Prediction, parametric analysis and bi-objective optimization of waste heat utilization in sinter cooling bed using evolutionary algorithm

Based on our previous work, the AEGs (annual energy gains) could be obtained on energy and exergy analysis for a sinter cooling bed. In the present study, a method synthesizing both economic cost and energy benefit aspects of the sinter cooling bed is proposed. Firstly, the GP (genetic programming) is employed to derive accurate correlations between the AEGs and operational parameters. Then, the economic cost model is established to evaluate effects of operational and economic parameters on the EAOC (equivalent annual operational cost). Finally, bi-objective optimization of the sinter cooling bed is performed to achieve the optimal operational conditions from both waste heat utilization and economic cost aspects using NSGA-II (non-dominated sorting genetic algorithm-II). In order to maximize the AEGs and minimize the EAOC, the EAOC and the AEGs based on the first and second laws of thermodynamics are selected as two objective functions. A Pareto frontier obtained shows that an increase in the AEGs can increase the EAOC of the sinter cooling bed. Under the given operational conditions, the optimum solutions with their corresponding decision variables are obtained. After considering both two Pareto frontiers curves, a set of suggested operational parameters for the decision-makers is also obtained.