State-of-the-art applications of machine learning in the life cycle of solid waste management

Liang, Rui; Chen, Chao; Kumar, Akash; Tao, Junyu; Ke, Yan; Han, Dawei; Jiang, Xianjia; Tang, Pei; Yan, Beibei; Chen, Guanyi

doi:10.1007/s11783-023-1644-x

Cited by 3 publications

(2 citation statements)

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Beyond resource utilization, ML also holds promise in waste management. Liang et al [74] applied ML to predict waste generation and characteristics, optimize waste collection and transportation, and simulate waste-to-energy processes. Velis et al [75] employed ML techniques, such as multivariate random forests and univariate nonlinear regression, to enhance urban waste management.…”

Section: Resource Utilization and Waste Managementmentioning

confidence: 99%

Advancing the Industrial Circular Economy: The Integrative Role of Machine Learning in Resource Optimization

Lin,

Wei

2023

JGELCD

View full text Add to dashboard Cite

In the face of escalating resource scarcity driven by the consumption of non-renewable resources, the industrial circular economy (ICE) emerges as a vital paradigm shift, pivotal for fostering resource-efficient societies and ensuring national resource security. This integrative review aims to critically assess the evolution and challenges inherent within the ICE over recent years, with a specific focus on the burgeoning role of machine learning (ML) in this domain. By synthesizing extant literature, this examination reveals several key findings. Firstly, the ICE significantly contributes to cost reduction through enhanced recycling and secondary utilization, underscoring its environmental stewardship. Secondly, it is evident that ML exhibits substantial promise in the manufacturing sector, not only augmenting production processes but also elevating product precision, reducing defect rates, and minimizing the likelihood of production mishaps. Most crucially, the application of ML within the ICE is identified as a potent catalyst, driving advancements across various facets -data analysis, model development, technological innovation, and equipment refinement. This analysis further elucidates the intrinsic value of ML in resource recycling and waste management, yielding improvements in resource recycling rates and methodologies, which in turn curtails production costs and amplifies output efficiency. Despite the strides made in replacing traditional industrial models with more sustainable ICE practices, challenges persist, particularly regarding the suboptimal levels of resource recycling and the continued generation of industrial waste. The integration of ML within ICE frameworks is posited as a transformative approach, offering not only enhanced resource recycling capabilities and superior product quality but also a sustainable trajectory for future industrial development. This study, therefore, contributes to the growing discourse on sustainable industrial practices, underscoring the synergistic potential of ML in revolutionizing the ICE, thereby aligning with the broader objectives of sustainable economic development.

show abstract

Section: Resource Utilization and Waste Managementmentioning

confidence: 99%

Advancing the Industrial Circular Economy: The Integrative Role of Machine Learning in Resource Optimization

Lin,

Wei

2023

JGELCD

View full text Add to dashboard Cite

show abstract

“…In order to optimize the phosphate adsorption process and direct the choice and preparation of adsorption materials, we can simultaneously apply the ML approach to the adsorption of phosphate. Previous documents have reported the applications of ML in solving multiple environmental problems such as heavy metal removal, , micropollutant oxidation, , seawater desalination, , carbon dioxide adsorption, , and municipal solid-waste treatment. , A number of ML models such as bagging, linear regression (LR), neural networks (NNs), and support vector machines (SVMs), and tree-based ML models have been developed in previous studies. Particularly, decision tree-based algorithms, including gradient boosting decision tree (GBDT), decision tree (DT), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), light gradient boosting machine (LightGBM), and random forest (RF), are a subcategory of supervised ML models. ,− DT is a tree structure that is like a binary tree or multitree.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Prediction of Phosphate Adsorption on Six Different Metal-Containing Adsorbents

Jiang

et al. 2023

ACS EST Eng.

View full text Add to dashboard Cite

Phosphate removal is a crucial objective in wastewater engineering to reduce harmful environmental impacts like eutrophication. Adsorption, a low-cost and efficient process for phosphate abatement, primarily relies on trapping phosphate on lowsolubility solid surfaces. Metal-based materials, due to their abundance, low cost, environmental friendliness, and chemical stability, are considered the most promising phosphate adsorbents. However, the synthesis of appropriate adsorbents is complex and timeconsuming. In addition, the diverse textural properties, the presence of various metals, and the selection of adsorption parameters make it challenging to the underlying mechanism of phosphate adsorption. In this study, we compiled a data set including 1800 data points mined from 128 peer-reviewed papers and adopted machine learning (ML) to systematically evaluate phosphate adsorption concerning textural properties, metal compositions, and adsorption parameters. We applied three different tree-based algorithms, including random forest (RF), decision trees (DTs), and extreme gradient boosting (XGBoost), to guide the design of adsorbents and predict the phosphate adsorption performances. Among the three algorithms, RF showed the best predictive performance with a high R 2 of 0.984 and a low root-mean-squared error (RMSE) of 0.650. Feature importance, based on the Shapley values, demonstrated the contributions of adsorbents' textural properties (e.g., surface area), adsorption parameters, and metal types in the order of precedence of phosphate adsorption, providing critical insights into guiding adsorbents design and synthesis for phosphate adsorption applications.

show abstract

Thermal kinetic parameters based classification method of residual waste oriented for efficient energy utilization

Liang,

Chen,

Tao

et al. 2024

Journal of Cleaner Production

View full text Add to dashboard Cite

State-of-the-art applications of machine learning in the life cycle of solid waste management

Cited by 3 publications

References 89 publications

Advancing the Industrial Circular Economy: The Integrative Role of Machine Learning in Resource Optimization

Advancing the Industrial Circular Economy: The Integrative Role of Machine Learning in Resource Optimization

Machine Learning Prediction of Phosphate Adsorption on Six Different Metal-Containing Adsorbents

Thermal kinetic parameters based classification method of residual waste oriented for efficient energy utilization

Contact Info

Product

Resources

About