Search results for: A.Rahimzadeh

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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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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Authors: Haleh Rezaei Zanjirabadi, Fatemeh Saberi, Bahman Rahimzadeh, Fariborz Masoudi, Mohammad Rahgosha

Abstract:

In this study, apatite minerals from the iron-phosphate deposit of Yazd have been investigated within the microcontinent zone of Iran in the Zagros structural zone. The geological units in the Esfordi area belong to the pre-Cambrian to lower-Cambrian age, consisting of a succession of carbonate rocks (dolomite), shale, tuff, sandstone, and volcanic rocks. In addition to the mentioned sedimentary and volcanic rocks, the granitoid mass of Bahabad, which is the largest intrusive mass in the region, has intruded into the eastern part of this series and has caused its metamorphism and alteration. After collecting the available data, various samples of Esfordi’s apatite were prepared, and their mineralogy and crystallography were investigated using laboratory methods such as petrographic microscopy, Raman spectroscopy, EDS (Energy Dispersive Spectroscopy), and Scanning Electron Microscopy (SEM). In non-destructive Raman spectroscopy, the molecular structure of apatite minerals was revealed in four distinct spectral ranges. Initially, the spectra of phosphate and aluminum bonds with O2HO, OH, were observed, followed by the identification of Cl, OH, Al, Na, Ca and hydroxyl units depending on the type of apatite mineral family. In SEM analysis, based on various shapes and different phases of apatites, their constituent major elements were identified through EDS, indicating that the samples from the Esfordi mining area exhibit a dense and coherent texture with smooth surfaces. Based on the elemental analysis results by EDS, the apatites in the Esfordi area are classified into the calcic apatite group.

Keywords: Petrology, apatite, Esfordi, EDS, SEM, Scanning Electron Microscopy, Raman spectroscopy.

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