Saeid R Dindarloo - Academia.edu (original) (raw)

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Papers by Saeid R Dindarloo

Ease of excavation or rippability, also called diggability or excavatibility, is a critical decis... more Ease of excavation or rippability, also called diggability or excavatibility, is a critical decision-making parameter in the selection of both the overburden removal method and the required equipment in surface mining and geotechnical engineering projects. The most widely used method of estimating rippability is rock mass classification. Although there are different classification schemes in the literature, all of them have the two major limitations of sharp transitions at class boundaries and subjective uncertainties in decision making. In this study, the decision trees method was employed as a classification tool for estimating ground rippability, using the four rock mass parameters of weathering degree, uniaxial compressive strength, joint spacing and bedding spacing. The results were compared with those of the widely used method of diggability index rating (DIR) and a fuzzy-based DIR. The problem of subjective uncertainty was resolved in the proposed decision trees method, as the tree rules were derived automatically from the training data sets. Compared with fuzzy-DIR, a decision tree classifier is simpler, needs less computational time and is more appreciable by industry practitioners. Nevertheless, the proposed method is not flawless and, similar to both the conventional and fuzzy DIR methods, yielded poor estimations in particular instances. The limitations of the three methods are discussed.

International Journal of Coal Geology, 2021

Abstract Predictions of the Hardgrove grindability index, a predictor of the crushing and pulveri... more Abstract Predictions of the Hardgrove grindability index, a predictor of the crushing and pulverization propensity of coal, have been made using both regression and neural network techniques. All techniques suffer from shortcomings. In general, input parameters must be selected based on a sound knowledge of coal chemistry and petrology, with avoidance of redundant parameters, avoidance of closure in the data sets that add to 100% (individually the proximate and ultimate analyses, petrology, and (approximately) major oxides), and a constrained coal rank and provenance setting. Predictions based on a specific set of coals are not necessarily translatable to different ranks or maceral suites. In general, for high volatile bituminous coals, combinations of coal rank (vitrinite reflectance or volatile matter), liptinite content, and ash percentage produce the best predictions.

Coal Combustion and Gasification Products, 2015

International Journal of Mineral Processing, 2016

International Journal of Mining Science and Technology, 2016

International Journal of Mining, Reclamation and Environment, 2016

Abstract The high ownership cost of mining equipment mean that downtimes are expensive and should... more Abstract The high ownership cost of mining equipment mean that downtimes are expensive and should be avoided with smart and efficient maintenance planning. Modern mines have large data sets on equipment performance and reliability, from dispatch and manufacturer health monitoring systems, that can be mined for more efficient maintenance planning. This study explores the application of classification and clustering approaches for pattern recognition and failure forecasting on mining shovels. The failure behaviour of a fleet of ten mining shovels during 1 year of operation was investigated using these techniques. The shovels were classified into four clusters using k-means clustering algorithms. Future failures were predicted using the support vector machine (SVM) classification technique. Historical failure and time to repair data were used to predict the next failure type for all shovels. The SVM technique was shown to be successful with prediction accuracy of over 75%. This is the first attempt (to the best of our knowledge) that the failure type is predicted based on historical failure/repair data for mining equipment. Clustering shovels based on their reliability can be used for equipment allocation and maintenance planning. These objectives cannot be achieved with traditional reliability modelling. Successful application of these techniques will be valuable input for decision-making during preventive maintenance scheduling.

Sedimentary Geology, 2015

Cable/hydraulic shovels are the main loading machinery in surface mining operations. Excellent kn... more Cable/hydraulic shovels are the main loading machinery in surface mining operations. Excellent knowledge of reliability, maintainability, and mechanical availability of shovels is a critical issue in mine planning, safety, and economics. In this study, the reliability of a hydraulic shovel was investigated through analysis of the number of failures of its hydraulic system. Failure data of the shovel, during one year of continuous operation, were obtained and preprocessed. Autoregressive integrated moving average (ARIMA) technique was employed for failure modeling and predictions. ARIMA showed to be a viable alternative to the traditional probability distribution fitting techniques. Mean absolute percentage error (MAPE) value of 2.5% was achieved for prediction of the number of failures by the best ARIMA model.

Truck-shovel systems are the dominant material loading and haulage machinery in open pit mining o... more Truck-shovel systems are the dominant material loading and haulage machinery in open pit mining operations. Optimal selection, sizing, and allocation of this capital intensive equipment is highly desirable for both achieving production targets and improving the mine’s economics. In this study, discrete event simulation technique was applied for modeling material transportation in Golegohar open pit iron ore mine in Iran. Direct observation of the material loading and haulage for duration of three months resulted in statistical modeling of the operation in the form of probability distribution functions. These probability models were used as the inputs of a stochastic simulation model constructed in GPSS/H simulation language. The model was validated through comparison with the actual operation and was used to test different what-if scenarios and sensitivity analysis of the current system. Some modifications in the number of trucks, shovels, and dispatching strategies were proposed. S...

Journal of the Southern African Institute of Mining and Metallurgy, 2015

Tunnelling and Underground Space Technology, 2015

Automation in Construction, 2015

Journal of Safety Research, 2016

ArXiv, 2016

Conventionally, many researchers have used both regression and black box techniques to estimate t... more Conventionally, many researchers have used both regression and black box techniques to estimate the unconfined compressive strength (UCS) of different rocks. The advantage of the regression approach is that it can be used to render a functional relationship between the predictive rock indices and its UCS. The advantage of the black box techniques is in rendering more accurate predictions. Gene expression programming (GEP) is proposed, in this study, as a robust mathematical alternative for predicting the UCS of carbonate rocks. The two parameters of total porosity and P-wave speed were selected as predictive indices. The proposed GEP model had the advantage of the both traditionally used approaches by proposing a mathematical model, similar to a regression, while keeping the prediction errors as low as the black box methods. The GEP outperformed both artificial neural networks and support vector machines in terms of yielding more accurate estimates of UCS. Both the porosity and the ...