Predictions of the Key Operating Parameters in Waste Incineration Using Big Data and a Multiverse Optimizer Deep Learning Model

Zheng Zhao, Ziyu Zhou (), Ye Lu, Zhuoge Li, Qiang Wei and Hongbin Xu
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Zheng Zhao: School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
Ziyu Zhou: School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
Ye Lu: School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
Zhuoge Li: Shenzhen Energy Environment Engineering Co., Ltd., Shenzhen 518048, China
Qiang Wei: Shenzhen Energy Environment Engineering Co., Ltd., Shenzhen 518048, China
Hongbin Xu: Shenzhen Energy Environment Engineering Co., Ltd., Shenzhen 518048, China

Sustainability, 2023, vol. 15, issue 19, 1-22

Abstract: In order to accurately predict the key operating parameters of waste incinerators, this paper proposes a prediction method based on big data and a Multi-Verse Optimizer deep learning model, thus providing a powerful reference for controlling the optimization of the incinerator combustion process. The key operating parameters that were predicted, according to the control objectives, were determined to be the steam flow, gas oxygen, and flue temperature. Firstly, a large amount of measurement data were collected, and 27 relevant control system parameters with a high correlation with the predicted variables were obtained via a mechanism analysis. The input variables of the prediction model were further determined using the improved WesselN symbolic transfer entropy algorithm. The delay time between the variables was found using a gray correlation coefficient, the prediction time was determined to be 6 min according to the delay time distribution of the flame feature, and the time delay compensation was applied to each parameter. Finally, the support vector machine was optimized using a Multi-Verse Optimization algorithm to complete the prediction of the key operating parameters. Experiments showed that the root mean square error of the proposed model for the three output variables—the steam flow, gas oxygen, and flue temperature—were 0.3035, 0.2477, and 1.6773, respectively, which provides a high accuracy compared to other models.

Keywords: waste incinerator; symbolic transfer entropy; Multi-Verse Optimization; support vector machine (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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