Intelligent Decision Making In Molasses Extraction Through Neural Network Modeling and Fuzzy Logic Control

In the sugarcane industry, molasses extraction is a critical process, influenced by various factors such as temperature, pressure, and feedstock quality. Traditional control methods often struggle to adapt to the dynamic nature of this process, leading to inefficiencies and suboptimal outcomes. This paper proposes a novel approach leveraging neural network modeling and fuzzy logic control to enhance decision-making in molasses extraction. By integrating neural networks for predictive modeling and fuzzy logic for adaptive control, the system can effectively handle the nonlinear and uncertain dynamics inherent in the process. Better efficiency, lower energy consumption, and higher product quality of the proposed approach are shown through simulation studies and real-world experimentation when compared to traditional methods. This work offers a feasible approach to optimise molasses extraction and may be used for other related processes in the food and beverage sector. It also advances intelligent control systems in industrial processes.