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

Omar F. Lutfy

ISBN 10: 3846584940 / ISBN 13: 9783846584941

Edité par LAP LAMBERT Academic Publishing, 2012

A propos de cet article

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9783846584941

Détails bibliographiques

• Titre: Design of an Intelligent Control System for Conveyor-Belt Grain Dryers : An Application of Soft Computing Techniques in Grain Drying Systems
• Éditeur: LAP LAMBERT Academic Publishing
• Date de parution: 2012
• Langue: anglais
• ISBN à 10 chiffres: 3846584940
• ISBN à 13 chiffres: 9783846584941
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

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.