TY - JOUR
T1 - Lower Explosion Limits Measurements and Prediction for Binary Liquid Mixtures
AU - Cao, Jiakai
AU - Ding, Li
AU - Pan, Yong
AU - Zhao, Jianping
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2019/8/13
Y1 - 2019/8/13
N2 - The lower explosion limit (LEL) is an important physicochemical parameter for characterizing the flammable and explosive hazard potential of chemicals. In this study, the LEL values of 21 groups of binary liquid mixtures with different compositions and ratios were tested, and the change law of LEL with compositions and ratios were revealed. Then four different physicochemical parameters were employed as the input parameters for the LEL prediction of mixtures. Both the multiple linear regression (MLR) and multiple nonlinear regression (MNR) methods were employed to model the possible quantitative relationships between these parameters and the LEL of binary mixtures. The resulted models showed satisfactory prediction ability, with the average absolute error being 0.188% for the MLR model and 0.196% for the MNR model, respectively. Model validations were also performed to check the stability and predictivity of the presented models, and the results showed that both models were valid and predictive requiring only some common physicochemical parameters of the pure components. This study can provide a simple, yet accurate way for engineering to predict the LELs of binary liquid mixtures as applied in the assessment of fire and explosion hazards and the development of inherently safer designs for chemical processes.
AB - The lower explosion limit (LEL) is an important physicochemical parameter for characterizing the flammable and explosive hazard potential of chemicals. In this study, the LEL values of 21 groups of binary liquid mixtures with different compositions and ratios were tested, and the change law of LEL with compositions and ratios were revealed. Then four different physicochemical parameters were employed as the input parameters for the LEL prediction of mixtures. Both the multiple linear regression (MLR) and multiple nonlinear regression (MNR) methods were employed to model the possible quantitative relationships between these parameters and the LEL of binary mixtures. The resulted models showed satisfactory prediction ability, with the average absolute error being 0.188% for the MLR model and 0.196% for the MNR model, respectively. Model validations were also performed to check the stability and predictivity of the presented models, and the results showed that both models were valid and predictive requiring only some common physicochemical parameters of the pure components. This study can provide a simple, yet accurate way for engineering to predict the LELs of binary liquid mixtures as applied in the assessment of fire and explosion hazards and the development of inherently safer designs for chemical processes.
UR - http://www.scopus.com/inward/record.url?scp=85072155149&partnerID=8YFLogxK
U2 - 10.1088/1757-899X/585/1/012072
DO - 10.1088/1757-899X/585/1/012072
M3 - 会议文章
AN - SCOPUS:85072155149
SN - 1757-8981
VL - 585
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
IS - 1
M1 - 012072
T2 - 5th Annual International Workshop on Materials Science and Engineering, IWMSE 2019
Y2 - 17 May 2019 through 18 May 2019
ER -