توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Estimation of Solubility of CO2 in Water With Artificial Neural Network And Mathematical Modeling
اطلاعات انتشار: چهاردهمین کنگره ملی مهندسی شیمی ایران، سال
تعداد صفحات: ۴
The solubility of carbon dioxide in water is measured at (278.15–348.15K) and (0.1–1MPa). The experimental data are compared with those obtained from equations of state such as van derWaals, Redlikh–Kwong, Soave– Redlikh–Kwong, Peng– Robinson coupled with two mixing rulesvan der Waals and Wong Sandler equations. Also, the solubility of carbon dioxide in water is modeled using the artificial neural network system. The results show that the artificial neuralnetwork system can accurately predict the solubility of carbon dioxide in water than the other equations of state.<\div>

۲Power Generation in a Cascade of Five Hydraulically and Electrically Connected Microbial Fuel Cells
اطلاعات انتشار: World Applied Sciences Journal، سي و يكم،شماره۱۲، ۲۰۱۴، سال
تعداد صفحات: ۶
In this paper bioelectricity production for more than 20 days in a two chambered microbial fuel cell (MFC) packed with granule activated carbon (GAC) was investigated. The system consisted of five individual MFC units that were hydraulically connected by feed flow and electrically connected either in series or parallel and operated in continuous mode. Glucose as substrate with a concentration of 1 g\l and anaerobic sludge were used for power generation. 400 μM of potassium permanganate was used in the cathode chambers as an electron acceptor. The maximum power density generated in the MFC was 989.70 mW\ 3, with respect to the net liquid volume of the anode chambers (0.00015 m3).

۳Modeling and Optimization of β–Cyclodextrin Production by Bacillus licheniformis using Artificial Neural Network and Genetic Algorithm
اطلاعات انتشار: Iranian Journal of Biotechnology، يازدهم،شماره۴(پياپي ۴۴)، ۲۰۱۳، سال
تعداد صفحات: ۱۰
Background: The complexity of the fermentation processes is mainly due to the complex nature of the biological systems which follow the life in a non–linear manner. Joined performance of artificial neural network (ANN) and genetic algorithm (GA) in finding optimal solutions in experimentation has found to be superior compared to the statistical methods. Range of applications of β–cyclodextrin (β–CD) as an enzymatic derivative of starch is diverse, where the complex performance of cyclodextrin glucanotransferase (CGTase) as the involved enzyme is not well recognized..Objectives: The aim of the present work was to use ANN systems with different training algorithms and defined architectures joined with GA, in order to optimize β–CD production considering temperature of the reaction mixture, substrate concentration, and the inoculum’s pH as the input variables..Materials and Methods: Commercially Neural Power, version 2.5 (CPC–X Software, 2004) was used for the numerical analysis according to the specifications provided in the software. β–CD concentration was determined spectrophotometrically according to phenolphthalein discoloration technique, described in the literature..Results: Randomly obtaining the experimental data for β–CD production in a fermentation process, could get explainable order using the ANN system coupled with GA. Changes of the β–CD as the function of each of the three selected input variables, were best quantified with use of the ANN system joined with the GA. The performance of the IBP learning algorithm was highly favorable (10300 epoch’s number within 5 second, with the lowest RMSE value) while the sensitivity analysis of the results which was carried out according to the weight method, were indicative of the importance of input variables as follows: substrate concentration temperature inoculum’s pH. For instance, small changes in the system’s pH are associated with the large variation in the β–CD production as has been described by the suggested model..Conclusions: Production of β–CD (enzymatic derivative of starch) by B. licheniformis was satisfactorily described based on multivariate data analysis application of the ANN system and the experimental data were optimized by considering ANN plus the GA where the IBP was used as the training method and with use of three neurons as the constructed variables in the hidden layer of the test network..
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