A neural network-based production process modeling and variable importance analysis approach in corn to sugar factory

doi:10.1007/s11705-022-2190-y

Frontiers of Chemical Science and Engineering

2023, Vol. 17

Issue (3): 358-371 https://doi.org/10.1007/s11705-022-2190-y

本期目录

A neural network-based production process modeling and variable importance analysis approach in corn to sugar factory

Yi Tong¹, Mou Shu², Mingxin Li³, Yingwei Liu², Ran Tao³, Congcong Zhou³, You Zhao¹, Guoxing Zhao¹, Yi Li¹(

), Yachao Dong³, Lei Zhang³, Linlin Liu³, Jian Du³(

)

¹. COFCO Biotechnology Co., Ltd., Beijing 100005, China
². COFCO Nutrition and Health Research Institute Co., Ltd., Beijing 102209, China
³. Institute of Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China

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Abstract：

Corn to sugar process has long faced the risks of high energy consumption and thin profits. However, it’s hard to upgrade or optimize the process based on mechanism unit operation models due to the high complexity of the related processes. Big data technology provides a promising solution as its ability to turn huge amounts of data into insights for operational decisions. In this paper, a neural network-based production process modeling and variable importance analysis approach is proposed for corn to sugar processes, which contains data preprocessing, dimensionality reduction, multilayer perceptron/convolutional neural network/recurrent neural network based modeling and extended weights connection method. In the established model, dextrose equivalent value is selected as the output, and 654 sites from the DCS system are selected as the inputs. LASSO analysis is first applied to reduce the data dimension to 155, then the inputs are dimensionalized to 50 by means of genetic algorithm optimization. Ultimately, variable importance analysis is carried out by the extended weight connection method, and 20 of the most important sites are selected for each neural network. The results indicate that the multilayer perceptron and recurrent neural network models have a relative error of less than 0.1%, which have a better prediction result than other models, and the 20 most important sites selected have better explicable performance. The major contributions derived from this work are of significant aid in process simulation model with high accuracy and process optimization based on the selected most important sites to maintain high quality and stable production for corn to sugar processes.

Key words： big data corn to sugar factory neural network variable importance analysis

收稿日期: 2022-04-05 出版日期: 2023-03-17

Corresponding Author(s): Yi Li,Jian Du

引用本文:

. [J]. Frontiers of Chemical Science and Engineering, 2023, 17(3): 358-371.
Yi Tong, Mou Shu, Mingxin Li, Yingwei Liu, Ran Tao, Congcong Zhou, You Zhao, Guoxing Zhao, Yi Li, Yachao Dong, Lei Zhang, Linlin Liu, Jian Du. A neural network-based production process modeling and variable importance analysis approach in corn to sugar factory. Front. Chem. Sci. Eng., 2023, 17(3): 358-371.

链接本文:

https://academic.hep.com.cn/fcse/CN/10.1007/s11705-022-2190-y
https://academic.hep.com.cn/fcse/CN/Y2023/V17/I3/358

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