In the future, renewable energies will be the driving force that transforms the world. Solar energy technology, including both photovoltaic and solar thermal, can be combined into one unit called hybrid photovoltaic thermal (PVT). This technology offers low-carbon electricity and provides heat energy simultaneously for various applications. Solar energy is a viable option to meet heat demand across varied industries due to environmental and energy management challenges. The paper discusses the development of a modified multi-input/multi-output fuzzy logic controller (MIMO-FLC) for industrial processes using PVT technology. It explains the design and implementation of the controller using MATLAB Simulink. In addition, the paper explores nature-inspired optimization techniques to obtain and tune the scaling parameters of MIMO-FLC. The main target was to determine the scaling parameters of the MIMO-FLC using three nature-inspired optimization methods: golden eagle optimization, gray wolf optimization, and whale optimization for control of the load temperature of multiple industrial processes. A multi-objective optimization was suggested to minimize the Integral of Time Absolute Error of MIMO-FLC to improve the dynamic operation of the system. Finally, comparing the proposed optimization techniques, simulations, and analyses shows that the proposed method outperforms effectively. These results indicate that the proposed techniques provide a better and more efficient solution to the problem under study. The primary finding of the study is that a centralized MIMO-FLC can effectively manage multiple factors and interrelationships within the system. The results are more coordinated and efficient temperature regulation across different processes. Additionally, lowering the need for many individual controllers simplifies the overall system structure, which makes the system easier to implement and maintain.
