Reza Mikaeil; Mostafa Piri; Sina Shaffiee Haghshenas; Nicola Careddu; Hamid Hashemolhosseini
Abstract
The noise of drilling in the dimension stone business is unbearable for both the workplace and the people who work there. In order to reduce the negative effects drilling has on the health of the environment, the drilling noise has to be measured, assessed, and controlled. The main purpose of this work ...
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The noise of drilling in the dimension stone business is unbearable for both the workplace and the people who work there. In order to reduce the negative effects drilling has on the health of the environment, the drilling noise has to be measured, assessed, and controlled. The main purpose of this work is to investigate an experimental-intelligent method to predict the noise value of drilling in the dimension stone industry. For this purpose,135 laboratory tests are designed on five types of rocks (four types of hard rock and one type of soft rock), and their results are measured in the first step. In the second step, due to the unpredicted and uncertain issues in this case, artificial intelligence (AI) approaches are applied, and the modeling is conducted using three intelligent systems (IS), namely an adaptive neuro-fuzzy inference system-SCM (ANFIS-SCM), an adaptive neuro-fuzzy inference system-FCM (ANFIS-FCM), and the radial basis function network (RBF) neural network. 75% of the samples are considered for training, and the rest for testing. Several models are constructed, and the results indicate that although there is no significant difference between the models according to the performance indices, the proposed construction of ANFIS-SCM can be considered as an efficient tool in the evaluation of drilling noise. Finally, several scenarios are designed with different input modes, and the results obtained prove that the types of rock and the drill bits are more important than the operational characteristics of the machine.
Akbar Esmaeilzadeh; Sina Shaffiee Haghshenas; Reza Mikaeil; Giuseppe Guido; Roohollah Shirani Faradonbeh; Roozbeh Abbasi Azghan; Amir Jafarpour; Shadi Taghizadeh
Abstract
Iran is one of the countries with the largest number of quarry mines in the world. Diamond cutting wire is usually used in quarries to cut dimension stone cubes, which is accompanied by hazardous events. Therefore, detecting and investigating the possible quarry risks is crucial to have a safe and sustainable ...
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Iran is one of the countries with the largest number of quarry mines in the world. Diamond cutting wire is usually used in quarries to cut dimension stone cubes, which is accompanied by hazardous events. Therefore, detecting and investigating the possible quarry risks is crucial to have a safe and sustainable mining operation. In mine exploitation, maintaining the safety of vehicles and increasing the knowledge of personnel regarding safety issues can considerably mitigate the number or radius of effect of hazards. Hence, the incidents and risks in the West-Azerbaijan quarries in Iran are investigated in this work. To do so, a list of the hazards and their descriptions are first prepared. Then the hazard risk rating is conducted using the Failure Modes and Effects Analysis (FMEA) method. The number of priorities is calculated for each incident based on probability, intensity, and risk detection probability. Finally, the main causes of risks in the studies quarries are identified. The results obtained show that the most likely dangers in dimensional stone mines in West Azerbaijan are diamond cutting wire breaking, rock-fall, and car accidents, with the priority numbers of 216, 180, and 135, respectively. These hazards can be mitigated by applying some preservative activities such as timely cutting wire replacement, utilizing an intelligent system for cutting tool control, necessary personal training, and considering some preservative points.
S. Shaffiee Haghshenas; R. Mikaeil; A. Esmaeilzadeh; N. Careddu; M. Ataei
Abstract
Predicting the amperage consumption of cutting machines could be one of the critical steps in optimizing the energy-consuming points for the dimension stone cutting industry. Hence, the study of the relationship between the operational characteristics of cutting machines and rocks with focusing ...
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Predicting the amperage consumption of cutting machines could be one of the critical steps in optimizing the energy-consuming points for the dimension stone cutting industry. Hence, the study of the relationship between the operational characteristics of cutting machines and rocks with focusing on the machine's energy-consuming is unavoidable. For this purpose, in the first step, laboratory studies under different operating conditions at different cutting depths and feed rates are performed on 12 soft and hard rock samples. In the continuation of the laboratory studies, the rock samples are transferred to the rock mechanics laboratory in order to determine the mechanical properties (uniaxial compressive strength and modulus of elasticity). The statistical studies are performed in the SPSS software in order to predict the electrical current consumption of the cutting machine according to the mechanical characteristics of the rock samples, cutting depth, and feed rate. The statistical models proposed in this work can be used with a high reliability in order to estimate the electrical current consumed in the cutting process.
Rock Mechanics
J. Mohammadi; M. Ataei; R. Kakaie; R. Mikaeil; S. Shaffiee Haghshenas
Abstract
Prediction of the production rate of the cutting dimensional stone process is crucial, especially when chain saw machines are used. The cutting dimensional rock process is generally a complex issue with numerous effective factors including variable and unreliable conditions of the rocks and cutting machines. ...
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Prediction of the production rate of the cutting dimensional stone process is crucial, especially when chain saw machines are used. The cutting dimensional rock process is generally a complex issue with numerous effective factors including variable and unreliable conditions of the rocks and cutting machines. The Group Method of Data Handling (GMDH) type of neural network and Radial Basis Function (RBF) neural network, as two kinds of the soft computing method, are powerful tools for identifying and assessing the unpredicted and uncertain conditions. Hence, this work aims to develop prediction models for estimating the production rate of chain saw machines using the RBF neural network and GMDH type of neural network, and then to compare the results obtained from the developed models based on the performance indices including value account for, root mean square error, and coefficient of determination. For this purpose, the parameters of 98 laboratory tests on 7 carbonate rocks are accurately investigated, and the production rate of each test is measured. Some operational characteristics of the machines, i.e. arm angle, chain speed, and machine speed, and also the three important physical and mechanical characteristics including uniaxial compressive strength, Los Angeles abrasion test, and Schmidt hammer (Sch) are considered as the input data, and another operational characteristic of the machines, i.e. production rate, is considered as the output dataset. The results obtained prove that the developed GMDH model is able to provide highly promising results in order to predict the production rate of chain saw machines based on the performance indices.
A. Aryafar; R. Mikaeil; F. Doulati Ardejani; S. Shaffiee Haghshenas; A. Jafarpour
Abstract
The process of pollutant adsorption from industrial wastewaters is a multivariate problem. This process is affected by many factors including the contact time (T), pH, adsorbent weight (m), and solution concentration (ppm). The main target of this work is to model and evaluate the process of pollutant ...
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The process of pollutant adsorption from industrial wastewaters is a multivariate problem. This process is affected by many factors including the contact time (T), pH, adsorbent weight (m), and solution concentration (ppm). The main target of this work is to model and evaluate the process of pollutant adsorption from industrial wastewaters using the non-linear multivariate regression and intelligent computation techniques. In order to achieve this goal, 54 industrial wastewater samples gathered by Institute of Color Science & Technology of Iran (ICSTI) were studied. Based on the laboratory conditions, the data was divided into 4 groups (A-1, A-2, A-3, and A-4). For each group, a non-linear regression model was made. The statistical results obtained showed that two developed equations from the A-3 and A-4 groups were the best models with R2 being 0.84 and 0.74. In these models, the contact time and solution concentration were the main effective factors influencing the adsorption process. The extracted models were validated using the t-test and F-value test. The hybrid PSO-based ANN model (particle swarm optimization and artificial neural network algorithms) was constructed for modelling the pollutant adsorption process under different laboratory conditions. Based on this hybrid modeling, the performance indices were estimated. The hybrid model results showed that the best value belonged to the data group A-4 with R2 of 0.91. Both the non-linear regression and hybrid PSO-ANN models were found to be helpful tools for modeling the process of pollutant adsorption from industrial wastewaters.
Rock Mechanics
A.R. Dormishi; M. Ataei; R. Khaloo Kakaie; R. Mikaeil; S. Shaffiee Haghshenas
Abstract
One of the most significant and effective criteria in the process of cutting dimensional rocks using the gang saw is the maximum energy consumption rate of the machine, and its accurate prediction and estimation can help designers and owners of this industry to achieve an optimal and economic process. ...
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One of the most significant and effective criteria in the process of cutting dimensional rocks using the gang saw is the maximum energy consumption rate of the machine, and its accurate prediction and estimation can help designers and owners of this industry to achieve an optimal and economic process. In the present research work, it is attempted to study and provide models for predicting the maximum energy consumption of the gang saw during the process of soft dimensional rocks with the help of an intelligent optimization model such as random non-linear techniques, i.e. the Hybrid ANFIS-DE and Hybrid ANFIS-PSO algorithms based upon 4 physical and mechanical parameters including uniaxial compressive strength, Mohs hardness, Schimazek’s F-abrasiveness factors, Young modulus, and an operational characteristic of the machine, i.e. production rate. During this research work, 120 samples are tested on 12 carbonate rocks. The maximum energy consumption of the cutting machine during this work is measured and used as a modeling output for evaluating the performance of cutting machine. Also meta-heuristic algorithms including DE and PSO algorithms are used for training the Adaptive Neural Fuzzy Inference System (ANFIS). In addition, the PSO algorithm has a higher ability in terms of model output and performance indices and has a superiority over the differential evolution algorithm. Furthermore, comparison between the measured datasets with the ANFIS-DE and ANFIS-PSO models indicate the accuracy and ability of the ANFIS-PSO model in predicting the performance of gang saw considering the machine’s properties and the cut rock.
R. Mikaeil; Y. Ozcelik; M. Ataei; S. Shaffiee Haghshenas
Abstract
Evaluation and prediction of performance of diamond wire saw is one of the most important factors involved in planning the dimension stone quarries. The wear rate of diamond wire saw can be investigated as a major criterion to evaluate its performance. The wear rate of diamond wire saw depends upon non-controlled ...
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Evaluation and prediction of performance of diamond wire saw is one of the most important factors involved in planning the dimension stone quarries. The wear rate of diamond wire saw can be investigated as a major criterion to evaluate its performance. The wear rate of diamond wire saw depends upon non-controlled parameters related to rock characteristics and controlled parameters related to characteristics of the cutting machine and operational parameters. Under the same working conditions, the wear rate of diamond wire saw is strongly affected by the rock properties. This is a key factor that required in evaluating the wear rate of diamond wire saw. In this work, the four major dimension stone properties uniaxial compressive strength, Schimazek F-abrasivity factor, Shore hardness, and Young's modulus were selected as the criteria to evaluate the wear rate of diamond wire saw using the harmony search algorithm (HSA). HSA was used to cluster the fifteen different andesite quarries located in Turkey. The studied dimension stones were classified into three classes. The results obtained show that the algorithm applied can be used to classify the performance of diamond wire saw according to its wear rate by only some famous physical and mechanical properties of dimension stone.
