Prediction Method of Sulfur Dioxide Emission

An accurate SO<sub>2</sub> prediction model of circulating fluidized bed (CFB) units can help operators make appropriate adjustments to unit operation. The SO<sub>2</sub> prediction accuracy of the mathematical model is limited due to the complexity of the combustion reaction...

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Autores principales:	Jiyu Chen, Mingming Gao
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	SO₂ prediction improved mean impact value method wide and deep LSTM deep learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/b56297d45b334998bd7b44510545b6e6
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id	oai:doaj.org-article:b56297d45b334998bd7b44510545b6e6
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spelling	oai:doaj.org-article:b56297d45b334998bd7b44510545b6e62021-11-18T00:04:16ZPrediction Method of Sulfur Dioxide Emission2169-353610.1109/ACCESS.2021.3123689https://doaj.org/article/b56297d45b334998bd7b44510545b6e62021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9591619/https://doaj.org/toc/2169-3536An accurate SO<sub>2</sub> prediction model of circulating fluidized bed (CFB) units can help operators make appropriate adjustments to unit operation. The SO<sub>2</sub> prediction accuracy of the mathematical model is limited due to the complexity of the combustion reaction in the circulating fluidized bed boiler. In order to obtain accurate predictions of SO<sub>2</sub>, a prediction model, which consists of the long short-term memory neural network (LSTM) using wide and deep structures, is proposed in this paper. Such structure improves the ability to extract linear relationships in the prediction model. The parameters of the wide structure are fixed using pre-training, which improves the prediction accuracy of the model. A differential prediction method is used for SO<sub>2</sub> prediction, which reduces the impact caused by the autocorrelation of the data. An improved mean impact value (MIV) algorithm is used to choose the best input variables combination scheme. Based on the original mean impact value algorithm (MIV), the temporal information is integrated, and repeated experiments are carried out to reduce the impact of model parameters initialization on the results. The improved MIV algorithm achieved higher prediction accuracy. The prediction model takes good prediction accuracy on the actual operating data of the 330MW CFB unit. The effectiveness of these changes is verified through comparative experiments. Compared to other existing prediction algorithms, the prediction model in this paper achieves the best prediction performance for several data sets.Jiyu ChenMingming GaoIEEEarticleSO₂ predictionimproved mean impact value methodwide and deep LSTMdeep learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147328-147342 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	SO₂ prediction improved mean impact value method wide and deep LSTM deep learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	SO₂ prediction improved mean impact value method wide and deep LSTM deep learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jiyu Chen Mingming Gao Prediction Method of Sulfur Dioxide Emission
description	An accurate SO<sub>2</sub> prediction model of circulating fluidized bed (CFB) units can help operators make appropriate adjustments to unit operation. The SO<sub>2</sub> prediction accuracy of the mathematical model is limited due to the complexity of the combustion reaction in the circulating fluidized bed boiler. In order to obtain accurate predictions of SO<sub>2</sub>, a prediction model, which consists of the long short-term memory neural network (LSTM) using wide and deep structures, is proposed in this paper. Such structure improves the ability to extract linear relationships in the prediction model. The parameters of the wide structure are fixed using pre-training, which improves the prediction accuracy of the model. A differential prediction method is used for SO<sub>2</sub> prediction, which reduces the impact caused by the autocorrelation of the data. An improved mean impact value (MIV) algorithm is used to choose the best input variables combination scheme. Based on the original mean impact value algorithm (MIV), the temporal information is integrated, and repeated experiments are carried out to reduce the impact of model parameters initialization on the results. The improved MIV algorithm achieved higher prediction accuracy. The prediction model takes good prediction accuracy on the actual operating data of the 330MW CFB unit. The effectiveness of these changes is verified through comparative experiments. Compared to other existing prediction algorithms, the prediction model in this paper achieves the best prediction performance for several data sets.
format	article
author	Jiyu Chen Mingming Gao
author_facet	Jiyu Chen Mingming Gao
author_sort	Jiyu Chen
title	Prediction Method of Sulfur Dioxide Emission
title_short	Prediction Method of Sulfur Dioxide Emission
title_full	Prediction Method of Sulfur Dioxide Emission
title_fullStr	Prediction Method of Sulfur Dioxide Emission
title_full_unstemmed	Prediction Method of Sulfur Dioxide Emission
title_sort	prediction method of sulfur dioxide emission
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/b56297d45b334998bd7b44510545b6e6
work_keys_str_mv	AT jiyuchen predictionmethodofsulfurdioxideemission AT mingminggao predictionmethodofsulfurdioxideemission
_version_	1718425246483087360

Prediction Method of Sulfur Dioxide Emission

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