Evaluation of a cascade artificial neural network for modeling and optimization of process parameters in co-composting of cattle manure and municipal solid waste

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.

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.

Semi-quantitative study on the secondary compression characteristics of municipal solid waste in aerobic and anaerobic bioreactors

Aeration plays a crucial role in accelerating the secondary compression of municipal solid waste (MSW) for the scientific implementation of aerobic bioreactor technology. There are few comparative reports on the secondary compaction characteristics of MSW in aerobic and anaerobic bioreactors. In this study, six long-term compression tests were conducted to analyze the impact of aeration on MSW compression characteristics, considering two degradation conditions (i.e. aerobic and anaerobic conditions) and three overburden stresses (i.e. 30, 50 and 100 kPa). Model-fitting analysis was employed to examine the data from the tests and exiting literatures. The results showed that aeration effectively increased the rate of secondary compression, and slightly enhanced the steady-state secondary compression strain. In addition, these enhancements tended to decrease with increasing stresses. The increment ratio of the secondary compression rate constant (Rk) was concentrated in the range of 25 % to 100 %, and increases with the increase of aeration rate. The increment ratio of the steady-state secondary compression strain (Rε) ranged from 10 % to 90 %, for the MSW with higher content of paper and wood exhibited higher Rε. The advance ratio of the secondary compression stabilization time (Rt) fell within the range of 20-50 %, and Rt is higher when the moisture content is in the range of 50-65 %. These findings provide valuable guidance on the accelerated stabilization in aerobic bioreactors, providing practical references for the application of aerobic technology to informal landfills.

Enhancing humification and microbial interactions during co-composting of pig manure and wine grape pomace: The role of biochar and Fe2O3

Phenol-rich wine grape pomace (WGP) improves the conversion of pig manure (PM) into humic acid (HA) during composting. However, the impact of using combinations of Fe2O3 and biochar known to promote compost maturation remains uncertain. This research explored the individual and combined influence of biochar and Fe2O3 during the co-composting of PM and WGP. The findings revealed that Fe2O3 boosts microbial network symbiosis (3233 links), augments the HA yield to 3.38 by promoting polysaccharide C-O stretching, and improves the germination index to 124.82 %. Limited microbial interactions, increased by biochar, resulted in a lower HA yield (2.50). However, the combination weakened the stretching of aromatics and quinones, which contribute to the formation of HA, resulting in reduced the humification to 2.73. In addition, Bacillus and Actinomadura were identified as pivotal factors affecting HA content. This study highlights Fe2O3 and biochar's roles in phenol-rich compost humification, but combined use reduces efficacy.

Designing a sustainable collection and transportation routes for domestic wastes in the agro-pastoral ecotone of the Tibetan Plateau

The absence of an efficient and safe routes for the timely collection and transportation of domestic waste (DW) may have negative effects on the environment and public health. However, the existing collection and transportation routes (CTR) for domestic waste (DW) based on territorial management are not suitable for the special socio-ecological system of the agro-pastoral ecotone (APE). Therefore, it is crucial to develop a low-cost, high-efficiency, and risk-free CTR to mitigate the threat of DW to the environmental sustainability in the APE of the Tibetan Plateau. This study selected Haidong as a research case and constructed a sustainable CTR optimization framework based on an integrated perspective on temporal, spatial and eco-safety risk. We used the improved Ant Colony Optimization (ACO) to simulate optimal spatial-temporal routes, and the eco-safety risk level of the CTR was assessed by using the Minimum Cumulative Resistance model (MCR). Results demonstrated that: (1) After the sustainable model was optimized, the total transportation mileage and the frequency of collection and transportation were reduced by 45.88% and 38.07% respectively, the economic cost savings were decreased by 32.29%. Optimized routes were more effective and can better adapt to the dispersed pollution-producing characteristics in the APE. (2) The optimized routes reduced greenhouse gas (GHG) emissions by 41.09%, and reduced the eco-safety risk of the high and relative high-risk routes, which account for 29.05% of total routes, can protect important ecological functions and reduce the adverse impacts of DW transportation on soil, atmosphere, water, and the living environment. (3) The cores of adaptive management for sustainable CTR in APE were to change from the current single-county administrative organization to a cross-county administrative organization; adjust the transportation cycle based on pollution-producing characteristics; sort the DW locally; and cultivate environmental awareness among farmers and herdsmen. This study designed new sustainable collection and transportation routes for domestic waste to improve environmental sustainability in the agro-pastoral ecotone.

Role of different natural materials in reducing nitrogen loss during industrial sludge composting: Modelling and optimization

In this study, the effects of pumice, expanded perlite, and expanded vermiculite on nitrogen loss were examined for industrial sludge composting using the Box-Behnken experimental design. The independent factors and their levels were selected as amendment type, amendment ratio, and aeration rate, and codded as x₁, x₂, and x₃ at 3 levels (low, center, and high). The statistical significance of independent variables and their interactions were determined at 95% confidence limits by Analysis of Variance. The quadratic polynomial regression equation produced to predict the responses was solved and the optimum values of the variables were predicted by analyzing the three-dimensional response surfaces plots. The optimum conditions for minimum nitrogen loss by the regression model were as pumice of amendment type, 40% of amendment ratio, and 6 L/min of aeration rate. In this study, it was observed that time-consuming and laborious laboratory work can be minimized with the Box-Behnken experimental design.

Life cycle assessment of Tehran Municipal solid waste during the COVID-19 pandemic and environmental impacts prediction using machine learning

Life cycle assessment and machine learning were combined to find the best option for Tehran's waste management for future pandemics. The ReCipe results showed the waste's destructive effects after COVID-19 were greater than before due to waste composition changes. Plastic waste has changed from 7.5 to 11%. Environmental burdens of scenarios were Sc-1 (increase composting to 50%) > Sc-3 > Sc-4 > Sc-b2 > Sc-5 > Sc-2 (increase recycling from 9 to 20%). The artificial neural network and gradient-boosted regression tree could predict environmental impacts with high R2. Based on the results, the environmental burdens of solid waste after COVID-19 should be investigated.

Evaluation of the influence of rice husk amendment on compost quality in the composting of sewage sludge

This study aimed to evaluate the influence of rice husk addition on compost quality and maturity in sewage sludge composting using a pilot scale aerated in-vessel reactor. During the composting process, changes in compost quality and physicochemical factors including pH, temperature, moisture content, electrical conductivity, total organic carbon (TOC), total nitrogen (TN), and carbon to nitrogen ratio (C/N) were monitored. In the pile containing 25% rice husk, the lowest losses occurred with 52.49% for TOC and 23.24% for TN, while C/N ratio in the final compost was 18.82, achieving mature and quality compost. The moisture contents of the final composts were found as 50.72% in the control group while it was 31.73% and 28.18% in the reactors containing 10% and 25% rice husk, respectively. These results suggested that rice husk addition was beneficial for reducing moisture content and balancing the C/N ratio in sewage sludge composting.

A first-order kinetic model for simulating the aerobic degradation of municipal solid waste

Aerobic degradation models are important tools for investigating the aerobic degradation behavior of municipal solid waste (MSW). In this paper, a first-order kinetic model for aerobic degradation of MSW was developed. The model comprehensively considers the aerobic degradation of five substrates, i.e., holocellulose, non-cellulosic sugars, proteins, lipids and lignin. The proportion ranges of the five substrates are summarized with the recommended values given. The effects of temperature, moisture content, oxygen concentration and free air space (FAS) on the reaction rates are considered, and the effect of settlement is accounted for in the FAS correction function. The reliability of the model was verified by comparing simulations of the aerobic degradation of low food waste content (LFWC-) and high food waste content (HFWC-) MSWs to the literature. Afterwards, a sensitivity analysis was carried out to establish the relative importance of aeration rate (AR), volumetric moisture content (VMC), and temperature. VMC had the greatest influence on the aerobic degradation of LFWC-MSW, followed by temperature and then AR; for HFWC-MSW, temperature was the most important factor, then VMC and last was AR. The degradation ratio of LFWC-MSW can reach 98.0% after 100 days degradation under its optimal conditions (i.e., temperature: 55 °C, VMC: 40%, AR: 0.16 L min^-1 kg^-1 DM), while it is slightly higher as 99.5% for HFWC-MSW under its optimal conditions (i.e., temperature: 55 °C, VMC: 40%, AR: 0.20 L min^-1 kg^-1 DM).

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.