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Sustainable Biomass Supply Chain Optimization Under Uncertainty: Multi-Objective Scenario-Based Modeling Approach

  1. Introduction The transition toward renewable energy systems requires sustainable and resilient biomass supply chain networks capable of operating under uncertainty and disruption risks. This research introduces an integrated multi-period, multi-objective mathematical modeling framework that simultaneously optimizes economic, environmental, and social objectives. By incorporating scenario-based uncertainty analysis, the model enhances decision-making robustness while addressing long-term sustainability challenges in biomass energy infrastructure planning. 2. Multi-Objective Mathematical Modeling Framework This topic explores the development of a comprehensive mathematical model that optimizes cost efficiency, carbon emissions, and social welfare simultaneously. Using multi-objective programming techniques, the study generates Pareto-optimal solutions that help decision-makers balance trade-offs between profitability, sustainability, and community development goals. 3. Scena...
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Machine Learning-Based Spatial Prediction of Nepal’s Forest Biomass Stocks | Data-Driven Bioregionalisation Study

  1. Introduction Forest biomass estimation plays a crucial role in understanding carbon sequestration potential, ecosystem productivity, and climate regulation. Nepal’s diverse topography and ecological gradients require spatially explicit modeling approaches to capture regional biomass variability. This research introduces a data-driven bioregionalisation framework combined with machine learning algorithms to enhance prediction accuracy across forest types. By integrating ecological zoning with predictive modeling, the study supports national climate commitments and sustainable forest governance strategies. 2. Data-Driven Bioregionalisation Framework This topic explores the methodological foundation of dividing Nepal into ecologically homogeneous bioregions using environmental variables such as elevation, precipitation, soil type, and vegetation indices. Bioregionalisation improves predictive consistency by reducing spatial heterogeneity and strengthening model generalization ...
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Integrated Radon Measurement & Electrical Prospecting for Geothermal Exploration: Lantian Section Case Study (Jiangxi, China)

  1. Introduction Geothermal energy exploration requires accurate subsurface characterization to reduce drilling risks and improve resource assessment. This research introduces an integrated methodology combining radon gas measurement and conventional electrical prospecting to enhance geothermal detection efficiency. By analyzing radon anomalies associated with subsurface fault zones and correlating them with resistivity data, the study demonstrates a multidisciplinary approach to identifying geothermal reservoirs. The Lantian Section case study provides a practical framework for sustainable geothermal exploration and contributes to advancing renewable energy resource mapping techniques. 2. Radon Measurement in Geothermal Prospecting Radon gas anomalies are closely linked to tectonic fractures and geothermal pathways. This topic examines radon concentration mapping techniques and their role in identifying permeable zones associated with geothermal reservoirs. The study highlight...
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Integrated Radon Measurement & Electrical Prospecting in Geothermal Exploration | Lantian, Jiangxi Case Study

  1. Introduction Geothermal energy exploration plays a crucial role in advancing sustainable and renewable energy systems. Accurate identification of geothermal reservoirs requires integrated geophysical and geochemical methods to reduce uncertainty and enhance exploration success rates. This study introduces the combined use of soil radon measurement and conventional electrical prospecting techniques to improve geothermal anomaly detection. By analyzing subsurface gas migration patterns alongside resistivity variations, researchers can better interpret fault structures and heat pathways, contributing to efficient geothermal resource assessment and sustainable energy planning. 2. Radon Measurement as a Geochemical Indicator This topic explores radon gas as a tracer for identifying geothermal activity. Elevated radon concentrations often correlate with fault zones and fracture systems that facilitate heat flow. The research evaluates radon anomaly mapping techniques and discusse...
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High-Temperature-Resistant Foam Drilling Fluid System: Advanced Thermal Stability Research for Deep Well Applications

  1. Introduction High-temperature and high-pressure (HTHP) drilling environments demand advanced fluid systems capable of maintaining structural integrity and operational efficiency under extreme thermal stress. Conventional drilling fluids often degrade at elevated temperatures, leading to reduced foam stability, viscosity loss, and compromised wellbore control. This research introduces a novel high-temperature-resistant foam drilling fluid system engineered to enhance thermal endurance, improve rheological stability, and ensure effective cuttings transport in deep and ultra-deep wells. 2. Thermal Stability and Rheological Performance Analysis This topic investigates the rheological behavior of foam drilling fluids under elevated temperature conditions. Laboratory testing evaluates viscosity retention, foam half-life, and shear resistance across various temperature ranges. The findings highlight the importance of polymer stabilizers and surfactant optimization in preserving fl...
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Renewable Energy Quality Trilemma & Coincident Wind–Solar Droughts: Risks, Reliability, and Energy Security

  1. Introduction The rapid expansion of renewable energy systems has intensified focus on the energy quality trilemma: maintaining reliability, affordability, and environmental sustainability simultaneously. However, correlated climatic events—such as coincident wind and solar droughts—pose systemic risks to power systems with high renewable penetration. This research investigates how simultaneous low-generation periods challenge grid stability, increase dependency on backup generation, and influence long-term energy planning. By combining climate variability analysis with energy system modeling, the study provides a structured understanding of renewable generation risks in decarbonized electricity systems. 2. Statistical Modeling of Coincident Renewable Droughts This research topic explores probabilistic and time-series modeling techniques used to detect and predict simultaneous low wind and solar generation events. By analyzing long-term meteorological datasets, researchers quan...
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How Rooftop Solar and Energy Storage Reduce Energy Insecurity in U.S. Households | Research Insights

  1. Introduction Energy insecurity remains a critical socio-economic challenge in the United States, disproportionately affecting low-income and marginalized communities. Households facing high energy burdens often engage in energy-limiting behaviors such as reducing heating or cooling, compromising health and well-being. Rooftop solar photovoltaic systems combined with residential energy storage present an innovative solution to improve affordability, reliability, and resilience. This research introduces the theoretical and empirical foundations for examining how distributed clean energy technologies can alleviate energy insecurity while supporting decarbonization and equitable energy transitions. 2. Measuring Energy Insecurity and Household Energy Burden This topic focuses on the quantitative assessment of energy insecurity using metrics such as energy burden ratios, payment arrears, service disconnections, and thermal discomfort indicators. The research evaluates socio-econo...
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