Multi-objective optimization of a rotary regenerator using tutorial training and self-learning inspired teaching-learning based optimization algorithm (TS-TLBO)

In the present work, tutorial training and self-learning inspired teaching-learning-based optimization (TS-TLBO) algorithm is proposed and investigated for the multi-objective optimization of a rotary regenerator. Two conflicting objectives, namely regenerator effectiveness and total pressure drop, are considered simultaneously for the multi-objective optimization. Six design variables such as frontal area, matrix rod diameter, matrix thickness, matrix rotation speed, split and porosity are considered for optimization. Application examples are presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimal designs are presented in a set of multiple optimum solutions, called Pareto-optimal solutions. Moreover, to reveal the level of conflict between these two objectives, the distribution of each design variables in their allowable range is also shown in two-dimensional objective spaces. Furthermore, the effect of change in the value of design variables on the objective function value is also performed in detail. Looking at the different design points of Pareto front, 99.4% reduction in total pressure drop is observed at the cost of 97.9% reduction in regenerator effectiveness. Also, the matrix rod thickness, split and porosity were found to be important design variables that caused a strong conflict between the objective functions.