The Application of Model PID or IMC-PID Advanced Process Control to Refinery and Petrochemical Plants

Xinping, Zhen; Li, Quanshan; Wei, Huan; Wang, Wenxin; Jin, Qibing; Li-deng, Pan

doi:10.1109/chicc.2006.4347098

Cited by 4 publications

(2 citation statements)

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“…Differential control tracks the rate of change of the error and adjusts the control input. This allows the system instability to be predicted and the control input to be adjusted appropriately to improve the response time and reduce oscillations [4,6].…”

Section: Background 21 Apc System 211 Pid Systemmentioning

confidence: 99%

“…Many continuous process petrochemicals are controlled using Proportional Integral Differential (PID) control, which is a feedback control technique that does not consider multiple variables [3]. Since this approach does not consider multiple variables, there has been a trend of switching to Advanced Process Control (APC), which is a feedforward control with excellent performance [4]. APC systems are advanced control technologies used in process and manufacturing systems to control the complex behavior of processes by utilizing advanced mathematical modeling and optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Prediction of Controlled Variables of APC Systems Using Time-Series Data in the Petrochemical Industry

Lee,

Yu,

Cheon

et al. 2023

Processes

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For decades, the chemical industry has been facing challenges including energy conservation, environmental protection, quality improvement, and increasing production efficiency. To address these problems, various methods are being studied, such as research on fault diagnosis for the efficient use of facilities and medium-term forecasting with small data, where many systems are being applied to improve production efficiency. The problem considered in this study is the problem of predicting time-series Controlled Variables (CV) with machine learning, which is necessary to utilize an Advanced Process Control (APC) system in a petrochemical plant. In an APC system, the most important aspect is the prediction of the controlled variables and how the predicted values of the controlled variables should be modified to be in the user’s desired range. In this study, we focused on predicting the controlled variables. Specifically, we utilized various machine learning techniques to predict future controlled variables based on past controlled variables, Manipulated Variables (MV), and Disturbance Variables (DV). By using a time delay as a parameter and adjusting its value, you can analyze the relationship between past and future data and improve forecasting performance. Currently, the APC system is controlled through mathematical modeling and research, The time-series data of controlled variables, manipulated variables, and disturbance variables are predicted through machine learning models to compare performance and measure accuracy. It is becoming important to change from mathematical prediction models to data-based machine learning predictions. The R-Squared (R2) and Mean Absolute Percentage Error (MAPE) metric results of this study demonstrate the feasibility of introducing an APC system using machine learning models in petrochemical plants.

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Section: Background 21 Apc System 211 Pid Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%