Multi-objective optimization of vapor absorption refrigeration system for the minimization of annual operating cost and exergy destruction

Abstract

A low-grade energy source-driven thermal compressor-based vapor absorption refrigeration system is considered a better alternative for an electrically operated mechanical compressor-based conventional vapor compression refrigeration system. In this article, multi-objective optimization of a single-stage LiBr–H2O vapor absorption refrigeration system (VARS) and a modified VARS are explored by minimizing the total annual operating cost and exergy destruction of the system. Exergy destruction during the operation is inversely proportional to the total annual cost of the system. Multi-objective optimization can be implemented to optimize these conflicting objectives for both systems. The nonlinear multi-objective model based on the concept of exergy and economic performance are minimized using multi-objective genetic algorithm (MOGA), multi-objective particle swarm optimization (MOPSO), and multi-objective sanitized teaching learning-based optimization (MOs-TLBO) for both VARS and MVARS. The current study also compares the decision variables obtained for minimizing the total annual cost and exergy destruction in basic and modified VARS thermo-economic models. The comparative analysis suggests that MOGA, MOPSO, and MOs-TLBO can be successfully employed to achieve these objectives. MOGA determines minimum annual cost, whereas minimum exergy destruction is reported by MOPSO. On the other hand, when compared to the other two algorithms (MOGA and MOPSO), MOs-TLBO reports a minimum total annual cost of plant operation for the reported minimum exergy destruction by effectively tuning the operating temperature of the system components for both basic and modified VARS. The current study reports Pareto points, which offer an optimal annual cost and exergy destruction based on the requirements using multi-objective algorithms.