A hybrid model combining mechanism with semi-supervised learning and its application for temperature prediction in roller hearth kiln

Chen, Jiayao; Gui, Weihua; Dai, Jiayang; Jiang, Zhaohui; Chen, Ning; Li, Xu

doi:10.1016/j.jprocont.2020.11.012

Cited by 17 publications

(4 citation statements)

References 31 publications

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“…Instead, it leverages a combination of labeled and unlabeled data to enhance model generalization and performance through the integration of these data sources. Chen et al (Chen et al, 2021) introduced a hybrid modeling method combining the mechanism with semi-supervised learning for temperature prediction in a roller hearth kiln, which implies the possibility of being employed in heat transfer.…”

Section: Classification Of Machine Learning Methodsmentioning

confidence: 99%

Recent advances in the applications of machine learning methods for heat exchanger modeling—a review

Zou,

Hirokawa,

et al. 2023

Front. Energy Res.

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Heat exchanger modeling has been widely employed in recent years for performance calculation, design optimizations, real-time simulations for control analysis, as well as transient performance predictions. Among these applications, the model’s computational speed and robustness are of great interest, particularly for the purpose of optimization studies. Machine learning models built upon experimental or numerical data can contribute to improving the state-of-the-art simulation approaches, provided careful consideration is given to algorithm selection and implementation, to the quality of the database, and to the input parameters and variables. This comprehensive review covers machine learning methods applied to heat exchanger applications in the last 8 years. The reviews are generally categorized based on the types of heat exchangers and also consider common factors of concern, such as fouling, thermodynamic properties, and flow regimes. In addition, the limitations of machine learning methods for heat exchanger modeling and potential solutions are discussed, along with an analysis of emerging trends. As a regression classification tool, machine learning is an attractive data-driven method to estimate heat exchanger parameters, showing a promising prediction capability. Based on this review article, researchers can choose appropriate models for analyzing and improving heat exchanger modeling.

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Section: Classification Of Machine Learning Methodsmentioning

confidence: 99%

Recent advances in the applications of machine learning methods for heat exchanger modeling—a review

Zou,

Hirokawa,

et al. 2023

Front. Energy Res.

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“…After analyzing the data characteristics of the ternary cathode material preparation industrial system, it is found that the production data has problems such as noise interference, dynamics, high dimension, and multi-modality [ 26 ]. First, in the sintering preparation process, the uncertainty of the production process such as the aging of the silicon carbide rod and the fluctuation of the exhaust pipe flow causes the acquisition device to be randomly interfered with by factors such as random errors and human errors.…”

Section: Description Of the Problem In Monitoring The Preparation Pro...mentioning

confidence: 99%

A Monitoring Method Based on FDALM and Its Application in the Sintering Process of Ternary Cathode Material

Chen

et al. 2022

Sensors

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In industrial processes, the composition of raw material and the production environment are complex and changeable, which makes the production process have multiple steady states. In this situation, it is difficult for the traditional single-mode monitoring methods to accurately detect the process abnormalities. To this end, a multimode monitoring method based on the factor dynamic autoregressive hidden variable model (FDALM) for industrial processes is proposed in this paper. First, an improved affine propagation clustering algorithm to learn the model modal factors is adopted, and the FDALM is constructed by combining multiple high-order hidden state Markov chains through the factor modeling technology. Secondly, a fusion algorithm based on Bayesian filtering, smoothing, and expectation-maximization is adopted to identify model parameters. The Lagrange multiplier formula is additionally constructed to update the factor coefficients by using the factor constraints in the solving. Moreover, the online Bayesian inference is adopted to fuse the information of different factor modes and obtain the fault posterior probability, which can improve the overall monitoring effect of the model. Finally, the proposed method is applied in the sintering process of ternary cathode material. The results show that the fault detection rate and false alarm rate of this method are improved obviously compared with the traditional methods.

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“…Although several thermal-uid models have been used to overcome the failure in temperature control caused by moisture formation and abnormal operating conditions, the kinetics of the dehydration reactions that occur during the synthesis process has not been resolved or implemented in the RHK models. [17][18][19][20][21] Since the temperature distribution in the RHK furnace can be inuenced by the generated moisture which has a comparatively high heat capacity, the dehydration reaction mechanism must be incorporated into the models to accurately predict the synthesis environment and control the process conditions.…”

Section: Introductionmentioning

confidence: 99%

Dehydration kinetics of the synthesis of high-nickel cathode materials used in lithium ion batteries

Jeon,

Kim,

Hwang

et al. 2024

J. Mater. Chem. A

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A hybrid model combining mechanism with semi-supervised learning and its application for temperature prediction in roller hearth kiln

Cited by 17 publications

References 31 publications

Recent advances in the applications of machine learning methods for heat exchanger modeling—a review

Recent advances in the applications of machine learning methods for heat exchanger modeling—a review

A Monitoring Method Based on FDALM and Its Application in the Sintering Process of Ternary Cathode Material

Dehydration kinetics of the synthesis of high-nickel cathode materials used in lithium ion batteries

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