Development of a Temperature Regulation System for Solar Dryers Based on Artificial Neural Network-Driven Intelligent Control †

Abstract

Solar drying is a sustainable and energy-efficient method for preserving agricultural products; however, its performance is strongly influenced by fluctuating environmental conditions. This study presents an artificial neural network (ANN)-based predictive temperature control system for an indirect forced-convection solar dryer. A data-driven dynamic model of the drying process was developed using experimental measurements and implemented in MATLAB R2014a (MathWorks, Natick, MA, USA). The proposed ANN-based controller was evaluated against a conventional PID controller under identical operating conditions. The results show that the ANN-based approach reduced the settling time by approximately 36% (160 s compared to 250 s for PID) and maintained drying chamber temperature stability within ±1.2 °C. These improvements demonstrate the effectiveness of neural predictive control for enhancing dynamic response and temperature regulation accuracy in solar drying systems. The study is limited to a prototype-scale dryer and short-term experimental data; therefore, further validation under varying climatic conditions and larger-scale systems is required.