A Many-Objective Optimization for an Eco-Efficient Flue Gas Desulfurization Process Using a Surrogate-Assisted Evolutionary Algorithm
Abstract
:1. Introduction
2. Literature Review
3. Problem Formulation
4. Method Description
4.1. Kriging-Based Surrogate Model
4.1.1. Preliminary of the Kriging Model
4.1.2. Model Accuracy Metrics
4.2. Surrogate-Assisted NSGA-III Algorithm
Algorithm 1: Input: The population Rt. Output: levels of each individual in Rt. | |
1: | for each individual i in Rt do |
2: | determine the set si of individuals dominated by i and number ni of individuals dominating i |
3: | end |
4: | save individuals with ni = 0 to the m-th level (Fm), and m = 1 |
5: | for k∈Fm do |
6: | for j∈sk do |
7: | nj = nj − 1 |
8: | if nj = 0 then |
9: | m = m + 1; save j to the level Fm |
10: | end |
11: | end |
12: | end |
13: | repeat step 5–12 |
4.3. TOPSIS for Multicriteria Decision-Making
5. Results and Discussion
5.1. Data Description and Validation for the Kriging Model
5.2. Many-Objective Optimization Using Kriging-Assisted NSGA-III
5.3. Multi-Criteria Decision-Making for the WFGD System
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Liu, C.; Gao, M.; Zhu, G.; Zhang, C.; Zhang, P.; Chen, J.; Cai, W. Data driven eco-efficiency evaluation and optimization in industrial production. Energy 2021, 224, 120170. [Google Scholar] [CrossRef]
- IEA. Country Profile of China. Available online: https://www.iea.org/countries/china#policies (accessed on 15 April 2020).
- National Bureau of Statistics of China. China Statistical Yearbook. 2020. Available online: http://www.stats.gov.cn/tjsj/ndsj/2020/indexch.htm (accessed on 16 May 2021).
- Jie, D.; Xu, X.; Guo, F. The future of coal supply in China based on non-fossil energy development and carbon price strategies. Energy 2021, 220, 119644. [Google Scholar] [CrossRef]
- Yue, L. Dynamics of clean coal-fired power generation development in China. Energy Policy 2012, 51, 138–142. [Google Scholar] [CrossRef]
- Zhao, Z.; Du, Q.; Zhao, G.; Gao, J.; Dong, H.; Cao, Y.; Han, Q.; Yuan, P.; Su, L. Fine Particle Emission from an Industrial Coal-Fired Circulating Fluidized-Bed Boiler Equipped with a Fabric Filter in China. Energy Fuels 2014, 28, 4769–4780. [Google Scholar] [CrossRef]
- Liu, S.; Sun, L.; Zhu, S.; Li, J.; Chen, X.; Zhong, W. Operation strategy optimization of desulfurization system based on data mining. Appl. Math. Model. 2020, 81, 144–158. [Google Scholar] [CrossRef]
- Liu, C.; Cai, W.; Zhai, M.; Zhu, G.; Zhang, C.; Jiang, Z. Decoupling of wastewater eco-environmental damage and China’s economic development. Sci. Total. Environ. 2021, 789, 147980. [Google Scholar] [CrossRef] [PubMed]
- Jain, H.; Deb, K. An Evolutionary Many-Objective Optimization Algorithm Using Reference-point Based Non-dominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach. IEEE Trans. Evol. Comput. 2014, 18, 602–622. [Google Scholar] [CrossRef]
- Deb, K.; Jain, H. An Evolutionary Many-objective optimization algorithm using reference-point based non-dominated sorting approach, part I: Solving problems with box constraints. IEEE Trans. Evol. Comput. 2014, 18, 577–601. [Google Scholar] [CrossRef]
- Del Valle-Zermeño, R.; Formosa, J.; Aparicio, J.; Guembe, M.; Chimenos, J.M. Transposition of wet flue gas desulfurization using MgO by-products: From laboratory discontinuous batch reactor to pilot scrubber. Fuel Process. Technol. 2015, 138, 30–36. [Google Scholar] [CrossRef]
- Zhu, Z.; Ma, Y.; Qu, Z.; Fang, L.; Zhang, W.; Yan, N. Study on a new wet flue gas desulfurization method based on the Bunsen reaction of sulfur-iodine thermochemical cycle. Fuel 2017, 195, 33–37. [Google Scholar] [CrossRef]
- Guo, R.-T.; Pan, W.-G.; Zhang, X.-B.; Xu, H.-J.; Ren, J.-X. Dissolution rate of magnesium hydrate for wet flue gas desulfurization. Fuel 2010, 90, 7–10. [Google Scholar] [CrossRef]
- Mehrara, H.; Roozbehani, B.; Shishehsaz, M.R.; Mirdrikvand, M.; Moqadam, S.I. Using Taguchi method to determine optimum process conditions for flue gas desulfurization through an amine scrubber. Clean Technol. Environ. Policy 2013, 16, 59–67. [Google Scholar] [CrossRef]
- Chen, Z.; Wang, H.; Zhuo, J.; You, C. Enhancement of mass transfer between flue gas and slurry in the wet flue gas desulfurization spray tower. Energy Fuels 2018, 32, 703–712. [Google Scholar] [CrossRef]
- Bandyopadhyay, A. Comment on “Flue gas desulfurization with an electrostatic spraying absorber”. Energy Fuels 2010, 24, 2787–2789. [Google Scholar] [CrossRef]
- Fang, S.L.; Du, Y.Q.; Huang, S.Y.; Wen, W.Q.; Liu, Y. Numerical simulation research for the optimization of the wet flue gas desulfurization tower. Appl. Mech. Mater. 2012, 170–173, 3662–3667. [Google Scholar] [CrossRef]
- Michalski, J.A. Aerodynamic characteristics of FGD spray towers. Chem. Eng. Technol. 1997, 20, 108–117. [Google Scholar] [CrossRef]
- Dou, B.; Pan, W.; Jin, Q.; Wang, W.; Li, Y. Prediction of SO2 removal efficiency for wet flue gas desulfurization. Energy Convers. Manag. 2009, 50, 2547–2553. [Google Scholar] [CrossRef]
- Shen, Z.; Guo, S.; Kang, W.; Zeng, K.; Yin, M.; Tian, J.; Lu, J. Kinetics and mechanism of sulfite oxidation in the magnesium-based wet flue gas desulfurization process. Ind. Eng. Chem. Res. 2012, 51, 4192–4198. [Google Scholar] [CrossRef]
- Lidong, W.; Juan, W.; Peiyao, X.; Qiangwei, L.; Wendi, Z.; Shuai, C. Selectivity of transition metal catalysts in promoting the oxidation of solid sulfites in flue gas desulfurization. Appl. Catal. A Gen. 2015, 508, 52–60. [Google Scholar] [CrossRef]
- Uddin, G.M.; Arafat, S.M.; Ashraf, W.M.; Asim, M.; Bhutta, M.M.A.; Jatoi, H.U.K.; Niazi, S.G.; Jamil, A.; Farooq, M.; Ghufran, M.; et al. Artificial intelligence-based emission reduction strategy for limestone forced oxidation flue gas desulfurization system. J. Energy Resour. Technol. 2020, 142, 1–38. [Google Scholar] [CrossRef]
- Qiao, Z.; Wang, X.; Gu, H.; Tang, Y.; Si, F.; Romero, C.E.; Yao, X. An investigation on data mining and operating optimization for wet flue gas desulfurization systems. Fuel 2019, 258, 116178. [Google Scholar] [CrossRef]
- Wang, W.; Luo, X.; Li, Q.; Xu, K.; Liu, J. Operation optimization and costs analysis of the wet desulfurization system in an ultra-supercritical coal-fired power plants. Environ. Prog. Sustain. Energy 2021, 40, 1–10. [Google Scholar] [CrossRef]
- Guo, Y.; Xu, Z.; Zheng, C.; Shu, J.; Dong, H.; Zhang, Y.; Weng, W.; Gao, X. Modeling and optimization of wet flue gas desulfurization system based on a hybrid modeling method. J. Air Waste Manag. Assoc. 2019, 69, 565–575. [Google Scholar] [CrossRef] [PubMed]
- Goodarzian, F.; Wamba, S.F.; Mathiyazhagan, K.; Taghipour, A. A new bi-objective green medicine supply chain network design under fuzzy environment: Hybrid metaheuristic algorithms. Comput. Ind. Eng. 2021, 160, 107535. [Google Scholar] [CrossRef]
- Goodarzian, F.; Kumar, V.; Ghasemi, P. A set of efficient heuristics and meta-heuristics to solve a multi-objective pharmaceutical supply chain network. Comput. Ind. Eng. 2021, 158, 107389. [Google Scholar] [CrossRef]
- Goodarzian, F.; Taleizadeh, A.A.; Ghasemi, P.; Abraham, A. An integrated sustainable medical supply chain network during COVID-19. Eng. Appl. Artif. Intell. 2021, 100, 104188. [Google Scholar] [CrossRef] [PubMed]
- Zhong, Y.; Gao, X.; Huo, W.; Luo, Z.-Y.; Ni, M.-J.; Cen, K.-F. A model for performance optimization of wet flue gas desulfurization systems of power plants. Fuel Process. Technol. 2008, 89, 1025–1032. [Google Scholar] [CrossRef]
- Liu, H.; Katagiri, A.S.; Okazaki, K. Drastic SOx removal and influences of various factors in O2/CO2 pulverized coal combustion system. Energy Fuels 2001, 15, 403–412. [Google Scholar] [CrossRef]
- Lophaven, S.N.; Nielsen, H.B.; Søndergaard, J. Dace—A Matlab Kriging Toolbox; Technical University of Denmark: Lyngby, Denmark, 2002. [Google Scholar]
- Krige, D. A statistical approach to some basic mine valuation problems on the witwatersrand. J. S. Afr. Inst. Min. Metall. 1951, 52, 119–139. [Google Scholar]
- Zhong, W.; Qiao, C.; Peng, X.; Li, Z.; Fan, C.; Qian, F. Operation optimization of hydrocracking process based on Kriging surrogate model. Control. Eng. Pr. 2019, 85, 34–40. [Google Scholar] [CrossRef]
- Shi, Y.; Lu, Z.; Xu, L.; Chen, S. An adaptive multiple-Kriging-surrogate method for time-dependent reliability analysis. Appl. Math. Model. 2019, 70, 545–571. [Google Scholar] [CrossRef]
- Joseph, R.V.; Ying, H. Orthogonal-Maximin Latin Hypercube Designs. Stat. Sin. 2012, 18, 171–186. [Google Scholar]
- Pan, L.; Xu, W.; Li, L.; He, C.; Cheng, R. Adaptive simulated binary crossover for rotated multi-objective optimization. Swarm Evol. Comput. 2020, 60, 100759. [Google Scholar] [CrossRef]
- Zeng, G.-Q.; Chen, J.; Li, L.-M.; Chen, M.-R.; Wu, L.; Dai, Y.-X.; Zheng, C.-W. An improved multi-objective population-based extremal optimization algorithm with polynomial mutation. Inf. Sci. 2016, 330, 49–73. [Google Scholar] [CrossRef]
- Das, I.; Dennis, J.E. Normal-boundary intersection: A new method for generating the pareto surface in nonlinear multicriteria optimization problems. SIAM J. Optim. 1998, 8, 631–657. [Google Scholar] [CrossRef] [Green Version]
- Ishizaka, A.; Nemery, P. Multi-Criteria Decision Analysis: Methods and Software; Wiley: Hoboken, NJ, USA, 2013. [Google Scholar]
- Fouedjio, F. Exact conditioning of regression random forest for spatial prediction. Artif. Intell. Geosci. 2020, 1, 11–23. [Google Scholar] [CrossRef]
- Peng, S.; Li, T.; Li, M.; Guo, Y.; Shi, J.; Tan, G.; Zhang, H. An integrated decision model of restoring technologies selection for engine remanufacturing practice. J. Clean. Prod. 2019, 206, 598–610. [Google Scholar] [CrossRef]
Output Variable | Regression Method | Training Set | Test Set | ||
---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | ||
y1 = g1(x) | Kriging model | 100% | 0 | 70.06% | 2.35 |
RSM | 83.05% | 2.00 | 65.87% | 2.76 | |
y2 = g2(x) | Kriging model | 100% | 0 | 74.65% | 0.21 |
RSM | 64.36% | 0.38 | 22.49% | 1.16 |
Indicator | Desulfurization Efficiency (%) | Power Load (kW) | Limestone Slurry Flow Rate (m3/min) |
---|---|---|---|
Optimal value | 98.11 | 4498.8 | 0.272 |
Empirical value | 97.88 | 8302.5 | 0.178 |
Rate of improvement | 0.23% | 45.8% | ×34.6% |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Dong, Q.; Wang, C.; Peng, S.; Wang, Z.; Liu, C. A Many-Objective Optimization for an Eco-Efficient Flue Gas Desulfurization Process Using a Surrogate-Assisted Evolutionary Algorithm. Sustainability 2021, 13, 9015. https://doi.org/10.3390/su13169015
Dong Q, Wang C, Peng S, Wang Z, Liu C. A Many-Objective Optimization for an Eco-Efficient Flue Gas Desulfurization Process Using a Surrogate-Assisted Evolutionary Algorithm. Sustainability. 2021; 13(16):9015. https://doi.org/10.3390/su13169015
Chicago/Turabian StyleDong, Quande, Cui Wang, Shitong Peng, Ziting Wang, and Conghu Liu. 2021. "A Many-Objective Optimization for an Eco-Efficient Flue Gas Desulfurization Process Using a Surrogate-Assisted Evolutionary Algorithm" Sustainability 13, no. 16: 9015. https://doi.org/10.3390/su13169015