Modeling and thermo-economic optimization of a biomass heat recovery exchanger operating on Al2O3-water nanofluid

Abstract

Given the energy limitations challenging industries, heat recovery systems have been given much attention during the past two decades. As power plants are continuously wasting energy to the environment, their potential for heat recovery has been promising from the early days and different approaches including using nanofluid-based coolants have been implemented to harvest the maximum amount of energy. Nevertheless, a systematic optimization design approach has not been introduced for designing these nanofluid-based heat recovery systems. In this study such an optimization method is presented where nanofluid-based heat recovery system for a biomass plant is mathematically modeled and optimized using a modified variant of particle swarm optimization (PSO). Al2O3 nanoparticles dispersed in distilled water with volume fractions of up to 2% are formed the coolant. For mathematical modeling, the properties of Al2O3-water nanofluid are taken from the available literature and an existing case study is employed for testing the efficiency of the proposed method. The advantage of using PSO, which is categorized as a population-based evolutionary algorithm, is that various design variables for the system could be considered simultaneously and the optimum characteristics would be achieved based on any given design target. For a more robust performance of the optimizer, the PSO is modified and multi leaders are being used for evolution process instead of the single global best particle employed in the conventional PSO. A simple, yet effective, adaptive penalty function is introduced to handle the inequality constraints of the problem. The results indicate that the optimum configuration uses a 2% Al2O3-water nanofluid and could harvest the same amount of energy with significantly lower annual cost (56.6% lower) and thus it is superior to the existing methods.