Flash point is one
of the most widely used properties in the risk
assessment and safe design of process industries. In the current work,
we have presented a novel and accurate model to predict the flash
points of pure organic compounds from diverse families. The proposed
model is a linear correlation between the flash point, normal boiling
point, and 42 predefined functional groups constituting the molecule.
Evaluation of the model through a data set of 1533 pure organic compounds
shows an average absolute deviation, an average absolute relative
error, and a correlation coefficient of 5.83, 1.61, and 0.992, respectively.
Comparing the results of the present study with other works shows
that the model proposed in this work is among the most accurate and
reliable ones to date.
Selecting compute nodes and solution grid generation are the first steps of numerical solutions. The most distinct manner is storing the values of dependent variables in the same set of nodes and using the identical control volumes for all variables. Such a grid is called Collocated. Collocated grid arrangement has many positive results in problems with complex solving range, especially with discontinuous boundary conditions. But this arrangement was not used for a long time for incompressible flow due to pressure and velocity isolation problems and creation of fluctuations in pressure. So the researchers in the mid-60s, have developed a new arrangement to reduce this isolation and increase the coupling between pressure and velocity. This new arrangement called staggered grid, provided the field of a new method for solving fluid flow problems called SIMPLE (Semi-Implicit Method for Pressure-Linked Equation) algorithm [1]. This report presents the solution to the continuity, Navier-Stokes equations. Standard fundamental methods like SIMPLER and primary variable formulation have been utilized. The results were analyzed for standard CFD test case-cavity flow. Different Reynold number (1000, 3000) and grid sizes with the finest meshes ie. (100×100), (1000×1000) have been studied.
The present study introduces a QSPR model to predict the flash point of pure organic compounds from diverse chemical families. We used the Maximum-Relevance Minimum-Redundancy (MRMR) as an efficient descriptor selection algorithm to select 20 the most effective out of 1926 calculated descriptors. The selected descriptors and their combination with the normal boiling point data were used as model inputs and their correlation with FP was mapped using feedforward artificial neural networks. Study-ing various models, the best result was obtained by a neural network with 2 neurons in the hidden layer for which a combination of the selected descriptors and normal boiling point data were used as model inputs. Evaluating the performance of this model for a dataset of 727 compounds resulted in average absolute relative errors of of 1.36 %, 1.34 %, 1.44 % and 1.42 % and average absolute deviations of 4.48, 4.41, 4.75 and 4.66 K for the overall, training, validation, and test datasets, respectively.
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