Prediction of cooling moisture content after cut tobacco drying process based on a particle swarm optimization-extreme learning machine algorithm

Ming Zhu; Kai Wu; Yuanzhen Zhou; Zeyu Wang; Junfeng Qiao; Yong Wang; Xing Fan; Yonghong Nong; Wenhua Zi; Ming Zhu; Kai Wu; Yuanzhen Zhou; Zeyu Wang; Junfeng Qiao; Yong Wang; Xing Fan; Yonghong Nong; Wenhua Zi

doi:10.3934/mbe.2021127

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 3: 2496-2507. doi: 10.3934/mbe.2021127

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Prediction of cooling moisture content after cut tobacco drying process based on a particle swarm optimization-extreme learning machine algorithm

1.
Honghe Cigarette Factory, Hongyunhonghe Tobacco Group Co., Ltd., Honghe 652300, China
2.
College of Energy and Environment Science, Yunnan Normal University, Kunming 650500, China
^† These authors contributed to this work equally

Received: 04 December 2020 Accepted: 02 March 2021 Published: 15 March 2021

The stability of the moisture content of the cigarette is an important index to evaluate the quality of the cigarette. The cooling moisture content after cut tobacco drying process is a key factor affecting the stability of the moisture content of the cigarette. In order to realize its accurate prediction and ensure the stability, in Honghe cigarette factory, a cooling moisture content prediction model is built based on a particle swarm optimization-extreme learning machine (PSO-ELM) algorithm via the historical production data. Besides, the proposed PSO-ELM algorithm is also compared with multiple linear regression (MLR), support vector machine (SVM) and the traditional extreme learning machine (ELM) algorithms in the same data set on the prediction. The prediction accuracy of PSO-ELM method is the highest and the average error of the prediction standard is the lowest. The results indicated the proposed method can achieve a better prediction performance over compared methods and it provides a new method to realize the prediction of the cooling moisture content after cut tobacco drying process.
- PSO-ELM,
- cooling moisture content,
- export moisture content,
- environment temperature,
- environment humidity
Citation: Ming Zhu, Kai Wu, Yuanzhen Zhou, Zeyu Wang, Junfeng Qiao, Yong Wang, Xing Fan, Yonghong Nong, Wenhua Zi. Prediction of cooling moisture content after cut tobacco drying process based on a particle swarm optimization-extreme learning machine algorithm[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2496-2507. doi: 10.3934/mbe.2021127

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Abstract

The stability of the moisture content of the cigarette is an important index to evaluate the quality of the cigarette. The cooling moisture content after cut tobacco drying process is a key factor affecting the stability of the moisture content of the cigarette. In order to realize its accurate prediction and ensure the stability, in Honghe cigarette factory, a cooling moisture content prediction model is built based on a particle swarm optimization-extreme learning machine (PSO-ELM) algorithm via the historical production data. Besides, the proposed PSO-ELM algorithm is also compared with multiple linear regression (MLR), support vector machine (SVM) and the traditional extreme learning machine (ELM) algorithms in the same data set on the prediction. The prediction accuracy of PSO-ELM method is the highest and the average error of the prediction standard is the lowest. The results indicated the proposed method can achieve a better prediction performance over compared methods and it provides a new method to realize the prediction of the cooling moisture content after cut tobacco drying process.

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