Multi-Objective Optimization of Steel Off-Gas in Cogeneration Using the ε-Constraint Method: A Combined Coke Oven and Converter Gas Case Study

Sergio García García, Vicente Rodríguez Montequín, Marina Díaz Piloñeta and Susana Torno Lougedo
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Sergio García García: EDP, Energías de Portugal, Plaza del Fresno, 2, 33007 Oviedo, Spain
Vicente Rodríguez Montequín: Area of Project Engineering, University of Oviedo, C/Independencia 3, 33004 Oviedo, Spain
Marina Díaz Piloñeta: Area of Project Engineering, University of Oviedo, C/Independencia 3, 33004 Oviedo, Spain
Susana Torno Lougedo: Area of Mining Exploitation, University of Oviedo, C/Independencia 3, 33004 Oviedo, Spain

Energies, 2021, vol. 14, issue 10, 1-21

Abstract: Increasingly demanding environmental regulations are forcing companies to reduce their impacts caused by their activity while defending the economic viability of their manufacturing processes, especially energy and carbon-intensive ones. Therefore, these challenges must be addressed by posing optimization problems that involve several objectives simultaneously, corresponding to different conditions, and often conflicting between. In this study, the residual gases of an integral steel factory were evaluated and modeled with the goal of developing an optimization problem considering two opposing objectives: CO 2 emissions and profit. The problem was first approached in a mono-objective manner, optimizing profit through Mixed Integer Linear Programming (MILP), and then was extended to a bi-objective problem solved by means of the ε-constraint method, to find the Pareto front relating profit and CO 2 emissions. The results show that multiobjective optimization is a very valuable resource for plant managers’ decision-making processes. The model makes it possible to identify inflection points from which the level of emissions would increase disproportionately. It gives priority to the consumption of less polluting fuels. The model also makes it possible to make the most of temporary buffers such as the gas holders, adapting to the hourly price of the electricity market. By applying this method, CO 2 emissions decrease by more than 3%, and profit amounts up to 14.8% compared to a regular case under normal operating conditions. The sensitivity analysis of the CO 2 price and CO 2 constraints is also performed.

Keywords: multiobjective optimization; ε-constraint; off-gas; steel gases; cogeneration process (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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