Multi-objective optimization of energy conservation and emission reduction in China's iron and steel industry based on dimensionality reduction

Abstract

With increasingly stringent environmental restrictions, the iron and steel industry is faced with multiple targets such as energy conservation, emission reduction, and cost control, hence, making industrial-environmental management a multi-objective optimization (MOO) problem. Existing studies adopt MOO models to address this issue, however, their methods result in high-dimension problems that increase computational cost and the difficulty to formulate optimal management schemes. This study builds a constrained MOO model in China's iron and steel industry, and introduces a dimensionality reduction technique to identify redundant objectives which can be omitted without altering the Pareto dominance structure of the original high-dimension problem. NSGA-II is then applied on the resulting lower-dimension problem to optimize the application of five types of Energy Conservation and Emission Reduction (ECER) measures, consisting of 23-process equipment and 55-ECER technologies. Results show that: (1) The high-dimension problem can be reduced to a four objective optimization problem. The four key-objectives are cost, energy, CO2, and PM intensity control. Hence, ECER policies should focus on these four key-objectives since optimizing them will synergistically minimize redundant objectives like NOx and SO2 emissions; (2) For the resulting lower-dimension problem, solution quality increases as reflected by algorithm verification metrics (spacing metric and hypervolume indicator), while computational cost is reduced by 73% and the decision making process is simplified significantly; (3) By-production reutilization and cleaner production technologies are the most attractive measures, contributing about 60% of ECER potential and only 30% of total cost. Equipment upgrading also has good ECER effects, but requires strong economic incentives, while end-of-pipe and renewable energy technologies have the lowest ECER potential. In sum, this study proposed a methodology to reduce the complexity of ECER optimization under multiple objectives, and provided suggestions to enhance industrial-environmental management under increasing environmental restrictions.