Online Identification and Control of pH in a Neutralization System

Obut, Salih; Özgen, Canan

doi:10.1021/ie070492p

Cited by 14 publications

(4 citation statements)

References 28 publications

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“…The process has different process gains at different pH interval values. The process has lower gains for the pH intervals of [3][4][5][6] and [9][10][11][12] whereas it has higher gains for the pH intervals of [6][7][8][9]. Since the inverse controller is the inverse definition of the fuzzy model of the pH NP, it inherently possesses the process gain information and exhibits significant control performance for different pH levels.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…Despite the fact that linear controllers are simple to design, adaptive control or nonlinear control methods provide better control performance than the classical linear control methods [5], [8]. In [2], [9], nonlinear model predictive and adaptive control structures are proposed for the control of pH processes. Although all of these techniques are successful, it is still hard to obtain an adequate model representing the pH NP in any operating condition for practical applications [5].…”

Section: Introductionmentioning

confidence: 99%

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Inverse Neuro-Fuzzy Model Based Controller Design for a Ph Neutralization Process

AKCA

Ulu

Obut

2023

Journal of Scientific Reports-A

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Since pH neutralization processes have extremely nonlinear characteristics, controlling it might be difficult. Therefore, a special controller design is needed to handle the high nonlinearities of the process. In this study, an inverse neuro-fuzzy model-based controller (NFMBC) design is presented for control of a pH neutralization process (NP). Input-output (IO) data set of the process is collected by applying a proper excitation signal. Then, forward and inverse neuro-fuzzy models of the process are constructed by using this data set after a training process. In terms of design simplicity, a two-input-one-output model structure is chosen for both neuro-fuzzy models. These forward and inverse neuro-fuzzy models are used in a nonlinear internal model control (NIMC) structure in order to provide robustness against disturbances and model mismatches. To examine the proposed controller's performance, simulation studies are carried out under setpoint variation and disturbance conditions. Additionally, the performance of the inverse NFMBC is compared to that of a fuzzy proportional integral derivative (FPID) controller with a 7x7 rule base. The results demonstrate that the designed controller provides more effective control performance for setpoint variations and also exhibits higher robustness against disturbances in the acid flow rate than the FPID controller.

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Section: Simulation Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inverse Neuro-Fuzzy Model Based Controller Design for a Ph Neutralization Process

AKCA

Ulu

Obut

2023

Journal of Scientific Reports-A

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“…This inference mechanism consisted of two modules; the first module was the fuzzy inference module with control error and its derivative as inputs and the parameter "z" as the output, while the second module tuned the controller gains by using the parameter "z". Recently, Obut and Ozgen 5 designed different controllers such as Model Predictive FLC, FLC, and PI Controller for pH control. They pointed out that FLC was a better controller than the others, which could be preferentially used under various process conditions without the need of complex identification and modeling issues.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Scaled Parameters and Setting Minimum Rule Base for a Fuzzy Controller in a Lab-Scale pH Process

Aras

Haşıloğlu

2011

Ind. Eng. Chem. Res.

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Experimental and simulation studies were conducted to design a multiregional fuzzy logic controller (FLC) for a lab-scale pH process system. Scaled coefficients of the controller were optimized offline to obtain the best controller performance in terms of cost function and function evaluation number using two widely used global optimization methods; namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Various tuning parameters of these optimization algorithms were investigated in detail to compare their performance on this subject. The cost functions obtained via two algorithms were very close to each other, but the function evaluation number was almost 4-fold due to complicated computation of the GA compared to the PSO. On the other side, working with the PSO was much easier owing to fewer adjustable parameters. In the second part of the study, the rule base of the controller was minimized using two different new methods. In this context, the number of rules was reduced by 43% and 56% using the fuzzy matching set and the personal initiative methods, respectively. Rule base reduction increased the performance and the effectiveness of the controller, an important issue for real-time applications.

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“…However, fuzzy control is blind to the process dynamics and its function may be impaired by system variations. On-line identification of the controlled process solves this problem (Obut and Özgen, 2008). Adaptive control with on-line system identification is another choice for pH processes (Su et al, 1998;Alpbaz et al, 2006;Altinten, 2007;Bölinga et al, 2007).…”

Section: Introductionmentioning

confidence: 99%