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RISS 인기검색어
- 검색키워드
- 저자 : Rezaie Fatemeh (검색결과 4 건)
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Effect of impeller speed on the Ni(II) ion flotation
Fatemeh Sadat Hoseinian,Bahram Rezai,Elaheh Kowsari,Mehdi Safaric 한국자원공학회 2019 Geosystem engineering Vol.22 No.3
In this study, the effect of impeller speed on Ni(II) removal and water removal was evaluated in ion flotation. The results show that the Ni(II) removal increases with increasing impeller speed from 600 to 800 rpm from less than 41% to 88%, respectively, and after that, it decreases to 79% with increasing impeller speed to 900 rpm in the first 4 min of flotation. The water removal was increased with increasing impeller speed. The Ni(II) removal and water removal were modelled and described as the function of variables such as flotation time and impeller speed using the gene expression programming (GEP). The kinetics study also showed that the removal rate of Ni(II) ions and water were increased with increasing impeller speed.
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홍수와 산사태 취약성도 작성을 위한 전 세계 지공간데이터
이사로,Rezaie Fatemeh (사)지오에이아이데이터학회 2023 GEO DATA Vol.5 No.4
Susceptibility mapping is an important component of natural hazard risk assessment and management. Susceptibility maps for floods and landslides, which are particularly damaging to human life and property, can provide a comprehensive understanding of risk areas and factors related to flood and landslide susceptibility. To create a global flood and landslide susceptibility map, global geospatial data for 37,984 landslide and 6,682 flood locations, as well as 11 selected environmental factors were used to construct a geographic information system database. The 11 environmental factors found to influence flood and landslide occurrence were rainfall, slope, terrain position index, plane curvature, terrain wetness index, distance from rivers, land use, soil texture, soil moisture, geology, and temperature. These data were then used directly to create a global flood and landslide susceptibility map.
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Lee, Saro,Rezaie, Fatemeh National Institute of Ecology 2021 국립생태원회보(PNIE) Vol.2 No.1
The study has been carried out with an objective to prepare Siberian roe deer habitat potential maps in South Korea based on three geographic information system-based models including frequency ratio (FR) as a bivariate statistical approach as well as convolutional neural network (CNN) and long short-term memory (LSTM) as machine learning algorithms. According to field observations, 741 locations were reported as roe deer's habitat preferences. The dataset were divided with a proportion of 70:30 for constructing models and validation purposes. Through FR model, a total of 10 influential factors were opted for the modelling process, namely altitude, valley depth, slope height, topographic position index (TPI), topographic wetness index (TWI), normalized difference water index, drainage density, road density, radar intensity, and morphological feature. The results of variable importance analysis determined that TPI, TWI, altitude and valley depth have higher impact on predicting. Furthermore, the area under the receiver operating characteristic (ROC) curve was applied to assess the prediction accuracies of three models. The results showed that all the models almost have similar performances, but LSTM model had relatively higher prediction ability in comparison to FR and CNN models with the accuracy of 76% and 73% during the training and validation process. The obtained map of LSTM model was categorized into five classes of potentiality including very low, low, moderate, high and very high with proportions of 19.70%, 19.81%, 19.31%, 19.86%, and 21.31%, respectively. The resultant potential maps may be valuable to monitor and preserve the Siberian roe deer habitats.
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Arash Sobouti,Fatemeh Sadat Hoseinian,Bahram Rezai,Sara Jalili 한국자원공학회 2019 Geosystem engineering Vol.22 No.6
Prediction of lead recovery during the leaching process is required to increase the process efficiency by proper modeling. In this study, a new artificial neural network predictive model based on the particle swarm optimization (ANN-PSO) was developed to predict the lead recovery by a hydrometallurgical method of lead concentrate leaching using fluoroboric acid. A multi-layer ANN-PSO model was trained for developing a predictive model based on the main effective parameters on the lead leaching process. The input parameters of the ANN-PSO model were leaching time, liquid/solid ratio, stirring speed, temperature and fluoroboric acid concentration, while the lead recovery during leaching was the output. The results indicate that the proposed ANN-PSO model can be effectively predicted the lead recovery during lead concentrate leaching using fluoroboric acid.
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