Intelligent control of a ph neutralization process plant - comparison of pid controller and fuzzy logic controller
Advanced control techniques for process industries have become more damaging due to the increasing complexity of the processes and stricter requirements for the product quality and environmental factors. Intelligent based control approach has become a trend for advanced control techniques mainly neu...
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my-utm-ep.484922017-08-23T06:50:04Z Intelligent control of a ph neutralization process plant - comparison of pid controller and fuzzy logic controller 2014 A. Hamid, Mohd. Zhafran TJ Mechanical engineering and machinery Advanced control techniques for process industries have become more damaging due to the increasing complexity of the processes and stricter requirements for the product quality and environmental factors. Intelligent based control approach has become a trend for advanced control techniques mainly neural network and fuzzy logic. Control of pH neutralization process is a challenging process because its inherent strong nonlinearity. Failure to control the process will have significant impact to the environment. In this project, a mathematical model of pH neutralization with specific plant parameters is developed. The model of the process and its simulation are implemented in MATLAB application. The model obtained will be used for application of classic PID and intelligent based fuzzy logic controller for evaluation. Each controller performances are analyzed for comparison based on preset control performance criteria. 2014 Thesis http://eprints.utm.my/id/eprint/48492/ masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering |
| institution |
Universiti Teknologi Malaysia |
| collection |
UTM Institutional Repository |
| topic |
TJ Mechanical engineering and machinery |
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TJ Mechanical engineering and machinery A. Hamid, Mohd. Zhafran Intelligent control of a ph neutralization process plant - comparison of pid controller and fuzzy logic controller |
| description |
Advanced control techniques for process industries have become more damaging due to the increasing complexity of the processes and stricter requirements for the product quality and environmental factors. Intelligent based control approach has become a trend for advanced control techniques mainly neural network and fuzzy logic. Control of pH neutralization process is a challenging process because its inherent strong nonlinearity. Failure to control the process will have significant impact to the environment. In this project, a mathematical model of pH neutralization with specific plant parameters is developed. The model of the process and its simulation are implemented in MATLAB application. The model obtained will be used for application of classic PID and intelligent based fuzzy logic controller for evaluation. Each controller performances are analyzed for comparison based on preset control performance criteria. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
A. Hamid, Mohd. Zhafran |
| author_facet |
A. Hamid, Mohd. Zhafran |
| author_sort |
A. Hamid, Mohd. Zhafran |
| title |
Intelligent control of a ph neutralization process plant - comparison of pid controller and fuzzy logic controller |
| title_short |
Intelligent control of a ph neutralization process plant - comparison of pid controller and fuzzy logic controller |
| title_full |
Intelligent control of a ph neutralization process plant - comparison of pid controller and fuzzy logic controller |
| title_fullStr |
Intelligent control of a ph neutralization process plant - comparison of pid controller and fuzzy logic controller |
| title_full_unstemmed |
Intelligent control of a ph neutralization process plant - comparison of pid controller and fuzzy logic controller |
| title_sort |
intelligent control of a ph neutralization process plant - comparison of pid controller and fuzzy logic controller |
| granting_institution |
Universiti Teknologi Malaysia, Faculty of Electrical Engineering |
| granting_department |
Faculty of Electrical Engineering |
| publishDate |
2014 |
| _version_ |
1747817403567308800 |