本文選題：安全工程　切入點(diǎn)：二元混合物　出處：《安全與環(huán)境學(xué)報(bào)》2017年04期

【摘要】：為了給工業(yè)界提供一種快速預(yù)測(cè)二元混合液體自燃溫度的有效途徑,將試驗(yàn)所測(cè)不同組分及配比的168個(gè)二元混合液體的自燃溫度作為期望輸出,將基于電性拓?fù)錉顟B(tài)指數(shù)(ETSI)理論、引入混合ETSI概念而計(jì)算出的9種原子類型所對(duì)應(yīng)的混合ETSI作為輸入,采用三層BP神經(jīng)網(wǎng)絡(luò)技術(shù)建立了根據(jù)原子類型混合ETSI來預(yù)測(cè)混合液體自燃溫度的BP神經(jīng)網(wǎng)絡(luò)模型,并應(yīng)用改進(jìn)的Garson算法進(jìn)行多參數(shù)敏感性分析。經(jīng)模型評(píng)價(jià)驗(yàn)證及穩(wěn)定性分析,得到訓(xùn)練集的決定系數(shù)R2為0.965,平均絕對(duì)誤差MAE為11.892 K,測(cè)試集的交叉驗(yàn)證系數(shù)Q2ext為0.923,平均絕對(duì)誤差MAE為15.530 K,發(fā)現(xiàn)該模型的預(yù)測(cè)性能優(yōu)于已有的多元非線性回歸(MNR)模型,表明BP神經(jīng)網(wǎng)絡(luò)模型具有較好的擬合能力和預(yù)測(cè)能力,對(duì)烷、醇類混合體系自燃溫度的預(yù)測(cè)精度最佳。
[Abstract]:In order to provide an effective way for industry to predict the spontaneous combustion temperature of binary mixed liquid, the spontaneous combustion temperature of 168 binary mixed liquids with different components and ratios measured in the experiment is regarded as the expected output.Based on the theory of electric topological state index (ETSI), the mixed ETSI corresponding to nine atomic types calculated by introducing the concept of mixed ETSI is used as input.A BP neural network model for predicting spontaneous combustion temperature of mixed liquids based on atomic type mixed ETSI is established by using three-layer BP neural network technology. The improved Garson algorithm is used to analyze the sensitivity of multiple parameters.After model evaluation and stability analysis,The determination coefficient R2 of the training set is 0.965, the average absolute error MAE is 11.892 K, the cross-validation coefficient Q2ext of the test set is 0.923, and the average absolute error MAE is 15.530 K. it is found that the prediction performance of this model is better than that of the existing multivariate nonlinear regression model.It is shown that the BP neural network model has better fitting ability and prediction ability, and the prediction accuracy of spontaneous combustion temperature of alkane and alcohol mixture system is the best.
【作者單位】： 南京工業(yè)大學(xué)安全科學(xué)與工程學(xué)院江蘇省危險(xiǎn)化學(xué)品本質(zhì)安全與控制技術(shù)重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(21436006,21576136) 江蘇省高校自然科學(xué)基金重大項(xiàng)目(12KJA620001)
【分類號(hào)】：X932

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本文編號(hào)：1716580