Soft measurement of dioxin emission concentration based on deep forest regression algorithm. (14th September 2021)

• Record Type: Journal Article
• Authors: Jian, Tang; Heng, Xia; Junfei, Qiao; Zihao, Guo
• Is Part Of: International journal of system control and information processing. Volume 3:Number 3(2021)
• Page Start: 208
• Page End: 228
• Publication Date: 2021-09-14
• Subjects: dioxin -- DFR -- deep forest regression -- layer regression vector -- augmented layer regression vector -- MSWI -- municipal solid waste incineration; System design -- Data processing -- Periodicals; Information technology -- Periodicals; 003.5
• Languages: English
• ISSNs: 1759-9334

Abstract

Dioxin (DXN) is an organic pollutant emitted by the municipal solid waste incineration (MSWI) process. In industrial process, the DXN emission concentration is detected by using offline laboratory analysis method with a monthly/seasonal or un-determined period. In this paper, a soft measurement method of DXN based on deep forest regression (DFR) algorithm is proposed. First, the input layer forest model consists of multiple sub-forest models is trained and the layer regression vector is obtained. Then, the augmented layer regression vector that serial combine the layer regression vector with the raw features is used to train the middle layer forest model. Finally, the augmented layer regression vector of the middle layer forest is fed into the output layer forest model to produce the final DXN prediction. The effectiveness of the proposed method was verified by the benchmark data and the DXN emission concentration data of the actual MSWI process.