Wavelet OHIF Elman neural network model and its predictive control of processing quality

Liu, Guixiong; Yang, Jie

doi:10.1117/12.889125

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…MPC is a multivariable controller which controls the output simultaneously by considering all factors. With the continuous development of MPC theory, MPC has been applied in petrochemical industry [12][13][14], HVAC [15], autopilot [16], sewage treatment [17] and other fields. The developed algorithm uses multiinput and multi-output MPC, coupled GT-ST process identification, mechanism model and neural network model.…”

Section: Calibration Process Identification Based On Gt-st Methodsmentioning

confidence: 99%

Fully automated controller for the real-flow calibration of natural gas flowmeters based

Han¹,

Zheng²,

Chaofan³

et al. 2023

Proceedings of the 19th International Flow Measurement Conference 2022 on Flow Measurement - FLOMEKO 2022

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With the rapid development of the natural gas industry, the natural gas measurement handover volume is increasing year by year, and the calibration of natural gas flowmeter is becoming more and more important. This paper defines and demonstrates the framework of a fully automated controller for natural gas flowmeter calibration stations, from conceptualization to field test. When the calibrated flowmeter is installed on bench, the controller will identify the corresponding process by success tree algorithm. It checks whether the current process is appropriate, and automatically switch the best process according to the diameter of the calibrated meter. In the regulation stage of different calibrated flow rates, the controller performs predictive control on different flow rates according to the actual hydraulic condition by the calculation of hybrid driven model based on neural network model and mechanism model. This process gives the initial position of regulating valves with fast and efficient movement. Under a mild PID control law, the actual flow will go to the setting flow rates neither overshoot nor fall back. Finally, the control scheme is demonstrated in three original case studies, one in the simulation environment built by hydraulic simulation software, and the other two in real systems with different complexity. The algorithm is verified by simulation software and the control scheme is implemented in the actual environment. The results show that the intelligent calibration process based on model predictive control can increase the calibration efficiency. The integrated system based on hybrid driven can promote the intelligent development of natural gas calibration stations.

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Section: Calibration Process Identification Based On Gt-st Methodsmentioning

confidence: 99%

Fully automated controller for the real-flow calibration of natural gas flowmeters based

Han¹,

Zheng²,

Chaofan³

et al. 2023

Proceedings of the 19th International Flow Measurement Conference 2022 on Flow Measurement - FLOMEKO 2022

View full text Add to dashboard Cite

show abstract

Wavelet OHIF Elman neural network model and its predictive control of processing quality

Cited by 1 publication

References 4 publications

Fully automated controller for the real-flow calibration of natural gas flowmeters based

Fully automated controller for the real-flow calibration of natural gas flowmeters based

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