Department of Mine Exploitation (2003 - Present)
Mining engineering
Mining Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, India
Mining Engineering, Mine Exploitation
Faculty of Engineering, University of Tehran, Tehran, Iran
Mining Engineering, Mine Exploitation
Faculty of Engineering, University of Tehran, Tehran, Iran
Dr Masoud Monjezi joined Tarbiat Modares University in 2002 as an assistant professor (currently professor) to be involved in the mining program as an academic. His primary research areas are mine blasting and open pit mine planning and design. He has made significant contributions to the mining program at exploitation division, and this along with his research output and engagement demonstrate level of his academic activities in the field of mining engineering. Dr. Monjezi has paid a lot of attention to his teaching in terms of educational development incorporating project-based learning, work integrated learning and field visits. Masoud has performed extremely well in research related to mine blasting and open pit mining, two fundamentally important areas of great concern in the mining industry. Overall, he has published more than 100 research papers in different reputed national and international journals with high impact factors and reputable conference proceedings. His career h-index is 26 (Scopus) and 1645 citations. He has also published a book (in Persian) entitled Blast Engineering in Open Pits using Intelligent Systems.
Blast-induced ground vibration is considered as one of the most hazardous phenomena of mine blasting, which can even cause casualties and severe damages to the adjacent properties. Measuring peak particle velocity (PPV) is helpful to know the actual vibration level but prediction of blast vibration prior to the blast is a tedious job due to involvement of blast design, explosive and rock parameters. Nowadays, efficient application of intelligent systems has been approved in different branches of science and technology. In this paper, a gene expression programming (GEP) model was developed to predict PPV using various blasting patterns as model inputs, which showed a high level of accuracy for the implemented model. Also, to optimize blast p
Waste rock dumping is very important in the production planning of open-pit mines. This subject is more crucial when there is a potential of acid-forming (PAF) by waste rocks. In such a type of mines, to protect the environment, the PAF materials should be encapsulated by non-harmful rocks. Therefore, block sequencing of the mined materials should be in such a way that both the environmental and economic considerations are considered. If non-acid forming (NAF) rocks are not mined in a proper time, then a stockpile is required for the NAF materials, which later on would be re-handled for encapsulation of PAF rocks. In the available models, the focus is on either block sequencing or waste dumping strategy. In this work, an attempt has been ma
Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (eg, price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting the global iron ore price. Considering the high number of effective parameters and existence of complex relationship among them, artificial intelligence-based approaches can be employed to predict iron ore price. In this paper, a new intelligence system namely group method of data handling (GMDH) was developed and introduced to predict the price of iron ore. For comparison purposes, four other techniques ie, autoregressive integrated moving average
Blasting operations create significant problems to residential and other structures located in the close proximity of the mines. Blast vibration is one of the most crucial nuisances of blasting, which should be accurately estimated to minimize its effect. In this paper, an attempt has been made to apply various models to predict ground vibrations due to mine blasting. To fulfill this aim, 112 blast operations were precisely measured and collected in one the limestone mines of Iran. These blast operation data were utilized to construct the artificial neural network (ANN) model to predict the peak particle velocity (PPV). The input parameters used in this study were burden, spacing, maximum charge per delay, distance from blast face to monit
It is of a high importance to introduce intelligent systems for estimation and optimization of blasting-induced ground vibration because it is one the most unwanted phenomena of blasting and it can damage surrounding structures. Hence, in this paper, estimation and minimization of blast-induced peak particle velocity (PPV) were conducted in two separate phases, namely prediction and optimization. In the prediction phase, an artificial neural network (ANN) model was developed to forecast PPV using as model inputs burden, spacing, distance from blast face, and charge per delay. The results of prediction phase showed that the ANN model, with coefficient of determinations of 0.938 and 0.977 for training and testing stages, respectively, can pro
Block sequencing is of great importance in an open-pit mining operation. Sequencing is usually performed to maximize the net present value (NPV). Also, from the environmental viewpoint, the sequence of dumping mined materials is of significant value in the sulfide mines. The potential acid-forming (PAF) waste rocks in these mines can seriously damage the environment due to the formation of acid mine drainage (AMD). To prevent the exposition of the PAF materials, it is essential to design suitable block sequencing. For this purpose, encapsulation of the PAF rocks by non-acid forming (NAF) rocks should be considered during waste dumping. However, this method can impose unnecessary re-handling costs. This issue is due to the determination of t
Acid rock drainage (ARD), produced from sulfide-bearing mine waste (e.g., waste rock, tailings) at active and abandoned mine sites, continues to be a global concern due to the significant impacts on water, soil, biodiversity, and the creation of public health risks. Many examples demonstrate that it is technically challenging to control and manage ARD, with common methods including costly additive treatments. Instead, an improved approach to ARD management is to minimize opportunities for generation from the outset. In this paper, a new mixed-integer programming (MIP) model is proposed to optimize the placement of waste rock into waste dumps with the objective of minimizing ARD formation. The MIP model considers the net neutralizing potenti
In this paper, the blasting data and rock mass characteristics of Chogart, Chadormalu, and Sechahum mines were used to predict the size distribution of rock fragmentation (D80). Rock fragmentation is affected by various parameters such as rock mass properties, in-situ blocks shape, blasting geometry, etc. To quantify the shape of in-situ blocks, fractal geometry is a suitable method. To predict the rock fragmentation (D80) based on independent variables (rock mass characteristics, in-situ block shape, and blasting geometry); linear/nonlinear regression and neural networks were used. The results showed that the nonlinear regression and neural network were the ability to predict the size distribution of rock fragmentation.? ? Introduction Due
In each project, there is always a possibility of occurrence of hazards and risks. Accidents cause many damages such as financial and psychological problems, that may have a negative effect on the workers life. To prevent or reduce the occurrence of incidents, it is necessary to identify and manage the relevant affecting factors. Blasting is one of the events that has frequently led to accidents. In this paper, 13 factors affecting the occurrence of blasting related accidents in the mining and construction projects, have been selected according to the opinion of experts and ranked to identify the most important one. For this purpose, Monte Carlo simulation method and analytical hierarchy process method were implemented. The factors were ran
Blasting is known as the most common approach for fragmenting rock in open-pit mines. Nevertheless, its side effects are not insignificant, for example, fly rock, ground vibration, dust, toxic by-products, air over-pressure, and back-break. These effects considerably alter the circumambient environment, particularly when pressure is higher than usual. This study proposed and compared four artificial intelligence models for predicting blast-induced air over-pressure, namely multi-layer perceptron (MLP), Random Forest (RF), isotonic regression (IR), and M5-Rules. The air over-pressure was selected as the output variable based on the input variables, ie, stemming length (T), explosive charge per delay (W), burden (B), monitoring distance (R),
Stope layout designing and production scheduling are main phases to determine the profitability of an underground mining project. These are mainly related to the output of one phase, which has a significant impact on generating of the next phase. Individual optimization of these two phases results only in a local optimal solution. To date, the integrated optimization of these phases has been carried out to maximize net present value (NPV). In this paper, a multi-objective integer programming model (MOIP) was developed to optimize this integrated problem in a sublevel stoping operation. The non-dominated sorting genetic algorithm (NSGA-II) was incorporated to solve the objective functions. The Pareto front generated by NSGA-II showed good co
An attempt was made to examine the relationship between various TBM operational factors, its performance, and muck geometry during the excavation of a short geologically uniform section of Golab II water transfer tunnel. A database based on nine field testing data derived from machine operating and performance parameters along with muck shape and size was formed. Subsequently, it was used for analyzing the correlation among variables. The analysis results point out that there is a strong inverse correlation between specific energy (SE), an indicator parameter of rock cutting efficiency, and three muck size indicators including: coarseness index (CI), mean particle size, and absolute grain size (last one is the correlation with R 2= 0.93). T
Truck-Shovel fleet, as the most common transportation system in open-pit mines, has a significant part of mining costs, for which optimal management can lead to substantial cost reductions. Among the available dispatch mathematical models, the multi-stage approach is well suited for allocating trucks to respected shovels in a dynamic dispatching program. However, with this kind of modeling sequencing of the allocated trucks is not possible though it is important to find out the best solution so that getting the minimum accrued cost. To comply with the shortcoming of the traditional model, in this paper, a new hybrid model is developed and applied in Copper Mine of Iran, in which for each truck an allocation matrix is considered as input to
We read the interesting paper by Oakes et al. 1 which present a 3D-0D model for airflow simulations in infant, child, and adult pulmonary conducting airways. However, we found some problems in their model. One of them is about their updating 0D resistance through
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