A NEW NEURAL NETWORK-GROUP CONTRIBUTION METHOD FOR ESTIMATION OF ISOBARIC EXPANSIVITY OF ORGANIC COMPOUNDS

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EBRAHIMI Z.; KALANTAR Z.; GOUDARZI N.

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Seminar: IRANIAN CHEMISTRY CONGRESS
Year:2013 | Issue:16

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Title

A NEW NEURAL NETWORK-GROUP CONTRIBUTION METHOD FOR ESTIMATION OF ISOBARIC EXPANSIVITY OF ORGANIC COMPOUNDS

Author(s)

EBRAHIMI Z. | KALANTAR Z. | GOUDARZI N. | Issue Writer Certificate

Keywords

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Abstract

THERMAL EXPANSIVITY (AP), IS A VERY IMPORTANT AND SENSITIVE THERMODYNAMIC PROPERTY WHICH KNOWLEDGE OF IT HELPS IN UNDERSTANDING THE VOLUMETRIC BEHAVIOR OF FLUIDS AGAINST TEMPERATURE AND PRESSURE, AND ALSO CALCULATING VARIOUS OTHER THERMODYNAMIC PARAMETERS.RELIABLE VALUES OF AP ARE ALWAYS DESIRABLE, AND SOME OF THEM CAN BE MEASURED BY CALORIMETRIC METHOD. HOWEVER, FOR MANY COMPOUNDS, THE EXPERIMENTALΑ P VALUES ARE SCARCE OR TOO EXPENSIVE TO OBTAIN. THEREFORE, PREDICTIVE METHODS HAVE BEEN CONSIDERED FOR PRACTICAL USES. ONE OF THE NEWEST METHODS TO ESTIMATE VARIOUS PHYSICAL AND CHEMICAL PROPERTIES IS COMBINATION OF GROUP CONTRIBUTION METHOD WITH NEURAL NETWORK [1-3]. IN THIS WORK, A NEW NEURAL NETWORK-GROUP CONTRIBUTIONS (NNGC) METHOD IS PRESENTED TO PREDICT THE THERMAL EXPANSIVITY (AP) OF PURE ORGANIC COMPOUNDS.THE DATA SET CONSISTS OF 3148 EXPERIMENTAL VALUES OF DENSITY FOR 5 ALKANES, 5 AMINES, 5 KETONES, 13 ALCOHOLS, 7 ESTERS AT DIFFERENT TEMPERATURE AND PRESSURE. THE DATA SET WAS RANDOMLY DIVIDED INTO THREE GROUPS: A TRAINING SET (2234), A VALIDATION SET (451) AND A TEST SET (463). THE SELECTION OF RELEVANT DESCRIPTORS IS AN IMPORTANT STEP TO CONSTRUCT A PREDICTIVE MODEL. THE FIRST SELECTED DESCRIPTORS WERE TEMPERATURE AND PRESSURE BECAUSEΑP IS RELATED TO THEM.THE OTHER DESCRIPTORSARE SELECTED BASED ON GROUP CONTRIBUTION METHOD. AFTER ANALYZING THE CHEMICAL STRUCTURE OF ALL COMPOUNDS IN THIS WORK, 9 FUNCTIONALLY GROUPS CONSIST OF METHYL, METHYLENE, METHYNE, -CH2NH2 >CHNH2,-CH2OH>CHOH, C=0 AND–COO- WERE FOUND USEFUL. THE NUMBER OF OCCURRENCES OF THESE FUNCTIONAL GROUPS FOR EACH COMPOUND AS WELL AS TEMPERATURE AND PRESSURE ARE USED AS INPUT PARAMETERS FOR MODEL. AFTER TRAINING AND OPTIMIZATION OF THE ANN PARAMETERS, THE PERFORMANCE OF THE MODEL WAS INVESTIGATED BY THE VALIDATION AND TEST SETS. THE MEAN SQUARE ERROR (MSE) AND DETERMINATION COEFFICIENT (R2) WERE 0.0003 AND 0.9918 FOR THE VALIDATION SET AND 0.0005 AND 0.9877 FOR TEST SET, RESPECTIVELY. THE RESULTS OBTAINED USING THIS MODEL IS IN GOOD AGREEMENT WITH THE EXPERIMENTAL VALUES.

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APA: Copy

EBRAHIMI, Z., KALANTAR, Z., & GOUDARZI, N.. (2013). A NEW NEURAL NETWORK-GROUP CONTRIBUTION METHOD FOR ESTIMATION OF ISOBARIC EXPANSIVITY OF ORGANIC COMPOUNDS. IRANIAN CHEMISTRY CONGRESS. SID. https://sid.ir/paper/915283/en

Vancouver: Copy

EBRAHIMI Z., KALANTAR Z., GOUDARZI N.. A NEW NEURAL NETWORK-GROUP CONTRIBUTION METHOD FOR ESTIMATION OF ISOBARIC EXPANSIVITY OF ORGANIC COMPOUNDS. 2013. Available from: https://sid.ir/paper/915283/en

IEEE: Copy

Z. EBRAHIMI, Z. KALANTAR, and N. GOUDARZI, “A NEW NEURAL NETWORK-GROUP CONTRIBUTION METHOD FOR ESTIMATION OF ISOBARIC EXPANSIVITY OF ORGANIC COMPOUNDS,” presented at the IRANIAN CHEMISTRY CONGRESS. 2013, [Online]. Available: https://sid.ir/paper/915283/en

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