Development of a new multi-layer perceptron based soft sensor for SO2 emissions in power plant

被引:21
|
作者
Sun, Kai [1 ,2 ,3 ]
Wu, Xiuliang [1 ]
Xue, Jingyu [1 ]
Ma, Fengying [1 ]
机构
[1] Shandong Acad Sci, Qilu Univ Technol, Sch Elect Engn & Automat, Jinan 250353, Shandong, Peoples R China
[2] State Key Lab Proc Automat Min & Met, Beijing 100160, Peoples R China
[3] Beijing Key Lab Proc Automat Min & Met, Beijing 100160, Peoples R China
关键词
Desulfurization process; Extremal optimization; Nonnegative garrote; Soft sensor; Multi-layer perceptron; ARTIFICIAL NEURAL-NETWORK; VARIABLE SELECTION; EXTREMAL OPTIMIZATION; QUALITY PREDICTION; REGRESSION; ALGORITHM; CHINA; MODEL;
D O I
10.1016/j.jprocont.2019.10.007
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the paper, a multi-layer perceptron (MLP) based soft sensor for SO2 emission in desulfurization process of thermal power plants is proposed. Firstly, the production process and variables involved in the desulfurization system of thermal power plants are analyzed. Secondly, the MLP network and its input variable selection algorithm with nonnegative garrote (NNG) and extremal optimization (EO) are studied. The proposed algorithm employs MLP to model the complex desulfurization process, and then conducts shrinkage on input weights of MLP by NNG. After that, further local variable selection is performed by EO and the final model is presented. Thirdly, the simulation on actual production data of a power plant and comparisons with other state-of-art soft sensors are made to demonstrate the performance of the proposed algorithm. The simulation results show that the proposed algorithm can accurately predict the target variable and has superior performance to other algorithms. Moreover, the variable importance analysis with our approaches are consistent with the field operating experience and can provide reference for the further optimization of the control system of desulfurization process. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码:182 / 191
页数:10
相关论文
共 50 条
  • [41] A multi-sensor satellite assessment of SO2 emissions from the 2012-13 eruption of Plosky Tolbachik volcano, Kamchatka
    Telling, J.
    Flower, V. J. B.
    Carn, S. A.
    JOURNAL OF VOLCANOLOGY AND GEOTHERMAL RESEARCH, 2015, 307 : 98 - 106
  • [42] A Trend Analysis of Development Projects in South Korea during 2007-2016 Using a Multi-Layer Perceptron Based Artificial Neural Network
    Park, Sung-Hwan
    Jung, Hyung-Sup
    Lee, Sunmin
    Yoo, Heon-Seok
    Cho, Nam-Wook
    Lee, Moung-Jin
    APPLIED SCIENCES-BASEL, 2021, 11 (15):
  • [43] Configurable Multi-Layer Perceptron-Based Soft Sensors on Embedded Field Programmable Gate Arrays: Targeting Diverse Deployment Goals in Fluid Flow Estimation
    Ling, Tianheng
    Qian, Chao
    Klann, Theodor Mario
    Hoever, Julian
    Einhaus, Lukas
    Schiele, Gregor
    SENSORS, 2025, 25 (01)
  • [44] A multi-sensor satellite-based archive of the largest SO2 volcanic eruptions since 2006
    Tournigand, Pierre-Yves
    Cigala, Valeria
    Lasota, Elzbieta
    Hammouti, Mohammed
    Clarisse, Lieven
    Brenot, Hugues
    Prata, Fred
    Kirchengast, Gottfried
    Steiner, Andrea K.
    Biondi, Riccardo
    EARTH SYSTEM SCIENCE DATA, 2020, 12 (04) : 3139 - 3159
  • [45] Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization
    Yeh, Wei-Chang
    Yeh, Yuan-Ming
    Chang, Po-Chun
    Ke, Yun-Chin
    Chung, Vera
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2014, 55 : 741 - 748
  • [46] MAX-DOAS measurements of ClO, SO2 and NO2 in the mid-latitude coastal boundary layer and a power plant plume
    Department of Environmental Science and Engineering, Gwangju Institute of Science and Technology, 1 Oryong-dong, 500-712 Buk-gu, Gwangju, Korea, Republic of
    不详
    不详
    Adv. Environ. Monitoring, (37-49):
  • [47] MAX-DOAS measurements of CIO, SO2 and NO2 in the mid-latitude coastal boundary layer and a power plant plume
    Lee, Chulkyu
    Kim, Young J.
    Lee, Hanlim
    Choi, Byeong C.
    ADVANCED ENVIRONMENTAL MONITORING, 2008, : 37 - +
  • [48] Development of a unique multi-layer perceptron neural architecture and mathematical model for predicting thermal conductivity of distilled water based nanofluids using experimental data
    Singh, Shiva
    Kumar, Sumit
    Ghosh, Subrata Kumar
    COLLOIDS AND SURFACES A-PHYSICOCHEMICAL AND ENGINEERING ASPECTS, 2021, 627
  • [49] New technology for control of NOx, SO2 and HCl emissions from large scale combustion plant using calcium magnesium acetate
    Nimmo, W.
    Patsias, A. A.
    Gibbs, B. M.
    Williams, P. T.
    JOURNAL OF THE ENERGY INSTITUTE, 2006, 79 (02) : 92 - 100