Design of an Intelligent Control System for Conveyor-Belt Grain Dryers. An Application of Soft Computing Techniques in Grain Drying Systems

      Аннотация: The major objective of this research is to improve the performance of conveyor-belt grain dryers by designing an intelligent control system utilizing the capabilities of the adaptive neuro-fuzzy inference system (ANFIS) to model and control the drying process. To achieve this objective, a laboratory-scale conveyor-belt grain dryer was specifically fabricated for this study. As the main controller in this work, a simplified ANFIS structure is proposed to act as a proportional-integral-derivative (PID)-like feedback controller to control nonlinear systems. This controller has several advantages over its conventional ANFIS counterpart, particularly the reduction in processing time. Moreover, three evolutionary algorithms (EAs), in particular a real-coded genetic algorithm (GA), a particle swarm optimization (PSO), and a global-best harmony search (GHS), were separately used to train the proposed controller and to determine its scaling factors. The simplified ANFIS controller was then applied to control the developed ANFIS-based dryer model. From all the simulation tests, the simplified ANFIS controller has proved its remarkable ability in controlling the grain drying process.

Ключевые слова: Intelligent Control, Neurofuzzy Systems, Adaptive Neuro Fuzzy Inference System, Particle swarm optimisation, Harmony Search, Grain Drying, Paddy Drying, System identification, Complex Fuzzy Basis Function Network, Control Engineering, Soft Computing, genetic algorithm, food processing, PID Controllers