Search results for: H. Rahimzadeh
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2

Search results for: H. Rahimzadeh

2 Experimental Study of Adsorption Properties of Acid and Thermal Treated Bentonite from Tehran (Iran)

Authors: H. R. Moghadamzadeh, M. Naimi, H. Rahimzadeh, M. Ardjmand, V. M. Nansa, A. M. Ghanadi

Abstract:

The Iranian bentonite was first characterized by Scanning Electron Microscopy (SEM), Inductively Coupled Plasma mass spectrometry (ICP-MS), X-ray fluorescence (XRF), X-ray Diffraction (XRD) and BET. The bentonite was then treated thermally between 150°C-250°C at 15min, 45min and 90min and also was activated chemically with different concentration of sulphuric acid (3N, 5N and 10N). Although the results of thermal activated-bentonite didn-t show any considerable changes in specific surface area and Cation Exchange Capacity (CEC), but the results of chemical treated bentonite demonstrated that such properties have been improved by acid activation process.

Keywords: Acid activation, Bentonite, CEC, Thermal activation.

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1 A Hybrid Neural Network and Traditional Approach for Forecasting Lumpy Demand

Authors: A. Nasiri Pour, B. Rostami Tabar, A.Rahimzadeh

Abstract:

Accurate demand forecasting is one of the most key issues in inventory management of spare parts. The problem of modeling future consumption becomes especially difficult for lumpy patterns, which characterized by intervals in which there is no demand and, periods with actual demand occurrences with large variation in demand levels. However, many of the forecasting methods may perform poorly when demand for an item is lumpy. In this study based on the characteristic of lumpy demand patterns of spare parts a hybrid forecasting approach has been developed, which use a multi-layered perceptron neural network and a traditional recursive method for forecasting future demands. In the described approach the multi-layered perceptron are adapted to forecast occurrences of non-zero demands, and then a conventional recursive method is used to estimate the quantity of non-zero demands. In order to evaluate the performance of the proposed approach, their forecasts were compared to those obtained by using Syntetos & Boylan approximation, recently employed multi-layered perceptron neural network, generalized regression neural network and elman recurrent neural network in this area. The models were applied to forecast future demand of spare parts of Arak Petrochemical Company in Iran, using 30 types of real data sets. The results indicate that the forecasts obtained by using our proposed mode are superior to those obtained by using other methods.

Keywords: Lumpy Demand, Neural Network, Forecasting, Hybrid Approach.

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