Robust Parameter Set Selection for a Hydrodynamic Model Based on Multi-Site Calibration Using Multi-Objective Optimization and Minimax Regret Approach

2018 ◽  
Vol 32 (12) ◽  
pp. 3979-3995
Author(s):  
Li Li ◽  
Eun-Sung Chung ◽  
Kyung Soo Jun
Keyword(s):  
Hydrodynamic Model ◽  
Minimax Regret ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Model Based ◽  
Robust Parameter ◽  
Selection For

scholarly journals Single- and Multi-Objective Optimization of a Dual-Chamber Microbial Fuel Cell Operating in Continuous-Flow Mode at Steady State

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 839
Author(s):  
Ibrahim M. Abu-Reesh
Keyword(s):  
Flow Rate ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Dual Chamber ◽  
Worst Case ◽  
Multi Objective ◽  
Model Based ◽  
Conflicting Objectives

Microbial fuel cells (MFCs) are a promising technology for bioenergy generation and wastewater treatment. Various parameters affect the performance of dual-chamber MFCs, such as substrate flow rate and concentration. Performance can be assessed by power density ( PD ), current density ( CD ) production, or substrate removal efficiency ( SRE ). In this study, a mathematical model-based optimization was used to optimize the performance of an MFC using single- and multi-objective optimization (MOO) methods. Matlab’s fmincon and fminimax functions were used to solve the nonlinear constrained equations for the single- and multi-objective optimization, respectively. The fminimax method minimizes the worst-case of the two conflicting objective functions. The single-objective optimization revealed that the maximum PD ,   CD , and SRE were 2.04 W/m2, 11.08 A/m2, and 73.6%, respectively. The substrate concentration and flow rate significantly impacted the performance of the MFC. Pareto-optimal solutions were generated using the weighted sum method for maximizing the two conflicting objectives of PD and CD in addition to PD and SRE   simultaneously. The fminimax method for maximizing PD and CD showed that the compromise solution was to operate the MFC at maximum PD conditions. The model-based optimization proved to be a fast and low-cost optimization method for MFCs and it provided a better understanding of the factors affecting an MFC’s performance. The MOO provided Pareto-optimal solutions with multiple choices for practical applications depending on the purpose of using the MFCs.

scholarly journals Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants

PLoS ONE ◽  
2016 ◽  
Vol 11 (3) ◽  
pp. e0152057 ◽  
Author(s):  
Qing-chun Meng ◽  
Xiao-xia Rong ◽  
Yi-min Zhang ◽  
Xiao-le Wan ◽  
Yuan-yuan Liu ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Air Pollutants ◽  
Emission Reduction ◽  
Multi Objective Optimization ◽  
Reduction Model ◽  
Multi Objective ◽  
Model Based
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