Work

Carbon emission prediction and reduction analysis of wastewater treatment plants based on hybrid machine learning models

Year: 2024

Type: article

Abstract: Accurate accounting and prediction of carbon emissions from sewage treatment plants is the basis for exploring low-carbon sewage treatment plants and measures to reduce pollution and carbon emissions. This study proposes a hybrid prediction framework based on machine learning, which integrates multiple algorithms and has strong adaptability and generalization ability. The prediction framework uses Pearson correlation coefficient to select feature values, constructs a combined prediction model based on the selected features using support vector machine (SVR) and artificial neural network (ANN), and optimizes the SVR model parameters and structure using Gray Wolf Optimization (GWO) algorithm. The results show that the model has stronger prediction performance compared with other prediction models, with a mean absolute percentage error (MAPE) of 0.49% and an R2 of 0.9926. In addition, this study establishes six future development scenarios based on historical data trends and policy outlines, which provide recommendations for the development of carbon emission reduction measures for wastewater treatment plants. This study can provide a reference for exploring efficient carbon management and achieving carbon neutrality in wastewater treatment plants.

Source: Environmental Engineering Research

DOI: 10.4491/eer.2024.403

Authors: Fangqin Liu, Ning Ding, Guanghua Zheng, Jiangrong Xu

Institutions: China Jiliang University, Hangzhou Dianzi University

Language: English

Topic: Water Quality Monitoring and Analysis

Subfield: Waste Management and Disposal

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Clean water and sanitation, Responsible consumption and production

Open Access status: diamond

Funders: National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province