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

Temel, Fulya Aydın; Yolcu, Özge Cağcağ; Turan, Nurdan Gamze

doi:10.1016/j.biortech.2022.128539

Cited by 35 publications

(6 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These observations made the authors focus on predicting the composting process using ML. As shown so far, an ML in composting focuses mostly on predicting the compost maturity and compost properties i.e., pH, EC, GI, TN, TOC, etc, with only a few papers concerned with emissions [46]. The accuracy of ML models used in composting process prediction changed in the range of 0.56-0.99 for R 2 , but in most cases showed good fit >0.7.…”

Section: Discussionmentioning

confidence: 99%

Optimizing the Composting Process Emissions – Process Kinetics and Artificial Intelligence Approach

Rosik,

Stegenta-Dąbrowska

2024

Preprint

View full text Add to dashboard Cite

Although composting has many advantages in the treatment of organic waste, there are still many problems and challenges associated with emissions, like NH3, VOCs, and H2S, as well as greenhouse gases such as CO2, CH4, and N2O. One promising approach to enhancing composting conditions is used of novel analytical methods bad on artificial intelligence. To predict and optimize the emissions (CO, CO2, H2S, NH3) during composting process kinetics thought mathematical models (MM) and machine learning (ML) models were utilized. Data about everyday emissions from laboratory composting with compost’s biochar with different incubation (50, 60, 70 °C) and biochar doses (0, 3, 6, 9, 12, 15% d.m.) were used for MM and ML models selections and training. MM has not been very effective in predicting emissions, (R2 0.1 - 0.9), while 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:0,693, CO2:0,80, NH3:0,93, H2S:0,65) have demonstrated satisfactory results. For the first time the ML models to predict CO and H2S during composting were demonstrated. Further research in a semi-scale and field study composting with biochar is needed to improve the accuracy of developments models.

show abstract

Section: Discussionmentioning

confidence: 99%

Optimizing the Composting Process Emissions – Process Kinetics and Artificial Intelligence Approach

Rosik,

Stegenta-Dąbrowska

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, the optimal model was selected for focused analysis [44]. Among them, the SVM algorithm was employed to establish a particular optimal decision hyperplane and maximize the 2 closest classes on both sides of this plane and itself to generalize each classification [45]. Meanwhile, the other three algorithms, based on Classification and Regression Trees (CARTs), completed the prediction by constructing a binary tree to recursively divide the samples, and the split node was established according to the minimum variance of the samples.…”

Section: Machine Learning Modelmentioning

confidence: 99%

Insightful Analysis and Prediction of SCOD Component Variation in Low-Carbon/Nitrogen-Ratio Domestic Wastewater via Machine Learning

Zhang,

Guo,

Luo

et al. 2024

Water

View full text Add to dashboard Cite

The rapid identification of the amount and characteristics of chemical oxygen demand (COD) in influent water is critical to the operation of wastewater treatment plants (WWTPs), especially for WWTPs in the face of influent water with a low carbon/nitrogen (C/N) ratio. Given that, this study carried out batch kinetic experiments for soluble chemical oxygen demand (SCOD) and nitrogen degradation for three WWTPs and established machine learning (ML) models for the accurate prediction of the variation in SCOD. The results indicate that four different kinds of components were identified via parallel factor (PARAFAC) analysis. C1 (Ex/Em = 235 nm and 275/348 nm, tryptophan-like substances/soluble microbial by-products) contributes to the majority of internal carbon sources for endogenous denitrification, whereas C4 (230 nm and 275/350 nm, tyrosine-like substances) is crucial for readily biodegradable SCOD composition according to the machine learning (ML) models. Furthermore, the gradient boosting decision tree (GBDT) algorithm achieved higher interpretability and generalizability in describing the relationship between SCOD and carbon source components, with an R2 reaching 0.772. A Shapley additive explanations (SHAP) analysis of GBDT models further validated the above result. Undoubtedly, this study provided novel insights into utilizing ML models to predict SCOD through the measurements of the excitation–emission matrix (EEM) in specific Ex and Em positions. The results could help us to identify the degradation and transformation relationship between different kinds of carbon sources and nitrogen species in the wastewater treatment process, and thus provide a novel guidance for the optimized operation of WWTPs.

show abstract

“…Waste incineration uses high-temperature and high-pressure pyrolysis oxidation to reduce the volume of waste and eliminate hazardous materials (Chen et al 2022a ). Waste composting involves the controlled decomposition of organic matter in waste and its conversion into substrates and fertilizers (Aydın Temel et al 2023 ; Wei et al 2022 ). Waste landfilling involves filling waste into depressions or large pits, followed by anti-seepage, drainage, and air-guiding treatments.…”

Section: Illegal Dumping and Waste Disposalmentioning

confidence: 99%

Artificial intelligence for waste management in smart cities: a review

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 35 publications

References 85 publications

Optimizing the Composting Process Emissions – Process Kinetics and Artificial Intelligence Approach

Optimizing the Composting Process Emissions – Process Kinetics and Artificial Intelligence Approach

Insightful Analysis and Prediction of SCOD Component Variation in Low-Carbon/Nitrogen-Ratio Domestic Wastewater via Machine Learning

Artificial intelligence for waste management in smart cities: a review

Contact Info

Product

Resources

About