Estimating the Iodine number of activated carbon during thermal activation using Artificial Neural Networks (ANNs)

سال انتشار: 1384
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 2,839

فایل این مقاله در 11 صفحه با فرمت PDF قابل دریافت می باشد

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

NICEC10_046

تاریخ نمایه سازی: 6 بهمن 1385

چکیده مقاله:

Artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving pattern recognition problems. In this study, a new approach based on artificial neural networks (ANNs) has been designed to estimate the Iodine number of activated carbon prepared from Iranian pistachio shell using the thermal activation in special activation conditions. 75% of 108 experimental data of preparation of activated carbon from pistachio shell have been used to train the network. This data include Iodine adsorption capacity (Iodine number) versus temperature, activation time and oxidizing gas type. The present work, applied the Tan-sigmoid transfer function in two layers in the feedforward neural network with backpropagation algorithm. The results from the network are in good agreement with the experimental data and the maximum error is 0.015%. Finally, it is shown that the application of artificial neural networks in estimating the Iodine number of activated carbon prepared from pistachio shell can help us as a valuable tool to predict behavior of the activation in other conditions.

نویسندگان

Baroutian

Chemical Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran

saeed.baroutian@Gmail.com Jeirani

Chemical Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran

Hashemipour Rafsanjani

Chemical Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran

مراجع و منابع این مقاله:

لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :
  • Lau, C., ،Neural Networks: theoretical foundation and analysis. New York: ...
  • Bhat, N., & McAvoy, T. J. (1990). Use of neural ...
  • Wang, H., Oh, Y., & Yoon, E. S. (1998). Strategies ...
  • Psichogios, D. C., & Ungar, L. H. (1992). A hybrid ...
  • ax Iranian Chemical Engineering Congress (IChEC10), Sistan & Balochestan University ...
  • Part 6: Modeling, Simulation, Proces Control 2812 _ [5] Molga, ...
  • Pollard, J. F., Broussard, M. R., Garrison, D. B., & ...
  • Galva n, I. M., Zaldivar, J. M., Herna ndez, H., ...
  • Ro j, E., & Wilk, M. (1998). Simulation of an ...
  • Ramani, S., & Miranda, R. (1996). Neural - network - ...
  • Daniel R. parisi and Miguel A. Laborde(200 1). Modeling steady-state ...
  • heterogeneous gas-solid reactors using feedforward neural networks. Computers and Chemical ...
  • Bryson, A. E., & Ho, Y. C. (1969). Applied optimal ...
  • Werbos, P. (1974). Beyond regression: new tools for prediction and ...
  • Parker, D.B. (1985). Learning logic. Technical Report TR-47. Center for ...
  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. ...
  • Hetch-Nielsen, R. (1987). Kolmogorov's apping neural networks existence theorem. In ...
  • Spretcher, D. A. (1965). On the structure of continuos function ...
  • S. Grossberg, (1987), Competitive learning: from interactive activation to adaptive ...
  • Abazari, A. "Activated carbon production from pistachio shell by thermal ...
  • Howard Demuth and Mark Beale, Neural Network Toolbox for use ...
  • ax Iranian Chemical Engineering Congress (IChEC10), Sistan & Balochestan University ...
  • ax Iranian Chemical Engineering Congress (IChEC10), Sistan & Balochestan University ...
  • نمایش کامل مراجع