Papers by Amir Taghizadeh Vahed
International Journal of Oil, Gas and Coal Technology
Coal consumption is one of the critical factors in the economy of China. Flotation separation of ... more Coal consumption is one of the critical factors in the economy of China. Flotation separation of coal from its inorganic part (ash) can reduce environmental problems of coal consumption and improve its combustion. This investigation used random forest (RF) as an advanced machine learning method to rank flotation operations by variable importance measurement and predict flotation responses based on operational parameters. Fifty flotation experiments were designed, and performed based on various flotation conditions and by different variables (collector dosage, frother dosage, air flowrate, pulp density, and impeller speed). Statistical assessments indicated that there is a significant negative correlation between yield and ash content. Experiments indicated that in the optimum conditions, yield and ash content would be 80 and 9%, respectively. Variable importance measurement by RF showed that frother has the highest effectiveness on yield. Outcomes of modelling released that RF can accurately be used for ranking flotation parameters, and generating models within complex systems in mineral processing. [Received: May 20, 2020; Accepted: July 19, 2020]
The objectives of this study are to find the best way to calibrate data that are collected from t... more The objectives of this study are to find the best way to calibrate data that are collected from the energy maps of Turkey and take advantage of alternative electric energy source in construction projects. To achieve these goals, renewable energy maps of Turkey were collected. Four types of energy maps such as solar, wind, geothermal, and wave energy were utilized. Cost effectiveness was proposed for the construction projects that have a total cost of more than a million dollars. Thus it is considered that alternative energy maps in the study could be utilized for construction projects instead of spending too much money using the traditional methods for on-site energy generation. This study is just a research that aims to figure out renewable energy availabilities on construction site(s) and it is also proposing that construction projects need much more research about green energy; thereby the real engineering methods will find their meanings.
Overburden stripping in open cast coal mines is extensively carried out by walking draglines. Dra... more Overburden stripping in open cast coal mines is extensively carried out by walking draglines. Draglines' unavailability and unexpected failures result in delayed productions and increased maintenance and operating costs. Therefore, achieving high availability of draglines plays a crucial role for increasing economic feasibility of mining projects. Applications of methodologies which can forecast the failure type of dragline based on the available failure data not only help to reduce the maintenance and operating costs but also increase the availability and the production rate. In this study, Machine Learning approaches have been applied for data which has been gathered from an operating coal mine in Turkey. The study methodology consists of three algorithms as: i) implementation of K-Nearest Neighbors, ii) implementation of Multi-Layer Perceptron, and iii) implementation of Radial Basis Function. The algorithms have been utilized for predicting the draglines' failure types. In this sense, the input data, which are mean time-to-failure, and the output data, failure types, have been fed to the algorithms. The regression analysis of methodologies have been compared and showed the K-Nearest Neighbors has a higher rate of regression which is around 70 percent. Thus, the K-Nearest Neighbor algorithm can be applied in order to preventive components replacement which causes to minimized preventive and corrective cost parameters. The accurate prediction of failure type, indeed, causes to optimized number of inspections. The novelty of this study is application of machine learning approaches in draglines' reliability subject for first time.
The availability and utilization of draglines, which are massive and expensive machines used for ... more The availability and utilization of draglines, which are massive and expensive machines used for overburden stripping, are a paramount concern for coal producers. Dragline's breakdown and prognostic of its failure types have a significant impact on a maximizing mine's production rate as well as minimizing maintenance and overall operating costs. In this sense, Machine Learning approaches have utilized in order to tackle the optimization issues. The main objective of this research study is prediction of a classification of the failure types for a walking dragline, operating in Tunçbilek coal mine in Turkey, using Multilayer Perceptron and Radial Basis Function approaches. It also aims to compare and assess the utilization and performance of two different algorithms in machine maintenance data classification. The research methodology essentially entails five stages: (i) acquisition of data and its preprocessing including whitening smoothing and noise elimination; (ii) coding and generalizing function approximation; (iii) tuning the function; (iv) implementation of multi-layer perceptron and radial basis function algorithms; (v) evaluation of results and comparison of two different machine learning algorithms for dragline reliability. The main novelty of this study is the utilization of machine learning approaches for dragline maintenance optimization for the first time.
Proceedings of the World …, Jan 1, 2011
The objectives of this study are to find the best way to calibrate data that are collected from t... more The objectives of this study are to find the best way to calibrate data that are collected from the energy maps of Turkey and take advantage of alternative electric energy source in construction projects. To achieve these goals, renewable energy maps of Turkey were collected. Four types of energy maps such as solar, wind, geothermal, and wave energy were utilized. Cost effectiveness was proposed for the construction projects that have a total cost of more than a million dollars. Thus it is considered that alternative energy maps in the study could be utilized for construction projects instead of spending too much money using the traditional methods for on-site energy generation. This study is just a research that aims to figure out renewable energy availabilities on construction site(s) and it is also proposing that construction projects need much more research about green energy; thereby the real engineering methods will find their meanings.
Drafts by Amir Taghizadeh Vahed
— Availability of mining machines plays a significant role in mine production. Dragline's reliabi... more — Availability of mining machines plays a significant role in mine production. Dragline's reliability has a great impact on sustaining economic feasibility of open cast coal mining projects. In that sense, reliability of draglines and optimizing its preventive maintenance are key issues to be addressed. The objective of this study is to apply machine learning methodologies for classifying failure types of a dragline based on a real data. The mean time between failure data was acquired from an operating open cast coal mine in Turkey. Three modified form of K-Nearest Neighbors algorithms have been used as a predictor for failure classification. An approximation function has been generated based on the time to failure and breakdown type. In case of parameter tuning, cross validation method has been utilized. This caused more reliable evaluation of the test sample, so average testing performance has been used for test data estimation. The basic model was for parameter tuning; Moreover, for achieving more efficient parameter Grid Search method was utilized. Since, usage of the algorithm is computationally expensive, so Randomized Search method has been carried out in order to figure out the functionality of modeled function in the high dimension datasets. The results of the study revealed that the application of K-Nearest Neighbors method reached the Regression Analysis of 73 percent. Thus, the higher accuracy of prediction of failure type can be helpful in prognostic of dragline's procedure. The main novelty of this study is utilization of machine learning approach for dragline maintenance for the first time.
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Papers by Amir Taghizadeh Vahed
Drafts by Amir Taghizadeh Vahed