Gaussian process regression modeling and prediction of temperature distribution in continuous tubular crystallizer

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Abstract For the newly developed tubular continuous crystallizer and production process, this paper proposes a modeling method for the temperature profile of the tubular crystallizer based on Gaussian Process Regression (GPR). By measuring the temperatures in four sections of the DN15 tubular crystallizer, temperature profile models for each section are constructed based on GPR. Moreover, prediction models are established for the temperature distribution of continuous crystallization sections under different jacket flow rate operating conditions. Meanwhile, the northern goshawk optimization algorithm is adopted to determine the hyperparameters of these models, aiming to improve the prediction accuracy. Through experimental tests on the temperature distribution of each section during the L-glutamic acid continuous crystallization process under different jacket flow rates, the effectiveness and advantages of the proposed method are verified.

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	Articles by authors
	LI Yuanjun
	ZHAO Mingyan
	SONG Bo
	LIU Tao

Keywords： continuous tubular crystallizer temperature distribution prediction model gaussian process regression intelligent optimization algorithm

Received 2024-05-24;

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LI Yuanjun, ZHAO Mingyan, SONG Bo, LIU Tao.Gaussian process regression modeling and prediction of temperature distribution in continuous tubular crystallizer[J] Chemcial Industry and Engineering, 2024,V41(6): 169-175

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