Nonlinear Observer-Based Online Optimal Control for Batch Reactor Using Particle Swarm Optimization | ||||
| Arab Journal of Nuclear Sciences and Applications | ||||
| Volume 58, Issue 1, January 2025, Page 1-8 PDF (1.02 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ajnsa.2024.310068.1838 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Manar Ouda   1; Hala Abdel-Halim 2; Ahmed A Abouelsoud3
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| 1Egyptian Atomic Energy Authority (EAEA), Nuclear Search Center, Engineering department | ||||
| 2Egyptian Atomic Energy Authority (EAEA), Nuclear & Radiological Safety Research Center, Egyptian Atomic Energy Authority (EAEA), Nuclear & Radiological Safety Research Center | ||||
| 3Cairo University, Electronics and Communications Engineering. Dept | ||||
| Abstract | ||||
| This research focuses on the online optimal control of reactor temperature in an exothermic batch reactor. Because a large amount of heat is rapidly emitted during an exothermic action, creating a suitable temperature controller is a challenging task. Exothermic activity speeds up the reaction rate and releases more heat. As a result, insufficient temperature control may cause the reaction to become unstable, threatening plant personnel and equipment. Thus, a particle swarm optimization algorithm (PSO)-based online optimal temperature management approach for an exothermic batch reactor is proposed as a modern intelligent control. PSO is used to get the ideal reaction temperature for the batch reactor type offline, maximizing the intended product. Additionally, online PSO is used based on the nonlinear generic model control (GMC) parameters according to the cost function to achieve the best performance for temperature management of the nonlinear batch reactor. An online nonlinear state estimation is created for the batch reactor's reacting component concentrations. This nonlinear estimator is proven to converge to the true state assuring the stability of the nonlinear batch reactor controller system and saving the cost. The simulation results show that the overall proposed strategy is effective and robust in terms of model mismatch and process parameter changes for the reference exothermic batch reactor type. | ||||
| Keywords | ||||
| : exothermic batch reactor; intelligent control; online optimal control; particle swarm optimization (PSO); Generic Model Control (GMC); nonlinear observer design | ||||
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