Exploring Key Components of Municipal Solid Waste in Prediction of Moisture Content in Different Functional Areas Using Artificial Neural Network

Tuo He, Dongjie Niu (), Gan Chen, Fan Wu and Yu Chen
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Tuo He: College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
Dongjie Niu: College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
Gan Chen: College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
Fan Wu: College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
Yu Chen: College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China

Sustainability, 2022, vol. 14, issue 23, 1-14

Abstract: Moisture content is a very important parameter for municipal solid waste (MSW) treatment technology selection and design. However, the moisture content of MSW collected from different urban areas is influenced by its physical composition in these areas. The aim of this study was to analyze the key components of MSW for predicting moisture content in different functional areas via the development of an artificial neural network (ANN) model. The dataset used in this study was collected in Shanghai from 2007 to 2019. Considering the influence of functional areas, the model obtained the performance with MAE of 2.67, RMSE of 3.29, and R 2 of 0.83, and an eight-fold cross validation showed acceptable results. The inter-quartile range (IQR) and isolation forest were compared to detect and remove outliers. In descending order, the moisture content was ranked as commercial/residential > office > cleaning areas. Based on a parameter exclusion method, kitchen, rubber, and plastic wastes show the greatest influence on moisture content in residential and commercial areas. In cleaning and office areas, paper, wood and bamboo waste products were the most important components. The determination of key components in different functional areas is of benefit for reducing the workload of moisture content estimation.

Keywords: moisture content prediction; functional areas; artificial neural network; parameter exclusion method; isolation forest; k-fold cross validation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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