Corresponding States Method for Estimation of Upper Flammability Limit Temperature of Chemical Compounds
The accuracy and predictability of predictive methods to determine the flammability characteristics of chemical compounds are of drastic significance in the chemical industry. This work aims at continuing application of the gene expression programming (GEP) mathematical strategy to modify the existing thermophysical properties correlations available in the literature to pursue the following objectives: optimization of the number of independent parameters, amplification of the generality, and improvement of the accuracy and predictability. This work deals with presenting a simple corresponding states model to predict the upper flammability limit temperature of 1462 organic compounds from 76 chemical families. The parameters of the correlation include the critical temperature and the acentric factor of the compounds. The obtained statistical parameters including average absolute relative deviation of the results from DIPPR 801 database values (1.7, 1.8, 1.7% for training, optimization, and prediction sets, ...
A corresponding states-based method for the estimation of natural gas compressibility factors
Abstract In this communication, a corresponding states-based model for the calculation/estimation of the gas compressibility factor ( z -factor) of natural gasses is proposed. The method applies the gene expression programming (GEP) algorithm. The parameters of the new model comprise the pseudo-reduced pressure and pseudo-reduced temperature. For assessing the performance and accuracy of the developed model, several statistical and graphical error analyses have been applied simultaneously. Additionally, comparisons have been made between this method and the most widely-used correlations and equations of state (EoS) available in the literature. Various statistical parameters are also used to evaluate the validity and the predictive capability of the newly developed method. Furthermore, the Leverage approach (Williams plot) is used to determine the realm of prediction capability of the new z-factor model and to detect any probable erroneous data points. The results obtained demonstrate that the newly proposed model is more reliable and more effective than the empirical models and EoS methods for prediction of z -factors of natural gasses.
Prediction of lateral outflow over triangular labyrinth side weirs under subcritical conditions using soft computing approaches
Highlights? ANN and GEP techniques could be employed in modeling discharge coefficient. ? The performance of ANN and GEP models is better than that of regression models. ? The value of RMSE of the RBNN model is lower than that of the other models. This paper presents the results of laboratory model testing of triangular labyrinth side weirs located on the straight open channel flume. The discharge capacity of triangular labyrinth side weirs is estimated by using two different artificial neural network (ANN) techniques, that is, the radial basis neural network (RBNN) and generalized regression neural network (GRNN), and gene-expression programming (GEP), which is an extension to genetic programming. 2500 laboratory test results are used for determining discharge coefficient of triangular labyrinth side weirs. The performance of the ANN and GEP models is compared with multi-linear and nonlinear regression models. Comparison results indicated that the neural computing and gene-expression programming techniques could be employed successfully in modeling discharge coefficient from the available experimental data.