Modeling and predicting of the flash point of chemical compounds

Document Type : Chemistry Article

Authors

1 Department of Mechanical Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran

2 Chemical Engineering,South Tehran Branch - Islamic Azad University , chemical and polymer engineering depatment, Tehran, Iran

Abstract

Flash point is one of the most important flammability characteristics of chemical compounds. In the present study, we developed a neural network model for accurate prediction of the flash point of chemical compounds, using the number of hydrogen and carbon atoms, critical temperature, normal boiling point, acentric factor and enthalpy of formation as model inputs. Using a robust strategy to efficiently assign neural network parameters and evaluate the authentic performance of the neural networks, we could achieve an accurate model which yielded average absolute relative errors of 0. 97, 0. 96, 0.99 and 1.0% and correlation coefficients of 0.9984, 0.9985, 0.9981 and 0.9979 for the overall, training, validation and test sets, respectively. These results are among the most accurate ever reported ones, to date.in this article method for selection the best learning algorithm and transfer function are clearly presented and relative error for these parameters are represented in detail .

Keywords

References

[1] T.A. Albahri, "MNLR and ANN structural group contribution methods for predicting the flash point temperature of pure compounds in the transportation fuels range", Process Safety and Environmental Protection, 93, 2015, pp. 182-191.
[2] J.A. Lazzús, "Prediction of flash point temperature of organic compounds using a hybrid method of group contribution+ neural network+ particle swarm optimization", Chinese Journal of Chemical Engineering, 18(5), 2010, pp. 817-823.
[3] Y. Pan, J. Jiang and Z. Wang, "Prediction of the flash points of alkanes by group bond contribution method using artificial neural networks", Frontiers of Chemical Engineering in China, 1(4), 2007, pp. 390-394.
[4] فاطمه کرد و کامیار موقرنژاد، «بررسی تجربی و مدل‌سازی شبکة عصبی برای پیش‌بینی ضریب شکست الکل‌های خالص و مخلوط دوتایی»، مجلة مدل‌سازی در مهندسی، 1398، دورة 17، شمارة 56، صفحة 375-387.
[5] A. Alibakhshi, H. Mirshahvalad and S. Alibakhshi, "Prediction of flash points of pure organic compounds: Evaluation of the DIPPR database", Process Safety and Environmental Protection, 105, 2017, pp. 127-133.
[6] A. Alibakhshi, H. Mirshahvalad and S. Alibakhshi, "A modified group contribution method for accurate prediction of flash points of pure organic compounds", Industrial & Engineering Chemistry Research, 54(44), 2015, pp. 11230-11235.
[7] G. Patil, "Estimation of flash point", Fire and Materials, 12(3), 1988, pp. 127-131.
[8] F.Y. Hshieh, "Correlation of closed‐cup flash points with normal boiling points for silicone and general organic compounds", Fire and materials, 21(6), 1997, pp. 277-282.
[9] M. Riazi and T. Daubert, "Predicting flash and pour points", Hydrocarbon processing, 66(9), 1987, pp. 81-83.
[10] A. Alibakshi, "Strategies to develop robust neural network models: Prediction of flash point as a case study", Analytica Chimica Acta, Vol. 1026, 2018, pp. 69-76.
[11] مصطفی لشکر بلوکی، «پیش‌بینی کشش سطحی مایعات یونی برپایة ایمیدازولیوم با به‌کارگیری شبکة عصبی مصنوعی»، مجلة مدل‌سازی در مهندسی، دورة 17، شمارة 58، صفحة 1-13.
[12] مسعود افرندو محمد همت اسفه، «مدل‌سازی با استفاده از شبکة عصبی مصنوعی جهت پیش‌بینی هدایت حرارتی نانوسیال نانولوله کربنی چندجداره عامل‌دار– آب و ارائه رابطه تجربی جدید»، مجلة مدل‌سازی در مهندسی، دورة 16، شمارة 53، صفحة 67-73.
[13] J.R. Rowley, R.L. Rowley and W.V. Wilding, "Prediction of pure‐component flash points for organic compounds", Fire and Materials, 35(6), 2011, pp. 343-351.
[14] L. Catoire and V. Naudet, "A unique equation to estimate flash points of selected pure liquids application to the correction of probably erroneous flash point values", Journal of physical and chemical reference data, 33(4), 2004, pp. 1083-1111.
[15] F. Gharagheizi et al., "Gene expression programming strategy for estimation of flash point temperature of non-electrolyte organic compounds", Fluid Phase Equilibria, 329, 2012, pp. 71-77.
[16] D. Mathieu, "Inductive modeling of physico-chemical properties: Flash point of alkanes", Journal of hazardous materials, 179(1), 2010, pp. 1161-1164.
[17] Y. Pan, J. Jiang and Z. Wang, "Quantitative structure–property relationship studies for predicting flash points of alkanes using group bond contribution method with back-propagation neural network", Journal of hazardous materials, 147(1), 2007, pp. 424-430.
[18] M.H. Keshavarz and M. Ghanbarzadeh, "Simple method for reliable predicting flash points of unsaturated hydrocarbons", Journal of hazardous materials, 193, 2011, pp. 335-341.
[19] D. Mathieu and T. Alaime, "Insight into the contribution of individual functional groups to the flash point of organic compounds", Journal of hazardous materials, 267, 2014, pp. 169-174.
[20] J. Rowley, R. Rowley and W. Wilding, "Estimation of the flash point of pure organic chemicals from structural contributions", Process Safety Progress, 29(4), 2010, pp. 353-358.
[21] J. Tetteh et al., "Quantitative structure− property relationships for the estimation of boiling point and flash point using a radial basis function neural network", Journal of chemical information and computer sciences, 39(3), 1999, pp. 491-507.
[22] A.S. Hukkerikar et al., "Estimation of environment-related properties of chemicals for design of sustainable processes: development of group-contribution+ (GC+) property models and uncertainty analysis", Journal of chemical information and modeling, 52(11), 2012, pp. 2823-2839.
[23] D. Mathieu, "Flash points of organosilicon compounds: how data for alkanes combined with custom additive fragments can expedite the development of predictive models", Industrial & Engineering Chemistry Research, 51(43), 2012, pp. 14309-14315.
[24] M.H. Keshavarz et al., "A simple and reliable method for prediction of flash point of alcohols based on their elemental composition and structural parameters", Process Safety and Environmental Protection, 102, 2016, pp. 1-8.
[25] A.R. Katritzky et al., "QSPR modeling of flash points: An update", Journal of Molecular Graphics and Modelling, 26(2), 2007, pp. 529-536.
[26] A. Khajeh and H. Modarress, "QSPR prediction of flash point of esters by means of GFA and ANFIS", Journal of hazardous materials, 179(1), 2010, pp. 715-720.
[27] C.-C. Chen, H.-J. Liaw and Y.-J. Tsai, "Prediction of flash point of organosilicon compounds using quantitative structure property relationship approach", Industrial & Engineering Chemistry Research, 49(24), 2010, pp. 12702-12708.
[28] A.R. Katritzky et al., "QSPR analysis of flash points", Journal of chemical information and computer sciences, 41(6), 2001, pp. 1521-1530.
 
 
Journal of Modeling in Engineering
Volume 19, Issue 64
April 2021
Pages 109-116

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History

  • Receive Date: 04 July 2020
  • Revise Date: 17 September 2020
  • Accept Date: 01 November 2020

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