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العنوان
Adaptive control of a decoupled chemical process /
المؤلف
El-Gendy, Eman Mohammed Anwa.
هيئة الاعداد
باحث / إيمان محمد أنور الجندي
مشرف / فايز فهمي جمعة عريض
مشرف / صبري فؤاد سرايه
مشرف / حمد شريف مصطفى
مشرف / محمود محمد سعفان
الموضوع
Temperature control. Neuro-fuzzy inference. Neural Networks.
تاريخ النشر
2019.
عدد الصفحات
online resource (149 pages) :
اللغة
الإنجليزية
الدرجة
الدكتوراه
التخصص
الهندسة
تاريخ الإجازة
1/1/2019
مكان الإجازة
جامعة المنصورة - كلية الهندسة - الحاسبات ونظم المعلومات
الفهرس
Only 14 pages are availabe for public view

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Abstract

As a result of the wide spread of chemical processes and their major effects in today’s developments, suitable control must be applied to these processes in order to achieve the best possible output with only minimum energy requirements. Distillation is one of the commonly used techniques in chemical industry to separate liquid mixtures to its pure components by the application and removal of heat. This process is not as simple as it might appear to be. The distillation columns are nonlinear and always subjected to external disturbances. The distillation process is also responsible for about 50% of the total operating cost, which is not acceptable due to the huge increase in the price of the fuel used by almost all processes all over the world as a result of limited energy resources. Therefore, the control of the distillation columns is very challenging to control engineers. One distillation column can be used to separate a binary mixture. However, the main problem arises from the separation of more than two components. The divided wall columns have appeared in industry in order to separate mixtures of three or more components to their pure components using only one thermally coupled column. This column can reduce 30% of total process energy as compared to the conventional columns sequence. In this thesis, conventional and different adaptive PID controllers are applied to control a divided wall column separating a ternary mixture of ethanol / propanol / n-butanol. The difference between conventional and adaptive PID controllers is the tuning parameters. In case of conventional PID controller, these parameters are calculated using Ziegler- Nichols method and remain constant despite the changing disturbances. On the other hand, the parameters of the adaptive PID controller change according to the operating conditions. Intelligent techniques are used for the online tuning of the adaptive PID controller, namely, Fuzzy logic control, neural networks, and adaptive neuro-fuzzy inference systems are applied. Furthermore, different optimization techniques are applied to tune either the adaptive PID controller directly, or the intelligent controller used to tune the adaptive PID controller. These techniques are genetic algorithm, particle swarm optimization, and hybrid genetic – particle swarm optimization. Model Reference adaptive controller based on neural network identification is also applied to control the divided wall column. Different disturbances are applied and comparative study between the suggested controllers and PI controller is performed in terms of different performance indexes, i.e. integral square error, integral time square error, integral absolute error, integral time absolute error. All simulations are done in MATLAB®/SIMULINK® 2015. from the results, adaptive PID controller tuned using adaptive neuro-fuzzy inference systems has the best performance. The results of the particle swarm optimization are much better than the results of the genetic algorithm. However, the use of hybrid genetic – particle swarm optimization could improve the performance.