Amine-based post-combustion CO2 capture is a promising technology for reducing CO2 emissions; however, oxidative degradation of amine solvents can generate ammonia (NH3), an important secondary emission. This study investigated the relationship between molecular structure and NH3 emission using 33 amines, including alkanolamines, sterically hindered alkanolamines, multi-amino compounds, and cyclic amines. Oxidative degradation experiments were conducted under controlled conditions, and molecular descriptors representing structural, electronic, steric, and hydrogen-bonding characteristics were evaluated. Thirty compounds were used for model development and three were retained as a descriptive holdout set. Multiple linear regression (MLR), Ridge regression, and Extreme Gradient Boosting (XGBoost) were compared using 30 repetitions of family-stratified five-fold cross-validation, with inner cross-validation used for tuning Ridge and XGBoost. XGBoost showed the best out-of-sample performance, with mean MAE and RMSE values of 0.477 and 0.630 log10 units, respectively, followed by Ridge regression. The unregularized MLR showed substantially poorer predictive stability. Addition of the hydrogen-bonding descriptor (Hb) and the interaction descriptors did not improve out-of-sample predictive performance, and the baseline descriptor set was retained for all models. Structural analysis indicated that nitrogen functionality, carbon substitution, steric environment, and electronic characteristics were associated with differences in NH3 emission. The three-compound holdout showed mixed model-specific performance and was interpreted descriptively. The models provide a quantitative framework for relating amine structure to NH3 emission within the represented chemical and experimental domain.
Nanopores widely developed in shale reservoirs can alter confined fluid phase behavior and affect CO2 based recovery and storage processes. Existing studies have mainly emphasized pore size effects, whereas the role of pore wall wettability is often treated qualitatively or indirectly. In this study, a modified Peng Robinson equation of state framework is developed to evaluate the coupled influence of pore size and representative pore wall wettability on confined phase behavior and miscibility. A dimensionless confinement factor is introduced by combining normalized pore radius with a contact angle dependent term, and a semi empirical critical property shift correlation is incorporated into the PR-EOS parameter correction. The confined phase equilibrium calculation is further coupled with the multiple mixing cell method to estimate minimum miscibility pressure. The model is compared with published saturation pressure data for ethane, propane, and butane in mesoporous materials, showing reasonable agreement within the investigated pore size and wettability range. Model predictions suggest that smaller pores and lower contact angles lead to larger apparent shifts in critical temperature and pressure, while the corrections weaken as pore size increases or the surface becomes less wetting. Under the present correction framework, CH4 exhibits larger predicted critical property deviations than CO2 under the same pore size and contact angle conditions. For a representative Eagle Ford condensate composition, pore size and representative pore wall wettability may affect vapor liquid composition redistribution, including the CH4 fraction in the vapor phase and the heavy component fraction in the liquid phase. The predicted MMP increases with contact angle and decreases with decreasing pore size, while higher CO2 content in the injection gas reduces both MMP and its sensitivity to wettability. This work provides an engineering thermodynamic framework for assessing model predicted confinement and wettability effects in shale nanopores, with potential relevance to CO2 enhanced recovery and storage applications.
Accurate mine stability assessment is crucial for ensuring worker safety, sustaining minerals production continuity, preserving mining infrastructure and operation integrity. Conventional geotechnical monitoring approach is limited in capturing nonlinear and transient failure precursors under complex and noisy underground mine conditions. This creates an urgent need for intelligent frameworks capable of accurately distinguishing between stable and unstable roof conditions to support proactive mine safety management. This study focuses on the implementation of novel deep learning frameworks for enhanced roof stability prediction and spatial hazard zoning using acoustic impact signal analysis in underground potash mines. A total of 9923 acoustic recordings were collected by striking the mine roof using an aluminum scaling bar at multiple mine locations. Each recording is characterized by experienced miners, labeled as stable (safe) or unstable (unsafe), forming the ground-truth hazard classification map. The deep learning (DL) architectures of multilayer perception (MLP) and residual neural network (ResNet) with mel frequency cepstral coefficient (MFCC)-based features were developed to perform mine stability classifications. The performance of optimized predictive models is assessed using confusion matrix-based evaluation metrics, including precision, recall, F1-score and overall accuracy. In addition, receiver operating characteristic curve analysis and the corresponding area under the curve (AUC) are employed to evaluate the model discriminatory capability and robustness. Results reveal that the MFCC-ResNet model exhibited superior generalization performance for MFCC-derived spectral characteristics of the acoustic impacts on potash mine roof stability, attaining 92% classification accuracy overall and AUC of 0.97 on testing data, compared to the other models of MFCC-MLP (accuracy: 90% and AUC: 0.96) and ResNet (accuracy: 86% and AUC: 0.94). The proposed novel DL-enhanced framework demonstrates high reliability as a decision-making tool, strong robustness against gradient degradation, and significant potential for real-time structural health monitoring of mine roof and sidewalls. This study develops scalable and explainable grounds for proactive mine ground control strategies, supporting improved hazard mitigation and operational safety planning in underground mining environments.
The fifth industrial revolution (I5.0), which is based on the utilization of interconnected data for efficient resource usage in meeting human requirements, proposes efficient solutions to resource constraint situations. However, the transition to I5.0 in the health sector is not easy and has to face several obstacles. This study dives deep into exploring and handling the obstacles in the integration of I5.0 practices into the existing Indian health system and suggests measures for overcoming them. Twenty-three obstacles were identified from literature analysis and experts' suggestions. The identified obstacles were further clubbed under organizational, technological, behavioral, financial, and regulatory and legal categories. Further, the obstacles were put to the fuzzy DEMATEL approach, which prioritized them based on their interaction/influence with each other. Triangular Fuzzy Number (TFN) approach was used to accommodate any uncertainty in responses from the experts. The findings from the study highlight the lack of integration of pertinent "I5.0" technologies in medical work, the lack of patient security law and general data protection regulation for healthcare services, and the lack of support from top management for "I5.0" adaptation as the top three ranked obstacles requiring immediate attention. The cause-effect classification in the study paved the direction to address first the causal obstacles, utilizing their interrelationship with other obstacles to achieve integration of I5.0 in the health sector. This study can be an important guiding document to the policymakers and various healthcare stakeholders to integrate the I5.0 concept in addressing the needs of the vast Indian population.
Many inland waters are shrinking due to shifts in climate and water diversion for human uses. As they dry out, their exposed sediments emit large amounts of carbon dioxide (CO2) to the atmosphere. However, current global estimates of CO2 emissions from dry inland waters are derived exclusively from bare sediment dark-chamber measurements that do not account for the colonization of desiccated areas by vegetation. To understand the impact of vegetation on CO2 emissions from dry sediments, we analyzed 164 dry inland water bodies across five climatic regions and five inland water body types (lakes, ponds, reservoirs, streams and wetlands). On average, within vegetated zones, vegetation occupied 47 +/- 35% in measured biomass quadrants. Light-induced decreases in instantaneous CO2 emissions in vegetated dry sediments were lower (mean +/- SD = -3.7 +/- 12.9 mmol CO2 m-2 hr-1) than increases during dark conditions (14.7 +/- 20.1 mmol CO2 m-2 hr-1). Diel (24-hr) CO2 emissions from dry, vegetated sediments (mean +/- SD = 100 +/- 261 mmol CO2 m-2 d-1) were 25% lower than in bare sediments (133 +/- 245 mmol CO2 m-2 d-1). These results indicate that vegetation can partially off-set sediment respiration, although the magnitude of this effect is insufficient to switch dry beds from net sources to net sinks of carbon.
