System identification using NARX and centrifugal compressor control through the intelligent, active method—Case study: K‐250 centrifugal compressor

Khosravi, Adel; Chatraei, Abbas; Shahgholian, Ghazanfar; Kargar, Seyed Mohamad

doi:10.1002/asjc.2799

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…The Output-Error (OE) estimator has the advantage of being more readily calculable than the predictionerror estimator. Using SI techniques such as the Hammerstein Weiner, Auto Regressive with Exogenous Input (ARX), Auto Regressive Moving Average with Exogenous Input (ARMX), Box-Jenkins (BJ) and OE models, a mathematical model was designed for a laboratory-based heating system [11][12][13][14][15][16][17]. The BJ model provides the greatest Final Prediction Error (FPE), correlation analysis, percentage of fitness, and loss function according to the simulated results [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…Then, the nonlinear ARX model and its parameters were determined by combining the local linear ARX models. The dependence of the parameter on the input and output was found numerically and roughly using polynomials [17]. Input-output data are taken from the Pseudo Random Binary Sequence (PRBS) experiment to find the heat exchanger system using the ARMAX model [13].…”

Section: Introductionmentioning

confidence: 99%

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Identification of heat exchanger process through Hammerstein and polynomial models

L D

2024

Phys. Scr.

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Process industries extensively use heat exchangers in their operations, playing a crucial role in various sectors by facilitating efficient heat transfer, conserving energy, and reducing operational costs. This paper focuses on identifying and validating the system model, with the primary task of designing the controller involving the determination of the mathematical representation of the system. The First Order Plus Dead Time (FODT) model, derived from basic principles, is used to represent the system. To analyze the system's behavior and construct a suitable controller, model development is essential, achieved by constructing principles models using energy balance equations to identify the heat exchanger process. Data collected from the model are utilized in the identification process, with the temperature at the outlet of the hot air blower being the controlled variable in this investigation. The research aims to determine the mathematical model based on time versus temperature data acquired from the Heat Exchanger. Various system identification methods, such as Hammerstein Wiener (HW), Auto Regressive with Exogenous Input (ARX), Box-Jenkins (BJ), Output-Error (OE), and Auto Regressive Moving Average with Exogenous Input (ARMAX) models, are implemented for the heat exchanger. The models obtained undergo validation, and the best-fit model closest to the physical system is considered for controller design. After conducting the analysis, it was found that the Output-Error (OE) model outperforms other models in terms of achieving the best fit.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of heat exchanger process through Hammerstein and polynomial models

L D

2024

Phys. Scr.

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“…While most of the PID controllers were used as analog in the past, controllers used with digital signals and computers are frequently encountered today [24]. In recent years; system identification and adaptation schemes are being developed to create PI and PID controllers that are optimal for the design objectives studied [23,[25][26][27]. The design of PI and PID controllers for linear systems and different design techniques for nonlinear systems are mysteries [23,[28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Heat transfer system and feedback temperature controller design for safety process operation of phosphorous acid potassium salts production

Hitit

AYGENER

YORGANCIOĞLU³

et al. 2022

International Advanced Researches and Engineering Journal

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The phosphorous acid salts are widely used in the industry because of the effective treatment against various fungal diseases encountered in plants. The production process is exothermic and, with high temperatures around 94℃ achieved, significant risks were observed in terms of occupational health and safety. Therefore, the aim is to design a control system that will make this production process reliable for human health, economic and ecological damage. For this purpose, studies were carried out to determine the optimum operating mode, heat transfer system, and temperature controller design to prevent a sudden temperature rise. First, the overall heat transfer coefficient between the reactor and the jacket was determined as 51.0930 W/m2℃ and, the refrigerant was chosen as cooling water with 1.271 g/s flow rate which is relatively more economical and accessible. The model parameters of the system were determined with a detailed dynamic analysis by giving positive and negative step inputs to the cooling water flow rate and then obtaining model parameters through reaction curve and linear regression methods. By using the obtained model parameters theoretical P, PI and PID parameters were calculated by Cohen Coon and, Ziegler-Nichols approaches, and the success of controller parameters was tested, simulated with the MATLAB Simulink program and lastly, successful temperature control was achieved in the experimental system.

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Model-based MIN/MAX override control of centrifugal compressor systems

Schulze,

Richter

2024

Franklin Open

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System identification using NARX and centrifugal compressor control through the intelligent, active method—Case study: K‐250 centrifugal compressor

Cited by 4 publications

References 40 publications

Identification of heat exchanger process through Hammerstein and polynomial models

Identification of heat exchanger process through Hammerstein and polynomial models

Heat transfer system and feedback temperature controller design for safety process operation of phosphorous acid potassium salts production

Model-based MIN/MAX override control of centrifugal compressor systems

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