Faculty Publications
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Item Prediction of Energy Efficiency of Main Transportation System Used in Underground Coal Mines – A Statistical Approach(Springer Nature, 2020) Sarathbabu Goriparti, N.V.; Murthy, C.S.N.; Mangalpady, A.Transport in underground mines i.e. belt conveyor is used for carrying extracted materials from one station to other. Transportation involves energy as its main consumer. An efficient energy system adapted for transporting extracted materials can minimize energy losses, hence resulting in reduced cost of energy. Energy to transportation is provided by means of an electric motor, the efficiency of the electric motor depend on load carried by the system, the length and height to which the material has to be delivered. The present study was carried on the energy efficiency of three different transportation systems in GDK-1&3 incline underground mine, The Singareni Collieries Company Limited, Ramagundam. The present study was carried out considering two cases with first, load varying from 20% to 100% keeping conveyor speed constant. Secondly, with 20% fixed loading and varying the conveyor speed from 1 m/s to 2 m/s. Estimation of the energy efficiency for a unique electric motor was estimated considering both the cases which involved three different lengths and heights. It was observed that with a constant conveyor speed of 2 m/s and filling rate varying from 27.775 kg/m to 5.555 kg/m, the amount of increase in efficiency was found to be 23.92%, 18.75% and 5.25% for Gantry, 5L and Surface conveyors respectively. Also with a constant filling rate of 5.555 kg/m and conveyor speed varying from 1 m/s to 2 m/s, the amount of decrease in efficiency was found to be 13.63%, 11.52% and 1.64% for Gantry, 5L and Surface conveyors respectively. Further a prediction study was carried on the energy efficiency based on the input parameters load, length and height. The model gives an R2 value 87% which is significant. © 2020, Springer Nature Switzerland AG.Item Evaluation of solar PV panel performance under humid atmosphere(Elsevier Ltd, 2020) Tripathi, A.K.; Ray, S.; Mangalpady, A.; Prasad, S.The main aim of this paper is to study the effects of humidity on the PV panel. In this paper, the panel performance was studied in the laboratory under varied humid atmosphere. The PV performance parameters were computed by measuring its output voltage and current, amount of solar radiation incident on the panel's surface and its surface temperature by varying humidity levels artificially in the laboratory. From the studies it was observed that with rising humidity levels, solar insolation and panel power output decrease. With an increment of 50.15% in the humidity level, the panel power output reduces by 34.22%. Moreover, it was found that due to the increase in humidity from 65.40% to 98.20% the panel temperature got lowered by 11.40%. © 2020 Elsevier Ltd. All rights reserved.Item Evaluation of Whole Body Vibration of Heavy Earth Moving Machinery Operators(Springer Nature, 2020) Jeripotula, S.K.; Mangalpady, A.; Raj, G.R.Operators of Heavy Earth Moving Machinery (HEMM) performing routine tasks in surface mines are highly vulnerable to whole body vibration (WBV) due to their continuous exposure to vibration. In the present study seventeen types of machinery were considered for the evaluation of the operator’s exposure to WBV. The measurements were made by placing the triaxial seat pad accelerometer on operator’s seat-surface as well as at the seat-back. Among these machinery one shovel, two front-end loaders, three drills, one grader and one water sprinkler were found to have RMS values in the severe zone as per ISO2631-1:1997 standards for seat-surface measurements. Similarly, for the seat-back measurements, one front-end loader, two drills, one grader and one water sprinkler were experienced the highest RMS value. For both seat-surface and seat-back measurements, Z-axis (i.e. vertical direction) was found to be a prominent axis for most of the machinery. © 2020, Springer Nature Switzerland AG.Item Evaluation of Whole-Body Vibration (WBV) of Dozer Operators Based on Job Cycle(Springer, 2019) Jeripotula, S.K.; Mangalpady, A.; Raj, G.R.Dozer operators are frequently exposed to high levels of occupational vibration. So far, no study reported component wise evaluation of dozer cycle of operation. In the present study, WBV data were collected by placing the trial accelerometer at operator’s seat-surface and at seat-back. Frequency-weighted root mean square (RMS), vibration dose value (VDV) and crest factor were collected for each dozer for two phases’ forward motion and return motion. All the dozers under study were found to be in severe zone with respect to measured RMS, during forward motion and return motion, irrespective of type of measurements (i.e., seat-surface and seat-back). As per VDV, out of eight dozers three dozers were found to be in caution zone during forward motion and three in return motion. According to EU Directive 2002 (as per RMS), all the dozers under study have reported exposure action value above 0.5 m/s2. Further, out of eight dozers, four dozers have shown exposure limit value above 1.15 m/s2 for seat-surface measurements and three dozers for seat-back measurements. Vibration mitigation strategies should be adapted not just based on intensity of vibration but also with respect to dominant axis of vibration. Considering the severe health risk due to the translational vibration (i.e., in x-direction), the vibration risk in the forward x-direction can be reduced by using seat belt; similarly in rear x-direction it can be attenuated by placing lumber-assisted back rest. © 2019, The Institution of Engineers (India).Item Evaluation of Whole Body Vibration (WBV) of Dumper Operators Based on Job Cycle(Springer, 2020) Jeripotula, S.K.; Mangalpady, A.; Raj, G.R.Dumper operators are frequently exposed to whole body vibration (WBV) in surface mines. Surface mining activities involve the amalgamation of comparatively high intensity of vibration and extended exposure durations. Efficient risk reduction mandates knowing of important phases of a task that poses a threat to health of dumper operators. So far in India very limited studies have been reported on WBV exposure with regard to surface mines. This paper evaluates WBV of dumper operators based on ISO 2631-1:1997 Standards and European Union (EU) Directive 2002 for different phases of job cycle. Six dumpers were considered for this study and the vibration measurements were made for all the four phases of the job cycle by placing the triaxial accelerometer on the operator’s seat-surface and at the seat-back, independently. The findings of the study revealed that the haulage task (loaded travel and empty travel) remains the chief contributor to vibration exposure according to seat-surface and seat-back measurements. Maximum frequency weighted root mean square (RMS) of 1.12 m/s2 and 1.09 m/s2 were reported for empty travel task for seat-surface and seat-back measurements, respectively. For seat-surface measurements based on RMS, Z axis remains as the dominant axis of vibration for all the dumpers during haulage task, whereas for seat-back measurements, the dominant axis varies between X and Y. Exposure action value (EAV) based on RMS has exceeded the threshold value of 0.5 m/s2 for all the dumpers during loaded travel and empty travel for seat-surface as well as for seat-back measurements. © 2019, Society for Mining, Metallurgy & Exploration Inc.Item Musculoskeletal Disorders Among Dozer Operators Exposed to Whole-Body Vibration in Indian Surface Coal Mines(Springer, 2020) Jeripotula, S.K.; Mangalpady, A.; Raj, G.R.Dozer operators are frequently exposed to whole-body vibration (WBV) during the execution of their work. Occupational exposure to WBV in Indian surface coal mines was evaluated by measuring vibration intensity and duration of exposure. A triaxial accelerometer was placed on the operator seat surface for taking the readings. Based on frequency-weighted root mean square acceleration equivalent to 8-hr shift duration, i.e., (A(8)) all dozer operators have exceeded an Exposure Action Value (EAV) of 0.5 m/s2, and 90% of dozers did not exceed Exposure Limit Value (ELV) of 1.15 m/s2. Based on Vibration Dose Value (VDV (8)), all dozer operators have exceeded Exposure Limit Value (EAV) of 9.1 m/s1.75, but no dozer operators have exceeded Exposure Limit Value (ELV) of 21 m/s1.75. Further, an epidemiological study was performed for identifying the extent of musculoskeletal disorders (MSDs) among dozer operators. For the detailed study, 42 dozer operators and 22 controls were selected from 2 surface coal mines. The control group was not exposed to WBV. It was seen from the cross-sectional study that pain in the lower back was predominantly higher (83.33%) in the exposed group when compared with the control group (31.81%). Likewise, pain in the neck (47.61%), shoulder (42.85%), knees (42.85%), and ankle (11.90%) was higher in the exposed group than that of the control group (22.71%, 0%, 45.45%, and 4.54%). A significant observation among the exposed group was that there was degradation in the quality of life. The outcome of the study would assist in monitoring and mitigation of machinery-induced vibration diseases (MIVD) in India and generally applicable to most of the mechanized mines as well. However, comprehensive studies are still needed to enunciate the magnitude extent. © 2020, Society for Mining, Metallurgy & Exploration Inc.Item Assessment of Exposure to Whole-Body Vibration of Dozer Operators Based on Postural Variability(Springer, 2020) Jeripotula, S.K.; Mangalpady, A.; Raj, G.R.The main aim of this work is to evaluate whole-body vibration (WBV) of dozer operators based on three sitting postures (i.e., with 15° lean forward inclination posture, vertically erect posture with no inclination, and with 15° lean backward inclination posture) in Indian surface coal mines. A seat pad tri-axial accelerometer was used to collect WBV data from six dozer operators for three different sitting postures. Results showed that except for Dozer-1, 2, 4, and 5 operators during lean forward sitting posture and Dozer-4 operator during vertical erected posture, no other dozer operators have exceeded an exposure limit value (ELV) of 1.15 m/s2 in any of the considered sitting postures. Similarly, the vibration dose value (VDV) based on exposure action value (EAV) of 9.1 m/s1.75 has surpassed for all the dozers. But no dozer operator has exceeded an exposure limit value of 21 m/s1.75. The outcome of the study infers that based on “above health guidance caution zone (HGCZ)” for daily vibration exposure, i.e., A(8) measurements, for the operator sitting in lean backward postures the vibration amplification was reduced by 32.89% less compared with lean forward posture and 16.23% less when compared with vertically erected posture. Similarly, based on VDV(8), the exposure to vibration for the lean backward posture was reduced by 33.34% when compared with lean forward posture and 17.11% less when compared with vertically erected posture. Based on the above observation, it is concluded that lean back inclination with a trunk flexion of 15° is a favorable sitting posture, as it exposes the dozer operators to minimum vibration. © 2020, Society for Mining, Metallurgy & Exploration Inc.Item Assessment of Driving Posture Ergonomics in LPDT Operators using the Rapid Upper Limb Assessment Method(World Researchers Associations, 2025) Prashanth, M.H.; Mangalpady, A.; Kar, M.B.This study aimed to determine the postural risk of Low-Profile Dump Truck (LPDT) operators working in an underground metal mine. A total of 38 LPDT operators aged between 18 and 56 years, with at least 6 months of professional driving experience and no history of injuries, were selected for this study. The postural data of operators were collected by placing the Nikon D5600 camera inside the 20-ton capacity LPDT cabins which are equipped with various ergonomic features such as gas seat suspension, adjustable seat height and backrest. The driving postures of operators were recorded in the sagittal plane while performing various job cycles such as loading, loaded travel, unloading and empty travel. The ergonomic assessment of these postures was done using the standard Rapid Upper Limb Assessment (RULA) chart. The results of this analysis showed that LPDT operators were sitting in the driving posture corresponding to the low (86%) and medium (14%) risk of Work-Related Musculoskeletal Disorders (MSDs). Further, it was observed that the mean RULA score during the dynamic operations (i.e. loaded and empty travel) was relatively high compared to the static operation (i.e. loading and unloading). The visual examination of the video footage showed that the operators faced visibility issues and were compelled to lean forward to see the road clearly. This resulted in a high RULA score during dynamic operation. The study highlighted the need for ergonomic intervention to prevent the LPDT operators from MSDs. © 2025, World Researchers Associations. All rights reserved.Item Human-in-the-Loop Data Analytics for Classifying Fatal Mining Accident Causes Using Natural Language Processing and Machine Learning Techniques(Springer Science and Business Media Deutschland GmbH, 2025) Sharma, A.; Kumar, A.; Vardhan, H.; Mangalpady, A.; Mandal, B.B.; Senapati, A.; Akhil, A.; Saini, S.Mining remains one of the most hazardous industries globally, marked by frequent fatalities resulting from complex operational risks. While accident investigation reports hold valuable insights for improving safety practices, the manual coding of fatality narratives remains labor-intensive, inconsistent, and impractical for large datasets. Although natural language processing (NLP) and machine learning (ML) techniques have gained traction for automating the analysis of safety narratives in other high-risk industries, their application to mining accident data, particularly within the Indian context, remains limited. Addressing this gap, the present study proposes a ML framework for the semi-automated classification of fatal accident causes from unstructured text narratives reported by the Directorate General of Mines Safety (DGMS) between 2016 and 2022. A total of 401 fatal accident descriptions were pre-processed and vectorized using Bag-of-Words, TF-IDF, and Word2Vec techniques, followed by model evaluation across multiple algorithms. A semi-automated classification scheme was developed to balance efficiency with expert oversight, where high-confidence predictions were assigned automatically and uncertain cases were flagged for manual review. Logistic regression combined with TF-IDF unigram features achieved the highest performance, with an F1 score of 0.78 and an accuracy of 0.81. Overall, the developed framework successfully auto-coded 68.75% of cases with 94% accuracy, 0.93 recall, and 0.91 precision. Word cloud visualizations were also employed to capture dominant words associated with different cause categories. The proposed framework offers a practical and operationally feasible solution for assigning fatality causes in the mining sector, contributing to active safety management, surveillance, and policy formulation. © Society for Mining, Metallurgy & Exploration Inc. 2025.