مشخصات پژوهش

عنوان	A Modeling Study by Response Surface Methodology (RSM) and Artificial Neural Network (ANN) on Nitrobenzene Hydrogenation Optimization Using Rh Nanocatalyst
نوع پژوهش	مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها	response surface methodology; RSM; artificial neural network; ANN; hydrogenation; nitrobenzene; fullerene
چکیده	In this study response surface methodology (RSM) and artificial neural network (ANN) were used to develop an approach for the evaluation of hydrogenation of nitrobenzene. Fullerene can be functionalized by reacting with an oxidizing agent. Rhodium was then added by the impregnation method on the functionalized fullerene. Rhodium nanoparticles stabilized on fullerene was used as a high performance catalyst the hydrogenation of nitrobenzene. Catalytic activity was evaluated over a temperature range: 25–150°C, hydrogen pressure: 1–30 atm, rhodium content: 1–15% (w/w), and reaction time: 30–180 min in a bench scale reactor. A comparison between the model results and experimental data gave a high absolute fraction of variance (R2 ANN = 0.9971, R2 RSM = 0.9945) and showed that two models were able to predict nitrobenzene hydrogenation by Rh nanocatalyst.
پژوهشگران	حسن کی پور (نفر اول)، محمد نوروزی (نفر دوم)، علیمراد رشیدی (نفر سوم)، رضا بیگزاده (نفر چهارم)