Control of pH neutralization system using nonlinear model predictive control with I-controller

Hermansson, Ayman William; Syafiie, S.

doi:10.1109/ieem.2014.7058759

Cited by 3 publications

(2 citation statements)

References 22 publications

(25 reference statements)

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“…One of the implementations of MPC is forming a multiple model predictive control (MMPC) with combination of multiple piecewise linear models (PWL) and the use of Bayesian weight calculator as optimization [4]. Referring to the research study by Hermansson and Syafiie, an adaptive integral controller is also augmented into the MMPC model (MMPC-I), leading to ability to reduce the tracking error formed from the external disturbances, contributing towards offset-free control [5]. However, there are several limitations to the current offset-free tracking approach, such as extra tuning effort, unguaranteed offset removal and increased computational load of the setup.…”

Section: Model Predictive Controlmentioning

confidence: 99%

Model Predictive Control – Sliding Mode Control of a pH system

Ooi

Hermansson

Lim

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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This paper studies the feasibility of using discrete sliding mode controller (SMC) to achieve offset-free control of nonlinear processes in the presence of disturbances. The performance of the SMC is compared to a multiple model predictive controller (MMPC) studying the ability of set-point tracking using the pH system as a case study. The results presented from the comparison show that SMC can perform offset-free control of a pH system, with the major drawback being slow response as well as oscillation at some pH values. Finally, a design of a combination between MMPC and SMC (MMPC-SMC) is proposed, with MMPC carrying out basic control response while the SMC fulfils the role of eliminating the offset. However, the inaccurate reading in the MATLAB simulation model does not generate the expected results on the pH control. Therefore, the modification on the MATLAB models is required to achieve the improved control system on the offset-free behaviour for the set-point tracking.

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Section: Model Predictive Controlmentioning

confidence: 99%

Model Predictive Control – Sliding Mode Control of a pH system

Ooi

Hermansson

Lim

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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“…A multi-model nonlinear predictive control scheme was applied in [8] to describe and handle the nonlinearities of a pH industry process. This approach included a parallel integral action in the controller to compensate unmeasured states.…”

Section: Introductionmentioning

confidence: 99%

Improving Process Management in a Water Treatment Plant Using Control Modelling

Dias

Santos

Librantz

et al. 2016

IFIP Advances in Information and Communication Technology

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This work presents a modelling and a simulation of a pH control for process management in a drinking water treatment plant. A range of historical data, or knowledge base, was used to define the behavior of each input and disturbances of the process, using the MATLAB/Simulink software. The present pH control modelling has been simulated and compared to some experimental tests, thus contributing to achieve and identify operational scenarios in a real water treatment plant. Therefore, the present results allows to predict not only the present scenarios but also new operational conditions, in order to estimate the better process parameters and reduce some costs related to raw materials, such as the lime consumption , in water treatment plants.

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Model predictive control of pH neutralization processes: A review

Hermansson

Syafiie

2015

Control Engineering Practice

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The paper provides a review of the different approaches of Model Predictive Control (MPC) to deal with the nonlinearities and transient behavior associated with pH and its control. Firstly a description of the pH system and what makes it difficult to control is presented, followed by the general description of the structure of MPC. The different applications of MPC vary mostly in the way the model is described and how the optimization is carried out to obtain the desired control action. The different modeling techniques applied to the MPC, which is used to describe the behavior of the pH are ranging from simple linear models, multiple linear models like piecewise linear descriptions and fuzzy models, to nonlinear descriptions like Wiener models and the use of artificial neural networks. The models and their respective ways of application are reviewed. Finally, the areas where more research is needed are addressed.

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Control of pH neutralization system using nonlinear model predictive control with I-controller

Cited by 3 publications

References 22 publications

Model Predictive Control – Sliding Mode Control of a pH system

Model Predictive Control – Sliding Mode Control of a pH system

Improving Process Management in a Water Treatment Plant Using Control Modelling

Model predictive control of pH neutralization processes: A review

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