Advancing management of mining-related lung diseases: Synergies between nutraceuticals and artificial intelligence

Mining-related occupational lung diseases remain a major global health burden, with occupational exposures such as coal mining dust exacerbating their incidence and severity. While conventional pharmacotherapies remain essential, their long-term efficacy is often constrained by adverse effects and a diminishing therapeutic response. Nutraceuticals, encompassing phytochemicals, probiotics, prebiotics, omega-3 fatty acids, vitamins, and trace elements, are emerging as promising adjuncts that offer antioxidant, anti-inflammatory, and immunomodulatory benefits, directly targeting the molecular mechanisms underlying respiratory pathology. Concurrently, advances in artificial intelligence (AI) have redefined disease management and therapeutic innovation. Machine learning, deep learning, and large language models (LLMs) now accelerate nutraceutical discovery and optimization through metabolomics integration. These computational tools not only predict bioactive compound efficacy but also facilitate biomarker identification, early diagnosis, and personalized treatment design. Clinical applications, ranging from convolutional neural network (CNN)-powered imaging diagnostics and AI-enabled wearable monitoring to ChatGPT-like LLMs, may assist clinicians by summarizing records or providing literature-informed suggestions, thereby improving respiratory care. However, these systems should be considered adjunctive, and their use requires careful validation, clinician oversight, and bias mitigation. This review uniquely bridges nutraceutical science with AI-enabled innovation, highlighting their synergistic potential to overcome the limitations of conventional therapies. By integrating molecular insights, digital intelligence, and clinical applications, the nutraceutical-AI alliance paves the way for predictive, preventive, and personalized respiratory medicine. Future research must now prioritize clinical validation, ethical governance, and multi-omics integration to translate this transformative paradigm into sustainable global healthcare

Bridging Geochemistry and Chemical Engineering: A Comprehensive Approach To Acid Mine Drainage Remediation and Water Recovery

Acid Mine Drainage is a persistent environmental problem associated with coal and metal mining. This is characterized by extreme low pH, elevated sulfate levels, and mobilized metals which causes considerable degradation of the surrounding ecosystems. In this study, we develop an integrated approach for assessing the viability of AMD mitigation combining statistical interpretation of water chemistry, geochemical modeling, and engineered treatment design. Multivariate analyses were conducted to determine how pH, sulfate, and dissolved metals are interrelated. It demonstrated distinct co-mobilization patterns that provided insight into the dominant controls on AMD composition. Geochemical modelling using PHREEQC on AMD samples collected from northeastern India was used to assess mineral stability trends correlating saturation index values with Pourbaix plots. The results highlighted pH-dependent precipitation of major metals such as Fe and Al, as well as the importance of redox conditions in determining sulfate degradation and metal speciation. The majority of variance in AMD chemistry was due to the collective behavior of sulfate and several trace metals. A smaller component represented the influence of pH-related parameters, with strong sulfate─metal linkages broadly confirming its key role in governing metal mobility. Utilizing the insights gained from geochemical modelling, a two-step treatment train was proposed. This consisted of an upstream chemical precipitation step (simulated in AMDTreat) followed by a downstream membrane-based separation step that was modelled in AquaGRID. The integrated system demonstrated removal of major contaminants from acidic discharge. The final water quality approached potable standards, while minimising sludge production as well as energy consumption

CFD Study of Indian Coal to Assess the Severity of Coal Dust Explosion and Its Classification as Per Explosibility

This paper presents a comprehensive study using computational fluid dynamics (CFD) to evaluate the explosibility of Indian coals and classify their explosion severity. A Siwek 20 L explosion chamber was simulated by ASTM standard 1226-19 to analyze coal samples collected from 24 coal mines across various coalfields of India. The explosibility parameters that is, maximum explosion pressure (Pmax), maximum rate of pressure rise ((dP/dt)max), explosion delay time (Ted), time to reach Pmax (Tep), and deflagration index (Kst) were estimated for each coal sample to evaluate the deflagration index, which measures the severity of explosions. The deflagration index (Kst) of all coal samples varied significantly between 47.90 bar·ms−1 and 109.43 bar·ms−1 indicating weak explosion potentials (0 < Kst < 200) as per OSHA 2009 standards. Based on this result, a classification system can be proposed for Indian coals depending on shared characteristics, which may be helpful in identifying coal according to their deflagration index (degree of severity). Presently, no formal classification system exists for Indian coal, and current assessments rely on USA OSHA regulations. Hence, multivariate statistical techniques, including feature selection, correlation analysis, multiple regression, and hierarchical clustering, were employed to identify the factors influencing explosion severity and to categorize the coal samples. Volatile matter dry (VMd) and crossing point temperature (CPT) were the most influential factors impacting Kst. A non-linear regression model yielded a polynomial equation with a strong fit (R2 = 0.909, std. error of estimate = 5.19%) for predicting the deflagration index and validated with test results. Hierarchical clustering further classified the coal samples into three distinct groups based on their explosion susceptibility: highly susceptible, moderately susceptible, and potentially susceptible. The proposed classification and prediction model can guide industry stakeholders to implement more effective explosion mitigation strategies and safety protocols

Environmentally sensitive trace elements in the Talcher and Ib valley coalfields: occurrence and environmental implications

Coal’s extensive use releases environmentally significant trace elements. This study investigates the occurrence and distribution of trace elements in coal from the Talcher and Ib Valley coalfields. Ten environmentally sensitive elements (Cr, Mn, Co, Ni, Cu, Zn, Cd, Ba, Sr and Pb) were examined, with Cr (306.69 mg/kg) and Ni (144.61 mg/kg) showing the highest average concentrations, followed by Ba (42.05 mg/kg) and Mn (17.95 mg/kg), while the remaining elements occur at <10 mg/kg. Enrichment factor analysis indicates a strong enrichment of Cr (3.06) and Ni (1.79) relative to the upper crustal levels, and the concentration coefficients show notable enrichment of Cr (18), Ni (10.3) and Mn (4.7) compared to world-coal averages. Sequential chemical extraction reveals that most elements (Cr, Mn, Co, Cu, Zn, Cd, Pb) are predominantly acid-soluble, while Ni (64.6%) and Ba (33.9%) are mainly silicate-bound, and Sr shows mixed silicate (25.4%) and pyritic (25.9%) affinity. A conceptual risk map was developed based on geochemical fractions, mobility, and toxicity. The ranking shows that Cd, Pb, Cr and Ni fall within the high-risk tier. Overall, the study delivers a quantitative, multiphase characterization of trace-element modes of occurrence, integrating sequential extraction with statistical correlations, and offers improved insight into environmental risk

A comparative study of thermal and mechanical properties of binder-free castor stalk pellets produced via pre- and post-torrefaction routes

This study investigates the thermal, mechanical, moisture-absorption, and energy balance characteristics of torrefied castor stalk (TCS) pellets made through two different processing methods. In route-1, biomass was first torrefied at elevated temperatures (250 ◦C, 275 ◦C, and 300 ◦C) and then pelletized (pre-TCS). In route-2, pellets were prepared from raw biomass and then torrefied (post-TCS) at various temperatures (250 ◦C, 275 ◦C, and 300 ◦C). At 300 ◦C, the post-TCS pellets showed a higher heating value (23.80 ±0.08 MJ/kg) compared to pre- TCS (21.96 ±0.165 MJ/kg) and CS-raw pellets (18.80 ±0.22 MJ/kg). Multi-linear regression analysis using SPSS version 28 revealed that impact of moisture, lignin, and extractive contents on the mechanical properties of TCS pellets. The post-TCS pellets absorbed less moisture than the pre-TCS pellets, resulting in only a slight decrease in HHV of 1.65 MJ/kg, even after 144 h of exposure to open laboratory conditions. The thermal stability was evaluated by STA/DSC, and FE-SEM confirmed a fused surface morphology, which increased the mechanical strength of post-TCS-300 pellets. The post-TCS-300 pellets demonstrated a higher energy balance of 60.97 MJ/kg compared to pre-TCS-300 pellets (55.51 MJ/kg). Overall, route-2 showed improved fuel qualities, including higher heating value, energy yield, mass yield, energy density, moisture-absorption resistance, thermal stability, durability, and O/C & H/C ratios. These results suggest that the post-torrefied pelletization process in route-2 is a more valuable option, suitable for both domestic and industrial uses

Prediction of overbreak and pull using machine learning approaches in the development face blasting of underground metalliferous mines

Drilling and blasting techniques are cost effective and efficient methods for the drivage of development headings in underground mines and tunnelling projects. Pull and overbreak are major challenges associated with these techniques, and it is important to maximise pull and minimise overbreak during blasting operations. Accurate prediction of these factors before blasting can significantly improve operational efficiency. In this study, multiple linear regression and machine learning models were employed to predict pull and overbreak. The K-nearest neighbor (KNN), Random Forest (RF) and Gradient Boosting Regressor (GBR) models were used for this purpose. Five input parameters namely number of holes, average hole depth, total explosive fired in the round, maximum charge weight per delay and uniaxial compressive strength of the rock, were collected from the experimental site. A total dataset of 155 points was compiled and split into training and testing sets in a 70: 30 ratio. The performance of the developed models was evaluated using root mean square error (RMSE) and the coefficient of determination (R2). Among the models, GBR exhibited the highest accuracy in predicting pull (RMSE = 1.09 and R2 = 0.94) and overbreak (RMSE = 4.11% and R2 = 0.95), indicating superior predictive capability compared to KNN and RF. These results suggest that GBR based predictive models can be effectively used for estimating pull and overbreak in underground development face blasting

Estimating anisotropy of fracture patterns using gray level Co-occurrence matrix (GLCM) approach: Implication on understanding permeability anisotropy

Fractures are the main pathways for fluid flow in reservoir rocks with low matrix permeability. Natural fractures are typically anisotropic, causing fluid flow to vary by direction. Accurate characterization of this anisotropy is essential for predicting reservoir behaviour and performance. This study applies a second-order statistical method—Gray-Level Co-occurrence Matrix (GLCM) analysis—to quantify fracture network anisotropy. Grayscale fracture images were examined in horizontal (0° East) and vertical (90° North) directions, and textural anisotropies were computed for key GLCM features: Contrast, Dissimilarity, Homogeneity, Energy, and Entropy. Permeability anisotropy was further estimated through numerical simulations, and its correlation with GLCM-based textural anisotropy was evaluated for both natural and synthetic fracture patterns. Results show that GLCM-based textural anisotropy captures not only pixel-level directional intensity variations but also is sensitive to the variation in underlying fracture geometrical attributes—orientation, density, aperture variation, and length distribution—that control directional flow. Across all datasets, permeability anisotropy correlated positively with Homogeneity and Energy anisotropies, and negatively with Contrast, Dissimilarity, and Entropy anisotropies. Among these, Entropy-anisotropy consistently emerged as the strongest predictor of permeability anisotropy. Applying this method to natural fracture networks, including fault damage zones, confirmed that GLCM-based textural anisotropy can non-invasively reveal fracture network anisotropy governing directional permeability. This approach has potential applications in reservoir characterization, hydrogeological modelling, and geothermal resource assessment

Comparative Analysis of Blast-Induced Vibration in Opencast and Underground Mines: A Case Study from Lower Gondwana Coal Seams, Dhanbad”

Title :“Comparative Analysis of Blast-Induced Vibration in Opencast and Underground Mines: A Case Study from Lower Gondwana Coal Seams, Dhanbad” Abstract Ground vibrations caused by blasting presents major challenges for safe and productive mining operations as it affects the slope stabilities, underground excavation and adjacent communication lines. In this paper, a comparative investigation of vibration propagation characteristics of opencast and underground blasting has been described based on a set of data collected from a sample of hundred blasts for a wide variety of geomining conditions. Peak particle velocity (PPV), attenuation trend and frequency characteristics have been studied using empirical, regression and machine learning methods. The results obtained reveal that ground vibrations (PPV) generated by underground blasting are twice of that generated in open cast case at locations near source owing to the high confinement effect, while wider spatial propagation with lower attenuation rate is observed in opencast blasting. A regression model based on scaled distance well predicts the ground vibrations with the best-fit relation; PPV=76.72×(SD) -0.56 which agrees well with the observed data. The geologic parameters such as Joint Factor (Jf) and Geological Strength Index (GSI) are found to have significant influence on the ground vibration attenuation trend. This study is representative of the general geomining scenarios typically encountered in Gondwana coal bearing formations of Jharia Coalfield collieries (Kuya collieries- Underground & Opencast Mines) of the Damodar Valley basin in Dhanbad. The coal-bearing formations, primarily Barakar and Raniganj formations, consist of alternating sequences of sandstone, shale, and coal sea

Oxy-blown bubbling fluidized bed gasification of high-ash Indian coal: A pilot – Scale demonstration

India's vast coal reserves offer a strategic pathway for clean energy via gasification; however, the high-ash content of indigenous coal renders most global gasifier designs techno-economically unviable. To address this gap, the CSIR-Central Institute of Mining and Fuel Research (CSIR-CIMFR) developed an indigenous Oxy-blown Bubbling Fluidized Bed Gasification (BFBG) pilot plant (TRL-6). The present findings evaluate the gasification performance of high-ash Indian coal at a thermal capacity of 107–150 kWth, utilizing a steam-oxygen mixture at atmospheric pressure and temperatures between ∼936 and 987 °C. The research systematically investigates the effects of the equivalence ratio (ER) and steam-to-coal (S/C) ratio on key gasification performance indicators, including Syngas composition and yield, H2/CO ratio, Carbon Conversion Efficiency (CCE), Cold Gas Efficiency (CGE), and Lower Heating Value (LHV) of the syngas. Under optimized conditions (ER: 0.40; S/C: 0.82), the process achieved a syngas composition of H2:CO:CH4:CO2 in the ratio of 38.45%:23.99%:7.34%:30.22%, yielding an H2/CO ratio of 1.60. Performance indicators achieved a CCE of 97.23%, CGE of 87.57%, and LHV of 9.03 MJ/Nm3 syngas. These results demonstrate the feasibility of producing hydrogen-rich syngas suitable for methanol synthesis, Direct Reduced Iron (DRI) production, and chemical feedstock. The findings provide the foundational data necessary for scaling up to pithead demonstration facilities in India