting in a maximum yield of 2997.282 mg/L. This value was 45.2 % higher than the average VFAs level in the operated fermenter. Our study offers valuable insights for predicting and optimizing VFAs production in sludge anaerobic fermentation, and it facilitates engineering practice in VFAs harvesting from WAS.

Photocatalytic degradation of drugs and dyes using a maching learning approach

The waste management industry uses an increasing number of mathematical prediction models to accurately forecast the behavior of organic pollutants during catalytic degradation. With the increasing quantity of waste generated, these models are critical for reinforcing the efficiency of wastewater treatment strategies. The application of machine-learning techniques in recent years has notably improved predictive models for waste management, which are essential for mitigating the impact of toxic commercial waste on global water supply. Organic contaminants, dyes, pesticides, surfactants, petroleum by-products, and prescription drugs pose risks to human health. Because traditional techniques face challenges in ensuring water quality, modern strategies are vital. Machine learning has emerged as a valuable tool for predicting the photocatalytic degradation of medicinal drugs and dyes, providing a promising avenue for addressing urgent demands in removing organic pollutants from wastewater. This research investigates the synergistic application of photocatalysis and machine learning for pollutant degradation, showcasing a sustainable solution with promising effects on environmental remediation and computational efficiency. This study contributes to green chemistry by providing a clever framework for addressing present-day water pollution challenges and achieving era-driven answers.

Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction

Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine learning models are applied for MSW-related trend prediction to provide insights on future waste generation or carbon emissions trends and assist the formulation of effective low-carbon policies. Yet, the existing machine learning models are diverse and scattered. This inconsistency poses challenges for researchers in the MSW domain who seek to identify and optimize the machine learning techniques and configurations for their applications. This systematic review focuses on MSW-related trend prediction using the most frequently applied machine learning model, artificial neural network (ANN), while addressing potential methodological improvements for reducing prediction uncertainty. Thirty-two papers published from 2013 to 2023 are included in this review, all applying ANN for MSW-related trend prediction. Observing a decrease in the size of data samples used in studies from daily to annual timescales, the summarized statistics suggest that well-performing ANN models can still be developed with approximately 33 annual data samples. This indicates promising opportunities for modeling macroscale greenhouse gas emissions in future works. Existing literature commonly used the grid search (manual) technique for hyperparameter (e.g., learning rate, number of neurons) optimization and should explore more time-efficient automated optimization techniques. Since there are no one-size-fits-all performance indicators, it is crucial to report the model's predictive performance based on more than one performance indicator and examine its uncertainty. The predictive performance of newly-developed integrated models should also be benchmarked to show performance improvement clearly and promote similar applications in future works. The review analyzed the shortcomings, best practices, and prospects of ANNs for MSW-related trend predictions, supporting the realization of practical applications of ANNs to enhance waste management practices and reduce carbon emissions.

Microalgal biorefineries: Advancement in machine learning tools for sustainable biofuel production and value-added products recovery

The microalgae can be converted into biofuels, biochemicals, and bioactive compounds in a biorefinery. Recently, designing and executing more viable and sustainable biofuel production from microalgal biomass is one of the vital challenges in the development of biorefinery. Scalable cultivation of microalgae is mandatory for commercializing and industrializing the biorefinery. The intrinsic complication in cultivation of microalgae is the physiological and operational factors that renders challenging impact to enable a smooth and profitable operation. However, this aim can only be successful via a simulation prospect. Machine learning tools provides advanced approaches for evaluating, predicting, and controlling uncertainties in microalgal biorefinery for sustainable biofuel production. The present review provides a critical evaluation of the most progressing machine learning tools that validate a potential to be employed in microalgal biorefinery. These tools are highly potential for their extensive evaluation on microalgal screening and classification. However, the application of these tools for optimization of microalgal biomass cultivation in industries in order to increase the biomass production, is still in its initial stages. Integrated hybrid machine learning tools can aid the industries to function efficiently with least resources. Some of the challenges, and perspectives of machine learning tools are discussed. Besides, future prospects are also emphasized. Though, most of the research reports on machine learning tools are not appropriate to gather generalized information, standard protocols and strategies must be developed to design generalized machine learning tools. On a whole, this review offers a perspective information about digitalized microalgal exploitation in a microalgal biorefinery.

Artificial intelligence methods for modeling gasification of waste biomass: a review

Gasification is a highly promising thermochemical process that shows considerable potential for the efficient conversion of waste biomass into syngas. The assessment of the feasibility and comparative advantages of different biomass and waste gasification schemes is contingent upon a multifaceted combination of interrelated criteria. Conventional analytical approaches employed to facilitate decision-making rely on a multitude of inadequately defined parameters. Consequently, substantial efforts have been directed toward enhancing the efficiency and productivity of thermochemical conversion processes. In recent times, artificial intelligence (AI)-based models and algorithms have gained prominence, serving as indispensable tools for expediting these processes and formulating strategies to address the growing demand for energy. Notably, machine learning (ML) and deep learning (DL) have emerged as cutting-edge AI models, demonstrating exceptional effectiveness and profound relevance in the realm of thermochemical conversion systems. This study provides an overview of the machine learning (ML) and deep learning (DL) approaches utilized during gasification and evaluates their benefits and drawbacks. Many industries and applications related to energy conversion systems use AI algorithms. Predicting the output of conversion systems and subjects linked to optimization are two of this science's critical applications. This review sheds light on the burgeoning utility of AI, particularly ML and DL, which have garnered significant attention due to their applications in productivity prediction, process optimization, real-time process monitoring, and control. Furthermore, the integration of hybrid models has become commonplace, primarily owing to their demonstrated success in modeling and optimization tasks. Importantly, the adoption of these algorithms significantly enhances the model's capability to tackle intricate challenges, as DL methodologies have evolved to offer heightened accuracy and reduced susceptibility to errors. Within the scope of this study, an exhaustive exploration of ML and DL techniques and their applications has been conducted, uncovering existing research knowledge gaps. Based on a comprehensive critical analysis, this review offers recommendations for future research directions, accentuating the pivotal findings and conclusions derived from the study.

Smart waste management 4.0: The transition from a systematic review to an integrated framework

Smart Waste Management (SWM) discusses the waste management process for different types of waste while introducing an intelligent approach to controlling the amount of waste. This paper introduces SWM4.0, which applies Industry4.0 (I4.0) technologies in various related events. First, the paper presents a systematic literature review on the role of I4.0 technologies in SWM activities regarding waste types, waste management processes, and 5R strategies. Then, existing solutions supporting SWM4.0 are extracted to develop a framework for exploring the use of I4.0 technologies. This framework includes sharing the four main pillars that contribute to the success of SWM4.0, namely smart people, smart cities, smart enterprises, and smart factories. Furthermore, this review suggests the possibility of unifying and extending existing solutions and identifying the necessary links and interfaces for researchers. For managerial implications, the framework identifies future strategies to fulfill specific SWM tasks and to foster new technological solutions for future research.

A comprehensive review on the source, ingestion route, attachment and toxicity of microplastics/nanoplastics in human systems

Microplastics (MPs)/nanoplastics (NPs) are widely found in the natural environment, including soil, water and the atmosphere, which are essential for human survival. In the recent years, there has been a growing concern about the potential impact of MPs/NPs on human health. Due to the increasing interest in this research and the limited number of studies related to the health effects of MPs/NPs on humans, it is necessary to conduct a systematic assessment and review of their potentially toxic effects on human organs and tissues. Humans can be exposed to microplastics through ingestion, inhalation and dermal contact, however, ingestion and inhalation are considered as the primary routes. The ingested MPs/NPs mainly consist of plastic particles with a particle size ranging from 0.1 to 1 μm, that distribute across various tissues and organs within the body, which in turn have a certain impact on the nine major systems of the human body, especially the digestive system and respiratory system, which are closely related to the intake pathway of MPs/NPs. The harmful effects caused by MPs/NPs primarily occur through potential toxic mechanisms such as induction of oxidative stress, generation of inflammatory responses, alteration of lipid metabolism or energy metabolism or expression of related functional factors. This review can help people to systematically understand the hazards of MPs/NPs and related toxicity mechanisms from the level of nine biological systems. It allows MPs/NPs pollution to be emphasized, and it is also hoped that research on their toxic effects will be strengthened in the future.

AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis

Inorganic and organic contaminants, such as fertilisers, heavy metals, and dyes, are the primary causes of water pollution. The field of artificial intelligence (AI) has received significant interest due to its capacity to address challenges across various fields. The use of AI techniques in water treatment and desalination has recently shown useful for optimising processes and dealing with the challenges of water pollution and scarcity. The utilization of AI in the water treatment industry is anticipated to result in a reduction in operational expenditures through the lowering of procedure costs and the optimisation of chemical utilization. The predictive capabilities of artificial intelligence models have accurately assessed the efficacy of different adsorbents in removing contaminants from wastewater. This article provides an overview of the various AI techniques and how they can be used in the adsorption of contaminants during the water treatment process. The reviewed publications were analysed for their diversity in journal type, publication year, research methodology, and initial study context. Citation network analysis, an objective method, and tools like VOSviewer are used to find these groups. The primary issues that need to be addressed include the availability and selection of data, low reproducibility, and little proof of uses in real water treatment. The provision of challenges is essential to ensure the prospective success of AI associated with technologies. The brief overview holds importance to everyone involved in the field of water, encompassing scientists, engineers, students, and stakeholders.

Spatial-temporal redundancy evaluation of the municipal solid waste incineration treatment capacity: the case study of China

In the process of marketization, the lack of redundancy evaluation of the MSW incineration treatment capacity leads to the regional imbalance of treatment capacity and waste of resources. Therefore, this study aimed to develop a spatial-temporal redundancy evaluation method for the MSW incineration treatment capacity based on the accurate MSW generation prediction using artificial intelligence. To achieve this aim, this study first proposed and finalized a prediction model of the provincial MSW generation by applying the artificial neuron network (ANN) technology and using the statistical data of Jiangsu Province of China from 1990 to 2020. In the finalized model, the input variables consist of three demographic variables, three social variables, and five economic variables; the model structure that includes four hidden layers and 16 neurons in each hidden layer performed best with a coefficient of determination (R2) of 0.995 on the training samples and an R2 of 0.974 on the test set, respectively. Using the finalized model and statistical data of all provinces in China, this study proposed a redundancy evaluation method for the MSW incineration treatment capacity and evaluated the spatial and temporal redundancy status of China. The results first confirm the effectiveness of the proposed method to model and quantify the redundancy problem. Second, according to the evaluation results, even if no new treatment plant will be built before 2025, 10 of China's 31 provinces still have redundancy problems, indicating the severity of this problem. This study first contributes to the body of knowledge by modeling the redundancy problem of the MSW incineration treatment capacity. Moreover, this study provides a tool to quantify temporal and spatial redundancy using advanced technology and publicly available data. Furthermore, the results can help waste-related authorities and organizations make optimal strategies and actions to better match MSW treatment capacity and MSW generation volume.

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.

Advances and applications of machine learning and deep learning in environmental ecology and health

Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm.

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.

Application of heavy metal immobilization in soil by biochar using machine learning

Biochar application is a promising strategy for the immobilization of heavy metal (HM)-contaminated soil, while it is always time-consuming and labor-intensive to clarify key influenced factors of soil HM immobilization by biochar. In this study, four machine learning algorithms, namely random forest (RF), support vector machine (SVR), Gradient boosting decision trees (GBDT), and Linear regression (LR) are employed to predict the HMimmobilization ratio. The RF was the best-performance ML model (Training R2 = 0.90, Testing R2 = 0.85, RMSE = 4.4, MAE = 2.18). The experiment verification based on the optimal RF model showed that the experiment verification was successful, as the results were comparable to the RF modeling results with a prediction error<20%. Shapley additive explanation and partial least squares path model method were used to identify the critical factors and direct and indirect effects of these features on the immobilization ratio. Furthermore, independent models of four HM (Cd, Cu, Pb, and Zn) also achieved better model prediction performance. Feature importance and interactions relationship of influenced factors for individual HM immobilization ratio was clarified. This work can provide a new insight for HM immobilization in soils.

Machine learning models for estimating contamination across different curbside collection strategies

Contaminated recyclables, which are frequently discarded as waste, pose a significant challenge to the implementation of a circular economy. These contaminated recyclables impede the circulation of resources, resulting in higher processing costs at material recovery facilities (MRFs). Over the past few decades, machine learning (ML) models such as linear regression (LR), support vector machine (SVM), and random forest (RF) have evolved to provide new methods for predicting inbound contamination rates in addition to traditional statistical models. In this study, we applied ML models to predict inbound contamination rates using demographic features from 15 counties in the U.S. with different curbside collection strategies. In general, we found that ML models outperformed linear mixed models. Specifically, SVM models had the highest performance (R² = 0.75; mean absolute error (MAE) = 0.06), which may be due to their ability to model nonlinear relationships between features and inbound contamination rates. The key predictor was population, with poverty rate being positively correlated and median age negatively correlated with inbound contamination rates. To improve the management of contamination and enhance the implementation of a circular economy, better models are needed to understand and estimate inbound contamination rates as well as identify critical factors in the present and future.

Environmental impact assessment of multi-source solid waste based on a life cycle assessment, principal component analysis, and random forest algorithm

As a national pilot city for solid waste disposal and resource reuse, Dongguan in Guangdong Province aims to vigorously promote the high-value utilization of solid waste and contribute to the sustainable development of the Greater Bay Area. In this study, life cycle assessment (LCA) coupled with principal component analysis (PCA) and the random forest (RF) algorithm was applied to assess the environmental impact of multi-source solid waste disposal technologies to guide the environmental protection direction. In order to improve the technical efficiency and reduce pollution emissions, some advanced technologies including carbothermal reduction‒oxygen-enriched side blowing, directional depolymerization‒flocculation demulsification, anaerobic digestion and incineration power generation, were applied for treating inorganic waste, organic waste, kitchen waste and household waste in the park. Based on the improved techniques, we proposed a cyclic model for multi-source solid waste disposal. Results of the combined LCA-PCA-RF calculation indicated that the key environmental load type was human toxicity potential (HTP), came from the technical units of carbothermal reduction and oxygen-enriched side blowing. Compared to the improved one, the cyclic model was proved to reduce material and energy inputs by 66%-85% and the pollution emissions by 15%-88%. To sum up, the environmental impact assessment and systematic comparison suggest a cyclic mode for multi-source solid waste treatments in the park, which could be promoted and contributed to the green and low-carbon development of the city.

Machine learning methods for predicting the key metabolic parameters of Halomonas elongata DSM 2581 T

Ectoine is generally produced by the fermentation process of Halomonas elongata DSM 2581 ^T, which is one of the primary industrial ectoine production techniques. To effectively monitor and control the fermentation process, the important parameters require accurate real-time measurement. However, for ectoine fermentation, three critical parameters (cell optical density, glucose, and product concentration) cannot be measured conveniently in real-time due to time variation, strong coupling, and other constraints. As a result, our work effectively created a series of hybrid models to predict the values of these three parameters incorporating both fermentation kinetics and machine learning approaches. Compared with the traditional machine learning models, our models solve the problem of insufficient data which is common in fermentation. In addition, a simple kinetic modeling is only applicable to specific physical conditions, so different physical conditions require refitting the function, which is tedious to operate. However, our models also overcome this limitation. In this work, we compared different hybrid models based on 5 feature engineering methods, 11 machine-learning approaches, and 2 kinetic models. The best models for predicting three key parameters, respectively, are as follows: CORR-Ensemble (R²: 0.983 ± 0.0, RMSE: 0.086 ± 0.0, MAE: 0.07 ± 0.0), SBE-Ensemble (R²: 0.972 ± 0.0, RMSE: 0.127 ± 0.0, MAE: 0.078 ± 0.0), and SBE-Ensemble (R²:0.98 ± 0.0, RMSE: 0.023 ± 0.001, MAE: 0.018 ± 0.001). To verify the universality and stability of constructed models, we have done an experimental verification, and its results showed that our proposed models have excellent performance. KEY POINTS: • Using the kinetic models for producing simulated data • Through different feature engineering methods for dimension reduction • Creating a series of hybrid models to predict the values of three parameters in the fermentation process of Halomonas elongata DSM 2581 ^T.

A strategic review on sustainable approaches in municipal solid waste management andenergy recovery: Role of artificial intelligence,economic stability andlife cycle assessment

The consumption of energy levels has increased in association with economic growth and concurrently increased the energy demand from renewable sources. The need under Sustainable Development Goals (SDG) intends to explore various technological advancements for the utilization of waste to energy. Municipal Solid Waste (MSW) has been reported as constructive feedstock to produce biofuels, biofuel carriers and biochemicals using energy-efficient technologies in risk freeways. The present review contemplates risk assessment and challenges in sorting and transportation of MSW and different aspects of conversion of MSW into energy are critically analysed. The circular bioeconomy of energy production strategies and management of waste are also analysed. The current scenario on MSW and its impacts on the environment are elucidated in conjunction with various policies and amendments equipped for the competent management of MSW in order to fabricate a sustained environment.

Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review

The increased water scarcity, depletion of freshwater resources, and rising environmental awareness are stressing for the development of sustainable wastewater treatment processes. Microalgae-based wastewater treatment has resulted in a paradigm shift in our approach toward nutrient removal and simultaneous resource recovery from wastewater. Wastewater treatment and the generation of biofuels and bioproducts from microalgae can be coupled to promote the circular economy synergistically. A microalgal biorefinery transforms microalgal biomass into biofuels, bioactive chemicals, and biomaterials. The large-scale cultivation of microalgae is essential for the commercialization and industrialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regarding physiological and illumination parameters renders it challenging to facilitate a smooth and cost-effective operation. Artificial intelligence (AI)/machine learning algorithms (MLA) offer innovative strategies for assessing, predicting, and regulating uncertainties in algal wastewater treatment and biorefinery. The current study presents a critical review of the most promising AI/MLAs that demonstrate a potential to be applied in microalgal technologies. The most commonly used MLAs include artificial neural networks, support vector machine, genetic algorithms, decision tree, and random forest algorithms. Recent developments in AI have made it possible to combine cutting-edge techniques from AI research fields with microalgae for accurate analysis of large datasets. MLAs have been extensively studied for their potential in microalgae detection and classification. However, the ML application in microalgal industries, such as optimizing microalgae cultivation for increased biomass productivity, is still in its infancy. Incorporating smart AI/ML-enabled Internet of Things (IoT) based technologies can help the microalgal industries to operate effectively with minimum resources. Future research directions are also highlighted, and some of the challenges and perspectives of AI/ML are outlined. As the world is entering the digitalized industrial era, this review provides an insightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae.

Prediction of biogas production of industrial scale anaerobic digestion plant by machine learning algorithms

In the anaerobic digestion (AD) process there are some difficulties in maintaining process stability due to the complexity of the system. The variability of the raw material coming to the facility, temperature fluctuations and pH changes as a result of microbial processes cause process instability and require continuous monitoring and control. Increasing continuous monitoring, and internet of things applications within the scope of Industry 4.0 in AD facilities can provide process stability control and early intervention. In this study, five different machine learning (ML) algorithms (RF, ANN, KNN, SVR, and XGBoost) were used to describe and predict the correlation between operational parameters and biogas production quantities collected from a real-scale anaerobic digestion plant. The KNN algorithm had the lowest accuracy in predicting total biogas production over time, while the RF model had the highest prediction accuracy of all prediction models. The RF method produced the best prediction, with an R² of 0.9242, and it was followed by XGBoost, ANN, SVR, and KNN (with R² values of 0.8960, 0.8703, 0.8655, 0.8326, respectively). Real-time process control will be provided and process stability will be maintained by preventing low-efficiency biogas production with the integration of ML applications into AD facilities.

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.

Evaluating the metal recovery potential of coal fly ash based on sequential extraction and machine learning

Given the depletion of metal resources and the potential leaching of toxic elements from solid waste, secondary recovery of metal from solid waste is essential to achieve coordinated development of resources and the environment. In this study, hybrid models combining the gradient boosting decision tree and particle swarm optimization algorithm were constructed and compared based on two different datasets. Additionally, a new, quantitative evaluation index for metal recovery potential (MRP) was proposed. The results showed that the model constructed using more elemental properties could more accurately predict metal fractions in coal fly ash (CFA) with an R² value of 0.88 achieved on the testing set. The MRP index revealed that the DAT sample had the greatest recovery potential (MRP = 43,311.70). Ca was easier to recover due to its high concentration and presence mostly in soluble fractions. Model post-analysis highlighted that the elemental properties and total concentrations generally exerted a greater influence on the metal fractions. The innovative evaluation strategy based on machine learning and sequential extraction presented in this work provides an important reference for maximizing metal recovery from CFA to achieve environmental and economic benefits with the goal of sustainable development.

Interpretable machine learning assisted spectroscopy for fast characterization of biomass and waste

The combination of machine learning and infrared spectroscopy was reported as effective for fast characterization of biomass and waste (BW). However, this characterization process is lack of interpretability towards its chemical insights, leading to less satisfactory recognition for its reliability. Accordingly, this paper aimed to explore the chemical insights of the machine learning models in the fast characterization process. A novel dimensional reduction method with significant physicochemical meanings was thus proposed, where the high loading spectral peaks of BW were selected as input features. Combined with functional groups attribution of these spectral peaks, the machine learning models established based on the dimensionally reduced spectral data could be explained with clear chemical insights. The performance of classification and regression models between the proposed dimensional reduction method and principal component analysis method was compared. The influence mechanism of each functional group on the characterization results were discussed. CH deformation, CC stretch & CO stretch and ketone/aldehyde CO stretch played essential roles in C, H/ LHV and O prediction, respectively. The results of this work demonstrated the theoretical fundamentals of the machine learning and spectroscopy based BW fast characterization method.

Machine-learning approaches in geo-environmental engineering: Exploring smart solid waste management

Over the past few decades, increased attention has been paid to domestic waste (DW) generation. DW comprises a large percentage of municipal solid waste (MSW), and its handling and processing involves serious technical issues while also consuming a major portion of municipal budgets. The accurate estimation, prediction, and characterization of DW is an ongoing challenge for many cities, municipalities, and local governments as they strive to implement sustainable strategies for MSW. The main objective of the present study is to estimate and correctly predict DW quantities using machine-learning (ML) algorithms. Several different ML algorithms are used in the research, including linear regression, regression trees, Gaussian process regression, support vector machine, and autoregressive integrated moving average methods for time series analysis. Two case studies are presented in this paper. In the first, domestic waste data covering the period from 2010 to 2021 were collected from the Saudi and Bahrain authorities, and in the second, the domestic waste-generating behavior of a family of eleven members was followed for one month. The results show that the biodegradable and non-biodegradable wastes generated by the family were in the range of 1.7-7.9 kg and 0.0-2.0 kg, respectively, and promising outcomes were obtained using an appropriate selection of input predictors in conjunction with time series analysis. The trained models are validated and tested using several types of evaluation metrics, including calculated residuals, mean square error, root mean square error, and coefficient determination (R²-Score). The latter values are in the range of 0.67-0.85 for the training and testing datasets for many of the predicted waste quantities. The results obtained from the study show that these algorithms can be used to reduce the environmental, economic, and societal impacts of waste by designing a smart waste management engineering system.

An optimal model for municipal solid waste management using hybrid dual faster R-CNN

Economic development, population growth, and rapid urbanization are the reasons for an increasing generation of waste all over the world. Recently, the statistics showed that 2.1 million of tons municipal solid waste (MSW) was produced in 2016, which is estimated to enhance by 3.4 million tons in 2050. In recent times, municipal solid waste generation is dramatically increasing due to factors such as rapid urbanization, altered living standard, and increased population. These factors make the municipal solid waste management system complex and break the pollution-controlling strategies. So it necessitates the system to accurately predict the waste composition. Based on the waste classification, a suitable decomposition technique is preferred. Therefore, this paper proposed CMSOA optimized dual faster R-CNN based waste management system to accurately classify the waste composition. The proposed system is formed by hybridizing dual faster RCNN along with complex-valued encoding multi-chain seeker optimization algorithm (CMSOA). Various evaluation measures, namely accuracy, precision, recall, F-measure, RMSE, MAE, and MAPE metrics, are computed, and the case study analysis is conducted on the major five cities of Maharashtra. The comparative analysis is carried out for various approaches, and from the analysis, the results revealed that the proposed method provides better classification results than other methods.

Optimizing the Quality of Machine Learning for Identifying the Share of Biogenic and Fossil Carbon in Solid Waste

Insights into carbon sources (biogenic and fossil carbon) and contents in solid waste are vital for estimating the carbon emissions from incineration plants. However, the traditional methods are time-, labor-, and cost-intensive. Herein, high-quality data sets were established after analyzing the carbon contents and infrared spectra of substantial samples using elemental analysis and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), respectively. Then, five classification and eight regression machine learning (ML) models were evaluated to recognize the proportion of biogenic and fossil carbon in solid waste. Using the optimized data preprocessing approach, the random forest (RF) classifier with hyperparameter tuning ranked first in classifying the carbon group with a test accuracy of 0.969, and the carbon contents were successfully predicted by the RF regressor with R² = 0.926 considering performance-interpretability-computation time competition. The above proposed algorithms were further validated with real environmental samples, which exhibited robust performance with an accuracy of 0.898 for carbon group classification and an R² value of 0.851 for carbon content prediction. The reliable results indicate that ATR-FTIR coupled with ML algorithms is feasible for rapidly identifying both carbon groups and content, facilitating the calculation and assessment of carbon emissions from solid waste incineration.

Reuse of Textile Waste in the Production of Sound Absorption Boards

Textile waste is formed in various stages, from the preparation of raw materials to the utilisation of textile products. One of the sources of textile waste is the production of woollen yarns. During the production of woollen yarns, waste is generated during the mixing, carding, roving, and spinning processes. This waste is disposed of in landfills or cogeneration plants. However, there are many examples of textile waste being recycled and new products being produced. This work deals with acoustic boards made from waste from the production of woollen yarns. This waste was generated in various yarn production processes up to the spinning stage. Due to the parameters, this waste was not suitable for further use in the production of yarns. During the work, the composition of waste from the production of woollen yarns was examined-namely, the amount of fibrous and nonfibrous materials, the composition of impurities, and the parameters of the fibres themselves. It was determined that about 74% of the waste is suitable for the production of acoustic boards. Four series of boards with different densities and different thicknesses were made with waste from the production of woollen yarns. The boards were made in a nonwoven line using carding technology to obtain semi-finished products from the individual layers of combed fibres and thermal treatment of the prepared semi-finished product. The sound absorption coefficients in the sound frequency range between 125 and 2000 Hz were determined for the manufactured boards, and the sound reduction coefficients were calculated. It was found that the acoustic characteristics of soft boards made from woollen yarn waste are very similar to those of classic boards or sound insulation products made from renewable resources. At a board density of 40 kg/m³, the value of the sound absorption coefficient varied from 0.4 to 0.9, and the noise reduction coefficient reached 0.65.

Estimation of Acetic Acid Concentration from Biogas Samples Using Machine Learning

In a biogas plant, the acetic acid concentration is a major component of the substrate as it determines the pH value, and this pH value correlates with the volume of biogas produced. Since it requires specialized laboratory equipment, the concentration of acetic acid in a biogas substrate cannot be measured on-line. The project aims to use NIR sensors and machine learning algorithms to estimate the acetic acid concentration in a biogas substrate based on the measured intensities of the substrate. As a result of this project, it was possible to determine whether the acetic acid concentration in a biogas substrate is higher or lower than 2 g/l using machine learning models.

Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction

Recent advances in machine learning (ML) have revolutionized an extensive range of research and industry fields by successfully addressing intricate problems that cannot be resolved with conventional approaches. However, low interpretability and incompatibility make it challenging to apply ML to complicated bioprocesses, which rely on the delicate metabolic interplay among living cells. This overview attempts to delineate ML applications to bioprocess from different perspectives, and their inherent limitations (i.e., uncertainties in prediction) were then discussed with unique attempts to supplement the ML models. A clear classification can be made depending on the purpose of the ML (supervised vs unsupervised) per application, as well as on their system boundaries (engineered vs natural). Although a limited number of hybrid approaches with meaningful outcomes (e.g., improved accuracy) are available, there is still a need to further enhance the interpretability, compatibility, and user-friendliness of ML models.

Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications

Hydrogen can be produced in an environmentally friendly manner through biological processes using a variety of organic waste and biomass as feedstock. However, the complexity of biological processes limits their predictability and reliability, which hinders the scale-up and dissemination. This article reviews contemporary research and perspectives on the application of machine learning in biohydrogen production technology. Several machine learning algorithems have recently been implemented for modeling the nonlinear and complex relationships among operational and performance parameters in biohydrogen production as well as predicting the process performance and microbial population dynamics. Reinforced machine learning methods exhibited precise state prediction and retrieved the underlying kinetics effectively. Machine-learning based prediction was also improved by using microbial sequencing data as input parameters. Further research on machine learning could be instrumental in designing a process control tool to maintain reliable hydrogen production performance and identify connection between the process performance and the microbial population.

Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.