Abstract
Based on numerical simulation work in sinter cooling bed of our previous work, AEG (annual energy gain) is obtained on energy and exergy analysis under WHCU (waste heat cascade utilization). In the present paper, a method that integrates economics and energy analysis of a sinter cooling bed is proposed. Firstly, the symbolic regression is employed to find an accurate correlation between operational parameters and AEG. In order to improve the performance of GPLAB (genetic programming toolbox) of MATLAB environment, the symbolic regression is modified by adding new function modules into GPLAB. Then, the cost model is established to evaluate effects of operational parameters on EAOC (equivalent annual operational cost). Finally, the CBR (cost benefits ratio) is calculated to assess the comprehensive performance of the sinter cooling bed. Furthermore, for the purpose of optimising CBR, the method of Genetic Algorithm (GA) is adopted. The studied cases show that, it is an effective way to obtain optimal sets of operational parameters in a sinter cooling bed within the range of operating conditions. The optimisation results show that, the CBR based on the first law and second law of thermodynamics could be reduced by 18.4 % and 29.8 % when the optimal sets of parameters are employed.
