Optimization-Based Input/Output Linearizing Control Strategy for a pH Process with Multiple Titrant Streams

Srihawan, Thana; Panjapornpon, Chanin; Tawai, Atthasit

doi:10.1021/acs.iecr.8b02973

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…Input/output (I/O) linearization is a model‐based control technique that provides the effectiveness of output tracking with a few required tuning parameters. The method has been extensively studied to handle various controller designs—including the process operation modes, minimum phase (MP)/nonminimum phase (NMP) behavior, or stable/unstable process . The I/O linearization method is typically applied for a process with an ordinary differential equation model with MP behavior, and it is later extended the concept to a class of partial differential equation system …”

Section: Introductionmentioning

confidence: 98%

Input/output linearizing controller with Taylor series expansion for a nonminimum phase process by hardware‐in‐the‐loop approach

Panjapornpon

Kajornrungsilp

Rochpuang

2020

Asia-Pacific J Chem Eng

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Input/output (I/O) linearization is a model-based control method that receives much attention for many research studies in chemical industries. Despite many advances in control theory, the I/O linearization is not widespread for a real-time implementation when compared with other model-based techniques. It requires differentiation and inversion of the process model-which becomes cumbersome as the complexity of the model increases. Thus, this work presents the development and experiment of an embedded I/O feedback control device with a pilot process that emulates an exothermic reactor with nonminimum phase behavior. Algorithms of an approximate I/O linearization control system is truncated by using Taylor series expansion, and it is embedded into a field-programmable gate array control device. The effectiveness of the embedded approximate I/O controller is compared with a digital proportional-integral (PI) controller for a test with the pilot process with a hardwarein-the-loop concept. The results show that the embedded model-based controller provides high performance for the servo problem and disturbance rejection, compared with the PI controller. The proposed embedded model-based controller shows the application to apply the advance control method into the field-programmable gate array hardware as a stand-alone control device, and it can perform the computation of a complex algorithm.

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Section: Introductionmentioning

confidence: 98%

Input/output linearizing controller with Taylor series expansion for a nonminimum phase process by hardware‐in‐the‐loop approach

Panjapornpon

Kajornrungsilp

Rochpuang

2020

Asia-Pacific J Chem Eng

Self Cite

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“…In the application of adaptive control, past data should be discounted to have a good closedloop performance with respect to changes in process conditions. Srihawan et al 28 presented an I/O linearization control law based on nonlinear optimization for coupled control of pH and its solution level using multiple titrants. The operating cost of the pH plant increases with these control schemes, as there is always a need for multiple control units of this sort.…”

Section: Introductionmentioning

confidence: 99%

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

Durairaj

Xavier

Patnaik

et al. 2023

Ind. Eng. Chem. Res.

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An essential step in the progression of nonlinear system identification is the inception of recurrent and convolutiontype deep learning methods in industrial units. Many chemical/ pharmaceutical/wastewater process units employ pH neutralization schemes to check the acidity and alkalinity of the product before bringing them for industrial production. The inherent nonlinear dynamics, especially during the neutralization of strong acid by a strong base, pose rigorous difficulties to model uncertainties, making it highly challenging to implement automatic pH control. This research endeavor focuses on building a deep Temporal Convolution Network (TCN) with a larger receptive field to learn the dynamics of the pH neutralization process. This method uses a 1-D causal convolution strategy with a residual learning framework to perform dilated causal convolutions. The simulation studies of the proposed TCN-based identification-scheme are adopted and executed in a Python environment for two important case studies, namely, (a) single tank pH process and (b) ETP (effluent treatment plant)-pH process. The proposed TCN framework manifested supremacy over predicted model responses of LSTM (long short-term memory) and MLP (multilayer-perceptron) architectures in terms of accuracy while requiring less training time. Furthermore, in this research, a state-of-art TCN-based NMPC (nonlinear model predictive control) and LSTM-based NMPC schemes are designed, implemented, and investigated where the control performance revealed the precision of the former.

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“…Westerink et al suggested that one‐dimensional mathematical model is quite reasonable to predict the hotspot formation inside a tubular reactor. Srihawan et al proposed a real‐time scheme to deal simultaneously with pH and level control of solution in a tank. They designed a linearized feedback controller for trajectory tracking.…”

Section: Introductionmentioning

confidence: 99%

Temperature control of real‐time identified fixed bed reactor by adaptive sliding mode control equipped with Arduino in Matlab

Pundir

Singh

2019

Asia-Pacific J Chem Eng

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Several control methodologies have been proposed in academia, but only a few of them have been implemented in industries due to their difficult implementation. This study is motivated by not only using low-cost data acquisition system such as Arduino in Matlab with little programming but also easy implementation of complex chemical process systems such as fixed bed reactor. An online identification and parameter estimation of a fixed bed reactor with Matlab package was developed. Designing a controller by using a nonlinear complicated model is a difficult task. A first-order discrete transfer function with unit delay has been identified for a fixed bed reactor for which a recursive least squares method with forgetting factor has been chosen. The Matlab program developed includes adaptive sliding mode control, determination of model order, and model verification. This work describes a Simulink-based model using Arduino, a data acquisition card used for interfacing with fixed bed reactor. A temperature trajectory tracking study was used in order to analyze the performance of the proposed strategy for control of the reactor. The controller implemented was developed using adaptive sliding mode control to control temperature of the fixed bed reactor. For safety concern, a constraint was applied to the control signal that is bounded between 0°C and 130°C. Temperature trajectory tracking using this algorithm is found to be good.The value of integral absolute error is found to be 387.6 for the experimental run up to 300 s.

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Optimization-Based Input/Output Linearizing Control Strategy for a pH Process with Multiple Titrant Streams

Cited by 5 publications

References 31 publications

Input/output linearizing controller with Taylor series expansion for a nonminimum phase process by hardware‐in‐the‐loop approach

Input/output linearizing controller with Taylor series expansion for a nonminimum phase process by hardware‐in‐the‐loop approach

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

Temperature control of real‐time identified fixed bed reactor by adaptive sliding mode control equipped with Arduino in Matlab

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