Download pdf
Path Analysis of Recycling of Urban Solid Waste Based on Multi-Objective Optimization Model
- 1 School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
* Author to whom correspondence should be addressed.
Abstract
With the acceleration of urbanization and rapid economic development, the problem of urban solid waste (SW) has become increasingly prominent. Traditional SW disposal treatment methods, mainly landfill and incineration, pose issues of environmental pollution and resource waste. This study aims to analyze the recycling pathways of urban SW and establish a multi-objective optimization model to achieve comprehensive optimization of economic, environmental, and social goals. Through data collection, quantification and standardization, establishment of a multi-objective optimization problem model, NSGA-II algorithm solution, and simulation and result analysis, this paper seeks to provide scientific decision support and technical guidance for urban SW management. Taking Wuhan as an example, this study analyzes the treatment methods of six different types of SW and seeks the optimal treatment pathways through the multi-objective optimization model. The results show that the multi-objective optimization model can effectively balance economic costs, environmental costs, and utilization efficiency in SW treatment and utilization, providing new ideas for achieving urban sustainable development and resource recycling.
Keywords
Recycling pathways, multi-objective optimization model, NSGA-II algorithm.
[1]. Dabbiru S, Munwar B B. (2024) Component and System Reliability Assessment of Landfill Cover Systems Using Pseudostatic and Pseudodynamic Methods. International Journal of Geomechanics, 24(11).
[2]. Ma H, Wei Q, Chen W, et al. (2024) Corrosion shielding effect of poly aluminium sulphate on metallic aluminium during the solidification of radioactive incineration bottom ash by low alkalinity cement. Construction and Building Materials, 447137970-137970.
[3]. Cerrato I S, Pacios D, Rodriguez E M J, et al. (2024) Optimized data management with color multiplexing in QR codes. Physica Scripta, 99(10): 105036-105036.
[4]. Harane P P, Unune R D, Ahmed R, et al.(2024) Multi-objective optimization for electric discharge drilling of waspaloy: A comparative analysis of NSGA-II, MOGA, MOGWO, and MOPSO. Alexandria Engineering Journal, 99(1): 1-16.
[5]. Alcaraz J. (2024) Redesigning a NSGA-II metaheuristic for the bi-objective Support Vector Machine with feature selection. Computers and Operations Research, 172106821-106821.
[6]. Mahesh N, Yogesh B, Sravanthi S, Vaibhav H, Ravindra N, Rahul Z, et al,(2023) Multi-objective optimization and experimental investigation of quarter car suspension system. International Journal of Dynamics and Control, 12(5): 1222-1238.
Cite this article
Chu,Z. (2024). Path Analysis of Recycling of Urban Solid Waste Based on Multi-Objective Optimization Model. Applied and Computational Engineering,110,29-35.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of CONF-MLA 2024 Workshop: Securing the Future: Empowering Cyber Defense with Machine Learning and Deep Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).