Optimizing Computational Efficiency for an Integrated Enset Processing Machine

Enset ( Ensete ventricosum ) is a staple crop for millions of people across Ethiopia. Nevertheless, the processing of this crop has remained inefficient, energy‐intensive, and labor‐intensive owing to the lack of proper integration of computational modeling and control optimization techniques. In this study, a framework for designing an enset processing machine was proposed. The framework integrates various techniques, including mechanical modeling of the machine, finite element method (FEM) analysis to assess machine integrity, multi‐objective optimization using a GA, and real‐time control using a PID controller. A mathematical model was created to represent the blade‐material interactions, torque‐load relationship, material removal rate, and energy consumption. FEM was used to analyze the integrity of the machine. The optimization algorithm was used to optimize the energy consumption per unit mass of material processed while maximizing the productivity of the machine. The results obtained showed that the optimized system has an efficiency of 38%, with an energy consumption of 0.242 kWh/kg of material processed. The system was also able to maximize the productivity to 4.13 kg/kWh. The optimized system has 16 blades, a clearance of 2.5–2.7 mm on the drum, a cutting angle of 15 degrees, and a drum speed of 90.71 rad/s. The optimal PID controller with parameters Kp = 2.5, Ki = 1.2, and Kd = 0.8 ensured a fast‐settling time, no overshoot, and stable speed regulation. The experimental results verified a high degree of similarity between simulation results and practical implementation. The proposed framework offers a robust, energy‐efficient, and scalable solution for modernizing enset processing machines, especially in small‐ and medium‐scale agro‐processing applications in resource‐constrained environments.