IMPLEMENTATION OF ADVANCED PROCESS CONTROL FOR FLOW CONTROL APPLICATION

In the process control industry in recent years, majority of control loops is using Proportional-Integral-Derivative (PID) controllers due to its structure that's easy to regulate the process output. The most difficult process variable (PV) to control is flow due to the property changes in flui...

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Main Author: ROSLI, NURFATIHAH SYALWIAH
Format: Final Year Project
Language:English
Published: Universiti Teknologi Petronas 2013
Subjects:
Online Access:http://utpedia.utp.edu.my/10039/1/14085_FinRep1.pdf
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spelling my-utp-utpedia.100392017-01-25T09:38:14Z http://utpedia.utp.edu.my/10039/ IMPLEMENTATION OF ADVANCED PROCESS CONTROL FOR FLOW CONTROL APPLICATION ROSLI, NURFATIHAH SYALWIAH TK Electrical engineering. Electronics Nuclear engineering In the process control industry in recent years, majority of control loops is using Proportional-Integral-Derivative (PID) controllers due to its structure that's easy to regulate the process output. The most difficult process variable (PV) to control is flow due to the property changes in fluid and fast response. Therefore when disturbance occurs, the system is becoming dynamic process. Thus, to reduce the waste, the conventional PID controller is not the best option. However, model predictive control (MPC) has a success story in the process industry and manufacturing of its high performance in controlling a system with multivariable control simultaneously. Therefore, this paper builds on the last three previous final year student that work on the comparison between PID, fuzzy logic and artificial intelligence controller. This work provides an overview of the basic concept of an MPC technology handling flow response. Based on the research done by previous students, Adaptive Neuro-Fuzzy Inference System (ANFIS) has the best control performance compared to Fuzzy Logic Control (FLC) and PID. Therefore this work also will illustrate the performance of MPC in terms of its stability and handling robustness compared to the conventional PID Controller. A general MPC control algorithm is developed using MATLAB/Simulink Toolboxes. After that, the designed controllers will be implemented in PcA SimExpert Mobile Pilot Plant SE231B-21-Flow Control and Calibration Process Unit. The Data Acquisition (DAQ) card is used for interfacing between hardware and software. The result will be analyzed to see the comparison of PID and MPC performance in flow control application. Based on the experiment results, MPC shows better performance compared to PID Controller. Universiti Teknologi Petronas 2013-12 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/10039/1/14085_FinRep1.pdf ROSLI, NURFATIHAH SYALWIAH (2013) IMPLEMENTATION OF ADVANCED PROCESS CONTROL FOR FLOW CONTROL APPLICATION. Universiti Teknologi Petronas. (Unpublished)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
ROSLI, NURFATIHAH SYALWIAH
IMPLEMENTATION OF ADVANCED PROCESS CONTROL FOR FLOW CONTROL APPLICATION
description In the process control industry in recent years, majority of control loops is using Proportional-Integral-Derivative (PID) controllers due to its structure that's easy to regulate the process output. The most difficult process variable (PV) to control is flow due to the property changes in fluid and fast response. Therefore when disturbance occurs, the system is becoming dynamic process. Thus, to reduce the waste, the conventional PID controller is not the best option. However, model predictive control (MPC) has a success story in the process industry and manufacturing of its high performance in controlling a system with multivariable control simultaneously. Therefore, this paper builds on the last three previous final year student that work on the comparison between PID, fuzzy logic and artificial intelligence controller. This work provides an overview of the basic concept of an MPC technology handling flow response. Based on the research done by previous students, Adaptive Neuro-Fuzzy Inference System (ANFIS) has the best control performance compared to Fuzzy Logic Control (FLC) and PID. Therefore this work also will illustrate the performance of MPC in terms of its stability and handling robustness compared to the conventional PID Controller. A general MPC control algorithm is developed using MATLAB/Simulink Toolboxes. After that, the designed controllers will be implemented in PcA SimExpert Mobile Pilot Plant SE231B-21-Flow Control and Calibration Process Unit. The Data Acquisition (DAQ) card is used for interfacing between hardware and software. The result will be analyzed to see the comparison of PID and MPC performance in flow control application. Based on the experiment results, MPC shows better performance compared to PID Controller.
format Final Year Project
author ROSLI, NURFATIHAH SYALWIAH
author_facet ROSLI, NURFATIHAH SYALWIAH
author_sort ROSLI, NURFATIHAH SYALWIAH
title IMPLEMENTATION OF ADVANCED PROCESS CONTROL FOR FLOW CONTROL APPLICATION
title_short IMPLEMENTATION OF ADVANCED PROCESS CONTROL FOR FLOW CONTROL APPLICATION
title_full IMPLEMENTATION OF ADVANCED PROCESS CONTROL FOR FLOW CONTROL APPLICATION
title_fullStr IMPLEMENTATION OF ADVANCED PROCESS CONTROL FOR FLOW CONTROL APPLICATION
title_full_unstemmed IMPLEMENTATION OF ADVANCED PROCESS CONTROL FOR FLOW CONTROL APPLICATION
title_sort implementation of advanced process control for flow control application
publisher Universiti Teknologi Petronas
publishDate 2013
url http://utpedia.utp.edu.my/10039/1/14085_FinRep1.pdf
http://utpedia.utp.edu.my/10039/
_version_ 1739831751369293824
score 13.160551