本文選題：高爐煉鐵　切入點(diǎn)：十字測溫　出處：《東北大學(xué)學(xué)報(bào)(自然科學(xué)版)》2017年05期 　論文類型：期刊論文

【摘要】：由于高爐中心溫度較高,十字測溫中心位置傳感器極易損壞,并且更換周期長,因而導(dǎo)致無法及時(shí)判斷爐頂煤氣流分布.采用多輸出支持向量回歸(M-SVR)和隨機(jī)權(quán)神經(jīng)網(wǎng)絡(luò)(RVFLNs)兩種數(shù)據(jù)驅(qū)動智能建模方法建立高爐十字測溫中心帶溫度估計(jì)模型,并基于實(shí)際工業(yè)數(shù)據(jù)對建立的模型進(jìn)行驗(yàn)證和比較分析.結(jié)果表明,在樣本數(shù)量較小時(shí),M-SVR模型和RVFLNs模型都具有較好的溫度估計(jì)效果,但當(dāng)樣本數(shù)量充足時(shí),M-SVR模型的泛化性能和估計(jì)精度更優(yōu)于RVFLNs模型.
[Abstract]:Because of the high temperature in the center of blast furnace, the cross temperature measuring center position sensor is easily damaged, and the replacement period is long. As a result, it is impossible to judge the gas flow distribution on the top of the furnace in time. Two data driven intelligent modeling methods, multi-output support vector regression (M-SVR) and random weight neural network (RVFLNs), are used to establish the temperature estimation model of the cross temperature measuring center belt of blast furnace. The results show that both M-SVR model and RVFLNs model have good temperature estimation effect when the number of samples is small. However, when the number of samples is sufficient, the generalization performance and estimation accuracy of M-SVR model are better than that of RVFLNs model.
【作者單位】： 東北大學(xué)流程工業(yè)綜合自動化國家重點(diǎn)實(shí)驗(yàn)室;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(61473064,61290323,61333007,61290321,61621004) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助項(xiàng)目(N160805001,N160801001) 遼寧省教育廳科技項(xiàng)目(L20150186)
【分類號】：TF321;TP18
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本文編號：1621708