State estimation and non-linear model based control of a continuous stirred tank reactor using unscented Kalman filter

2017 ◽  
Vol 95 (7) ◽  
pp. 1323-1331 ◽  
Author(s):  
Atanu Panda ◽  
Prakash Jagadeesan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Linear Model ◽  
Unscented Kalman Filter ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Model Based Control ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Continuous Stirred Tank

scholarly journals Continuous Stirred Tank Reactor Fault Detection Using Higher Degree Cubature Kalman Filter

2019 ◽  
Vol 18 (3-2) ◽  
pp. 47-50
Author(s):  
Muhammad Naguib Ahmad Nazri ◽  
Zool Hilmi Ismail ◽  
Rubiyah Yusof
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Computational Cost ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Current State ◽  
Cubature Kalman Filter ◽  
Continuous Stirred Tank

Continuous Stirred Tank Reactor (CSTR) plays a major role in chemical industries, it ensures the process of mixing reactants according to the attended specification to produce a specific output. It is a complex process that usually represent with nonlinear model for benchmarking. Any abnormality, disturbance and unusual condition can easily interrupt the operations, especially fault. And this problem need to detect and rectify as soon as possible.  A good knowledge based fault detection using available model require a good error residual between the measurement and the estimated state. Kalman filter is an example of a good estimator, and has been exploited in many researches to detect fault. In this paper, Higher degree Cubature Kalman Filter (HDCKF) is proposed as a method for fault detection by estimation the current state. Cubature Kalman filter (CKF) is an extension of the Kalman filter with the main purpose is to estimate process and measurement state with high nonlinearities. It is based on spherical radial integration to estimate current state by generating cubature points with specific value. Conventional CKF use 3rd degree spherical and 3rd degree radial, here we implement Higher Degree CKF (HDCKF) to have better accuracy as compared to conventional CKF. High accuracy is required to ensure no false alarm is detected and furthermore good computational cost will improve its detection. Finally, a numerical example of CSTR fault detection using HDCKF is presented. Implementation of HDCKF for fault detection is compared with other filter to show effective results.

Improving the Performance of a Continuous Stirred Tank Reactor Using Moving Horizon State Estimation and Model Predictive Control

2011 ◽  
Vol 403-408 ◽  
pp. 3454-3460
Author(s):  
Fazlollah Armoon ◽  
Hooshang Jazayeri-Rad
Keyword(s):  
Model Predictive Control ◽  
State Estimation ◽  
Predictive Control ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Inlet Concentration ◽  
Product Concentration ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Continuous Stirred Tank

Since chemical reactors are utilized to produce specific and valuable products, concentration of products should be regulated at a specified level. As a disturbance input, a change in the inlet concentrations can vary the product concentration. So, in order to regulate the product concentration, the inlet concentrations and the product concentration should be measured. However, measurement of concentration encounters some problems such as high cost and time delay. For compensation of these failures, estimation of concentration has been proposed. In this work, the inlet concentration and the product concentration of a continuous stirred-tank reactor (CSTR) are estimated based on the moving horizon state estimation (MHSE), and the product concentration is regulated based on the model predictive control (MPC). Simulation results indicate that the proposed strategy improves the performance of the CSTR compared with the method in which the inlet concentration is not estimated.

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