Search

It is believe that 80% industrial of carbon dioxide can be controlled by separation and storage technologies which use the blended ionic liquids absorber. Among the blended absorbers, the mixture of water, N-methyldiethanolamine (MDEA) and guanidinium trifluoromethane sulfonate (gua) has presented the superior stripping qualities. However, the blended solution has illustrated high viscosity that affects the cost of separation process. In this work, the blended fabrication was scheduled with is the process arranging, controlling and optimizing. Therefore, the blend’s components and operating temperature were modeled and optimized as input effective variables to minimize its viscosity as the final output by using back-propagation artificial neural network (ANN). The modeling was carried out by four mathematical algorithms with individual experimental design to obtain the optimum topology using root mean squared error (RMSE), R -squared ( R 2 ) and absolute average deviation (AAD). As a result, the final model (QP-4-8-1) with minimum RMSE and AAD as well as the highest R 2 was selected to navigate the fabrication of the blended solution. Therefore, the model was applied to obtain the optimum initial level of the input variables which were included temperature 303–323 K, x [gua], 0–0.033, x [MDAE], 0.3–0.4, and x [H 2 O], 0.7–1.0. Moreover, the model has obtained the relative importance ordered of the variables which included x [gua] > temperature > x [MDEA] > x [H 2 O]. Therefore, none of the variables was negligible in the fabrication. Furthermore, the model predicted the optimum points of the variables to minimize the viscosity which was validated by further experiments. The validated results confirmed the model schedulability. Accordingly, ANN succeeds to model the initial components of the blended solutions as absorber of CO 2 capture in separation technologies that is able to industries scale up. [ABSTRACT FROM AUTHOR]

In this study, estimation capabilities of the artificial neural network (ANN) and the wavelet neural network (WNN) based on genetic algorithm were investigated in a synthesis process. An enzymatic reaction catalyzed by Novozym 435 was selected as the model synthesis process. The conversion of enzymatic reaction was investigated as a response of five independent variables; enzyme amount, reaction time, reaction temperature, substrates molar ratio and agitation speed in conjunction with an experimental design. After training of the artificial neurons in ANN and WNN, using the data of 30 experimental points, the products were used for estimation of the response of the 18 experimental points. Estimated responses were compared with the experimentally determined responses and prediction capabilities of ANN and WNN were determined. Performance assessment indicated that the WNN model possessed superior predictive ability than the ANN model, since a very close agreement between the experimental and the predicted values was obtained. [ABSTRACT FROM AUTHOR]

Background: The complexity of reactions and kinetic is the current problem of photodegradation processes. Recently, artificial neural networks have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study, an artificial neural network was applied for modeling p-cresol photodegradation. To optimize the network, the independent variables including irradiation time, pH, photocatalyst amount and concentration of p-cresol were used as the input parameters, while the photodegradation% was selected as output. The photodegradation% was obtained from the performance of the experimental design of the variables under UV irradiation. The network was trained by Quick propagation (QP) and the other three algorithms as a model. To determine the number of hidden layer nodes in the model, the root mean squared error of testing set was minimized. After minimizing the error, the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. Results: The comparison indicated that the Quick propagation algorithm had minimum root mean squared error, 1.3995, absolute average deviation, 3.0478, and maximum coefficient of determination, 0.9752, for the testing data set. The validation test results of the artificial neural network based on QP indicated that the root mean squared error was 4.11, absolute average deviation was 8.071 and the maximum coefficient of determination was 0.97. Conclusion: Artificial neural network based on Quick propagation algorithm with topology 4-10-1 gave the best performance in this study. [ABSTRACT FROM AUTHOR]

This paper deals with the comparative study on glycerolysis of palm fatty acid distillate (PFAD) in a solvent free system at different reaction conditions in an attempt to get maximum degree of FFA% reduction for biodiesel feedstock. Initially, optimization of varied reaction parameters was performed under all the different reaction conditions using artificial neural network (ANN) based on the genetic algorithm (GA). It has been found that the reduction of acidity varies with varying reaction conditions with maximum reaction rate observed in case of reaction carried-out in open reactor system with inert gas flow, followed by the reaction in open reactor system without inert gas flow and then in case of reaction under the close reactor system. In the most favorable case, 1.5 mg KOH /g PFAD of FFA (free fatty acid) was achieved after 90 min of reaction time with an excess glycerol of 4% at 220 °C. The results from the ANN model show good agreement with experimental results. Thus, the glycerolysis in open reactor system with inert gas flow (N 2 ) option is much-preferred option compared to acid esterification for the same biodiesel plant capacity, particularly for high-FFA feedstocks. [ABSTRACT FROM AUTHOR]