This study presents a scientometric analysis of renewable energy applications in low-temperature regions, focusing on green hydrogen production, carbon storage, and emerging trends. Using bibliometric tools such as RStudio and VOSviewer, the research evaluates publication trends from 1988 to 2024, revealing an exponential growth in renewable energy studies post-2021, driven by global policies promoting carbon neutrality. Life cycle assessment (LCA) plays a crucial role in evaluating the environmental impact of energy systems, underscoring the need to integrate renewable sources for emission reduction. Hydrogen production via electrolysis has emerged as a key solution in decarbonizing hard-to-abate sectors, while carbon storage technologies, such as bioenergy with carbon capture and storage (BECCS), are gaining traction. Government policies, including carbon taxes, fossil fuel phase-out strategies, and renewable energy subsidies, significantly shape the energy transition in cold regions by incentivizing low-carbon alternatives. Multi-objective optimization techniques, leveraging artificial intelligence (AI) and machine learning, are expected to enhance decision-making processes, optimizing energy efficiency, reliability, and economic feasibility in renewable energy systems. Future research must address three critical challenges: (1) strengthening policy frameworks and financial incentives for large-scale renewable energy deployment, (2) advancing energy storage, hydrogen production, and hybrid energy systems, and (3) integrating multi-objective optimization approaches to enhance cost-effectiveness and resilience in extreme climates. It is expected that the research will contribute to the field of knowledge regarding renewable energy applications in low-temperature regions.

The objective of this research was to evaluate the sustainability of a co-treatment system that combines Municipal Wastewater (MW) and Acid Mine Drainage (AMD) through the technique of intermittent electrocoagulation, applied as an advanced solution to improve contaminant removal efficiency and optimize energy balance. Four scenarios were analyzed: Treatment I (with a 1/7 ratio of urban wastewater to AMD), Treatment II (which includes an artificial wetland), Treatment IIIa (which introduces electrocoagulation to enhance sulfate removal and pH regulation), and Treatment IIIb (which employs a 1/15 ratio of AMD to eutrophic water). The methodology focused on calculating key sustainability indicators such as the Net Yield Ratio (EYR), Emergy Inversion Ratio (EIR), Environmental Loading Ratio (ELR), and Sustainability Index (SI), in order to assess the impact of each technology on the energy efficiency and environmental load of the system. The results showed that, although Treatment IIIa was effective in contaminant removal, the EIR increased to 0.18 and the ELR rose to 0.62, indicating a higher reliance on non-renewable inputs due to increased energy demand. However, Treatment IIIb, which combines electrocoagulation with eutrophic water, significantly improved the sustainability of the system, achieving an SI of 2.31 and an ELR of 1.22, reflecting a reduction in energy efficiency due to intensive use of external resources, but overall greater sustainability compared to the other scenarios. This research concludes that intermittent electrocoagulation, when integrated with synergistic resources like eutrophic water, can enhance contaminant removal efficiency and improve the use of renewable resources, minimizing environmental load and increasing the sustainability of wastewater treatment systems.

This study presents a scientometric analysis of renewable energy applications in low-temperature regions, focusing on green hydrogen production, carbon storage, and future trends. The research covers three distinct periods from 1988 to 2024, using bibliometric tools such as R-Studio and VOSviewer to evaluate scientific publications and keyword co-occurrence. The study highlights an exponential growth in renewable energy research post-2021, driven by global initiatives to achieve carbon neutrality. Life Cycle Assessment (LCA) plays a crucial role in identifying the environmental impacts of energy systems, emphasizing the need to integrate renewable energy sources to reduce carbon emissions. The analysis reveals that hydrogen production via electrolysis has emerged as a key topic in reducing carbon footprints, particularly in hard-to-abate sectors. Carbon storage technologies, including bioenergy with carbon capture and storage (BECCS), have gained traction as essential components of future energy systems. The results indicate a growing interest in optimizing renewable energy systems for low-temperature environments, with significant progress in solar, wind, and hydrogen technologies. The study underscores the importance of local and regional adaptations, integrating energy storage solutions, and diversifying energy matrices to enhance energy security. Future trends suggest a focus on multi-objective optimization using deep learning and AI to improve energy system performance. The findings contribute to understanding the global research landscape on renewable energy applications in cold regions, offering valuable insights into sustainable solutions for extreme climates.

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Although interior-point methods (IPMs) have transformed mathematical programming since 1984, the original projective Karmarkar algorithm is rarely documented step by step on reproducible engineering examples that combine algorithmic transparency with real resource allocation constraints. This article therefore does not propose a new variant of Karmarkar’s algorithm; rather, its scientific contribution is the reproducible MATLAB implementation, canonical-form conversion, and comparative validation of the original projective method against the revised Simplex method and Barnes’ affine scaling variant in two engineering settings. The case studies are (i) the minimum-weight plastic design of a rigid frame with seven candidate plastic hinge locations and six collapse mechanisms and (ii) the optimal allocation of crop patterns in the Caplina Valley (Tacna, Southern Peru), an arid irrigated system with an irrigated command area of 1253 ha, monthly labor availability of 22,239 jornales, and water availability derived from Caplina River discharges at 75% persistence. For Case I, the algorithm reached F = 1.001 in the normalized dual space, which corresponds to F = 4.251 in the original structural objective after applying the scaling factor 17/4; relative to the analytical optimum F* = 4.25, this gives |4.251 − 4.25|/4.25 = 2.4 × 10⁻⁴ after 20 iterations. For Case II, the model yielded the maximum net production value of USD 703,135.92, allocating 948.47 ha among 12 crops while satisfying water, labor, market, and land constraints. The double validation confirms the algorithm’s strictly interior trajectory, polynomial-time rationale, and transparent internal parameters (α = 0.7968, ε = 10⁻⁸), making the implementation a reproducible benchmark for educational use and for future AI–operations research hybrid solvers in regions with limited access to commercial optimization software.

Acid-generating mining waste and polymer waste are two of the most persistent environmental problems facing the mining and manufacturing sectors, respectively. We have investigated the co-recovery of these disparate waste streams for the production of unfired cementitious paving blocks. We established a statistically optimized formulation using response surface methodology (RSM) and a central composite design (CCD). We systematically evaluated three process variables: air-curing time (4–37 days), dosage of the waste mixture (5–68% by weight of dry solids: acid-generating mining waste, hydrated lime, and recycled polymer in a waste-to-polymer mass ratio of 1:1), and type of polymeric aggregate (recycled PET flakes versus granulated rubber). Compressive strength ranged from 4.5 to 42.1 MPa across the 40 experimental conditions. The resulting quadratic model was clearly significant (F = 186.31, p < 0.0001) with solid predictive parameters (R² = 0.9796; R²pred = 0.9627; adequate precision = 42.47). Desirability-based optimization, which limited air curing to industrially feasible timeframes (7–28 days) and maximized waste utilization within a 10–50% by weight, identified PET with 12.4 days of curing and a 50% by weight waste mixture as the optimal configuration, predicting a compressive strength of 37.3 MPa. This value exceeds the 32 MPa threshold for Type I heavy-traffic paving blocks; however, confirmatory tests yielded 34.09 ± 1.08 MPa, indicating that production-scale use should include control of moisture content, compaction, and batch homogeneity. Scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS) and X-ray diffraction (XRD) demonstrated that PET inclusions promoted a denser and more continuous interfacial transition zone than shredded rubber, driven by physical entanglement and the pronounced microfilling effect of the fine waste particles.