کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5756568 | 1622618 | 2017 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
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
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کلمات کلیدی
Training timeACCSNEBCARTDCsPEBANFISMLPMSWCFBHeating value - ارزش گرمایشBack-propagation - بازگشت به عقبPSO - بهینه سازی ازدحام ذراتParticle swarm optimization - بهینه سازی ازدحام ذراتCirculating fluidized bed - تخت مایعRandom forest - جنگلهای تصادفی یا جنگلهای تصمیم تصادفیSubtractive clustering - خوشه بندی کمکیMunicipal solid waste - زباله جامد شهریAdaptive neuro-fuzzy inference system - سیستم استنتاج فازی عاملی سازگارDistributed control system - سیستم کنترل توزیع شدهZero - صفرClassification and regression tree - طبقه بندی و درخت رگرسیونSVM - ماشین بردار پشتیبانیSupport vector machine - ماشین بردار پشتیبانیMultilayer perceptron - پرسپترون چندلایه
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
مهندسی ژئوتکنیک و زمین شناسی مهندسی
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Waste Management - Volume 68, October 2017, Pages 186-197
Journal: Waste Management - Volume 68, October 2017, Pages 186-197
نویسندگان
Haihui You, Zengyi Ma, Yijun Tang, Yuelan Wang, Jianhua Yan, Mingjiang Ni, Kefa Cen, Qunxing Huang,