Energy optimization using metaheuristic bat algorithm assisted controller tuning for industrial and residential applications

The advent of model based control provides optimization and constrained control capabilities that can be tailored to specific goals. Metaheuristic algorithms are being researched in various fields owing to their efficiency in providing global optimization. In this paper, both model regression and energy efficiency based controller tuning are attempted using the chaotic bat algorithm (CBA). Two sets of plant data, one from an industrial precalciner temperature loop (PTL) and another from a domestic heating ventilation and air conditioning system (HVAC) are considered. Explicit model predictive control is designed. Minimizing the energy consumption (HVAC) and coal feed rate (PTL) by tuning the controllers is attempted. Results indicate that CBA can be successfully deployed in both regression and achieving control objectives as observed from the case studies.