Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators. (October 2017)

• Record Type: Journal Article
• Authors: You, Haihui; Ma, Zengyi; Tang, Yijun; Wang, Yuelan; Yan, Jianhua; Ni, Mingjiang; Cen, Kefa; Huang, Qunxing
• Is Part Of: Waste management. Volume 68(2017)
• Page Start: 186
• Page End: 197
• Publication Date: 2017-10
• Subjects: MSW municipal solid waste -- HV heating value -- RF random forest -- SVM support vector machine -- DCS distributed control system -- PSO particle swarm optimization -- TT training time -- BP back-propagation -- NEB negative extremely big -- NM negative medium -- ZE zero -- PM positive medium -- CFB circulating fluidized bed -- MLP multilayer perceptron -- ANFIS adaptive neuro-fuzzy inference system -- ACCS automatic combustion control system -- CART classification and regression tree -- SC subtractive clustering -- PP prediction precision -- PEB positive extremely big -- NB negative big -- NS negative small -- PS positive small -- PB positive big; Circulating fluidized bed incinerators -- Heating value -- MLP -- SVM -- ANFIS -- RF; Hazardous wastes -- Periodicals; Refuse and refuse disposal -- Periodicals; 363.728
• DOI: 10.1016/j.wasman.2017.03.044
• Languages: English
• ISSNs: 0956-053X

Abstract

Highlights: A rapid, cost-effective, and comparative methodology was proposed to evaluate the HVs of burning MSW online. Models ranked in descending order of performance are ANFIS, RF, SVM, MLP. A well trained model is capable of characterizing the variation trend of HVs accurately. Abstract: The heating values, particularly lower heating values of burning municipal solid waste are critically important parameters in operating circulating fluidized bed incineration systems. However, the heating values change widely and frequently, while there is no reliable real-time instrument to measure heating values in the process of incinerating municipal solid waste. A rapid, cost-effective, and comparative methodology was proposed to evaluate the heating values of burning MSW online based on prior knowledge, expert experience, and data-mining techniques. First, selecting the input variables of the model by analyzing the operational mechanism of circulating fluidized bed incinerators, and the corresponding heating value was classified into one of nine fuzzy expressions according to expert advice. Development of prediction models by employing four different nonlinear models was undertaken, including a multilayer perceptron neural network, a support vector machine, an adaptive neuro-fuzzy inference system, and a random forest; a series of optimization schemes were implemented simultaneously in order to improve the performance of each model. Finally, a comprehensive comparison study was carried out to evaluate the performance of the models. Results indicate that the adaptive neuro-fuzzy inference system model outperforms the other three models, with the random forest model performing second-best, and the multilayer perceptron model performing at the worst level. A model with sufficient accuracy would contribute adequately to the control of circulating fluidized bed incinerator operation and provide reliable heating value signals for an automatic combustion control system.