Search Results (1,621)

The rising demand for energy resources and industrial goods presents significant challenges to sustainable development. Sunflower oil, commonly utilized in the food sector, biofuels, and various industrial applications, is notably affected by this demand. In Bulgaria, it serves as a primary source of vegetable fats, ranking second to butter in daily consumption. The aim of this study is to evaluate and propose methods to improve energy and resource efficiency in sunflower oil production in Bulgaria. The analysis is based on data from an energy audit conducted in 2023 at an industrial sunflower oil production facility. Reconstruction and modernization initiatives, which included the installation of high-performance, energy-efficient equipment, led to a 34% increase in energy efficiency. The findings highlight the importance of adjusting the technological parameters such as temperature, pressure, grinding level, and pressing time to reduce energy use and operational costs. Additionally, resource efficiency is improved through more effective raw material utilization and waste reduction. These strategies not only enhance the economic and environmental performance of sunflower oil production but also support sustainable development and competitiveness within the industry. The improvement reduces hexane use by approximately 2%, resulting in energy savings of 12–15 kWh/t of processed seeds and a reduction in CO₂ emissions by 3–4 kg/t, thereby improving the environmental profile of sunflower oil production. Full article

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO₂ emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO₂ emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article

This research considers a preliminary comparative technical evaluation of two micro-gas turbine (MGT) systems in combined heat and power (CHP) mode (100 kWe), aimed at supplying heat to a residential community of 15 average-sized buildings located in Central Europe over a year. Two systems were modelled in Ebsilon 15 software: a natural gas case (benchmark) and an ammonia-fueled case, both based on the same on-design parameters. Off-design simulations evaluated performance over variable ambient temperatures and loads. Idealized, unrecuperated cycles were adopted to isolate the thermodynamic impact of the fuel switch under complete combustion assumption. Under these assumptions, the study shows that the ammonia system produces more electrical energy and less excess heat, yielding marginally higher electrical efficiency and EUF (26.05% and 77.63%) than the natural gas system (24.59% and 77.55%), highlighting ammonia’s utilization potential in such a context. Future research should target validating ammonia combustion and emission profiles across the turbine load range, and updating the thermodynamic model with a recuperator and SCR accounting for realistic pressure losses. Full article

In response to increasingly stringent emission regulations, low-carbon fuels have received significant attention as sustainable energy sources for internal combustion engines. This study investigates four representative low-carbon fuels, methane, methanol, hydrogen, and ammonia, by systematically summarizing their combustion characteristics and emission profiles, along with a review of existing after-treatment technologies tailored to each fuel type. For methane engines, unburned hydrocarbon (UHC) produced during low-temperature combustion exhibits poor oxidation reactivity, necessitating integration of oxidation strategies such as diesel oxidation catalyst (DOC), particulate oxidation catalyst (POC), ozone-assisted oxidation, and zoned catalyst coatings to improve purification efficiency. Methanol combustion under low-temperature conditions tends to produce formaldehyde and other UHCs. Due to the lack of dedicated after-treatment systems, pollutant control currently relies on general-purpose catalysts such as three-way catalyst (TWC), DOC, and POC. Although hydrogen combustion is carbon-free, its high combustion temperature often leads to elevated nitrogen oxide (NO_x) emissions, requiring a combination of optimized hydrogen supply strategies and selective catalytic reduction (SCR)-based denitrification systems. Similarly, while ammonia offers carbon-free combustion and benefits from easier storage and transportation, its practical application is hindered by several challenges, including low ignitability, high toxicity, and notable NO_x emissions compared to conventional fuels. Current exhaust treatment for ammonia-fueled engines primarily depends on SCR, selective catalytic reduction-coated diesel particulate filter (SDPF). Emerging NO_x purification technologies, such as integrated NO_x reduction via hydrogen or ammonia fuel utilization, still face challenges of stability and narrow effective temperatures. Full article

Volatile organic compounds (VOCs) are highly volatile chemicals in natural and anthropogenic environments, significantly affecting indoor air quality. Major sources of indoor VOCs include emissions from building materials, furnishings, and consumer products. Natural wood products release VOCs, including terpenes and aldehydes, which exert diverse health effects ranging from mild respiratory irritation to severe outcomes, such as formaldehyde-induced carcinogenicity. The temporal dynamics of VOC emissions were investigated, and the toxicological and physiological effects of the VOCs emitted by two types of natural wood, Korean Red Pine (Pinus densiflora) and Japanese Cypress (Chamaecyparis obtusa), were evaluated. Using female C57BL/6 mice as an animal model, the exposure setups included phytoncides, formaldehyde, and intact wood samples over short- and long-term durations. The exposure effects were assessed using oxidative stress markers, antioxidant enzyme activity, hepatic and renal biomarkers, and inflammatory cytokine profiles. Long-term exposure to Korean Red Pine and Japanese Cypress wood VOCs did not induce significant pathological changes. Japanese Cypress exhibited more distinct benefits, including enhanced oxidative stress mitigation, reduced systemic toxicity, and lower pro-inflammatory cytokine levels compared to the negative control group, attributable to its more favorable VOC emission profile. These findings highlight the potential health and environmental benefits of natural wood VOCs and offer valuable insights for optimizing timber use, improving indoor air quality, and informing public health policies. Full article

According to the National Institutes of Health, approximately 465 million individuals are affected by type II diabetes mellitus (T2DM) and could benefit from managing their condition with a high-quality diet based on proper, nutrient-rich food choices. A plant-based diet not only has health benefits but also helps mitigate climate change by reducing greenhouse gas emissions, but the Mediterranean diet has the most beneficial effect on overall health. In contrast, ultra-processed foods have a negative impact on T2DM outcomes. Reviewing the nutritional profile of different meals, snacks and desserts would be helpful in enhancing their quality, strengthening the role of dietitians and doctors and protecting against T2DM complications. This approach would also increase simplification and education for consumers. The PubMed-Medline, Web of Science, Scopus and Cochrane Library databases were searched for relevant articles published up to May, from 2000 (based on publication date). The results support the need to reinforce health claims and highlight public demand for food choices while also improving patient quality of life. Full article

Geographical origin authentication of agrifood products is essential for ensuring their quality, preventing fraud, and maintaining consumers’ trust. In this study, we used proton nuclear magnetic resonance (¹H NMR) and excitation–emission matrix (EEM) fluorescence spectroscopy combined with chemometric methods for the geographical origin characterization of olive drupes and leaves from different Tuscany subregions, where olive oil production is relevant. Single-block approaches were implemented for individual datasets, using principal component analysis (PCA) for data visualization and Soft Independent Modeling of Class Analogy (SIMCA) for sample classification. ¹H NMR spectroscopy provided detailed metabolomic profiles, identifying key compounds such as polyphenols and organic acids that contribute to geographical differentiation. EEM fluorescence spectroscopy, in combination with Parallel Factor Analysis (PARAFAC), revealed distinctive fluorescence signatures associated with polyphenolic content. A mid-level data fusion strategy, integrating the common dimensions (ComDim) method, was explored to improve the models’ performance. The results demonstrated that both spectroscopic techniques independently provided valuable insights in terms of geographical characterization, while data fusion further improved the model performances, particularly for olive drupes. Notably, this study represents the first attempt to apply EEM fluorescence for the geographical classification of olive drupes and leaves, highlighting its potential as a complementary tool in geographic origin authentication. The integration of advanced spectroscopic and chemometric methods offers a reliable approach for the differentiation of samples from closely related areas at a subregional level. Full article

The life-cycle carbon emissions (LCCE) assessment of dairy barns is crucial for identifying low-carbon transition pathways and promoting the sustainable development of the dairy industry. We applied a life cycle assessment approach integrated with building information modeling and EnergyPlus to establish a full life cycle inventory of the material quantities and energy consumption for dairy barns. The LCCE was quantified from the production to end-of-life stages using the carbon equivalent of dairy barns (CEDB) as the functional unit, expressed in kg CO₂e head⁻¹ year⁻¹. A carbon emission assessment model was developed based on the “building–process–energy” framework. The LCCE of the open barn and the lower profile cross-ventilated (LPCV) barn were 152 kg CO₂e head⁻¹ year⁻¹ and 229 kg CO₂e head⁻¹ year⁻¹, respectively. Operational carbon emissions (OCE) accounted for the largest share of LCCE, contributing 57% and 74%, respectively. For embodied carbon emissions (ECE), the production of building materials dominated, representing 91% and 87% of the ECE, respectively. Regarding carbon mitigation strategies, the use of extruded polystyrene boards reduced carbon emissions by 45.67% compared with stone wool boards and by 36% compared with polyurethane boards. Employing a manure pit emptying system reduced carbon emissions by 76% and 74% compared to manure scraping systems. Additionally, the adoption of clean electricity resulted in a 33% reduction in OCE, leading to an overall LCCE reduction of 22% for the open barn and 26% for the LPCV barn. This study introduces the CEDB to evaluate low-carbon design strategies for dairy barns, integrating building layout, ventilation systems, and energy sources in a unified assessment approach, providing valuable insights for the low-carbon transition of agricultural buildings. Full article

Designing optimal heritage tourism routes in secondary cities involves complex trade-offs between cultural richness, travel time, carbon emissions, spatial coherence, and group satisfaction. This study addresses the Personalized Group Trip Design Problem (PGTDP) under real-world constraints by proposing DRL–IMVO–GAN—a hybrid multi-objective optimization framework that integrates Deep Reinforcement Learning (DRL) for policy-guided initialization, an Improved Multiverse Optimizer (IMVO) for global search, and a Generative Adversarial Network (GAN) for local refinement and solution diversity. The model operates within a digital twin of Warin Chamrap’s old town, leveraging 92 POIs, congestion heatmaps, and behaviorally clustered tourist profiles. The proposed method was benchmarked against seven state-of-the-art techniques, including PSO + DRL, Genetic Algorithm with Multi-Neighborhood Search (Genetic + MNS), Dual-ACO, ALNS-ASP, and others. Results demonstrate that DRL–IMVO–GAN consistently dominates across key metrics. Under equal-objective weighting, it attained the highest heritage score (74.2), shortest travel time (21.3 min), and top satisfaction score (17.5 out of 18), along with the highest hypervolume (0.85) and Pareto Coverage Ratio (0.95). Beyond performance, the framework exhibits strong generalization in zero- and few-shot scenarios, adapting to unseen POIs, modified constraints, and new user profiles without retraining. These findings underscore the method’s robustness, behavioral coherence, and interpretability—positioning it as a scalable, intelligent decision-support tool for sustainable and user-centered cultural tourism planning in secondary cities. Full article

The International Maritime Organization (IMO) has set a goal to reach net-zero greenhouse gas emissions from international shipping by or around 2050. The ship energy efficiency framework has played a positive role over the past decade in improving carbon intensity and reducing greenhouse gas emissions by employing the technical and operational energy efficiency metrics as effective appraisal tools. To quantify the ship energy efficiency performance and review the existing energy efficiency framework, this paper analyzed the data for the reporting year of 2023 extracted from the European Union (EU) monitoring, reporting, and verification (MRV) system, and investigated the operational profiles and energy efficiency for the ships calling at EU ports. The results show that the data accumulated in the EU MRV system could provide powerful support for conducting ship energy efficiency appraisals, which could facilitate the formulation of decarbonization policies for global shipping and management decisions for stakeholders. However, data quality, ship operational energy efficiency metrics, and co-existence with the IMO data collection system (DCS) remain issues to be addressed. With the improvement of IMO DCS system and the implementation of IMO Net-Zero Framework, harmonizing the two systems and avoiding duplicated regulation of shipping emissions at the EU and global levels are urgent. Full article

With the increasing integration of renewable energy and smart technologies in residential energy systems, proactive household energy management (HEM) have become critical for reducing costs, enhancing grid stability, and achieving sustainability goals. This study proposes a ultra-short-term forecasting-driven proactive energy consumption optimization strategy that integrates advanced forecasting models with multi-objective scheduling algorithms. By leveraging deep learning techniques like Graph Attention Network (GAT) architectures, the system predicts ultra-short-term household load profiles with high accuracy, addressing the volatility of residential energy use. Then, based on the predicted data, a comprehensive consideration of electricity costs, user comfort, carbon emission pricing, and grid load balance indicators is undertaken. This study proposes an enhanced mixed-integer optimization algorithm to collaboratively optimize multiple objective functions, thereby refining appliance scheduling, energy storage utilization, and grid interaction. Case studies demonstrate that integrating photovoltaic (PV) power generation forecasting and load forecasting models into a home energy management system, and adjusting the original power usage schedule based on predicted PV output and water heater demand, can effectively reduce electricity costs and carbon emissions without compromising user engagement in optimization. This approach helps promote energy-saving and low-carbon electricity consumption habits among users. Full article

This study investigates the effects of diffusion modeling and swirl intensity on flow fields and NO emissions in CH₄/NH₃ non-premixed swirling flames using large eddy simulations (LESs). Simulations are performed for a 50/50 ammonia–methane blend at three global equivalence ratios of 0.77, 0.54, and 0.46 and two swirl numbers of 8 and 12, comparing the unity Lewis number (ULN) and mixture-averaged diffusion (MAD) models against the experimental data includes OH-PLIF and ON-PLIF reported in a prior study by the KAUST group. Both models produce similar flow fields, but the MAD model alters the flame structure and species distributions due to differential diffusion (DD) and limitations in its Flamelet library. Notably, the MAD library lacks unstable flame branch solutions, leading to extensive interpolation between extinction and stable branches. This results in overpredicted progress variable source terms and reactive scalars, both within and beyond the flame zone. The ULN model better reproduces experimental OH profiles and localizes NO formation near the flame front, whereas the MAD model predicts broader NO distributions due to nitrogen species diffusion. Higher swirl intensities shorten the flame and shift NO production upstream. While a low equivalence ratio provides enough air for good mixing, lower ammonia and higher NO contents in exhaust gases, respectively. Full article

The maritime industry faces growing pressure to reduce greenhouse gas (GHG) emissions, reflected in the progressive adoption of stricter international energy regulations. Wind-Assisted Propulsion Systems (WAPS) offer a promising solution by significantly contributing to decarbonization. This paper presents a versatile simulation and optimization platform that supports the conceptual design of WAPS-equipped vessels and evaluates the viability of such investments. The platform uses a steady-state force equilibrium model to simulate vessel performance along predefined routes under realistic weather conditions, incorporating regulatory frameworks and economic assessments. A multi-objective optimization framework identifies optimal designs across user-defined criteria. To demonstrate its capabilities, the platform is applied to a bulk carrier operating between China and the USA, optimizing for capital expenditure, net present value (NPV), and CO₂ emissions. Results show the platform can effectively balance conflicting objectives, achieving substantial emissions reductions without compromising economic performance. The final optimized design achieved a 12% reduction in CO₂ emissions, a 7% decrease in capital expenditure, and a 6.6 million USD increase in net present value compared to the reference design with sails, demonstrating the platform’s capability to deliver balanced improvements across all objectives. The methodology is adaptable to various ship types, WAPS technologies, and operational profiles, offering a valuable decision-support tool for stakeholders navigating the transition to zero-carbon shipping. Full article

The primary objective of this paper is to quantify a realistic pathway for Canada to reach net-zero emissions by 2050. This study analyzed greenhouse gas (GHG) emissions from the 10 provinces and 3 territories of Canada based on the emissions from their economic sectors. A time series analysis was performed to understand the trajectory of the emissions profile from 1990 to 2023. Using the 2023 emissions as the baseline, a linear reduction, based on the GHG proportions from each jurisdiction, was performed and projected to 2050 (except for Prince Edward Island (PEI), where net zero was targeted for 2040). Moreover, a machine learning technique (k-means unsupervised algorithm) was used to group all the jurisdictions into homogeneous regions for national strategic climate policy initiatives. The within-cluster sum of squares identified the following clusters: Cluster 1: Manitoba (MB), New Brunswick, Nova Scotia, and Newfoundland and Labrador; Cluster 2: Alberta (AB); Cluster 3: Quebec (QC) and Saskatchewan; Cluster 4: Ontario (ON); and Cluster 5: PEI, Northwest Territories, Nunavut, and Northwest Territories. Considering the maximum GHG reductions needed per cluster (Clusters 1–5), the results show that 0.309 Mt CO₂ eq/year, 5.447 Mt CO₂ eq/year, 1.293 Mt CO₂ eq/year, 2.217 Mt CO₂ eq/year, and 0.04 Mt CO₂ eq/year must be targeted from MB (transportation), AB (stationary combustion), QC (transportation), ON (stationary combustion) and PEI (transportation), respectively. The concept of climate stabilization wedges, which provides a practical framework for addressing the monumental challenge of mitigating climate change, was introduced to each derived region to cut GHG emissions in Canada through tangible, measurable actions that is specific to each sector/cluster. The clustering-based method breaks climate mitigation problems down into manageable pieces by grouping the jurisdictions into efficient regions that can be managed effectively by fostering collaboration across jurisdictions and economic sectors. Actionable and strategic recommendations were made within each province to reach the goal of net-zero. The implications of this study for policy and climate action include the fact that actionable strategies and tailored policies are applied to each cluster’s emission profile and economic sector, ensuring equitable and effective climate mitigation strategies in Canada. Full article

Liquefied natural gas (LNG) is increasingly used as a marine fuel due to its capacity to significantly reduce emissions of particulate matter, sulfur oxides (SO_x), and nitrogen oxides (NO_x), compared to conventional fuels. In addition, LNG combustion produces less carbon dioxide (CO₂) than conventional marine fuels, and the use of non-fossil LNG offers further potential for reducing greenhouse gas emissions. However, this benefit can be partially offset by methane slip—the release of unburned methane in engine exhaust—which has a much higher global warming potential than CO₂. This study presents an experimental evaluation of methane emissions from a RoPax vessel powered by low-pressure dual-fuel four-stroke engines with a direct mechanical propulsion system. Methane slip was measured directly during onboard testing and combined with a year-long analysis of engine operation using an Engine Load Monitoring (ELM) method. The yearly average methane slip coefficient (C_slip) obtained was 1.57%, slightly lower than values reported in previous studies on cruise ships (1.7%), and significantly lower than the default values specified by the FuelEU (3.1%) Maritime regulation and IMO (3.5%) LCA guidelines. This result reflects the ship’s operational profile, characterized by long crossings at high and stable engine loads. This study provides results that could support more representative emission assessments and can contribute to ongoing regulatory discussions. Full article