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Integration of Production Scheduling and Process Control Using Intelligent Controller

Namdar Moghaddam, Mostafa | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 54413 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Vafa, Ehsan
  7. Abstract:
  8. In the present work, the integration of production scheduling and process control in a multi-product CSTR reactor is addressed. In order to reduce the complexity of solving this integrated problem, in the scheduling layer, a reduced closed-loop model, named scale bridging model, based on neural network concept is used. In the control layer, a neural network-based controller is used to achieve control objectives such as tracking, stability and robustness against model uncertainty. In addition, the designed closed loop system is used to obtain offline setpoint-output data set in order to train neural network based scale bridging model. In a comparative study, both simultaneous and sequential solution methods are applied to solve the integrated scheduling and control problem. The simulation results show that in the absence of process disturbances, the application of these two approaches leads to the same production sequence and almost the same control performance. However, in the presence of process disturbances in the control layer, it is shown that the rescheduling of the production sequence through a simultaneous solution of the integrated problem is required to achieve higher economic profitability. Through a simulation study, it is illustrated that when the concentration of one of the products deviates to 0.28 molar due to the entrance of a disturbance, rescheduling of the production sequence increases the economic profitability of the multiproduct CSTR by $799 per hour. This result shows that the integration of production scheduling and control through a simultaneous framework is important from the economic point of view
  9. Keywords:
  10. Process Control ; Scheduling and Control Integration ; Neural Network Based System Identification ; Neural Network Based Intelligent Controller ; Intelligent Controller ; Production Scheduling ; Continous Stirred Tank Reactor (CSTR)

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