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Fast and relatively accurate determination of the fragment size distribution of a muck-pile is still a challenge in mining operations and the existing measurement methods are inefficient. In this research, a new algorithm to determine fragment size distribution due to blasting was presented, using the image processing technique. In the newly proposed approach, delineating of the fragmented rock particles, as the main core of processing, was carried out, using a convolutional neural network. Two networks were defined and trained by 150 laboratory and 150 field data images. Also, 30 laboratory and 30 field data images were applied to carry out the validation visually, and by using F1-scores. For the two laboratory and field networks and results obtained by Split-Desktop software automatic edge detection on the same images, the F1-scores are equal to (0.98, 0.74) and (0.99, 0.85) respectively. Also, for determination of fragment size distribution by laboratory data network and Split-Desktop software automatic edge detection on the same images, the Root Mean Square Error (RMSE) for F30 and F80 are equal to (0.36, 1.20) and (0.31, 1.24) respectively. These indicate better performance of the proposed approach for both rock edge detection and fragment size distribution over Split-Desktop software automatic edge detection.

The aim of introducing occupational exposure limits (OELs) is to use them as a risk management tool in order to protect workers’ health and well-being against harmful agents at the workplace. In this review we identify OELs for benzene, toluene, ethylbenzene, xylene (BTEX), and styrene concentrations in air and assess occupational exposure to these compounds through a systematic literature search of publications published in West Asian countries from 1980 to 2021. OELs for BTEX and styrene have been set in Iran and Turkey to levels similar to those in European countries and the US. The search yielded 49 full-text articles that cover studies of exposure assessment in six countries, but most (n=40) regard Iran. Average occupational exposure to benzene of workers in oil-related industries is higher than recommended OEL, while average occupational exposure to other compounds is lower than local OELs (where they exist). Currently, information about levels of occupational exposure to BTEX and styrene is insufficient in West Asian countries, which should be remedied through OEL regulation and application. Furthermore, coherent research is also needed to determine actual levels of occupational exposure, dose-responses, and the economic and technical capacity of local industries to address current issues.

Face failure is a typical phenomenon in longwall coal mines that can have a wide range of consequences. Face failure, which includes wall spall and roof collapse occurrences, is a hazardous virus that, if not managed, spreads fast across all stages of coal mining and has the potential to disable the mine. Based on this research, face failure may have a detrimental influence on technical, environmental, community, safety, and economic concerns, and its negative effects will leave an unfavourable legacy for the future. As a result, these impacts can be mitigated by effective management and risk management approaches. The quantitative and qualitative face failure risk model provided in this study has a considerable potential as a suitable tool for decision makers to analyse failure risk. Face failure-related high-risk variables can be discovered using this approach, which also makes comparing various mines easier from a face failure aspect. For validation, the model was evaluated in the Parvadeh, Negin and Pabedana coal mines. The study’s findings revealed that Parvadeh’s face failure risk factor was 5058, indicating a high risk in this mine due to mechanized mining. Furthermore, the scores of the Negin and Pabedana mines were computed as 3019 and 3165, respectively, indicating that they were in the moderate risk category owing to traditional mining.

Nowadays, the dimension stone industry performs a crucial role in the world economy. Accordingly, dimension stone quarries’ importance grows due to their different applications in various construction, building, and decorative industries. Some issues threaten this industry and provide a financial risk that should be taken into account to make the smallest possible risk for investment. The presence of discontinuities in the rock mass has a key function as far as it concerns the overall quality of in situ rock blocks. It impacts the feasibility of dimension stone quarries and overall mine exploitation efficiency. Therefore, it is recommended to survey discontinuities and rock blocks and estimate the average geometry of a rock block, including the shape and size, before mining the benches to maximize mining exploitation efficiency and minimize waste ore production. This investigation aims to survey the discontinuities of the limestone quarry mine located in Josheghan, Iran, to determine and calculate rock blocks’ suitable geometry and an extraction’s direction for active mine benches. For this purpose, the scanline method was applied to survey discontinuities in seven active benches. 3DEC software was used to indicate discontinuities and model the rock blocks for all active benches. It was concluded that the benches’ cutting line make a 13.14 degree with the discontinuities main’s direction. The result of this study proved that by changing the direction of mining and extraction for active mine benches, the unnecessary waste production would decrease. The production rate with the recommended extraction direction will increase by about 1.13% compared to the current extraction direction, which makes 13.14 degrees with the discontinuities main’s direction. Currently, bench seven recorded the minimum production rate, which is 97.60; by applying the new extraction’s direction, it is predicted that this bench will achieve a 99.83 production rate. Consequently, it is concluded by improving the production rate, exploitation efficiency would increase considerably.

This study aims to analyze the level of geotechnical risks and predict the advance rate in rock Tunnel Boring Machine (TBM) tunnelling, using a multi-stage fuzzy logic modelling. Twelve parameters, affecting the geotechnical hazard scenario occurrence, which were clustered into five groups, were used as input parameters and the risk level was used as an output parameter. Also, based on the relation between the risk levels and advance rates, a predictive model for advance rate prediction was proposed. To validate the performance of modelling carried out, data from 58 geological zones in section two of the Zagros tunnel, Iran were used. The obtained results showed that by using the fuzzy logic-based model, in most zones, the risk levels estimated are in good agreement with field observations. Moreover, as expected, the high coefficient of determination (R2) of 0.91 between the risk level estimated and the average advance rate achieved in 58 analyzed zones, confirms the ability of the model proposed to predict the level of geotechnical risks. Furthermore, R2= 0.93, Root Mean Square Error (RMSE) of 0.62 and Variance Accounted For (VAF) of 97.51 between the measured and predicted advance rates show the good performance of the new predictive model developed for the advance rate estimation.

The prediction of the advance rate of a Tunnel Boring Machine (TBM) in hard rock conditions is one of the most important concerns for estimating the time and costs of a tunnel project. In this paper, in the first step, a model based on Rock Engineering Systems (RES) is proposed to predict geotechnical risks (representing media characteristics) in rock TBM tunnelling. Fifteen main parameters that influence the geotechnical hazards were used in the modelling. In establishing an interaction matrix and also a parameter rating, the views of five experts were taken into account. The Vulnerability Index (VI) (geotechnical risk levels) for 2058 datasets out of 2168 sets of data from 53 geological zones in 11 km of the Zagros long tunnel was obtained. In the second step, based on the machine operating parameters such as torque, cutter head rotation per minute, cutter normal force and media characteristics (represented by VIs), which were used as input parameters and advance rate was used as an output parameter, while using 2058 datasets, linear and non-linear multiple regression analyses were carried out. 110 datasets (out of 2168 datasets), which were not used in the modelling, were applied to evaluate the performance of regression models and other models in literature and the results were compared. The obtained results showed that the new linear model proposed with R2=0.83 and RMSE=0.12 has a better performance than the other models.

The general aim of this research was to optimise textural properties and to evaluate the possibility of producing ultra-filtrated low-fat cheese (7-9 % (w/w)), containing various concentrations of galactomannan and Novagel (0.1-0.5 % w/w), and assessing textural properties of produced low-fat cheeses and comparing them with full fat ones. According to the results, reducing fat implies increasing the hardness, cohesiveness, gumminess and chewiness of the tested samples. On the other hand, adding galactomannan gum and Novagel, and increasing their concentration, implies reducing all of the above mentioned textural properties. According to the results, increasing the amount of fat and using galactomannan and Novagel gum, implies increasing the adhesiveness and springiness of the tested treatments. The results showed that textural properties including hardness, adhesiveness, cohesiveness, gumminess and chewiness, of sample containing 9 % (w/w) fat, 0.5 % (w/w) galactomannan, and 0.3 % (w/w) Novagel were not of significantly different from the control sample and was selected as the superior sample. Multiple optimization of the low-fat cheeses textural properties via Response Surface Method (RSM) software showed that the treatment containing 9 % (w/w) fat, 0.1 % (w/w) Novagel and 0.46% (w/w) galactomannan fulfils 84 % of desirable properties of a full fat cheese.

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