2. Thesis and Dissertations

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    Development of Iot Enabled Lorawan Based Real Time Early Warning Monitoring System for Underground Mine Environmental Parameters
    (National Institute of Technology Karnataka, Surathkal., 2024) Naik, Anil S; Reddy, Sandi Kumar; Mandela, Govinda Raj
    In underground mines, real time monitoring of environmental parameters is crucial for detecting hazardous scenarios during mining operations. This research study explores wireless communication technology and the Internet of Things (IoT) to enhance safety and prevent underground mining accidents, benefitting workers and organizations. Gas parameters like oxygen(O2), Carbon monoxide (CO), Carbon dioxide (CO2), Methane (CH4), Hydrogen sulfide (H2S), Nitrous oxide (NO), Nitrogen dioxide (NO2), Sulfur dioxide (SO2), and Ethylene oxide (EO) and environmental factors like temperature and humidity are monitored using portable multi-gas detectors certified by DGMS and a hygrometer once per shift. A hardware prototype employing IoT-enabled Sx1278 Ra-02 LoRa 433 MHz and ZigBee modules enables wireless communication from underground mine tunnels to the surface. This system was successfully tested in two Indian underground gold mines. Additionally, an IoT-enabled real-time monitoring system using HPD13A LoRa 868 MHz modules integrates CO, CO2, CH4, H2S, H2, temperature, and humidity sensors. Data is stored locally and uploaded to the cloud via LoRa receivers, providing a reliable, power-efficient solution for continuous real time monitoring in underground mines. However, the developed hardware prototype communication range and sensor power consumption limit are deployed in underground mines, especially in harsh environmental conditions. To address these challenges, an IoT-enabled LoRaWAN Gateway based system is proposed. This system integrates industrial RS485 sensors, RS485-LN converter, and LoRaWAN Gateway to monitor environmental parameters from the surface continuously. The system promptly generates an email alert notification on the surface to the concerned authority and initiates an audible alarm alert sound in underground mine tunnels and at the surface when the specified parameters exceed the predetermined thresholds. The developed LoRaWAN system was tested in an underground gold mine 832 meters below the surface, demonstrating effective wireless communication over distances up to 1000 meters. The system facilitates the transmission of vi environmental parameters data of approximately 1800 meters from an underground mine of a specific location to the surface. Real-time data displayed in a surface control room dashboard offers immediate insights into underground mine environment conditions, complementing traditional multi-gas detectors' measurements. The environmental parameters measured by the IoT-enabled LoRaWAN system are compared with those of DGMS-approved multi-gas detection devices. The measurement accuracy for gases like CO2 and NO was recorded at 86.95% and 88.57%, respectively. CO levels spiked during blasting activities. The H2S, CH4, and H2 concentrations were not detected in underground mine tunnels, while N2 concentration was noted at 77.8%. Temperature and humidity readings from the IoT-enabled LoRaWAN system ranged between 28°C to 33°C and 55% to 61%, respectively. In contrast, a portable recorder device reported temperature variations from 31°C to 33.5°C and humidity levels from 58.9% to 61.5%. Environmental data gathered through an IoT-enabled LoRaWAN system is processed using the LSTM and XGBoost machine learning algorithm to predict environmental conditions accurately. The standard validation metric RMSE validates the accuracy of these predictions. Furthermore, the system's design is robust, with intrinsic safety features, flameproof construction, and an IP65-rated panel, making it exceptionally suitable and secure for hazardous underground mine environments. The system design includes inherent safety features and IP65-rated panels for robustness in hazardous environments. In conclusion, this research emphasizes the need for standardized strategies to manage and mitigate hazardous gases in underground mines, particularly from diesel vehicles. Implementation of the IoT-enabled LoRaWAN system proves cost effective and efficient for continuous monitoring, ensuring safety and productivity in underground mining operations.
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    Investigations on the Role of Green Synthesized Iron Nanoparticles in the Fenton’s Oxidation of Triclosan in Wastewater
    (National Institute of Technology Karnataka, Surathkal, 2024) K N, Rashmishree; Shrihari, S.; Thalla, Arun Kumar
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    Design of Operational Strategies for Public Bus Transit System Considering Variations in Passenger Mobility Pattern
    (National Institute of Technology Karnataka, Surathkal, 2024) K S, Nithin; Mulangi, Raviraj H
    In developing countries, the migration of people to urban areas has led to an increase in the urban population. Growth in urban population directly influences the vehicle population, leading to traffic congestion, pollution and accidents. To overcome this, people should be made to choose public transit over private vehicles by making it more efficient and attractive. The transit system can be made efficient and attractive when the travel time of passengers is reduced. This can be achieved by the implementation of operational strategies including limited-stop services, short-turning, dead-heading and so on. However, before the implementation of these strategies, variability in passenger mobility patterns should be assessed to identify which strategy is best suited for a given corridor. Thus, the present study aimed to design operational strategies for public bus transit system (PBTS) considering variability in passenger mobility patterns. Initially, seasonal variations in passenger mobility patterns were analysed to evaluate the impact of weather on passenger behaviour. Based on the factors influencing passenger flow, passenger demand forecasting models were developed at route and stop levels. Then, the operational strategy was designed for PBTS by integrating limitedstop service with the existing all-stop service. Further, a regression model was developed based on variability in passenger mobility patterns to evaluate the feasibility of limited-stop service before implementing it in any corridor. Based on the outcomes obtained from the analysis, recommendations were given to the transport planners to improve the efficiency of the PBTS. The research work was carried out by considering PBTS from two cities i.e., Davanagere and Udupi of Karnataka state, India. The PBTS of both cities is operated by Karnataka State Road Transport Corporation (KSRTC). The Electronic Ticketing Machine (ETM) is used by the systems to collect ticket data. The ETM data of both cities was procured from KSRTC to obtain passenger flow information. Two cities were considered because of their different weather conditions, making it possible to compare the influence of weather on the variability in passenger mobility patterns. To analyse the impact of different intensities of weather on passenger flow, the weather data of both cities was collected from the Indian Meteorological Department (IMD). Further, land use data was also procured from urban development authorities to evaluate variations in passenger mobility patterns at different spatial regions. The impact of weather on passenger mobility patterns was analysed at different temporal and spatial scales. The change in passenger flow with a change in the intensity of weather was computed to evaluate the impact of different intensities of weather on passenger mobility. The analysis was carried out at system, stop and route levels to have microscopic insights into the relationship between weather and passenger flow. Time-series analysis was carried out at the system and stop levels to evaluate the hourly variations in passenger flow at different intensities of weather. At the system level, the were categorised based on land use and aggregated to evaluate the impact of weather on different spatial regions with different land use. Further, at the route level, the variation in passenger flow between the stops of a route was analysed based on the coefficient of variation (CV). The outcome from the analysis indicated that, at high rainfall, the passenger flow increased in Davanagere and decreased in Udupi. At moderate rainfall, only during weekend variation in passenger flow was observed. The village stops of Davanagere experienced a significant increase in passenger demand during the rainy season. Further, high CV was observed at the routes of Davanagere and Udupi under different weather conditions indicating the concentration of demand among a few stops of a route. These results proved that there is a weather-related impact on passenger flow, but it is region-specific. It was not possible to identify a specific pattern in the relationship between passenger flow and weather. Passenger demand forecasting models were developed with the incorporation of factors influencing passenger flow. The models were developed at the route level and stop level using long short-term memory (LSTM) and graph convolutional neural network (GCN), respectively. Based on the analysis of variability in passenger mobility patterns it was evident that passenger flow is influenced by weather attributes at different temporal and spatial scales. As a result, the LSTM model was developed with the incorporation of weather and temporal attributes and the GCN model was developed with the incorporation of land use as a spatial attribute. In the LSTM model, temporal features such as recent time intervals (R), daily periodicity (P) and weekly trend (T) were included along with the weather attributes (W) and named RPTW-LSTM. Recent time intervals are the most recent steps in time-series data that account for short-term fluctuations in the data. Periodicity refers to patterns that repeat at regular intervals in time-series data. Daily periodicity specifically involves daily cyclic repetitions at the same hour. The weekly trend is encompassed in the model to account for long-term changes observed in the passenger flow data. The output (predictions) of hourly variation, daily periodicity, weekly trend and weather models are fused in the final model. The fused model uses LSTM for multivariate time series analysis, accounting for multiple factors that change over time. In the GCN model, the bus network is constructed as a graph with bus stops were considered as nodes and link between two nodes specifying the passenger flow between the stops. For each node, the land use around the bus stops within a service range of 500m was extracted and included as a node feature to capture spatial correlations in passenger flow. The developed models were compared with the baseline models to evaluate the impact of different features. The developed model showed better accuracy than the baseline models. This indicates that the performance of the model can be improved by incorporating the factors that influence passenger flow. Thus, the development of the prediction models assists transport planners in identifying the possible passenger flow variations in future, based on which transit schedules and frequencies can be designed. The operational strategies were designed for the PBTS by integrating limitedstop service with the existing all-stop service. The frequency of the existing all-stop service was shifted to the limited-stop service to avoid expansion of the fleet size, thereby avoiding additional investments. An optimisation model was developed to optimise stopping strategy and frequency of limited-stop service by maximising the total cost savings (TCS). The optimisation model included maximisation of the operator cost and in-vehicle travel time of passengers and minimisation of waiting time for passengers not served by limited-stop service. The limited-stop service was designed for PBTS of both cities. Genetic Algorithm (GA) was used to optimise the stopping pattern and frequency of limited-stop service by maximising the TCS. During each iteration, a single-point crossover and bit-flip mutation were applied to the population to introduce genetic diversity. To maintain the quality of the solutions, two elite individuals with the best fitness values were preserved in each generation. From the results, it was observed that the implementation of limited-stop service resulted in significant savings for both PBTS. The model strategically included important stops and skipped stops with low demand. Further, it was observed that the savings and stopping patterns were dependent on the number of stops and demand variability, respectively. As a result, to further identify the impact of different characteristics of corridors on the performance of the limited-stop service, a feasibility analysis was carried out. The feasibility analysis was carried out to identify when and where limited-stop service can be implemented. A regression model was developed to evaluate the influence of different characteristics of corridors on the savings incurred due to the implementation of limited-stop services. Based on the factors identified, the adaptability of limited-stop service can be verified before implementing it in any corridor. The scenarios were generated based on variability in passenger mobility patterns at different weather and temporal conditions. The limited-stop service was optimised for each of these scenarios and TCS was determined. Then, cost percentage savings (CPS), achieved due to the implementation of limited-stop service compared to total cost (TC) from all-stop service, was computed. Employing CPS as the dependent variable and various characteristics of corridors as independent variables, a regression model was calibrated. The model was found to have a better fit with the variables considered. The features like CV, dwell time, operator cost and number of stops had a significant positive impact on the model and wait time cost had a negative impact on the model. The variable CV with a positive impact indicates that the limited-stop service could be feasible when variability in passenger mobility patterns is high and it was observed that variability was high in different weather and temporal conditions. Similarly, limited-stop service could be feasible in the corridors where operator cost, dwell time and number of stops are high. However, care must be taken to reduce the waiting time of passengers whose stops are not served. Thus, the developed methodology can be adopted by transport planners to assess the variability in passenger mobility patterns and based on the observed variations different operational strategies can be designed to make the PBTS efficient and attractive.
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    Advanced Slope Monitoring System Todevelop Trigger Action Response Plan (Tarp) in Opencast Coal Mines Using Internet of Things (Iot)
    (National Institute of Technology Karnataka, Surathkal., 2024) M., Sathish Kumar; K., Ram Chandar
    In India, coal is the main energy source used to generate electricity and for other industrial purposes. Since coal-based thermal power plants account for a sizable share of India's electricity generation, the demand for coal rises drastically. India currently imports coal from other nations to meet its domestic needs. In India more than 96% coal is produced from opencast mines. Opencast (OC) mines are progressively becoming deeper to meet the increasing demand of coal. Lager and deeper opencast mines result in unstable slopes, leading to slope failures, which pose a major challenge. Slope stability and its monitoring is a serious issue in OC mines. In current scenario, conventional methods are being used for slope monitoring in opencast mine. Such monitoring typically requires a person to be physically present at the site and can only be carried out during the day. On the other hand, Slope Stability Radar (SSR) and Light Detection and Ranging (LiDAR) can monitor slope movements effectively but these are expensive, works on day time only and physical presence of persons required. In order to address this ambiguity, an advanced slope monitoring system is essential. This system should utilize low-cost sensors to monitor parameters affecting slope stability and provide early alerts by analyzing sensor data in real-time. Based on a comprehensive literature review identified, moisture content, vibrations, and displacement are the key factors contributing to slope instability. So, in this study, a slope monitoring system was designed using soil moisture, vibration and displacement sensors to detect and monitor these parameters. This system incorporates Wireless Sensor Networks (WSN) and Cloud Computing Technologies (CCT), enabling early warning alerts via email and SMS when pre-set threshold values are exceeded. The system was developed and implemented in three case study opencast mines. Additionally, a Trigger Action Response Plane (TARP) was formulated based on the rate of displacement and total displacement by the means of Wireless Sensor Networks, existing total station monitoring system, numerical modelling parametric study, that provides guidelines for actions to be taken at various levels of slope stability based on alerts from the monitoring system aided to develop a i user-friendly Advanced Slope Monitoring System (ASMS) software. This system was evaluated for its functions and performance through a laboratory experimentation on a physical slope model after the sensors were calibrated using reliable instruments. Based on the laboratory experiments, soil moisture sensors recorded a maximum of 82% and minimum of 25%. For vibration sensor, a maximum of 80 Hz and minimum of 0 Hz was detected, and 0.25 mm and 5.3 mm displacement is recorded without load and with load condition respectively. While evaluating the effect of soil moisture and vibration on slope displacement, it is identified that the moisture content in the slope has more impact on slope displacement than vibration. Laboratory investigations gave encouraging results on reliability and effectiveness of the developed system to perform field investigations in three different mines. From the field investigations, Kakatiya Khani Opencast-2 Project case study recorded the highest average rate of displacement of 2.12 mm/day, 75% moisture content and 36 Hz vibration. Khairagura Opencast Project recorded 3.27 mm/day rate of displacement, soil moisture content 78% and 28 Hz vibration, at Srirampur Opencast 2 Project rate of displacement is 3.57 mm/day with moisture 82% and 30 Hz vibration. Data collected from the mines of existing total station monitoring system and previous slope failure cases revealed the following observations, upto 50 mm displacement slopes are stable, between 50 mm to 100 mm cracks are generated and from 100 mm to 150 mm indicates potential failures are observed and above 150 mm failures observed. Later, Slope displacement obtained from Wireless Sensor Networks system of case study mines were compared with the displacement readings from the total station and numerical model of the slope that was being monitored. Results obtained at case study-1 mine for displacement through Wireless Sensor Networks system is 25.50 mm (minimum) and 46.80 mm (maximum), through total station monitoring system is 27 mm (minimum) and 49.30 mm (maximum). Similarly, the minimum and maximum displacement through numerical modeling are 29.77 mm and 46.26 mm ii respectively. The percentage of error while comparing with Wireless Sensor Networks and Total Station is below 11.47%, and WSN and NMM methods is not more than 16.75%. Hence, Wireless Sensor Networks based slope monitoring system data is very reliable. Parametric study conducted using numerical modelling studies with varying rock properties and slope geometry. A regression equation is developed for displacement and Factor of Safety (FoS). Advanced Slope Monitoring System (ASMS) software is developed based on the derived equations to track the behavior of slopes in opencast mines. Trigger Action Response Plan (TARP) has been developed based on the field investigations of case study mines. Level 1 indicates, for displacement rates below 0.3 mm/day and total slope movement under 10 mm, recommends a weekly monitoring. Level 2 indicates, rate of displacement between 0.3 mm/day to 10 mm/day and total displacement between 10 mm to 50 mm suggests weekly monitoring and slope indicates no cracks, Level 3 indicates, rates between 10 mm/day to 50 mm/day, and 50 mm to 100 mm total displacement, suggests monitoring every two days and slope indicates with crack. Level 4, rate of displacement between 50 mm/day to 100 mm/day, and total displacement between 100 mm to 150 mm recommends daily monitoring indicating potential failure. Level 5 for rate of displacement exceeding 100 mm/day and total displacement exceeding 150 mm failure takes place and suggesting clearing the area.
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    Improvement of Indian Residential Real Estate Asset Demand and Delivery for Enhancement of Construction Industry Output
    (National Institute of Technology Karnataka, Surathkal, 2024) D K, Apoorva; Mahesh, Gangadhar
    In the context of an absence of studies examining the interrelationship between Indian construction industry and residential real estate sector, the study aims to develop and test a conceptual framework aimed at stimulation of construction industry output through optimization of housing market, followed by development of improvement frameworks for demand and supply side forces of housing market. Means of stimulation of construction industry by residential real estate sector were categorized based on the intent behind purchase of residential real estate assets or inflow of capital into housing development entities. Housing market was examined to identify factors constituting consumer-centric delivery and consumer-empowered demand. Supply side of housing market was probed to identify underlying factors stifling the delivery of housing assets. The identified factors were put together to form the conceptual framework. Questionnaires were developed and administered to both demand and delivery-side stakeholders of housing market, along with carrying out of interviews and document analyses. The study demonstrates significant correlations between real estate investment-led construction industry output stimulation and consumer-centric residential real estate asset delivery. The deterrents to consumer-centric housing delivery have been ascertained to be having an impact on time, cost and scope of housing projects. Significant correlations have been ascertained between these deterrents. On the demand-side, skills, awareness and engagement of consumers are strongly correlated with each other. Affordability of housing is rightfully correlated with all the three means of stimulation of construction industry output. Improvement frameworks for the delivery-side of housing market have been identified to be required to be developed to cover the interfaces of interaction between promoters/developers, judicial/quasijudicial bodies, urban local bodies and governments. Improvement frameworks for the demand-side of the housing market were ascertained to be required to be centered around the different phases of asset acquisition. Specific to the Indian context, the study presents and validates a novel conceptual framework aimed at stimulation of construction industry output through interventions in housing market, along with development of improvements frameworks catering to both demand and supply-side forces of housing market.
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    Development of Eco-Friendly Concretes – A Step Towards Sustainable Construction
    (National Institute of Technology Karnataka, Surathkal., 2024) Reddy, Ramala Rakesh Kumar; Yaragal, Subhash C
    Owing to the rapid advancements in industrialization and urbanization, the utilization of concrete has witnessed an exponential surge over the past few decades. This escalating demand for concrete necessitates a proportional increase in natural resources for both cement production and the procurement of coarse aggregates. Notably, the production of cement not only depletes limestone resources but also entails environmentally unfriendly processes. Furthermore, the surge in concrete demand is accompanied by a substantial increase in the generation of construction and demolition (C&D) waste, owing to evolving trends, economic development, and the heightened demolition of existing structures in the 21st century. This surge in C&D waste generation poses a significant environmental challenge. To render concrete production more sustainable, it is imperative to mitigate the reliance on conventional cement. This can be achieved through the incorporation of supplementary cementitious materials and the utilization of C&D waste as aggregates in concrete. Such measures not only diminish the demand for natural aggregates but also contribute to reducing landfill volumes, aligning with contemporary principles of environmental conservation and sustainable development. The present study focuses on the processing of C&D waste into the quality of Recycled Coarse Aggregates (RCA) and uses them in the production of cement-less concrete. This study proposes an alternative method for processing the demolition waste into high-quality recycled coarse aggregate using the ball mill. Taguchi’s design of experiments based on orthogonal array was used to minimize the number of trials for saving material and time. Experiments were carried out based on L25 orthogonal array with three processing parameters: charge, revolution duration, and aggregate weight with five levels. The revolution speed of the ball mill was set to 60 revolutions per minute. The Taguchi method was then combined with grey relational analysis to achieve the best combination of processing parameters for producing high-quality aggregate. Experimental studies on water absorption, specific gravity, impact value, and abrasion value were used to assess the quality of recycled coarse aggregates. The best combination for each performance characteristic was achieved by using the mean of Signal to Noise ratio graphs. The optimal combination of processing parameter levels i to generate superior quality recycled aggregates and the most significant processing parameter were identified based on the response table of means of grey relation grade. The processed RCA along with Ferrochrome Slag aggregates (FCSA) was used for the production of One-Part Alkali-Activated concrete (OPAAC) by replacing cement with Fly ash (FA), Micro silica (MS), and Ground granulated blast furnace slag (GGBS). The proportion of MS is maintained at 20% of FA, while the maximum replacement of FA with GGBS is set to 60%, varying in 20% intervals (i.e., 0%, 20%, 40%, and 60%). Moreover, the natural aggregates (NA) are substituted with RCAs, FCSAs, or a combination of both. Additionally, microstructural and mineralogical investigations are conducted to determine the formation of distinct hydration products, utilizing scanning electron microscopy (SEM) and X-ray diffractometry (XRD) techniques. In OPAAC containing FA, the primary hydration products identified are alkaline alumino silicate hydrates (CASH and NASH). As the GGBS content increases, calcium silicate hydrate (CSH) becomes the predominant hydration product. Furthermore, in order to assess the sustainability of OPAAC, an analysis of embodied CO2 emissions is performed, and the results are compared with CC and alkali-activated concrete. Notably, OPAAC comprising 40% FA replaced with GGBS, 50% RCAs, and 50% FCSAs demonstrates the most favourable mechanical properties and exhibits lower CO2 emissions. In this study, an examination of the performance of OPAAC mixes under elevated temperatures was also conducted. The mechanical properties results dictated a fixed combination of RCAs and FCSAs)at 50% each. The binder composition was identified as a critical factor influencing the performance of concrete at elevated temperatures. Consequently, OPAAC mixes were meticulously formulated using various combinations of FA, MS, and GGBS. These mixes underwent exposure to temperatures ranging from 200℃ to 800℃, with increments of 200℃. Notably, the mix comprising 60% FA and 40% GGBS exhibited superior performance compared to all other OPAAC mixes and conventional concrete under the specified elevated temperature conditions.
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    Uncertainty Quantification in Structural Systems Using Universal Grey Theory
    (National Institute of Technology Karnataka, Surathkal, 2024) Kumar, Akshay; Balu, A S.
    In recent years, increasing attention has been directed toward addressing uncertainties in engineering systems, which may arise from both aleatory and epistemic sources. Aleatory uncertainty originates from the inherent randomness of physical systems, whereas epistemic uncertainty stems from incomplete or limited knowledge. The present study focuses exclusively on the quantification of epistemic uncertainty in engineering systems. When system information is imprecise or partially available in the form of intervals or ranges, methods such as the combinatorial approach, interval methods (IM), and Universal Grey Theory (UGT) are commonly employed. However, as system dimensionality increases, significant challenges arise. Combinatorial optimization becomes computationally expensive for large-scale systems, while interval methods often lead to overestimation due to dependency issues and violations of physical laws. To overcome these limitations, UGT offers a promising alternative by satisfying distributive laws, thereby effectively addressing dependency problems and improving computational efficiency. Nevertheless, traditional UGT faces limitations in situations where one or both interval bounds are negative and the upper bound has a smaller absolute value. To address this issue, the present study proposes a necessary modification to the arithmetic operations within the UGT framework. Another critical aspect of efficiently quantifying epistemic uncertainty in large-scale systems is computational cost. Finite element–based uncertainty analysis is often computationally demanding for high-dimensional systems. Although metamodeling techniques such as the Response Surface Method (RSM) and Kriging can approximate original models, their computational expense increases significantly with system dimensionality. High Dimensional Model Representation (HDMR), a variant of RSM, emerges as a computationally efficient alternative for handling large-scale systems. To effectively quantify epistemic uncertainty in high-dimensional systems, this study proposes an integrated HDMR–UGT–based formulation. In this approach, HDMR is employed to construct response surfaces using finite element simulations, while UGT is utilized for explicit uncertainty propagation and prediction of response bounds. The proposed methodology is validated through numerical examples, demonstrating high accuracy and significant computational efficiency in exclusively quantifying epistemic uncertainty. Comparative studies with conventional techniques further confirm the effectiveness of the proposed HDMR–UGT formulation in reducing computational effort while maintaining high accuracy in the analysis of engineering systems subjected to epistemic uncertainty.
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    Performance of a Boost Multi Level Inverter for Mining Applications
    (National Institute of Technology Karnataka, Surathkal., 2023) N.V.V., Prudhvi Krishna B; B.M., Kunar; Ch.S.N., Murthy
    In recent, much advancement has been witnessed in the field of drive systems. Electrical variable speed drives (VSDs) are integral to mineral processing and mining. VSDs are widely used in mining, drilling, traction, air compressors, water pumps, and conveyors. In fact, drives are back bone for any application, In the field of mining, drilling, traction systems drives are the only important parts of the entire control units. In this regard, the inner structure of drive i.e., inverter is key unit for the same. In the present study a novel inverter structure is designed for different mining, traction and conveyor applications. Although applications are plenty but still the proposed inverter is capable to meet the aforesaid applications. The back ground of drives system is that, for many years DC motors were used in mining applications. Steadily those motors are being replaced with AC motors due to several advancements in the AC drive technology. In addition to better control, AC motors require less maintenance than DC motors. Thus, controlling AC motors with drive system is a challenging issue. This aspect is key for new research and developments in the field of drives. Although voltage control and current control drives exists, still VSD’s have their own functions and features in terms of utilization. Traditionally VSDs are the part of the drive systems, VSD’s are mainly based on the conventional voltage source inverters (VSIs). Nevertheless, the conventional VSIs suffer from the barriers like output voltage limitations, harmonics, and control complexities. Since the loads are variable in the mining applications, continuous and reliable power converters are much needed. Multilevel inverters (MLIs) have become a promising alternative to conventional VSIs due to efficiency, ample power capacity, high-quality injected currents, and less complexity. Most of the available MLIs possess unity or stepped-down voltage feature. In such cases, a front-end boosting stage or step-up transformer at the output is used to meet the voltage requirement. Using MLIs with inherent boosting ability in such a scenario is more logical. Capacitor-based MLIs with boosting ability and self-voltage balancing are termed switched capacitor MLIs (SC-MLIs). Likewise, MLI topologies presented in this thesis amplify the voltage level using a single dc source. These units can be cascaded to produce higher levels by considering a proper choice of the magnitude of the multiple dc sources. The motivation of this research work is to emanate a novel switched-capacitor-based boost MLI for mining and drilling applications. In particular, the proposed inverter is designed for v nine-levels by using two capacitors as virtual sources. To support the proposed configurations, detailed operating principles, modulation, and real time results are presented. The Proposed MLIs is capable to generate large number of levels with a single source if it extended further. However, to achieve nine levels only 11 switches and 2 capacitors are used and this enables the proposed topology in compact and efficient manner.
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    Influence Of Enhanced Production and Pit Dimensions of Opencast Coal Mines on Dust Concentration
    (National Institute of Technology Karnataka, Surathkal., 2024) Podicheti, Ravi Kiran; K., Ram Chandar
    Opencast coal mines play a crucial role in meeting the energy demands of a country. With significant availability of indigenous coal reserves and its affordability, coal is likely to continue as primary source of energy to meet the developmental needs of rising economy. However, the operations will result in deterioration of ambient air quality, particularly due to emission of particulate matter (PM), PM10 and PM2.5. PM10 consists of PM size less than 10 microns and PM2.5 consists of PM size less than 2.5 microns. The PM disperses to different directions with varying concentrations based on the quantum of mining operations and local meteorological parameters. Monitoring of PM dispersion is essential due to its associated health impacts. These particles can penetrate deep into lungs, potentially triggering respiratory and cardiovascular illnesses. Prediction of ambient dispersion of PM helps in taking mitigation measures for reducing the dispersion and thus exposure. Development of region-specific dispersion models with respect to changing mine parameters and local meteorological conditions for PM10 and PM2.5 emanating from opencast coal mines are necessary for accurate prediction of PM dispersion. Though number of techniques exists for prediction of dust concentration due to opencast mining, machine learning offers several advantages over traditional modeling techniques in terms of data driven insights, non-linearity, flexibility, handling of complex interactions, anomaly detection, etc. An approach is made in this research to analyze the concentration of PM in and around 6 opencast coal mines of the Singareni Collieries Company Limited located in Godavari Valley Coal Field, Telangana State of India and to predict the PM concentrations in core and buffer zones using machine learning techniques. The study involved collection of 10 years historical data comprising of 31,680 observations related to mine operating parameters, meteorological and PM data for processing and subsequent dust predictions. Data has been analyzed using multivariate regression and different machine learning techniques like Decision Tree, Bagging and Random Forest (RF). i The performance metrics of test data compared in order to find the best fit model among these techniques. It has been found that RF method has given better accuracy. A software program PMPOM (Particulate Matter Prediction in Opencast Mines), has been developed using Python with RF algorithm to predict PM10 and PM2.5 values at select locations in each mine. Further, contour plots have been developed using Quantum Geographic Information System (QGIS), a geospatial modelling application, for visualization and spatial analysis of PM dispersion patterns within and surrounding the mine. This comprehensive approach has been instrumental in creating more accurate and robust dust dispersion model for the entire region. The study also aimed at determining the ratio of PM2.5/PM10 which is helpful in arriving at the concentration of PM2.5 from PM10 or vice-versa in case of non measurement of any one parameter. It has been concluded that a relationship is not possible to establish between PM2.5 and PM10 in the core zone, whereas in the buffer zone is feasible.
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    A Framework for Enhancing Sustainable Competency of Small and Medium Contractors in the Ethiopian Construction Industry
    (National Institute of Technology Karnataka, Surathkal, 2024) Bekele, Abraham Aboneh; Mahesh, Gangadhar
    Small and medium contractors (SMCs) are vital in promoting socioeconomic development, particularly in developing economies, as they constitute a significant portion of the construction industry (CI). Their significance lies in their ability to create employment opportunities, generate revenue, develop infrastructure, and have strong links with other sectors of the economy, which have multiple effects on the country's growth. While acknowledging their significant importance and contribution, it is evident that there is a need to enhance and maintain their competency in light of various challenges affecting their growth. This research aims to devise a sustainable competency development framework for enhancing the competitiveness of SMCs in the Ethiopian CI and establish management mechanisms to facilitate their business sustenance. The specific objectives are to: identify factors affecting sustainable competency of SMCs; assess the effectiveness of the development programs in enhancing progress in the CI; evaluate the prevailing opportunities to create sustainable SMCs and develop appropriate improvement mechanisms to exploit these opportunities; and develop sustainable competency development framework for SMCs. The study employed qualitative and quantitative research methods. This approach allowed for gathering input from industry stakeholders, which was then used to develop the framework. The findings of this study provide a comprehensive understanding of the factors impacting the sustainable competency of SMCs in Ethiopia. The study identified the major underlying factors or challenges, such as the lack of project management skills; low-profit margin due to high competition; inability to access plants and equipment; and the inability to access financial resources emanating from endogenic core sources. Additionally, the study also identified factors or challenges stemming from exogenic core sources including unfavourable financial policy, lack of trust between parties in the industry, and uncertainty in supplies of materials and prices. Furthermore, the study's findings offer valuable insights into potential improvements that could enhance the prospects of sustainable SMCs development in Ethiopia. These improvements encompass encouraging local construction material producers and enhancing their capacity, advocating for an industry-based education system, introducing sector-specific financing programs, and implementing project planning, scheduling, and performance tracking practices. The study's findings highlight priority areas for enhancing competitiveness, providing valuable guidance for policymakers, regulators, entrepreneurs, and other stakeholders in making informed decisions.