Modeling and Model Predictive Control of Semi-Batch Chemical Reactor

2013 ◽  
Vol 791-793 ◽  
pp. 822-825
Author(s):  
Lubomír Macků ◽  
David Novosad ◽  
David Sámek
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Chemical Engineering ◽  
Chemical Reactor ◽  
Batch Process ◽  
Online Identification ◽  
Whole Process ◽  
Process Simulations ◽  
Reactor Geometry

The paper presents a control mechanism design for a semi-batch chemical reactor. The data obtained by chemical engineering analysis of real experiments are used to simulate the semi‑batch process. A mathematical model based on the real reactor geometry and size is used to simulate the whole process. The process simulations are implemented in MATLAB / Simulink environment and suitable PID and Model Predictive Control are also proposed. Because of that the chemical reactor is a complex and nonlinear system, the PID controller has to use an online identification to be able to deal with nonlinearities. Results obtained by simulations are compared and discussed.

Classical and Neural Network–Based Approach of Model Predictive Control for Binary Continuous Distillation Column

2014 ◽  
Vol 9 (1) ◽  
pp. 71-87 ◽  
Author(s):  
Amit Kumar Singh ◽  
Barjeev Tyagi ◽  
Vishal Kumar
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Distillation Column ◽  
Chemical Engineering ◽  
Research Work ◽  
Settling Time ◽  
Training Data ◽  
Main Process

Abstract The objective of present research work is to develop a neural network–based model predictive control scheme (NN-MPC) for distillation column. To fulfill this objective, an existing laboratory setup of continuous binary-type distillation column (BDC) is used. An equation-based model that uses the fundamental physical and chemical laws along with valid normal assumptions is validated for this experimental setup. Model predictive control (MPC) is one of the main process control techniques explored in the recent past for various chemical engineering applications; therefore, the conventional MPC scheme and the proposed NN-MPC scheme are applied on the equation-based model to control the methanol composition. In NN-MPC scheme, a three-layer feedforward neural network model has been developed and is used to predict the methanol composition over a prediction horizon using the MPC algorithm for searching the optimal control moves. The training data is acquired by the simulation of the equation-based model under the variation of manipulated variables in the defined range. Two cases have been considered, one is for set point tracking and another is for feed flow disturbance rejection. The performance of the control schemes is compared on the basis of performance parameters namely overshoot and settling time. NN-MPC and MPC schemes are also compared with conventional PID controller. The results show the improvement in settling time with NN-MPC scheme as compared to MPC and conventional PID controller for both the cases.

scholarly journals Modeling and Control of Crane Overload Protection During Marine Lifting Operation Based on Model Predictive Control

2017 ◽  
Author(s):  
Zhengru Ren ◽  
Roger Skjetne ◽  
Zhen Gao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Degrees Of Freedom ◽  
Multiple Shooting ◽  
Control Input ◽  
Lumped Mass ◽  
Modeling And Control ◽  
Overload Protection ◽  
And Control

This paper deals with a nonlinear model predictive control (NMPC) scheme for a winch servo motor to overcome the sudden peak tension in the lifting wire caused by a lumped-mass payload at the beginning of a lifting off or a lowering operation. The crane-wire-payload system is modeled in 3 degrees of freedom with the Newton-Euler approach. Direct multiple shooting and real-time iteration (RTI) scheme are employed to provide feedback control input to the winch servo. Simulations are implemented with MATLAB and CaSADi toolkit. By well tuning the weighting matrices, the NMPC controller can reduce the snatch loads in the lifting wire and the winch loads simultaneously. A comparative study with a PID controller is conducted to verify its performance.

scholarly journals Nonlinear model predictive control of LDPE autoclave reactors

2021 ◽  
Author(s):  
Noel C Jacob
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Chemical Engineering ◽  
Closed Loop ◽  
Control Strategies ◽  
Linear Process ◽  
Mechanistic Modeling ◽  
Nonlinear Model Predictive Control ◽  
Highly Nonlinear

Polymerization reactors are characterized by highly nonlinear dynamics, multiple operating regions, and significant interaction among the process variables, and are therefore, usually difficult to control efficiently using conventional linear process control strategies. It is generally accepted that nonlinear control strategies are required to adequately handle such processes. In this work, we develop, implement, and evaluate via simulation a nonlinear model predictive control (NMPC) formulation for the control of two classes of commercially relevant low-density polyethylene (LDPE) autoclave reactors, namely, the single, and multi-zone multi-feed LDPE autoclave reactors. Mathematical models based on rigorous, first-principles mechanistic modeling of the underlying reaction kinetics, previously developed by our research group, were extended to describe the dynamic behaviour of the two LDPE autoclave reactors. Unscented Kalman filtering (UKF) based state estimation, not commonly used in chemical engineering applications, was implemented and found to perform quite well. The performance of the proposed NMPC formulation was investigated through a select number of simulation cases on the mathematical ‘plant’ models. The resulting closed-loop NMPC performance was compared with performance obtained with conventional linear model predictive control (LMPC) and proportional-integral-derivative (PID) controllers. The results of the present study indicate that the closed-loop disturbance rejection and tracking performance delivered by the NMPC algorithm is a significant improvement over the aforementioned controllers.

Neural Network-based Model Predictive Control with CPSOGSA for SMBR Filtration

2017 ◽  
Vol 7 (3) ◽  
pp. 1538 ◽  
Author(s):  
Zakariah Yusuf ◽  
Norhaliza Abdul Wahab ◽  
Abdallah Abusam
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Membrane Fouling ◽  
Pid Controller ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Gravitational Search Algorithm ◽  
Permeate Flux ◽  
Hybrid Particle Swarm Optimization

This paper presents the development of neural network based model predictive control (NNMPC) for controlling submerged membrane bioreactor (SMBR) filtration process.The main contribution of this paper is the integration of newly developed soft computing optimization technique name as cooperative hybrid particle swarm optimization and gravitational search algorithm (CPSOGSA) with the model predictive control. The CPSOGSA algorithm is used as a real time optimization (RTO) in updating the NNMPC cost function. The developed controller is utilized to control SMBR filtrations permeate flux in preventing flux decline from membrane fouling. The proposed NNMPC is comparedwith proportional integral derivative (PID) controller in term of the percentage overshoot, settling time and integral absolute error (IAE) criteria. The simulation result shows NNMPC perform better control compared with PID controller in term measured control performance of permeate flux.

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