Search Results (121)

Societies are aiming to have a higher ecological consciousness in wastewater treatment operations and achieve a more sustainable future. With this said, global demands for larger quantities of resources and the consequent waste generated will inevitably lead to the exhaustion of current municipal wastewater treatment works. The utilization of biosolids (particularly microbial proteins) from wastewater treatment operations could generate a sustainable bio-adhesive for the wood industry, reduce carbon footprint, mitigate health concerns related to the use of carcinogenic components, and support a more circular economic option for wastewater treatment. A techno-economic analysis for three 10 MGD wastewater treatment operations producing roughly 11,300 dry pounds of biosolids per day, in conjunction with co-feedstock defatted soy flour protein at varying ratios (i.e., 0%, 15%, and 50% wet weight), was conducted. Aspen Capital Cost Estimator V12 was used to design and estimate installed equipment additions for wastewater treatment plant integration into an urban biorefinery process. Due to the mechanical attributes and market competition, the chosen selling prices of each adhesive per pound were set for analysis as USD 0.75 for Plant Option P1, USD 0.85 for Plant Option P2, and USD 1.00 for Plant Option P3. Over a 20-year life, each plant option demonstrated economic viability with high NPVs of USD 107.9M, USD 178.7M, and USD 502.2M and internal rates of return (IRRs) of 24.0%, 29.0%, and 44.2% respectively. The options examined have low production costs of USD 0.14 and USD 0.19 per pound, minimum selling prices of USD 0.42–USD 0.51 per pound, resulting in between 2- and 4-year payback periods. Sensitivity analysis shows the effects biosolid production fluctuations, raw material market price, and adhesive selling price have on economics. The results proved profitable even with large variations in the feedstock and raw material prices, requiring low market selling prices to reach the hurdle rate of examination. This technology is economically enticing, and the positive environmental impact of waste utilization encourages further development and analysis of the bio-adhesive process. Full article

Virtual power plants achieve the flexible scheduling and management of power systems by integrating distributed energy resources such as renewable energy sources, energy storage systems, and controllable loads. However, due to the instability of renewable energy generation, load demand fluctuations, and market price uncertainty, virtual power plants face a gigantic challenge operating and participating in electricity markets. First, this paper outlines the functions and uncertainties of virtual power plants; then, it describes the uncertainties of virtual power plants in terms of aggregation, participation in market bidding, and optimal dispatch; finally, it summarizes the review. Full article

In distribution networks, energy storage serves as a crucial means to mitigate power fluctuations from renewable energy sources. However, due to its high cost, energy storage remains a resource whose large-scale adoption in power systems faces significant challenges. In recent years, the emergence of shared energy storage business models has provided new opportunities for the efficient operation of multi-distribution networks. Nevertheless, distribution network operators and shared energy storage operators belong to different stakeholders, and traditional centralized scheduling strategies suffer from issues such as privacy leakage and overly conservative decision-making. To address these challenges, this paper proposes a Nash bargaining game-based optimal energy management and trading strategy for multi-distribution networks with shared energy storage. First, we establish optimal scheduling models for active distribution networks (ADNs) and shared energy storage operators, respectively, and then develop a cooperative scheduling model aimed at maximizing collaborative benefits. The interactive variables—power exchange and electricity prices between distribution networks and shared energy storage operators—are iteratively solved using the Alternating Direction Method of Multipliers (ADMM). Finally, case studies based on modified IEEE-33 test systems validate the effectiveness and feasibility of the proposed method. The results demonstrate that the presented approach significantly outperforms conventional centralized optimization and distributed robust techniques, achieving a maximum improvement of 3.6% in renewable energy utilization efficiency and an 11.2% reduction in operational expenses. While maintaining computational performance on par with centralized methods, it effectively addresses data privacy concerns. Furthermore, the proposed strategy enables a substantial decrease in load curtailment, with reductions reaching as high as 63.7%. Full article

Rapid industrialization, widespread transportation electrification, and significantly rising household energy consumption are rapidly increasing global electricity demand. Climate change and dependency on fossil fuels to meet this demand underscore the critical need for sustainable energy solutions. Microgrids (MGs) provide practical applications for renewable energy, reducing reliance on fossil fuels and mitigating ecological impacts. However, renewable energy poses reliability challenges due to its intermittency, primarily influenced by weather conditions. Additionally, fluctuations in fuel prices and the management of multiple devices contribute to the increasing complexity of MGs and the necessity to address a range of objectives. These factors make the optimization of Energy Management Strategies (EMSs) essential and necessary. This study contributes to the field by categorizing the main aspects of MGs and optimization EMS, analyzing the impacts of weather on MG performance, and evaluating their effectiveness in handling multi-objective optimization and data considerations. Furthermore, it examines the pros and cons of different methodologies, offering a thorough overview of current trends and recommendations. This study serves as a foundational resource for future research aimed at refining optimization EMS by identifying research gaps, thereby informing researchers, practitioners, and policymakers. Full article

Driven by increased integration of renewable energy sources, the widespread decarbonization of power systems has led to energy price fluctuations that require greater adaptability and flexibility from grid users in order to maximize profits. Industrial loads equipped with flexible resources can optimize energy consumption rather than merely reacting to immediate events, thereby capitalizing on volatile energy prices. However, the absence of sufficient measured data in industrial processes limits the ability to fully harness this flexibility. To address this challenge, we presents a black-box optimization model for optimizing the energy consumption of cooling systems in the aquaculture industry using Extreme Gradient Boosting (XGBoost) and Bayesian Optimization (BO). XGBoost is employed to establish a nonlinear relationship between cooling system power consumption and available measured data. Based on this model, Bayesian Optimization with the Lower Confidence Bound (LCB) acquisition function is used to determine the optimal discharge temperature of water into breeding pools, minimizing day-ahead electricity costs. The proposed approach is validated using real-world data from a case study at the Port of Hirtshals, Denmark based on measurements from 2023. Our findings illustrate that leveraging the inherent flexibility of industrial processes can yield financial benefits while providing valuable signals for grid operators to adjust consumption behaviors through appropriate price mechanisms. Furthermore, machine learning techniques prove effective in optimizing energy consumption for industries with limited measured data, delivering accurate and practical estimations. Full article

In natural resource-dependent economies, global resource price volatility makes financial systems more vulnerable to economic shocks. The relationship between natural resource rent and bank stability lies in how fluctuations in resource revenues can affect financial institutions’ stability. The purpose of this paper is twofold. First, it explores the effect of natural resource rent (NRR) on bank stability (BS) in the Middle East and North Africa (MENA) region. Second, it examines whether institutional quality (IQ) moderates the association between BS and NRR. To achieve these goals, we used a sample of 68 conventional banks located in the MENA region between 2005 and 2020 and performed the System Generalized Method of Moments (SGMM) as an econometric approach. The empirical findings show that NRR is negatively and significantly associated with BS, while IQ significantly enhances BS in the MENA region. Additionally, the outcomes support evidence that the MENA banks benefit from an interaction between IQ and NRR. This result was confirmed for both the Z-ROA and Z-ROE as measures of BS. The results of this paper could have several useful applications for policymakers and bankers. Policymakers should prioritize strengthening institutional frameworks to mitigate the adverse effects of resource dependence on financial stability. In addition, bankers are invited to focus on improving institutional quality by fostering an institutional environment, including compliance with anti-corruption standards and coordination with regulatory bodies to boost financial resilience. Full article

The increasing complexity of highway electromechanical systems has created a critical need to improve the accuracy of resource consumption standards. Traditional deterministic methods often fail to capture inherent variability in resource usage, resulting in significant discrepancies between budget estimates and actual costs. To address this issue for a specific device, this study develops a probabilistic framework based on Monte Carlo simulation, using manual barrier gate installation as a case study. First, probability distribution models for key parameters were established by collecting and statistically analyzing field data. Next, Monte Carlo simulation generated 100,000 pseudo-observations, yielding mean labor consumption of 1.08 workdays (SD 0.29), expansion bolt usage of 6.02 sets (SD 0.97), and equipment shifts of 0.20 (SD 0.10). Comparison with the “Highway Engineering Budget Standards” (JTG/T 3832-2018) revealed deviations of 1% to 4%, and comparison with market bid prices showed errors below 2%. These results demonstrate that the proposed method accurately captures dynamic fluctuations in resource consumption, aligning with both national norms and actual tender data. In conclusion, the framework offers a robust and adaptable tool for cost estimation and resource allocation in highway electromechanical projects, enhancing budgeting accuracy and reducing the risk of cost overruns. Full article

The building sector significantly contributes to global resource depletion and greenhouse gas emissions, necessitating integrated approaches to evaluate both environmental and economic performance. This study developed a sustainability-oriented assessment framework—applied in a Chinese context—that integrates life cycle assessment (LCA), life cycle costing (LCC), and carbon financial optimization to evaluate the life cycle performance of prefabricated steel buildings. Using publicly available databases (CEADs, Ecoinvent, and the Chinese Life Cycle Database), the framework quantified cradle-to-grave environmental impacts across raw material extraction, prefabrication, transport, on-site assembly, operation, and end-of-life stages. Emissions were monetized using standardized emission factors and official cost coefficients, enabling environmental costs to be expressed in financial terms. A dynamic financial simulation module was incorporated to assess the effects of carbon price fluctuations and quota allocation schemes. Sensitivity analyses were performed to examine the influence of key variables such as retrofit investment costs, emission reduction efficiency, and carbon policy scenarios on financial returns. The results show that material production and operational energy use dominate life cycle carbon emissions, jointly contributing more than 90% of the total impacts. Moderate decarbonization investments—such as HVAC upgrades and improved insulation—can achieve positive net economic returns under baseline carbon pricing. This integrated, data-driven framework serves as a practical decision-support tool for policymakers and industry stakeholders. It is adaptable across different regions and material systems, supporting the global transition toward low-carbon and financially viable construction practices. Full article

The main objective of energy balance analysis is to guide farmers in making informed decisions that promote the efficient management of natural resources, optimise the use of agricultural inputs, and improve the overall economic performance of their farms. In addition, it supports the adoption of sustainable agricultural practices, such as crop diversification, the use of renewable energy sources, and the recycling of agricultural by-products and residues into natural energy sources or fertilisers. This paper analyses the variation in energy efficiency between 2019 and 2022 of the main crops in Angola: maize, soybean, and rice, and the forest production of eucalyptus biomass in agroforestry farms. The research was based on the responses to interviews conducted with the managers of the farms regarding the machinery used, fuels and lubricants, labour, seeds, phytopharmaceuticals, and fertilisers. The quantities are gathered by converting data into Megajoules (MJ). The results show variations in efficiency and energy balance. In corn, efficiency fluctuated between 1.32 MJ in 2019 and 1.41 MJ in 2020, falling to 0.94 MJ in 2021 due to the COVID-19 pandemic before rising to 1.31 MJ in 2022. For soybeans, the energy balance went from a deficit of −8223.48 MJ in 2019 to a positive 11,974.62 MJ in 2022, indicating better use of resources. Rice stood out for its high efficiency, reaching 81,541.33 MJ in 2021, while wood production showed negative balances, evidencing the need for more effective strategies. This research concludes that understanding the energy balance of agricultural operations in Angola is essential not only to achieve greater sustainability and profitability but also to strengthen the resilience of agricultural systems against external factors such as climate change, fluctuations in input prices, and economic crises. A comprehensive understanding of the energy balance allows farmers to assess the true cost-effectiveness of their operations, identify energy inefficiencies, and implement more effective strategies to maximise productivity while minimising environmental impacts. Full article

This paper examines the financial health risks that private sector water, sanitation, and hygiene (WASH) businesses in rural Vietnam face. It investigates the challenges faced by water operators and sanitation suppliers involved in donor-funded development projects aimed at supporting poor and vulnerable households. Through surveys and focus group discussions with 15 suppliers who worked in public–private partnerships, this research examines the financial risk factors affecting water and sanitation suppliers and their impact on financial viability through two case studies. For water operators, the risks primarily involve infrastructure management, operational costs, and revenue instability. In the sanitation sector, risks center around fluctuating material prices, limited business expansion capital, and household affordability. This study highlights the dual role of government and donor subsidies, which enhance service accessibility but potentially distort market dynamics. It also underscores the need for targeted financial and policy interventions, including better access to microfinance, regulatory improvements, and human resource development. The findings aim to inform strategies for government, donors, and private sector actors in similar WASH development contexts to enhance financial sustainability, ensuring inclusive WASH services in underserved areas. This paper contributes to policy discussions by proposing mechanisms to balance public–private collaboration while fostering market resilience and equitable access to WASH services in emerging economies similar to that of Vietnam. Full article

The integration of the agricultural product market is of great significance to reducing price fluctuations and improving social welfare. In this study, we employ the relative price method to measure the integration of the agricultural product market in 31 Chinese provinces from 2003 to 2022. We use the Dagum–Gini coefficient and its decomposition and the σ convergence and β convergence models to analyze regional variations, time trends, and convergence. It is found that the degree of integration of the Chinese agricultural product market did not increase continuously but fluctuated with increasing intensity. The spatial differentiation degree of agricultural market integration fluctuated. The integration degree of the agricultural product market has σ convergence, absolute β convergence, and conditional β convergence. The marginal contribution of this study is the systematic analysis of the dynamic evolution and convergence of the integration of the Chinese agricultural product market. In order to improve the integration degree of the agricultural product market, in this paper, we put forward policy suggestions from three aspects: strengthening policy support, optimizing resource allocation, and building agricultural product market information centers. Full article

This study proposes a nonlinear 0-1 mixed-integer programming model for optimizing the location of logistics distribution centers within the Hohhot–Baotou–Ordos–Ulanqab urban agglomeration, integrating transportation costs, carbon emissions, and operational coefficients. The optimization problem is solved using a genetic algorithm (GA), whose robustness is systematically validated through comparative analyses with linear programming (LP) and alternative heuristic optimization methods including simulated annealing (SA) and particle swarm optimization (PSO). Comprehensive sensitivity analyses are conducted on critical parameters—including transportation costs, demand fluctuations, carbon pricing mechanisms, the logistics center capacity, land use impact, and water resource constraints—to evaluate the model’s adaptability under diverse operational scenarios. The research methodology incorporates environmental impact factors, including carbon emission costs, land resource utilization, and water resource management, thereby extending traditional optimization frameworks to address region-specific ecological sensitivity concerns. The empirical results demonstrate that the optimized location configuration significantly reduces logistics operational costs while simultaneously enhancing both the economic efficiency and environmental sustainability, thus fostering regional economic coordination. This study makes several key contributions: (1) developing an integrated decision-making framework that balances economic efficiency and environmental sustainability; (2) systematically incorporating environmental impact factors into the optimization model; (3) establishing calibration methods specifically tailored for ecologically sensitive regions; and (4) demonstrating the potential for the synergistic optimization of economic and environmental objectives through strategic logistics network planning. Full article

This paper presents a deep neural network (DNN)-embedded mixed-integer linear programming (MILP) model for fault prediction and production optimization in tablet pressing machines. The DNN predicts the probability of failures during the tablet pressing process by analyzing key operational parameters such as pressure, temperature, humidity, speed, vibration, and number of maintenance cycles. The MILP model optimizes the temperature and humidity settings, production schedules, and maintenance planning to maximize total profit while minimizing penalties for fault pressing, energy consumption, and maintenance costs. To integrate DNN into the MILP framework, Big-M constraints are applied to linearize the Rectified Linear Unit (ReLU) activation functions, ensuring solvability and global optimality of the optimization problem. A case study using the Kaggle dataset demonstrates the model’s ability to dynamically adjust production and maintenance schedules, enhancing profitability and resource utilization under fluctuating electricity prices. Sensitivity analyses further highlight the model’s robustness to variations in maintenance and energy costs, striking an effective balance between cost efficiency and production quality, which makes it a promising solution for intelligent scheduling and optimization in complex manufacturing environments. Full article

Efficient supply–demand management in Product-as-a-Service (PaaS) markets requires tools to evaluate pricing strategies while integrating sustainability goals like reuse, efficiency, and carbon footprint reduction. This paper introduces a declarative modeling framework aimed at balancing the three pillars of profitability, cost optimization, and sustainability in PaaS markets. The framework addresses risks such as equipment failure, usage variability, and economic fluctuations, helping providers optimize pricing and operating costs while enabling customers to manage expenses. A declarative model is developed to assess the PaaS market balance to determine optimal leasing offers and requests for quotations. A case study is used to validate the framework, involving devices with specific rental prices and failure rates, as well as customer expectations and budget constraints. Computational experiments demonstrate the model’s practical applicability in real-world scenarios and it can be used by PaaS providers to develop competitive leasing strategies, policymakers to assess market stability, and enterprises to optimize procurement decisions. The findings show that the framework can guide decision making, offering insights into the impact of new technologies, compatibility conditions for leasing offers, and strategies for balancing providers’ profits and customers’ costs. The proposed framework has broad applicability across industries such as manufacturing, healthcare, logistics, and IT infrastructure leasing, where efficient resource allocation and lifecycle management are crucial. Full article

Under the guidance of high-quality development goals, the energy industry should not only pay attention to the development level but also to the coordination effect among multiple elements. In the process of low-carbon development, natural gas plays an important transitional role as a clean fossil energy. In this study, by introducing the theoretical perspective of energy trilemma, a comprehensive measurement system of the three-dimensional development level of the regional natural gas industry was constructed. Then, in order to overcome the limitation that the coordination effect is weakened due to the concentration of function values, an improved coupling coordination model was established based on the redefined coupling degree distribution function. Next, based on actual data from Beijing from 2006 to 2022, the safety–economy–environmental protection development level of the natural gas industry was empirically analyzed, and the coupling coordination degree of multi-dimensional factors was deeply investigated. The empirical results reveal the following: (1) Beijing is one of the largest natural gas consumption markets in China, so the economy level of its natural gas industry was relatively high. However, the safety level and environmental protection level needed to be improved. This is mainly due to the scarce resource endowment, and the dependence of economic growth on fossil energy. (2) The coupling coordination degree showed a fluctuating upward trend. The coordination degree of safety and environmental protection was the best, mainly because they coexisted and promoted each other at the policy level. The coordination degree of safety and economy was also relatively high, mainly because supply security could provide resource support for market expansion and stabilize price levels. Meanwhile, a prosperous market would stimulate energy exploration and infrastructure extension. This study will help to provide a high-quality development plan for the natural gas industry for solving the regional energy trilemma. Full article