Improving kitchen waste composting maturity by optimizing the processing parameters based on machine learning model

Enhanced organic degradation and microbial community cooperation by inoculating Bacillus licheniformis in low temperature composting

Microbial activity and interaction are the important driving factors in the start-up phase of food waste composting at low temperature. The aim of this study was to explore the effect of inoculating Bacillus licheniformis on the degradation of organic components and the potential microbe-driven mechanism from the aspects of organic matter degradation, enzyme activity, microbial community interaction, and microbial metabolic function. The results showed that after inoculating B. licheniformis, temperature increased to 47.8°C on day 2, and the degradation of readily degraded carbohydrates (RDC) increased by 31.2%, and the bioheat production increased by 16.5%. There was an obvious enhancement of extracellular enzymes activities after inoculation, especially amylase activity, which increased by 7.68 times on day 4. The inoculated B. licheniformis colonized in composting as key genus in the start-up phase. Modular network analysis and Mantel test indicated that inoculation drove the cooperation between microbial network modules who were responsible for various organic components (RDC, lipid, protein, and lignocellulose) degradation in the start-up phase. Metabolic function prediction suggested that carbohydrate metabolisms including starch and sucrose metabolism, glycolysis / gluconeogenesis, pyruvate metabolism, etc., were improved by increasing the abundance of related functional genes after inoculation. In conclusion, inoculating B. licheniformis accelerated organic degradation by driving the cooperation between microbial network modules and enhancing microbial metabolism in the start-up phase of composting.

Co-composting of dewatered sludge and wheat straw with newly isolated Xenophilus azovorans: Carbon dynamics, humification, and driving pathways

Composting is a biological reaction caused by microorganisms. Composting efficiency can be adequately increased by adding biochar and/or by inoculating with exogenous microorganisms. In this study, we looked at four methods for dewatered sludge waste (DSW) and wheat straw (WS) aerobic co-composting: T1 (no additive), T2 (5% biochar), T3 (5% of a newly isolated strain, Xenophilus azovorans (XPA)), and T4 (5% of biochar-immobilized XPA (BCI-XPA)). Throughout the course of the 42-day composting period, we looked into the carbon dynamics, humification, microbial community succession, and modifications to the driving pathways. Compared to T1 and T2, the addition of XPA (T3) and BCI-XPA (T4) extended the thermophilic phase of composting without negatively affecting compost maturation. Notably, T4 exhibited a higher seed germination index (132.14%). Different from T1 and T2 treatments, T3 and T4 treatments increased CO2 and CH4 emissions in the composting process, in which the cumulative CO2 emissions increased by 18.61-47.16%, and T3 and T4 treatments also promoted the formation of humic acid. Moreover, T4 treatment with BCI-XPA addition showed relatively higher activities of urease, polyphenol oxidase, and laccase, as well as a higher diversity of microorganisms compared to other processes. The Functional Annotation of Prokaryotic Taxa (FAPROTAX) analysis showed that microorganisms involved in the carbon cycle dominated the entire composting process in all treatments, with chemoheterotrophy and aerobic chemoheterotrophy being the main pathways of organic materials degradation. Moreover, the presence of XPA accelerated the breakdown of organic materials by catabolism of aromatic compounds and intracellular parasite pathways. On the other hand, the xylanolysis pathway was aided in the conversion of organic materials to dissolved organics by the addition of BCI-XPA. These findings indicate that XPA and BCI-XPA have potential as additives to improve the efficiency of dewatered sludge and wheat straw co-composting.

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.

Deciphering and predicting changes in antibiotic resistance genes during pig manure aerobic composting via machine learning model

Livestock manure is one of the most important pools of antibiotic resistance genes (ARGs) in the environment. Aerobic composting can effectively reduce the spread of antibiotic resistance risk in livestock manure. Understanding the effect of aerobic composting process parameters on manure-sourced ARGs is important to control their spreading risk. In this study, the effects of process parameters on ARGs during aerobic composting of pig manure were explored through data mining based on 191 valid data collected from literature. Machine learning (ML) models (XGBoost and Random Forest) were utilized to predict the rate of ARGs changes during pig manure composting. The model evaluation index of the XGBoost model (R2 = 0.651) was higher than that of the Random Forest (R2 = 0.490), indicating that XGBoost had better prediction performance. Feature importance was further calculated for the XGBoost model, and the XGBoost black box model was interpreted by Shapley additive explanations analysis. Results indicated that the influencing factors on the ARGs variation in pig manure were sequentially divided into thermophilic period, total composting period, composting real time, and thermophilic stage average temperature. The findings gave an insight into the application of ML models to predict and decipher the ARG changes during manure composting and provided suggestions for better composting manipulation and optimization of process parameters.

Predicting maturity and identifying key factors in organic waste composting using machine learning models

The measurement of germination index (GI) in composting is a time-consuming and laborious process. This study employed four machine learning (ML) models, namely Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), and Decision Tree (DT), to predict GI based on key composting parameters. The prediction results showed that the coefficient of determination (R2) for RF (>0.9) and ANN (>0.9) was higher than SVR (<0.6) and DT (<0.8), suggesting that RF and ANN displayed superior predictive performance for GI. The SHapley additive exPlanations value result indicated that composting time, temperature, and pH were the important features contributing to GI. Composting time was found to have the most significant impact on GI. Overall, RF and ANN were suggested as effective tools for predicting GI in composting. This study offers the reliable approach of accurately predicting GI in composting processes, thereby enabling intelligent composting practices.

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.

Microbiome data analysis via machine learning models: Exploring vital players to optimize kitchen waste composting system

Composting, reliant on microorganisms, effectively treats kitchen waste. However, it is difficult to precisely understand the specific role of key microorganisms in the composting process by relying solely on experimental research. This study aims to employ machine learning models to explore key microbial genera and to optimize composting systems. After introducing a novel microbiome preprocessing approach, Stacking models were constructed (R2 is about 0.8). The SHAP method (SHapley Additive exPlanations) identified Bacillus, Acinetobacter, Thermobacillus, Pseudomonas, Psychrobacter, and Thermobifida as prominent microbial genera (Shapley values ranging from 3.84 to 1.24). Additionally, microbial agents were prepared to target the identified key genera, and experiments demonstrated that the composting quality score was 76.06 for the treatment and 70.96 for the control. The exogenous agents enhanced decomposition and improved compost quality in later stages. In summary, this study opens up a new avenue to identifying key microorganisms and optimizing the biological treatment process.

Prediction of composting maturity and identification of critical parameters for green waste compost using machine learning

Ensuring the maturity of green waste compost is crucial to composting processes and quality control of compost products. However, accurate prediction of green waste compost maturity remains a challenge, as there are limited computational methods available. This study aimed to address this issue by employing four machine learning models to predict two indicators of green waste compost maturity: seed germination index (GI) and T value. The four models were compared, and the Extra Trees algorithm exhibited the highest prediction accuracy with R2 values of 0.928 for GI and 0.957 for T value. To identify the interactions between critical parameters and compost maturity, The Pearson correlation matrix and Shapley Additive exPlanations (SHAP) analysis were conducted. Furthermore, the accuracy of the models was validated through compost validation experiments. These findings highlight the potential of applying machine learning algorithms to predict green waste compost maturity and optimise process regulation.

Optimization of olive oil extraction wastes co composting procedure based on bioprocessing parameters

Organic waste generation has increased massively around the world during the last decades, especially the waste produced by the olive-growing industry. In order to manage the waste accumulation, composting process is an appropriate biotechnological solution which allows the waste organic matter biotransformation into a useful product the "compost", used as an amendment for agricultural soils. The classical composting process presents several disadvantages; the major difficulty is to find the best feedstocks proportion to be used, leading to a final C/N ratio ranged between 12 and 15, a neutral pH, a humidity between 40% and 60% and organic matter (OM) content of 20-60%, at ambient temperature. Consequently, an accurate optimization of the composting process is needed for predicting the process parameters progress. To optimize these parameters and the waste rates initially mixed, the multiple regression method was used to determine the compost final parameters values, referring to the initial mixture of the different waste types. The best model filling the required standardized values included 49% of olive mill wastewater, 19.5% of exhausted olive mill cake, 15.5% of poultry manure, and 16% of green waste. This combination provides a pH of 7.5, a C/N ratio of 12.5 and an OM content of 44%. Such modelization would enshorten the composting required time.

Electric Heating Promotes Sludge Composting Process: Optimization of Heating Method through Machine Learning Algorithms

Composting with electric heating has attracted extensive attention for the advantage of high treatment efficiency for sludge. However, there are challenges in investigating how electric heating affects the composting process and how to reduce its energy consumption. This study investigated the effects of different electric heating methods on composting. The highest temperature, water content reduction, organic matter reduction, and weight reduction rate in group B6 (heating in the first and second stages) were 76.00 ° C, 16.76 %, 4.90 %, and 35.45 %, respectively, indicating that electric heating promoted water evaporation and organic matter degradation. In conclusion, electric heating promoted the sludge composting process and the heating method of group B6 was optimal for composting characteristics. This work contributes to the understanding of the mechanism of electric heating promoting composting process and providing theoretical support for the engineering application of composting with electric heating.

A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach

Biowaste remediation and valorization for environmental sustainability focuses on prevention rather than cleanup of waste generation by applying the fundamental recovery concept through biowaste-to-bioenergy conversion systems - an appropriate approach in a circular bioeconomy. Biomass waste (biowaste) is discarded organic materials made of biomass (e.g., agriculture waste and algal residue). Biowaste is widely studied as one of the potential feedstocks in the biowaste valorization process due to its being abundantly available. In terms of practical implementations, feedstock variability from biowaste, conversion costs and supply chain stability prevent the widespread usage of bioenergy products. Biowaste remediation and valorization have used artificial intelligence (AI), a newly developed idea, to overcome these difficulties. This report analyzed 118 works that applied various AI algorithms to biowaste remediation and valorization-related research published between 2007 and 2022. Four common AI types are utilized in biowaste remediation and valorization: neural networks, Bayesian networks, decision tree, and multivariate regression. The neural network is the most frequent AI for prediction models, the Bayesian network is utilized for probabilistic graphical models, and the decision tree is trusted for providing tools to assist decision-making. Meanwhile, multivariate regression is employed to identify the relationship between experimental variables. AI is a remarkably effective tool in predicting data, which is reportedly better than the conventional approach owing to its characteristics of time-saving and high accuracy. The challenge and future work in biowaste remediation and valorization are briefly discussed to maximize the model's performance.

Application of machine learning on understanding biomolecule interactions in cellular machinery

Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.

Artificial intelligence and machine learning approaches in composting process: A review

Studies on developing strategies to predict the stability and performance of the composting process have increased in recent years. Machine learning (ML) has focused on process optimization, prediction of missing data, detection of non-conformities, and managing complex variables. This review investigates the perspectives and challenges of ML and its important algorithms such as Artificial Neural Networks (ANNs), Random Forest (RF), Adaptive-network-based fuzzy inference systems (ANFIS), Support Vector Machines (SVMs), and Deep Neural Networks (DNNs) used in the composting process. In addition, the individual shortcomings and inadequacies of the metrics, which were used as error or performance criteria in the studies, were emphasized. Except for a few studies, it was concluded that Artificial Intelligence (AI) algorithms such as Genetic algorithm (GA), Differential Evaluation Algorithm (DEA), and Particle Swarm Optimization (PSO) were not used in the optimization of the model parameters, but in the optimization of the parameters of the ML algorithms.

Artificial intelligence technologies in bioprocess: Opportunities and challenges

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

Modelling and optimization of sewage sludge composting using biomass ash via deep neural network and genetic algorithm

In this study, the use of Deep Cascade Forward Neural Network (DCFNN) was investigated to model both linear and non-linear chaotic relationships in co-composting of dewatered sewage sludge and biomass fly ash (BFA). Model results were evaluated in comparison with RSM, Feed Forward Neural Network (FFNN) and Feed Back Neural Network (FBNN), and Cascade Forward Neural Network (CFNN). DCFNN produced predictive results with MAPE values less than 1% for all datasets in all experimental designs except one with 1.99%. Furthermore, the decision variables were optimized by Genetic Algorithm (GA). The desirability level obtained from the optimization results was found to be 100% in a few designs and above 95% in all other designs. The results showed that DCFNN is a reliable and consistent tool for modeling composting process parameters, also GA is a satisfactory tool for determining which outputs the input parameters will produce in an experimental setup.

Biodegradation of high di-(2-Ethylhexyl) phthalate (DEHP) concentration by food waste composting and its toxicity assessment using seed germination test

Di-(2-ethylhexyl) phthalate (DEHP), a plasticizer derived from phthalate ester, is used as an additive in industrial products such as plastics, paints, and medical devices. However, DEHP is known as an endocrine-disrupting chemical, causing cancers and adverse effects on human health. This study evaluated DEHP biodegradation efficiency via food waste composting during 35 days of incubation. At high DEHP concentrations (2167 mg kg^-1) in food waste compost mixture, the DEHP biodegradation efficiency was 99% after 35 days. The highest degradation efficiency was recorded at the thermophilic phase (day 3 - day 11) with the biodegradation rate reached 187 mg kg^-1 day^-1. DEHP was metabolized to dibutyl phthalate (DBP) and dimethyl phthalate (DMP) and would be oxidized to benzyl alcohol (BA) and mineralized into CO₂ and water via various metabolisms. Finally, the compost's quality with residual DEHP was evaluated using Brassica chinensis L. seeds via 96 h of germination tests. The compost (at day 35) with a trace amount of DEHP as the end product showed no significant effect on the germination rate of Brassica chinensis L. seeds (88%) compared to that without DEHP (94%), indicating that the compost can be reused as fertilizer in agricultural applications. These results provide an improved understanding of the DEHP biodegradation via food waste composting without bioaugmentation and hence facilitating its green remediation and conversion into value-added products. Nevertheless, further studies are needed on DEHP biodegradation in large-scale food waste composting or industrial applications.

Extensive data analysis and kinetic modelling of dosage and temperature dependent role of graphene oxides on anammox

Anaerobic ammonium oxidation (anammox) is carbon friendly biological nitrogen removal process, and recently more focus is given to improving the anammox activity. Because of its high adsorption and modifiability, graphene and its derivative in wastewater treatment have received much attention. However, the specific effects and mechanisms of graphene oxide (GO) and reduced graphene oxide (RGO) on anammox are still controversial. Extensive data analysis was performed to explore the effects of GO and RGO on anammox. Statistical analysis revealed that 100 mg/L GO significantly promoted the anammox process, while 200 mg/L of GO inhibited the anammox process. The promotion of anammox performance under the influence of RGO was dependent on the temperature. The Logistic model was utilized for depicting the variation of nitrogen removal efficiency under promoting dosage of graphene oxides. A neural network model-based analysis was performed to reach anammox's potential mechanisms under the influence of two graphene oxides. Spearman correlation analysis showed that GO and RGO had significant positive correlations with nitrogen removal efficiency and specific anammox activity (p < 0.01), especially for RGO. In addition, the abundance of Planctomycetes and Nitrospirae was positively correlated with the addition of graphene oxides. This work comprehensively unraveled the role of graphene oxide materials on the anammox process and provided practical directions for the enhancement of anammox.