Iranian Journal of Chemical Engineering (IJChE)

Abstract The Ethanol-water separation involves a well-known azeotrope that confines the achievement of the ethanol purity to the values higher than 95 wt% using straightforward distillation. Many attempts have been made to identify how it can be possible to produce ultra-pure ethanol (99.95 wt%) for various valuable applications. In practice, minimizing the total cost of the process is of high importance beside having the finished product with utmost purity. As a consequence, finding the best process conditions imposed to apply the simulation and statistical optimization methods in combination. Numerical optimization provides the best trade-offs to achieve the goals. In this research, the separation of the ethanol/water mixture (87 wt%) was simulated using azeotropic distillation in Aspen plus^© environment. Indeed, cyclohexane was chosen as an effective azeotrope-former. The UNIQUAC equation was used to describe the phase behavior. The two-column arrangement, in which the first column was used to dehydrate ethanol and the second to recover the entrainer, was applied in this simulation. The effect of important process variables, including the number of the trays in columns and the feed-tray position in each tower on the total capital cost were investigated. Finally, the process variables were optimized via the Response Surface Methodology to minimize the total cost of the process. The results uncovered that the total capital cost would be minimized if the number of the trays in the azeotropic (C1) and recovery (C2) columns were set to 34 and 40, whereas, the feed-tray numbers were adjusted to 19 and 9 respectively.

Abstract An adaptive neuro-fuzzy inference system (ANFIS) was applied to simulate the batch adsorption of triglyceride (TG) from the human blood serum using the cinnamon powder, which has appeared as a potential biosorbent for serum purification, in our previous work. The obtained experimental results were used to train and evaluate the ANFIS model. Temperature (°C), the adsorption time (h), the stirring rate (rpm), the dose of adsorbent (g) and the adsorbent milling time (min) (or the particle sizes of the powder) were considered as the model inputs and TG removal (%) was chosen as the model response. The ANFIS model was trained with 75 % of the available data while 25 % of the remaining data was used to verify the validity of the obtained model. Sobol sensitivity analysis results indicated that the cinnamon dose with 71 % and the adsorbent milling time (or the particle size of the powder) with 15 % impact share were the most influential variables on the TG removal. Furthermore, the specific surface area and the number of reactive adsorption sites were found to be the most important characteristics of the adsorbent. Generally, the results of this study confirmed the advantages of applying the ANFIS and Sobol approaches for the data-based modeling of the bioprocesses.

Abstract The artificial neural network (ANN) approach was applied to develop simple correlations for predicting the thermal conductivity of nitrogen-methane and carbon dioxide-methane mixtures. The genetic algorithm method was used to obtain global optimum parameters (weights and biases) of the ANNs. The methane mole fraction, temperature, pressure, and density as effective parameters on thermal conductivity were network input variables. 171 and 180 data points related to the nitrogen-methane and carbon dioxide-methane gas mixtures, respectively, divided to test and train datasets. Simple correlations were obtained due to the small number of optimal neurons in the ANN structures. The mean relative errors of 0.206% and 0.199% for the testing dataset indicate the high accuracy and validation of the correlations. The work indicates that artificial intelligence approaches are very useful for thermal conductivity modeling in natural gases. A sensitivity analysis was performed on all input variables that indicates that the gas mixture density has the greatest impact on the thermal conductivity.