本文選題：居民生活垃圾 + 熱值��； 參考：《西南交通大學(xué)》2015年碩士論文

【摘要】：居民生活垃圾可燃成分具有較高的熱值,若能充分回收利用將其能源化,對能源危的可持續(xù)利用具有非常重大的意義。.本文選擇人工神經(jīng)網(wǎng)絡(luò)進行數(shù)據(jù)挖掘,以在居民小區(qū)采取的生活垃圾所測定的試驗數(shù)據(jù)為基礎(chǔ),選擇PE/PP、紙類、橡膠、含水率以及干基氫各含量作為模型的輸入?yún)?shù),居民生活垃圾可燃成分的低位熱值為輸出參數(shù),并分別基于BP神經(jīng)網(wǎng)絡(luò)、RBF神經(jīng)網(wǎng)絡(luò)和自適應(yīng)神經(jīng)模糊推理系統(tǒng)(ANFIS),對低位熱值建立了預(yù)測模型。BP神經(jīng)網(wǎng)絡(luò)模型預(yù)測準(zhǔn)確率為93.36%,RBF神經(jīng)網(wǎng)絡(luò)模型預(yù)測準(zhǔn)確率為96.87%,自適應(yīng)神經(jīng)模糊推理系統(tǒng)(ANFIS)的模型預(yù)測準(zhǔn)確為91.06%。對比三種模型的預(yù)測結(jié)果可知：BP、RBF神經(jīng)網(wǎng)絡(luò)和自適應(yīng)神經(jīng)模糊推理系統(tǒng)(ANFIS)模型均可用于可燃成分熱值的預(yù)測。ANFIS模型具有極高的模型擬合效果,但模型的驗證誤差相對較大,使該模型平均預(yù)測準(zhǔn)確率較低。BP模型的預(yù)測準(zhǔn)確率相對較高,能較好的對可燃成分熱值進行預(yù)測,但其效果要低于RBF模型。RBF模型在BP模型的基礎(chǔ)上加入了線性控制,使模型的預(yù)測準(zhǔn)確率有了較大提高。由此證明：RBF模型更適用于居民生活垃圾可燃成分的低位熱值預(yù)測,能獲得令人更為滿意的結(jié)果。當(dāng)PE/PP、紙類、橡膠、含水率以及干基氫各含量分別為31.66%、59.94%、0.03%、31.37%、8.74%時,經(jīng)RBF模型預(yù)測后得到的低位熱值為16352.61kJ/kg,其處于RDF物料所要求熱值范圍,故用居民生活垃圾中的可燃成分生產(chǎn)RDF具有一定的理論可行性。
[Abstract]:The combustible composition of domestic refuse has a high calorific value, if it can be fully recycled and utilized, it is of great significance to the sustainable utilization of energy. In this paper, artificial neural network is selected for data mining. Based on the experimental data measured by domestic waste in residential areas, PE-P, paper, rubber, moisture content and dry base hydrogen content are selected as input parameters of the model. The low calorific value of combustible components of resident domestic waste is the output parameter. Based on the BP neural network RBF neural network and the adaptive neurofuzzy inference system, the prediction model of low calorific value is established. The prediction accuracy of BP neural network model is 93.3636. The prediction accuracy of RBF neural network model is 96.87, and the prediction accuracy of BP neural network model is 96.87 and adaptive. The model prediction of neurofuzzy inference system (ANFIS) is 91.06. Compared with the prediction results of the three models, it can be seen that the two models can be used to predict the calorific value of combustible components. ANFIS model has a very high fitting effect, but the verification error of the model is relatively large. The prediction accuracy of the model is lower than that of the BP model, and the prediction accuracy of the model is relatively high, which can better predict the calorific value of combustible composition, but its effect is lower than that of the RBF model. The BP model has linear control on the basis of the BP model. The prediction accuracy of the model has been greatly improved. It is proved that the 1: RBF model is more suitable for the prediction of low calorific value of combustible components of household solid waste and can obtain more satisfactory results. When P / P, paper, rubber, moisture content and dry base hydrogen content were 31.660.93 and 8.74, respectively, the low calorific value predicted by RBF model was 16352.61 kJ / kg, which was within the required calorific value range of RDF material. Therefore, it is theoretically feasible to produce RDF from combustible components in household garbage.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：X799.3;TP183

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