Gas holdup in a bubble column reactor filled with oil-based liquids was estimated by an artificial neural network (ANN). The ANN was trained using experimental data from the literature with various sparger pore diameters and a bubbly flow regime. The trained ANN was able to predict that the gas holdup of data did not seen during the training period over the studied range of physical properties, operating conditions, and sparger pore diameter with average normalized square error\0.05. Comparisons of the neural network predictions to correlations obtained from experimental data show that the neural network was properly designed and could powerfully estimate gas holdup in bubble column with oily solutions. KeywordsArtificial neural network (ANN) Á Bubble column reactor Á Liquid properties Á Oil-based liquids Á Total gas holdup List of symbols Ar Archimedes number d C Column diameter (m) d p Mean pore diameter (m) d S Sparger diameter (m) Eo Eotvos number Fr Froude number H L Liquid heights before gas injection (m) H D Liquid heights after gas injection (m) n 1

In the present study, bubble size distribution (BSD) within a bubble column reactor was modeled using an artificial neural network (ANN). The fluids tested in the bubble column consisted of 11 different oil mixtures, each containing two different oils. Pure water was also tested. BSD was determined for various superficial gas velocities by photographing the state of the fluid. It was found that bubble size as well as distribution depended on parameters, such as gas flow rate, liquid properties, sparger pore diameter and distance from the sparger in the column. The proposed ANN model is based on more than 4500 data points collected for BSD estimation. Through statistical testing, it was found that the model has a correlation coefficient greater than 70% and upon experimental testing was found to better predict BSD than currently used correlations found in the literature. 

The main objective of this research is to present a model to assess the situation of human resource management.To do this, the Organizational Excellence Model of performance appraisal has been selected among the other models. Thus, based on the criteria of the model, we will present a model to assess the performance of the human resource. Moreover, equipped with the mentioned model, the research will evaluate the existing gap in the human resource management of the studied organization and will offer some suggestions to improve the situations. In current era, the organizations will survive who can be responsible for the needs and wants of their customers and beneficiaries. Using such models the organizations can evaluate the rate of their success in administering the improvement programs at different periods of times at one hand, and compare their performance with other (and best) organizations at the other hand. This research is an applied research in its objective, and it is a descriptive-analytic one in its data gathering. Since the subject of the research has been available for the researchers and the study has being done at the place of the research, so it is a field study. According to the findings of the research, the deepest gap is observable in the results of the customers and "human resource management customer results" and the "people results".