(681f) Multi-Objective Optimization of the Energy System in an Iron and Steel Plant Considering the Economic Cost and Life Cycle Environmental Impact

Zeng, Y., Institute of Process Engineering, Chinese Academy of Sciences
Li, J., The University of Manchester
Xiao, X., Institute of Process Engineering, Chinese Academy of Sciences
Song, F., Institute of Process Engineering, Chinese Academy of Sciences
Nie, Y., University of Chinese Academy of Sciences
Zhu, M., Institute of Process Engineering, Chinese Academy of Sciences
The iron and steel industry is one of the largest energy consumers and emission sources releasing large amount of CO2 and some air pollutants such SO2 and NOx [1]. As reported, the energy consumption from the iron and steel industry could account for 18% of total industry final energy consumption, whilst CO2 emissions could account for about 7% of the total CO2 emissions in the world [2]. Such large energy consumption and CO2 emissions mainly comes from the inefficient operation of its energy system involving the distribution of byproduct gases, steam and electricity among production units, boilers, turbines, combined heat and power (CHP) units, and waste heat and energy recovery units.

Many optimization models have been successfully developed for production processes in the iron and steel industry without consideration of its energy system, such as sintering process [3], blast furnace iron-making process [4], and steelmaking and continuous casting process [5]. Early work with respect to the energy system optimization in an iron and steel plant mainly focused on optimization of the byproduct gases distribution in gasholders and boilers [6-7] without simultaneous optimization of byproduct gases, steam and electricity distribution. Recently, Zeng et al. [8] developed a multi-period mixed-integer linear programming model for simultaneous optimization of byproduct gases, steam and electricity distribution, incorporating many significant operational features such as fuel selection, gasholder level control, ramp rate variation, mixing of different byproduct gases, piecewise constant generation rates of byproduct gases, and piecewise constant demand profiles of byproduct gases, steam and electricity. However, they mainly achieved the minimization of the economic cost without investigating the environmental impact of the operational performance. The inclusion of environmental impact makes the optimization problem more challenging, since the environmental and economic objectives are usually contradictive.

In this presentation, we use our previous work [8] as basis to develop a multi-period multi-objective planning model for optimization of the integrated energy system in an iron and steel plant. The objective is to minimize the economic cost and environmental impact simultaneously. The environmental performance is represented by the environmental impact (EI) potential rather than by emission release rates. The EI potential considers several environmental impacts caused by both the generation of the imported electricity, and the direct emissions from the operating process of the energy system, which are evaluated using life cycle assessment (LCA) method. To solve the proposed multi-objective optimization model, the augmented ε-constraint method is adopted to generate the Pareto frontier. A fuzzy preference selection method is used to determine the best compromising solution in which a fuzzy set is used to define the membership function of objectives and the non-dominated solution with the maximum normalized membership function value is generated. A real industrial example is investigated to demonstrate the capability of the proposed model and solution method. The computational results show that the total operating cost could be reduced by 6% and the environment impact could be reduced by 30% using our proposed model and solution approach in comparison with those from the actual operation.

Keywords: multi-objective optimization, environmental impact, Life cycle assessment, integrated energy system, iron and steel


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