Prediction of carbon dioxide production from green waste composting and identification of critical factors using machine learning algorithms

Effects of seasonal variations of the physio-chemical properties of municipal solid waste on effective materials and resources recovery

Municipal solid waste (MSW) generation rate is on the rise as it is estimated to reach 3,539 million tonnes by 2050 from the 1,999 million tonnes in 2015. The seasonal variations of the physio-chemical properties of the MSW among others exacerbates its management challenges. This study aimed to conduct in-depth investigations on the seasonal variations of physio-chemical properties of the MSW generated in Kabale Municipality, southwestern Uganda to inform sustainable MSW management systems. This study revealed that this MSW is majorly plastics, with concentrations of 21.45% and 26.94% in the dry and wet seasons, respectively, which presents a more recycling potential for these plastics in the wet season. The biodegradable MSW fraction (food, paper, cardboard and garden trimming wastes), which were 35.6% and 35.34% for the dry and wet seasons, respectively, supports energy recovery from the waste in the form of biogas, with a higher potential in the wet season as supported by its higher volatile solid content for the same of 48.92% as compared to that of the dry season of 34.92%. Based on these findings, it is recommended among others that the masses be sensitized on how to generate biogas from the biodegradable fraction of this MSW.

Optimizing the early-stage of composting process emissions - artificial intelligence primary tests

Although composting has many advantages in treating organic waste, many problems and challenges are still associated with emissions, like NH3, CO and H2S, as well as greenhouse gases such as CO2. One promising approach to enhancing composting conditions is using novel analytical methods based on artificial intelligence. To predict and optimize the emissions (CO, CO2, H2S, NH3) during the early-stage of composting process machine learning (ML) models were utilized. Data about emissions from laboratory composting with compost's biochar with different incubation (50, 60, 70 °C) and biochar doses (0, 3, 6, 9, 12, 15% dry mass) were used for ML models selections and training. ML models such as acritical neural network (ANN, Bayesian Regularized Neural Network; R2 accuracy CO:0.71, CO2:0.81, NH3:0.95, H2S:0.72) and decision tree (DT, RPART; R2 accuracy CO:0.69, CO2:0.80, NH3:0.93, H2S:0.65) have demonstrated satisfactory results. The ML models to predict CO and H2S during composting were demonstrated for the first time. Utilizing emission data to predict other noxious gases presents a cost-effective and expeditious alternative to the empirical analysis of compost properties.

Simulation and optimization of cheese whey additive for value-added compost production: Hyperparameter tuning approach and genetic algorithm

Cheese whey is a difficult and costly wastewater to treat due to its high organic matter and mineral content. Although many management strategies are conducted for whey removal, its use in composting is limited. In this study, the effect of cheese whey in the composting of sewage sludge and poultry waste on compost quality and process efficiency was investigated. Also, valid and consistent simulations were developed with Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Neural Network Regression (NNR) Machine Learning (ML) algorithms. The results of all physicochemical parameters determined that 3% of cheese whey addition for both feedstocks improved the composting process's efficiency and the final product's quality. The best results obtained through hyperparameter tuning showed that Gaussian Process Regression (GPR) was the most effective modeling tool providing realistic simulations. The reliability of these simulations was verified by running the GPR process 50 times. MdAPE demonstrated the validity and consistency of the created process simulations. Moreover, a genetic algorithm was used to optimize these dependent simulations and achieved almost 100% desirability. Optimization studies showed that the effective cheese whey ratios were 3.2724% and 3.1543% for sewage sludge and poultry waste, respectively. Optimization results were compatible with the results of experimental studies. This study provides a new strategy for the recovery of cheese whey as well as a new perspective on the effect of cheese whey on both physicochemical parameters and composting phases and the modeling and optimization processes of the results.

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.

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.

Composting municipal solid waste and animal manure in response to the current fertilizer crisis - a recent review

The dynamic price increases of fertilizers and the generation of organic waste are currently global issues. The growth of the population has led to increased production of solid municipal waste and a higher demand for food. Food production is inherently related to agriculture and, to achieve higher yields, it is necessary to replenish the soil with essential minerals. A synergistic approach that addresses both problems is the implementation of the composting process, which aligns with the principles of a circular economy. Food waste, green waste, paper waste, cardboard waste, and animal manure are promising feedstock materials for the extraction of valuable compounds. This review discusses key factors that influence the composting process and compares them with the input materials' parameters. It also considers methods for optimizing the process, such as the use of biochar and inoculation, which result in the production of the final product in a significantly shorter time and at lower financial costs. The applications of composts produced from various materials are described along with associated risks. In addition, innovative composting technologies are presented.

Development of experimental error-Driven model for prediction of corrosion rates of amines based on their chemical structures

This work investigated the relationships between amine corrosion rates and their chemical structural properties for application in the development of a Gaussian Process Regression (GPR) model for chemical structure-based prediction of corrosion rate of any amine. The GPR model accounted for experimental errors, which widened its scope to accurately predict the true corrosion rates, being restricted only to error associated with the trained model. The Average Absolute Deviation (AAD) between experimental corrosion rates and model predicted rates was 4.26 % for the test data, and 5.32 % for two test data unknown to the model. This showed that the model is generalizable and its predictions are accurate. This work also developed a user-friendly Graphical-User Interface, which allows a user to define any amine's structure to provide needed information to calculate its surface tension and steric effects for use as input variables to the model in predicting the corrosion rate of the amine.

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.

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.

A Method to Predict CO2 Mass Concentration in Sheep Barns Based on the RF-PSO-LSTM Model

In large-scale meat sheep farming, high CO₂ concentrations in sheep sheds can lead to stress and harm the healthy growth of meat sheep, so a timely and accurate understanding of the trend of CO₂ concentration and early regulation are essential to ensure the environmental safety of sheep sheds and the welfare of meat sheep. In order to accurately understand and regulate CO₂ concentrations in sheep barns, we propose a prediction method based on the RF-PSO-LSTM model. The approach we propose has four main parts. First, to address the problems of data packet loss, distortion, singular values, and differences in the magnitude of the ambient air quality data collected from sheep sheds, we performed data preprocessing using mean smoothing, linear interpolation, and data normalization. Second, to address the problems of many types of ambient air quality parameters in sheep barns and possible redundancy or overlapping information, we used a random forests algorithm (RF) to screen and rank the features affecting CO₂ mass concentration and selected the top four features (light intensity, air relative humidity, air temperature, and PM2.5 mass concentration) as the input of the model to eliminate redundant information among the variables. Then, to address the problem of manually debugging the hyperparameters of the long short-term memory model (LSTM), which is time consuming and labor intensive, as well as potentially subjective, we used a particle swarm optimization (PSO) algorithm to obtain the optimal combination of parameters, avoiding the disadvantages of selecting hyperparameters based on subjective experience. Finally, we trained the LSTM model using the optimized parameters obtained by the PSO algorithm to obtain the proposed model in this paper. The experimental results show that our proposed model has a root mean square error (RMSE) of 75.422 μg·m^-3, a mean absolute error (MAE) of 51.839 μg·m^-3, and a coefficient of determination (R²) of 0.992. The model prediction curve is close to the real curve and has a good prediction effect, which can be useful for the accurate prediction and regulation of CO₂ concentration in sheep barns in large-scale meat sheep farming.

Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams

Biorefinery systems are playing pivotal roles in the technological support of resource efficiency for circular bioeconomy. Meanwhile, artificial intelligence presents great potential in handling scientific tasks of high-dimensional complexity. This review article scrutinizes the status of machine learning (ML) applications in four critical biorefinery systems (i.e. composting, fermentation, anaerobic digestion, and thermochemical conversions) as well as their advancements against traditional modeling techniques of mechanistic approach. The contents cover their algorithm selections, modeling challenges, and prospective improvements. Perspectives are sketched to further inform collective efforts on crucial aspects. The multidisciplinary interchange of modeling knowledge will enable a more progressive digital transformation of sustainability efforts in supporting sustainable development goals.

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.

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.