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Dai H, Wang Z, Zhao J, Jia X, Liu L, Wang J, Abbasi HN, Guo Z, Chen Y, Geng H, Wang X. Modeling and optimizing of an actual municipal sewage plant: A comparison of diverse multi-objective optimization methods. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 328:116924. [PMID: 36525736 DOI: 10.1016/j.jenvman.2022.116924] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/16/2022] [Accepted: 11/27/2022] [Indexed: 06/17/2023]
Abstract
The activated sludge process of an actual municipal sewage treatment plant was systematically modeled, calibrated, and verified in this study. Identified multi-objective optimization (MOO) methods were employed to optimize the process parameters of the validated model, and the optimal MOO algorithm was obtained by comparing Pareto solution sets. The optimization model consisted of three key evaluation indicators (objective functions), which are the average effluent quality (AEQ), overall cost index (OCI), and total volume (TV) of the biochemical tank, along with 12 more process parameters (decision variables). Three optimization algorithms, i.e., adaptive non-dominated sorting genetic algorithm III (ANSGA-III), non-dominated sorting genetic algorithm II (NSGA-II), and particle swarm algorithm (PSO), were adopted using MATLAB. The comparison of these algorithms demonstrated that the ANSGA-III algorithm had better Pareto solution sets under the triple objective optimization, and the effluent quality of COD, TN, NH4+-N, and TP after optimization decreased by 2.22, 0.47, 0.13, and 0.02 mg/L, respectively. Additionally, the simulated AEQ was reduced by 13% compared to the original effluent, and the OCI and TV decreased from 21,023 kWh d-1 and 17,065 m3 to 20,226 kWh d-1 and 16,530 m3, respectively. The reported ANSGA-III algorithm and the proposed multi-objective method have a promising ability for energy conservation, emission reduction, and upgrading of municipal sewage treatment plants.
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Affiliation(s)
- Hongliang Dai
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China; School of Environmental and Engineering, Huazhong University of Science and Technology, Wuhan, China.
| | - Zeyu Wang
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China.
| | - Jinkun Zhao
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China.
| | - Xiaoyu Jia
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China.
| | - Lan Liu
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China.
| | - Jun Wang
- Zhengrunzhou Sewage Treatment Plant of Everbright Sponge City Development Co., Ltd, Zhenjiang, China. wangjun-@ebwater.com
| | - Haq Nawaz Abbasi
- Department of Environmental Science, Federal Urdu University of Arts, Science and Technology, Karachi, Pakistan.
| | - Zechong Guo
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China; School of Environmental and Engineering, Huazhong University of Science and Technology, Wuhan, China.
| | - Yong Chen
- School of Environmental and Engineering, Huazhong University of Science and Technology, Wuhan, China.
| | - Hongya Geng
- Department of Materials, Imperial College London, Prince Consort Road, London, SW7 2AZ, UK.
| | - Xingang Wang
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China.
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Rapid Multi-Objective Optimization of Periodically Operated Processes Based on the Computer-Aided Nonlinear Frequency Response Method. Processes (Basel) 2020. [DOI: 10.3390/pr8111357] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The dynamic optimization of promising forced periodic processes has always been limited by time-consuming and expensive numerical calculations. The Nonlinear Frequency Response (NFR) method removes these limitations by providing excellent estimates of any process performance criteria of interest. Recently, the NFR method evolved to the computer-aided NFR method (cNFR) through a user-friendly software application for the automatic derivation of the functions necessary to estimate process improvement. By combining the cNFR method with standard multi-objective optimization (MOO) techniques, we developed a unique cNFR–MOO methodology for the optimization of periodic operations in the frequency domain. Since the objective functions are defined with entirely algebraic expressions, the dynamic optimization of forced periodic operations is extraordinarily fast. All optimization parameters, i.e., the steady-state point and the forcing parameters (frequency, amplitudes, and phase difference), are determined rapidly in one step. This gives the ability to find an optimal periodic operation around a sub-optimal steady-state point. The cNFR–MOO methodology was applied to two examples and is shown as an efficient and powerful tool for finding the best forced periodic operation. In both examples, the cNFR–MOO methodology gave conditions that could greatly enhance a process that is normally operated in a steady state.
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