03 تیر 1400
مجيد محدثي

مجید محدثی

مرتبه علمی: استادیار
نشانی: ایران، کرمانشاه، بزرگراه امام خمینی (ره)، دانشگاه صنعتی کرمانشاه، دانشکده مهندسی، گروه مهندسی شیمی
تحصیلات: دکترای تخصصی / مهندسی شیمی
تلفن: 083-38305000 (1167, 1058, 1025)
دانشکده: دانشکده مهندسی

مشخصات پژوهش

عنوان
PSO-ANFIS and ANN Modeling of Propane/Propylene Separation using Cu-BTC Adsorbent
نوع پژوهش مقاله چاپ شده
کلیدواژه‌ها
Adsorptio, ANN, Cu-BTC, Propylene/Propane, PSO-ANFIS
پژوهشگران سهراب فتحی (نفر اول)، عباس رضایی (نفر دوم)، مجید محدثی (نفر سوم)، منا نظری (نفر چهارم)

چکیده

In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these computational intelligence (CI) approaches, the input parameters such as adsorbent shape (SA), temperature (T), and pressure (P) were related to the output parameter which is propylene or propane adsorption. A thorough comparison between the experimental, artificial neural network and particle swarm optimization-adaptive neuro-fuzzy inference system models was carried out to prove its efficiency in accurate prediction and computation time. The obtained results show that both investigated methods have good agreements in comparison with the experimental data, but the proposed artificial neural network structure is more precise than our proposed PSO-ANFIS structure. Mean absolute error (MAE) for ANN and ANFIS models were 0.111 and 0.421, respectively.