管式连续结晶器温度分布的高斯过程回归建模与预测

摘要针对新发展的管式连续结晶器和生产工艺,提出一种基于高斯过程回归(Gaussian Process Regression, GPR)的管式结晶器温度分布区间建模方法。通过测量DN15管式结晶器4个管段区间的温度,构建基于高斯过程回归的各区间温度分布模型。而且,建立针对不同夹套流速操作条件的连续结晶管段温度分布预测模型,采用北方苍鹰优化算法确定模型中超参数,以提高模型预测准确性。通过L-谷氨酸连续结晶过程在不同夹套流速下的各管段温度分布测试实验,验证了本研究提出方法的有效性与优点。

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	李元鋆
	赵洺延
	宋博
	刘涛

关键词：管式连续结晶器温度分布预测模型高斯过程回归智能优化算法

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.

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

Received 2024-05-24;

Fund:国家自然科学基金面上项目(62173058);国家重大科研仪器研制项目(62327807)。

Corresponding Authors: 刘涛,教授,E-mail:tliu@dlut.edu.cn。 Email: tliu@dlut.edu.cn

About author: 李元鋆(2000—),男,硕士生,现从事结晶过程建模与控制方面的研究。

引用本文:

李元鋆, 赵洺延, 宋博, 刘涛.管式连续结晶器温度分布的高斯过程回归建模与预测[J]. 化学工业与工程, 2024,41(6): 169-175

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, 2024,41(6): 169-175

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