Search Results (1,245)

This paper develops a novel coupled model to predict the thermal behavior of a high-temperature fast heat storage unit, integrating Power-to-Heat technology with steam generation. A phase change material (PCM) made of a ZnAl₆ metal alloy is used for heat storage. Electricity is used to charge the battery, and the stored energy is used to produce superheated steam during discharge. The coupled model was based on a 3D multiphase CFD model of the heat storage unit and a 1D multiphase water boiling model implemented in Python language. The CFD model solves the transient conservation equations of mass, momentum, and energy using the enthalpy–porosity method to describe phase change, while heat transfer to water is represented by a coupled 1D boiling model. The paper also presents a preliminary design, a computational strategy, and boundary conditions for the operating modes, providing an analytical foundation for detailed engineering, production, and implementation in real-world industrial environments. The presented results confirmed the correct operation of the model and enabled the evaluation of system performance, discharge behavior, and validation of the geometric assumptions required to achieve the target steam parameters. The proposed modular design allows for system scalability, while the entire system is a response to the daily variability of electricity prices resulting from periodic reductions in demand and overproduction of electricity from renewable sources. Estimated thermal behavior of the thermal storage unit for the discharging scenario allows reaching constant output power at the level of 200 kW for 85 min. Integration with a cooling reduction station allows constant system power output to be maintained by increasing the mass flow rate as the steam parameters decrease from over 400 ${}^{\circ }$C to 200 ${}^{\circ }$C with a lowering state of charge. Full article

Over the past two decades, extreme climate and weather events have become increasingly frequent in the United States, and the carbon–water cycle of corn ecosystems has shown high sensitivity to climate change. However, traditional simulation methods that rely on coarse-scale reanalysis data are unable to reflect changes in local water and heat conditions accurately. This study combines in situ meteorological observations with remote sensing, using a long short-term memory model to simulate the daily gross primary productivity (GPP) and evapotranspiration (ET) of 684 corn-growing meteorological stations in the United States. In summer, GPP and ET showed a spatial pattern of gradual decrease from the humid eastern region to the arid western region, and the multi-year daily averages at meteorological stations showed a single-peak pattern. The sensitivity of GPP and ET changes is mainly influenced by leaf area index (LAI) and shortwave radiation downward changes, which together explain more than 90% of the main variation in GPP and ET at the meteorological stations. The 2012 drought caused a general decline in GPP and ET, with the peak occurring approximately 15 days earlier than usual. Water use efficiency (GPP/ET) decreased at 85% of the sites (p < 0.05), but photosynthesis per unit leaf area (GPP/LAI) increased at 63% of the sites (p < 0.05). This study demonstrates the importance of meteorological station-scale data for understanding carbon–water flux dynamics in cornfields. Integrating the models developed in this study with medium-to-long-term climate projections will further guide climate-informed agricultural water management and provide reliable accounting and pricing tools for agricultural land carbon markets and carbon trading. Full article

This study explores a photovoltaic/thermal (PV/T)-based electrolysis system designed for dual production of hydrogen fuel and domestic hot water (DHW), providing a sustainable energy solution amid rising global emissions. A dynamic rule-based control mechanism with hysteresis thresholds on hydrogen-storage state of charge (SoC) is implemented to balance electrolyzer operation with intermittent solar availability, maintaining PV/T power outputs while preventing storage overfilling and minimizing start–stop cycling. The system is assessed across 27 geographically diverse cities spanning a wide range of solar irradiation and energy price structures. Annual hydrogen yields range from 20 kg/yr in high-latitude locations (Helsinki, Stockholm) to 33.5 kg/yr in high-irradiation regions (Riyadh, Abu Dhabi), while the levelized cost of hydrogen (LCOH) spans from 6.47 USD/kg (Riyadh) to 22.86 USD/kg (Helsinki). Economically, the system achieves its strongest performance in solar-rich, high-energy-cost environments: Rome records the highest net annual cash flow (858.9 USD/yr) and shortest payback period (2.47 years), followed by Davos, Madrid, Brasília, and Canberra. In contrast, locations with subsidized energy tariffs—such as Algiers, Kyiv, and Tehran—yield low or negative net cash flows, rendering the system economically unviable without policy support. Environmental analysis reveals annual CO₂ avoidance ranging from 0.33 ton/yr (Stockholm) to 2.97 ton/yr (Riyadh), with a global mean of 1.095 ton/yr and a combined total of approximately 29.6 tons/yr across all examined sites. A machine learning model is developed to generalize performance predictions across unseen locations, achieving leave-one-out (LOO) R² values of 0.953 (net cash flow), 0.935 (LCOH), and 0.947 (LCO-DHW), with mean absolute errors below ±1 USD/kg and ±0.03 USD/kWh. The findings confirm that, under fixed capital cost assumptions, local electricity price and solar irradiation are the dominant drivers of economic viability, while grid carbon intensity and solar resource jointly govern environmental performance, with markets offering irradiation above 1500 kWh/m²·yr and electricity prices exceeding 0.2 USD/kWh representing the most promising deployment targets. Full article

With the expansion of offshore oil and gas exploration into deep-water regions, the efficient scheduling of platform supply vessels (PSVs) is critical to offshore operations. The platform supply vessel routing and scheduling problem (PSVRSP) is an NP-hard combinatorial optimization problem, which is further complicated by uncertainty in offshore demand. Existing studies reveal a methodological gap: exact optimization algorithms have rarely been applied to this problem, as most prior research relies on heuristic methods that cannot guarantee optimality. To address this gap, this study proposes a novel enhanced branch-and-price (B&P) algorithm for the platform supply vessel routing and scheduling problem with uncertain demand (PSVRSP-UD). The proposed approach integrates NG-route labeling, a group-representative label mechanism, and a two-level branching strategy to efficiently obtain globally optimal solutions under demand uncertainty. A scenario-based mixed-integer linear programming (MILP) model is formulated, in which demand uncertainty is captured using Latin hypercube sampling (LHS) combined with Cholesky decomposition and sample-based reduction (SBR). Based on Dantzig–Wolfe decomposition, the proposed B&P algorithm integrates NG-route labeling and a two-level branching strategy to achieve global optimization. Computational experiments show that the B&P algorithm outperforms CPLEX in both computational efficiency and solution quality. Sensitivity analyses examine the impacts of scenario number, demand fluctuation, time window tightness, and weight coefficients on the results. The new results in this study can provide a practical decision-support tool for offshore logistics operations. Full article

This study presents an integrated environmental assessment of plastic waste-to-hydrogen systems with varying CO₂ utilization ratios, combining process-level simulation with life-cycle assessment (LCA). The environmental impacts are evaluated across key categories, including global warming potential (GWP), fine particulate matter formation (PM), fossil resource scarcity (FRC), and water consumption (WC). The results reveal a non-linear relationship between CO₂ utilization and environmental impacts. As the CO₂ utilization ratio increases from the N₂ baseline to moderate levels (CO₂-40 to CO₂-50), environmental impacts decrease due to improved carbon utilization and reduced direct CO₂ emissions. However, further increases in CO₂ utilization lead to a reversal of this trend, with environmental burdens rising significantly due to increased energy and utility demand associated with intensified CO₂ recycling. Process contribution analysis shows that the dominant impact drivers shift from direct CO₂ emissions to utility-related contributions, particularly heat (steam) and electricity, at higher utilization levels. A trade-off analysis between direct CO₂ emissions and utility-related impacts identifies an optimal environmental operating range around CO₂-50. An integrated comparison with techno-economic performance, represented by the minimum hydrogen selling price (MHSP), reveals a divergence between environmental and economic optima. While environmental impacts are minimized at CO₂-40 to CO₂-50, the economic optimum occurs at higher utilization levels (CO₂-60 to CO₂-70). These results highlight that CO₂ utilization acts as a key design variable governing the trade-off between carbon efficiency and energy demand. An optimal compromise region is identified around CO₂-50 to CO₂-60, providing a balanced operating window for both environmental and economic performance. This study demonstrates that maximizing CO₂ utilization is not necessarily optimal from a system-level sustainability perspective and provides practical insights for the design and optimization of integrated plastic waste-to-hydrogen systems. Full article

At the Second Water Intake Pump Station of the Chenhang Reservoir in Shanghai, suboptimal pump scheduling resulted in electricity consumption cost attributable to pump-motor equipment accounting for an exceptionally large proportion of the total power expenditure. In response to the economical operation issues, a mathematical model of power consumption cost for the pump station was established by introducing time-of-use electricity pricing and constraint suppression terms. Taking the minimum cost as the research objective, the differential evolution (DE) algorithm was employed to establish a fitness function for electricity cost, aiming to find the most economical and reliable scheduling strategy. However, owing to its low computational speed and high complexity, machine learning was introduced to establish neural network surrogate models of the DE algorithm. By comparing three surrogate models, the Multilayer Perceptron (MLP) neural network model was adopted as the most appropriate surrogate model. It was optimized for robustness improvement and verified on site. The results demonstrate that implementing the surrogate model achieves over 25% savings in electricity cost per thousand cubic meters of water, while slashing the solution time by 88.53% compared to the standard DE algorithm. Furthermore, the overall power consumption is reduced by 2.20% under a cost-priority strategy and by 15.89% under a power-priority strategy, thereby directly mitigating the carbon footprint of the pump station. The proposed hybrid computational framework in this study bridges the gap between the computationally expensive heuristic optimization and the strict real-time control requirements in engineering, highlighting its significant contribution to the sustainable and low-carbon operation of water infrastructure. Full article

Water utilities are increasingly expected to combine technical reliability with social inclusion, risk communication, and service continuity. This empirical paper reports a cross-sectional mixed-mode household survey conducted in Rzeszów, Poland, based on 384 complete questionnaire records. For a city of approximately 200,000 inhabitants, this sample size matched the conventional planning benchmark associated with a 95% confidence level and a 5% maximum error under simple-random-sampling assumptions; however, because recruitment was mixed-mode and non-probabilistic, the results are interpreted as evidence from the realized sample rather than as formally weighted population estimates. The questionnaire covered routine service evaluation, interruption experience, preparedness, communication preferences, vulnerability-related burden, and willingness to support reliability enhancement. The analytical workflow combined descriptive statistics, reliability analysis, Bartlett’s test of sphericity, the Kaiser–Meyer–Olkin measure, principal component analysis, Mann–Whitney U tests, Kruskal–Wallis tests, chi-square tests, Spearman correlation, binary logistic regression, correspondence analysis, and CHAID-type segmentation. The highest ratings were recorded for continuity of supply (mean = 4.18) and pressure stability (mean = 4.15), whereas price fairness received the lowest mean score (3.17). Interruptions were reported by 40.1% of respondents and were associated with lower overall satisfaction. Logistic regression showed that continuity rating (OR = 4.029) and water quality rating (OR = 2.305) increased the odds of high satisfaction, whereas longer interruptions reduced them (OR = 0.354). Additional analyses showed that interruptions lasting 12 h or more markedly increased the odds of high nuisance among affected households (OR = 5.914), while respondents aged 51 years or more had lower odds of declaring emergency-information awareness (OR = 0.468). Internal bootstrap validation indicated only mild optimism (optimism-corrected AUC = 0.825). The findings indicate that customer satisfaction in urban water services is shaped primarily by continuity, perceived water quality, and disruption burden, while communication and preparedness needs remain socially differentiated. Full article

Freshwater scarcity is emerging as a critical constraint on industrial clusters, production networks, and urban service systems, where water functions simultaneously as an essential production input and a shared regional resource. This study investigates how post-allocation water-quota imbalances in large inter-basin diversion systems can be addressed through adaptive secondary water rights trading. Focusing on China’s South-to-North Water Diversion Project (SNWDP), the research aims to explain under what institutional and efficiency conditions water rights trading can enhance corporate social responsibility, environmental management, and sustainable supply chain resilience. The study’s main innovation lies in the development of a corporate-oriented evolutionary game model that links water governance with corporate production, urban–industrial demand, and responsible supply chain management. Unlike conventional models, it incorporates bounded rationality, heterogeneous water-use efficiency, information asymmetry, transaction costs, primary allocation water pricing, and the risk of unrecovered basic water fees. Using a case inspired by the Zhengzhou–Nanyang transaction along the Middle Route of the SNWDP, the model simulates the strategic interaction between a water-rich node with surplus quota and a water-scarce node facing deficit demand. The findings show that a socially desirable Trade–Trade equilibrium emerges only when efficiency expectations and institutional conditions are favorable. Lower transaction costs and basic water prices, higher sunk-fee risk, and clearer efficiency differentials significantly increase trading willingness. The study demonstrates the practical value of transparent secondary water markets in improving allocative flexibility, reducing governance rigidity, and promoting more responsible and environmentally efficient regional water management. Full article

This study investigates the tariff-oriented operation of residential air-to-water heat pump systems integrated with thermal energy storage under long-term real climatic conditions. In contrast to studies based on short-term simulations or advanced predictive control, this work evaluates a simple rule-based control strategy with a focus on practical applicability. The analysis is based on hourly simulations using measured meteorological data over an eight-year period for multiple locations characterized by continental climatic conditions. Two system configurations were considered: a reference system without thermal energy storage and a storage-integrated system operating under a dual-tariff electricity pricing scheme. The results show that thermal energy storage enables effective load shifting toward lower tariff periods, resulting in consistent electricity cost reductions of 19–23% across all analyzed years and locations. These savings are achieved without significant changes in seasonal performance. However, the economic analysis indicates that the payback period remains relatively long (20 years), exceeding typical thresholds for residential investments under current conditions. Overall, the findings highlight the importance of operational flexibility and demonstrate that simple control strategies can improve the economic performance of residential heat pump systems. Full article

As China advances high-quality agricultural development, promoting green production among farmers has become an important policy priority. Using survey data from 1787 rice farmers in seven major rice-producing provinces in southern China, this study examines whether enterprise-led contract farming can promote farmers’ green production behavior. Green production behavior is measured by a composite index based on six practices, including green control technology, soil testing and formulated fertilization, organic fertilizer substitution, water-saving irrigation, agricultural film recycling, and straw return. Empirical analysis results show that enterprise-led contract farming can significantly promote farmers’ green production behavior. Further analysis suggests that food safety certification, planting technology training, and lower perceived price volatility are important pathways through which contract farming is linked to green production practices. The promoting effect is weaker among older farmers, stronger for farmers cultivating land with medium soil fertility, and more pronounced among small-scale rice farmers. These findings highlight the role of enterprise-led contract farming in promoting farmers’ green production and offer policy implications for encouraging wider participation in green production practices. Full article

The Yanshangou landslide, located in the Baihetan Reservoir area, poses severe potential threats to the normal operation of the reservoir due to its distinct deformation characteristics and high sensitivity to reservoir water level fluctuations. This study systematically investigates the geological background, deformation characteristics, stability evolution, and landslide-induced surge hazards of the Yanshangou landslide in the Baihetan Reservoir area. This work only considers the influence of reservoir water level fluctuations, which is the dominant factor controlling the current progressive deformation of the landslide. Field surveys and GNSS/deep displacement monitoring results revealed that the Yanshangou landslide exhibits obvious staged deformation characteristics, and the landslide deformation rate was closely coupled with the dynamic changes in reservoir water level. A slope stability evaluation method integrating the Morgenstern–Price limit equilibrium method and Richard’s equation was established, and the results indicated that the Yanshangou landslide has low saturated permeability. Therefore, its factor of safety (FOS) presents a clear four-stage variation trend in response to reservoir water level fluctuations. A Smoothed Particle Hydrodynamics (SPH)-based numerical model was further developed to simulate the landslide-induced surges under two typical reservoir water level scenarios (815 m and 765 m). The simulation results demonstrated that a high reservoir water level led to more intense surges with greater height and higher velocity, while a low reservoir water level resulted in surges with a wider propagation range along the reservoir bank. The research findings of this study provide a comprehensive theoretical basis and detailed data support for the prevention and mitigation of geological hazards in the Baihetan Reservoir area, and also offer a reference for the hazard management of similar reservoir landslides worldwide. Full article

Guar gum (GG), a typical biopolymer, has found widespread use in packaging applications due to its biodegradability, non-toxicity, and low price. However, the further application of GG is significantly limited by its poor flexibility and water resistance. In this study, GG/natural rubber (NR) films were prepared by thermo-compressing hand-kneaded pastes made from GG powder and fresh NR latex. Various NR contents—5, 10, 20, and 40 wt%—were investigated. Water-resistant properties were determined by moisture absorption, water dissolution, surface wettability, and water vapor permeability. Fourier transform infrared spectroscopy indicated interactions between the dispersed NR phases and the GG matrix. Scanning electron microscopy revealed distinct phase separation between the GG and NR phases in the films. All GG/NR films exhibited excellent interfacial adhesion between GG and NR phases. Tensile results indicated that an increase in the amount of NR in the GG-based films led to a decrease in both maximum tensile strength and Young’s modulus, while elongation at break increased. GG/40% NR films exhibited an elongation at break of 17.5%, which is a substantial increase of 415% compared to pure GG films. The addition of NR showed improved water-resistant properties of GG-based films; however, the rate of biodegradation during soil burial decreased as the NR ratios increased. These thermo-compressed GG/NR blends hold promise as sustainable alternatives to single-use plastic packaging applications. Full article

The increasing frequency and severity of extreme droughts caused by climate change has emerged as a key risk factor exerting complex effects on the overall national economy through a structure of interconnected industries. The Water Input–Output Linear Programming (WIOLP) model was applied to data from 2015 to 2018 to quantitatively assess the effects of drought-induced water use constraints on production and socioeconomic potential losses. By modeling scenarios in which water use decreased by 10% from 100%, changes in the gross output, the value added, the socioeconomic potential loss, and the shadow price by industry were evaluated. Results showed that socioeconomic potential losses increased nonlinearly, with maximum potential losses of 311,118 billion Korean Won (KRW) in 2015 and 355,260 billion KRW in 2018. The shadow price rose from 7311 to 73,186 KRW/m³ in 2015 and from 3291 to 89,586 KRW/m³ in 2018, confirming that the marginal productivity of water increased exponentially under stricter constraints. Industry-level analysis revealed the largest losses in high water use industries (e.g., agriculture, forestry, fisheries, chemicals, and non-metals), whereas electricity, electronics, and machinery sectors maintained relatively stable production. This study demonstrates that the WIOLP model can empirically analyze nonlinear economic ripple effects under resource constraints, overcoming limitations of conventional input–output and computable general equilibrium models. Full article

Heat recovery from greywater is one solution for improving the energy efficiency of buildings and reducing greenhouse gas emissions. Particular attention is paid to systems utilizing heat from shower water, which, due to its high temperature and regularity, represents a promising energy source. However, the interplay of parameters determining the financial and environmental effectiveness of such a solution has not yet been fully explored. Therefore, the aim of this paper was to identify key variables influencing the feasibility of using a shower heat exchanger operating under unbalanced flow conditions and to assess the consistency between financial and environmental effects. The analyzed net present values ranged from −€1381 to €52,168. Greenhouse gas emission reduction values ranged between 61 kgCO₂e and 37,207 kgCO₂e. The analysis was conducted using predictive modeling and the SHAP (SHapley Additive exPlanations) method, which allows for the interpretation of the impact of individual variables on the forecasted net present value and potential greenhouse gas emission reduction. A global analysis was carried out to determine the relative importance of variables, as well as a local analysis for selected cases. The results showed that operational variables related to shower use, particularly shower length and mixed water flow rate, significantly influenced the prediction results of both models. In the case of emission reduction, greenhouse gas emission intensity and its change over time also had a significant impact, whilst the financial effects were determined by the energy price from the perspective of the subsequent years of the system’s operation. Full article

Climate change is intensifying hydroclimatic extremes and agricultural water scarcity, sharpening trade-offs among yield stability, water saving, and farm incomes in major grain regions. Existing studies often optimise cropping patterns or irrigation schedules separately, seldom embedding yield robustness and policy instruments in one decision framework. We propose an integrated Machine-learning–System-dynamics–Non-dominated-sorting-genetic-algorithm-II (ML–SD–NSGA-II) framework linking long-horizon meteorological scenario generation, crop–water–economy feedback and multi-objective optimisation of crop areas and irrigation depths. ML models generate daily climate sequences to drive an SD model of soil moisture, yield formation, basin-scale allocable water, and farm returns; NSGA-II searches Pareto-optimal strategies that maximise profit and irrigation water productivity while minimising yield deviation. Applied to a rice–wheat irrigation system in the middle Yangtze River Basin, knee-point solutions lift irrigation water productivity by about 14%, maintain near-baseline profits, and reduce yield deviation. Scenario tests with block tariffs, quota-based subsidies, and extreme drought show pricing mainly curbs low-value water use in normal years, while under drought, physical scarcity dominates and economic tools offer limited buffering. This reveals the existence of a scarcity-regime threshold beyond which economic instruments become second-order relative to binding biophysical constraints. The framework supports transparent ex ante testing of tariff–subsidy packages for irrigation governance and adaptation. Full article