Control Of pH Neutralization Process Research Articles

Control of pH neutralization process using NMPC based on discrete time NARX-Laguerre model

In this paper, we propose a NMPC scheme based on NARX-Laguerre model in its discrete-time structure in order to control the pH neutralization process. This model is the result of the development of discrete-time NARX model parameters on five independent Laguerre bases introducing the benefit of an essentially diminished number of parameters compared to the classical NARX model. The decrease in parametric complexity actually depends on choosing the ideal poles that characterize the Laguerre bases. In this paper, we develop an analytical technique to optimize the NARX-Laguerre poles. The parameters of the NARX-Laguerre model are reached using a recursive technique. The proposed model is utilized to integrate a nonlinear model predictive control, where we create a j-step ahead predictor on the prediction horizon [k+1,k+Np] and formulate the optimization problem using a performance criterion taking into consideration the restrictions imposed on the input and output of the process. The proposed nonlinear model predictive control strategy is validated on pH neutralization process.

Deep Learning Based System Identification and Nonlinear Model Predictive Control of pH Neutralization Process

Deep Learning Based System Identification and Nonlinear Model Predictive Control of pH Neutralization Process

Adaptive Control of pH Neutralization Process based on Feedback Linearization Technique

This paper presents a nonlinear adaptive controller for pH neutralization process, which is difficult to control due to its nonlinear dynamics, sensibility to small disturbance and time-varying characteristics. Special attention is paid to the effect of internal perturbations and external disturbances typically present in the chemical process. In this work, the performance of the adaptive nonlinear controller based on feedback linearization (FL) is compared with the performance of a conventional PID controller for pH control. It is shown that conventional PID controller rejects input disturbances with poor performance, and the proposed control technique can reject the input disturbances from the control signal and then recovers linear reference tracking very well.

Genetic Optimization based Adaptive Fuzzy Logic Control of a pH Neutralization Process

Presence of severe nonlinearity in pH neutralization process makes the problem of pH control a difficult and challenging one. Further, optimized control of such highly nonlinear system requires tuning of various parameters with a suitable evolutionary algorithm (EA). This paper presents genetic algorithm (GA) based unconstrained, continuous and single-objective optimization of conventional and adaptive nonlinear fuzzy logic control (FLC) of pH neutralization process for servo and regulatory operations. The GA optimized adaptive FLC scheme changes the universe of discourse of input-output membership functions based on the region of operation and amount of step change in set-point and load. The performance of controllers are evaluated using integral of squared errors (ISE) and tested for random step changes in set-point and load.

Local linear model tree and Neuro-Fuzzy system for modelling and control of an experimental pH neutralization process

This paper describes the modelling and control of a pH neutralization process using a Local Linear Model Tree (LOLIMOT) and an adaptive neuro-fuzzy inference system (ANFIS). The Direct and Inverse model building using LOLIMOT and ANFIS structures is described and compared. The direct and inverse models of the pH system are identified based on experimental data for the LOLIMOT and ANFIS structures. The identified models are implemented in the experimental pH system with IMC structure using a GUI developed in the MATLAB -SIMULINK platform. The main aim is to illustrate the online modelling and control of the experimental setup. The results of real-time control of an experimental pH process using the Internal Model Control (IMC) strategy are also presented.

Adaptive fuzzy tuning of PID controllers

In this paper, the performances of fuzzy proportional-integral-derivative (PID) and classic PID controllers are compared through simulation studies. For this purpose, the level control of a two interacting tanks system, temperature control of unstable continuous stirred tank reactor (CSTR), and pH control of pH neutralization process were selected. In the level control process, results indicated that both of classic and fuzzy PID controllers have approximately the same performance. However, adjusting the classic PID controller is simpler than fuzzy PID controller. Therefore, in simple processes like level control in two interacting tanks, classic PID controllers are preferred. In an unstable CSTR, classic PID controller is not suitable due to the instability of the system. Fuzzy PID controller is more useful than classic PID controller in this type of systems. In pH neutralization process, using classic PID controller is inappropriate because of nonlinearity of the system and the fuzzy PID controller is more efficient.

Control of pH neutralization process using simulation based dynamic programming

The pH neutralization process has long been taken as a representative benchmark problem of nonlinear chemical process control due to its nonlinearity and time-varying nature. For general nonlinear processes, it is difficult to control with a linear model-based control method so nonlinear controls must be considered. Among the numerous approaches suggested, the most rigorous approach is the dynamic optimization. However, as the size of the problem grows, the dynamic programming approach suffers from the curse of dimensionality. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach was proposed by Bertsekas and Tsitsiklis [1996]. The NDP approach is to utilize all the data collected to generate an approximation of optimal cost-to-go function which was used to find the optimal input movement in real time control. The approximation could be any type of function such as polynomials, neural networks, etc. In this study, an algorithm using NDP approach was applied to a pH neutralization process to investigate the feasibility of the NDP algorithm and to deepen the understanding of the basic characteristics of this algorithm. As the approximator, the neural network which requires training and the k-nearest neighbor method which requires querying instead of training are investigated. The approximator has to use data from the optimal control strategy. If the optimal control strategy is not readily available, a suboptimal control strategy can be used instead. However, the laborious Bellman iterations are necessary in this case. For pH neutralization process it is rather easy to devise an optimal control strategy. Thus, we used an optimal control strategy and did not perform the Bellman iteration. Also, the effects of constraints on control moves are studied. From the simulations, the NDP method outperforms the conventional PID control.