Energy efficiency assessment based on IMO indices for LNG dual-fuel engine based electric propulsion coastal passenger ship

Assessing Energy Efficiency and Cost Optimization through Data Analytics: A Case Study of CEAT Tyres Manufacturing Plant, Butibori Nagpur

Abstract Mathematical modeling plays a crucial role in analyzing and designing control systems, offering insights into system behavior and aiding the development of efficient controllers. Similarly, electronic circuit models can be simulated using various commercial or open-source platforms, enabling flexible and accessible system analysis. This study presents a thermal model of a chest freezer using LTspice, an electronic circuit simulator, to address the challenges of evaluating the performance of the freezer under various operating conditions. Although standardized tests effectively assess energy efficiency and safety, they are limited in capturing real-world scenarios such as variable ambient temperatures, load types, and frequent door openings, which require significant time and resources. The proposed simulation model based on LTspice allows adjustable parameters for ambient temperature, internal thermal load, and door openings, making it possible to explore different operating conditions efficiently. The accuracy of the model is assessed through experimental validation using data from a climatic chamber. The results indicate that both the simulated and measured temperature profiles follow similar trends, with comparable oscillation patterns and average values. Minor differences in curve shapes are observed, primarily due to simplifications in the model, but overall alignment remains strong. The compressor on–off cycles in the simulation also closely match the experimental observations. These results validate the LTspice model as a reliable and adaptable representation of freezer thermal dynamics. The method offers a cost-effective and scalable alternative to traditional thermal simulation tools, with potential applications in design optimization, control strategy development, and evaluation of other freezer models with minimal modification.

This study presents the results of a comprehensive energy audit conducted at an industrial textile dyeing facility in Türkiye with the aim of evaluating its energy performance and identifying energy efficiency improvement potentials. Electricity and natural gas consumption patterns were analyzed using historical production and energy data, field measurements, and on-site observations. The audit focused particularly on thermal energy systems, including steam boiler operation, heat distribution lines, and auxiliary equipment, as well as electrical systems such as lighting. Flue gas analysis, thermal imaging, and energy monitoring were employed to determine system efficiencies and quantify energy losses. Based on the audit findings, three main energy efficiency measures were proposed: recovery of waste heat from boiler flue gases, insulation of uninsulated piping and installation elements, and conversion of conventional lighting systems to high-efficiency LED luminaires. The results indicate that natural gas accounts for more than 90% of total energy consumption, highlighting the critical importance of improving thermal systems. The proposed measures demonstrate significant energy saving and economic benefits, with simple payback periods ranging between 0.6 and 3.9 years. The findings confirm that systematic energy audits constitute an effective and economically feasible approach for reducing energy consumption, operating costs, and greenhouse gas emissions in energy-intensive textile dyeing facilities.

The thermal conductivity of thermal insulation materials is a critical parameter for assessing energy efficiency and performance in building, industrial, and aerospace applications. This study combined numerical simulation, parameter inversion optimization and experimental measurement to evaluate the transient hot-wire method for measuring the thermal conductivity of expanded polystyrene (EPS) foam. Using a nickel wire as the hot wire, the effects of various parameters—including wire length and width, heating power, Kapton film thickness and end effect—were systematically analyzed through finite element analysis and Bayesian optimization algorithm. Following the simulation and inversion conclusions, a series of hot-wire sensors with a fixed length of 30 mm and widths of 25 μm, 50 μm, 100 μm, 150 μm, and 200 μm were fabricated for experimental validation. Measurement results were compared against a reference value obtained by the guarded hot plate method. It was found that the sensor with a length of 30 mm and a width of 100 μm demonstrated optimal performance among the configurations tested, with deviations between the experimental measurements and the reference value remaining within approximately ±1.5%.

Retraction Note: Trilemma assessment of energy intensity, efficiency, and environmental index: evidence from BRICS countries.

Hospitals are resource-intensive facilities with continuous operations that result in high energy and water consumption, making the implementation of green building technology essential to improve environmental performance during the operational phase. Objective: This study aims to identify the energy, water, and material efficiency measures implemented at RSUD dr. Soeratno Gemolong, Sragen, and to evaluate the level of efficiency achieved based on EDGE (Excellence in Design for Greater Efficiencies) standards. Methods: The research employed an applied evaluative approach by collecting primary and secondary data through direct observation, interviews, documentation review (as-built drawings and material data), and literature review. The collected data were analyzed using the EDGE application and compared with EDGE benchmarks and relevant Indonesian regulations. Findings: The results indicate that baseline energy efficiency was 19.85%, slightly below the EDGE minimum requirement of 20%, but increased to 24.09% after targeted improvements such as reducing building envelope air infiltration and improving cooling system efficiency. Water efficiency reached 25.75%, and material efficiency achieved 34%, both exceeding the EDGE minimum standard. Implications: These findings demonstrate that EDGE-based evaluation can support maintenance-driven optimization strategies in hospital buildings and provide practical guidance for facility managers and policymakers in prioritizing high-impact efficiency interventions. Originality/Value: This study provides an integrated empirical assessment of energy, water, and material efficiency (EEM, WEM, MEM) in an operational public hospital, showing how targeted improvements can shift energy performance from near-compliance to compliant status within the EDGE framework.

Industrial wastewater treatment facilities are characterized by high energy consumption, which significantly affects their operational costs and environmental performance. This study analyzes the structure of energy consumption in industrial wastewater treatment plants and identifies key areas for improving energy efficiency. Particular attention is given to aeration systems, pumping equipment, sludge energy recovery through anaerobic digestion, and the implementation of automated process control systems. Quantitative indicators for assessing energy efficiency are presented, including specific energy consumption per unit volume of treated wastewater and per unit mass of removed pollutants. A comparative analysis of conventional and energy-efficient technological solutions demonstrates that integrated modernization measures can reduce electricity consumption by 20–40% while enhancing environmental sustainability and economic performance.

ABSTRACT The assessment of energy efficiency in water facilities is receiving increasing attention as a key strategy in the transition toward an energy‐neutral urban water cycle. This study evaluates the energy efficiency of 146 drinking water treatment plants (DWTPs) in Chile by applying a latent class stochastic frontier analysis (LCSFA) to account for both observable and unobservable sources of heterogeneity. Unlike traditional models that assume homogeneity across units, the LCSFA approach identified two distinct groups of DWTPs with significantly different operational characteristics, pollutant loads, and efficiency profiles. Group 1 exhibited an average energy efficiency score of 0.534, while Group 2 showed a significantly higher average of 0.737. In Group 1, efficiency scores estimated under the pooled frontier are higher than those obtained from the latent class model for 73% of DWTPs, whereas in Group 2 the pooled frontier yields lower efficiency scores for 58% of plants. The analysis also revealed substantial energy‐saving opportunities. Group 1 facilities could reduce energy use by an average of 0.112 kWh/m 3 , compared to 0.044 kWh/m 3 in Group 2. These findings demonstrate the importance of adopting heterogeneity‐aware methods for fair and accurate benchmarking. The study offers methodological innovation and practical insights for regulators and utility managers aiming to design targeted energy efficiency interventions and performance‐based incentives in the water sector.

This study assesses the effect of energy efficiency labels on the private housing market using information on energy efficiency assessments of housing and property transactions in France between 2016 and 2021. We take into consideration two energy labels assigned to dwellings, one that calculates energy efficiency based on energy consumption and the other on greenhouse gas emissions. The results of the hedonic regressions show that having a higher efficiency rating has a significantly positive effect on housing prices. We also show that this effect increases with the number of annual heating degree days of the locality of the dwelling, indicating the importance of the energy-saving aspect in the market valuation of energy-efficient housing. Finally, using regression discontinuity estimates, we differentiate between the effect of the cognitive perception of labels and of the real energy efficiency gain on housing prices. Our findings reveal that the cognitive effect is predominantly observed in the least efficient dwellings.

Greenhouse gas (GHG) emissions from anthropogenic activities are causing an increase in global average temperatures and climate change. Buildings and construction contribute significantly to global climate change, accounting for about 21 percent of global greenhouse gas emissions, including the higher education sector. Therefore, universities play a role in achieving the Net Zero Emission 2060 target to reduce carbon emissions on campus so that it requires energy efficiency efforts on energy consumption and the provision of green land in carbon sequestration. This study aims to analyze (1) energy consumption and EUI with various scenarios, (2) carbon footprint with various scenarios, (3) the value of carbon sequestration by trees and carbon balance with various scenarios. It was found that the laboratory building produced the highest electrical energy consumption with an average of 6,916.94 kWh/month. The highest EUI was obtained by laboratory building D12 and has efficient criteria, which is 10.08 kWh/m2/month. The energy-saving scenario shows that all buildings have very efficient criteria. The carbon footprint in existing conditions, wasteful and efficient scenarios respectively amounted to 52,680 kgCO2eq and has the potential to increase to 71,269.76 kgCO2eq and decrease to 39,715.93 kgCO2eq. Vegetation in the form of existing trees in the area has an absorption capacity of 1,067,414.24 kgCO2eq so that trees are able to absorb all CO2 emissions produced so that the carbon balance produces a negative value.

The rapid adoption of electric vehicles (EVs) has highlighted the need for intelligent systems that optimize performance, extend battery life, and ensure sustainable mobility. This study investigated AI-based energy management, battery health prediction, and adaptive control strategies for electrified mobility systems. A hybrid approach integrating machine learning, reinforcement learning, and predictive analytics was employed to monitor real-time driving conditions, forecast battery state-of-health (SoH) and remaining useful life (RUL), and dynamically adjust energy distribution. The methodology involved simulation-based evaluations across urban, highway, and mixed driving cycles to assess energy efficiency, system responsiveness, and predictive accuracy. Results demonstrated that AI-driven energy management significantly reduced energy losses during acceleration and deceleration, while predictive models accurately anticipated battery degradation, enabling proactive maintenance interventions. Adaptive control mechanisms improved vehicle stability, optimized load distribution, and minimized battery stress during dynamic driving scenarios. Comparative analysis indicated that AI-based systems outperformed conventional rule-based strategies in terms of efficiency, reliability, and scalability. These findings underscore the potential of intelligent electrified mobility systems to enhance operational performance, prolong battery lifespan, and support sustainable transportation solutions. Future implementations are recommended to integrate explainable AI techniques and real-world validation to further improve transparency, reliability, and adoption. Overall, the study establishes a framework for AI-enabled EV systems, highlighting their transformative role in achieving energy-efficient, adaptive, and resilient electrified mobility.

Nationwide assessment of energy efficiency gains from electric air taxi integration in U.S. cities

The global shipping industry faces severe carbon emission challenges. Harbor tugs, as significant contributors to port emissions, require improved energy efficiency. However, their sailing conditions are complex and dynamic, making temporal feature characterization difficult with traditional static or simplistic clustering methods. To address this, this study proposes a novel method for constructing typical sailing conditions by integrating an enhanced clustering approach with Hidden Markov Models (HMM). First, kinematic segments are extracted from processed ship speed data, and key features are selected and reduced via Principal Component Analysis (PCA). Subsequently, an improved clustering model combining the Whale Optimization Algorithm (WOA) and K-means++ is developed to categorize segments into six distinct condition types. These clustered states then serve as the hidden states of an HMM, whose learned transition matrix synthesizes a 3600 s typical sailing condition profile. The constructed profile is validated through multi-dimensional comparison with original data, demonstrating high fidelity in statistical characteristics, temporal properties, and distribution similarity. The results confirm that the proposed method can accurately replicate the operational patterns of harbor tugs. This study provides a reliable data foundation for the energy efficiency assessment and optimization of harbor tugs and offers a new methodological perspective for constructing operational profiles for ships and other mobile machinery.

In the era of the climate crisis, the availability of drinking water is becoming a growing concern. In Poland and in the world, rivers and water reservoirs used for drinking water abstraction and treatment are increasingly drying up. Drought is forcing greater reliance on groundwater, which often requires aeration during treatment to enable the removal of dissolved iron and manganese compounds. Therefore, this article presents the results of tests on the effectiveness of groundwater aeration in a PVC pipe aerator packed with steel Białecki rings. The tested pipe aerator was made according to patent PL 235924 B1. The article presents a critical literature review, the research methodology, an evaluation of measurement accuracy, an analysis of groundwater aeration performance, as well as an assessment of energy efficiency (eQ) of the investigated pipe aerator. A nomogram was developed for design purposes to determine the air flow rate (Qa) required in the pipe aerator depending on the desired oxygen content dissolved in the water aerated in the PVC pipe aerator. The investigations showed that the lowest oxygen dissolution occurred at Qa = 0.5 m3∙h−1, and the highest at Qa = 3.0 m3∙h−1, both for the rings with diameters of 12 mm and 25 mm.

This study systematically investigates the influence of capacitor energy storage parameters on the energy utilization efficiency of the underwater electrochemical explosion process. By integrating spherical and cylindrical shock wave propagation models, the pulse shock wave energy under different capacitor energy storage levels was theoretically calculated and experimentally validated. The results indicate that the applicability of the shock wave propagation model depends on the distance and aquatic environment: the spherical model is more suitable for short-distance, deep-water conditions, whereas the cylindrical model performs better for long-distance or shallow-water conditions. Within the energy storage range of up to 100 J, increasing the capacitance significantly enhances both the pulse energy output and energy utilization efficiency. Specifically, as the stored energy increased from 13 J to 100 J, the shock wave energy rose from 0.051 J to 2.45 J, and the energy utilization rate improved from 0.39% to 2.45%. Nevertheless, the overall energy utilization efficiency remains below 10%. This study confirms that rationally configuring capacitor parameters can effectively regulate the discharge process, providing important experimental and theoretical support for optimizing energy utilization efficiency.

This study presents a comprehensive energy audit of the University of Eastern Philippines (UEP) aimed at assessing electricity consumption patterns, identifying inefficiencies, and proposing energy conservation strategies for institutional sustainability. The audit followed a three-phase framework—pre-audit planning, detailed audit and analysis, and post-audit evaluation—guided by the standards of the International Energy Agency (IEA) and ASHRAE. Data were collected from 25 major campus facilities using electricity billing records (2016–2018), field measurements, and stakeholder interviews. Results revealed a 51.4% increase in total electricity expenditure over three years, from ₱4.77 million to ₱7.22 million, driven by infrastructure expansion, increased equipment usage, and reliance on air-conditioning systems. The Cocofed Building, College of Business Administration (CBA) Building, and Bio-Physical Laboratory were identified as the highest energy-consuming structures, accounting for a substantial portion of total campus demand. Seasonal variations showed peak energy use from May to October, corresponding to high cooling loads. Based on audit findings, the implementation of targeted Energy Conservation Measures (ECMs)—including LED retrofitting, HVAC optimization, behavioral reforms, and potential solar photovoltaic integration—could achieve up to 15% reduction in energy use, equivalent to approximately ₱692,000 in annual savings. The study concludes that systematic energy auditing provides an essential framework for data-driven decision-making and long-term sustainability in higher education institutions, particularly in tropical and developing regions.

This study proposes an optimized ensemble learning framework for energy-efficiency assessment in low-voltage distribution networks by integrating multiple data sources. The framework integrates heterogeneous data from smart meters, SCADA systems, meteorological stations, and network topology databases, employing advanced feature engineering to extract 89 essential predictors from 147 initial features. Three gradient boosting algorithms—Random Forest, XGBoost, and LightGBM—are combined through an elastic net stacking strategy with Bayesian hyperparameter optimization. The stacking ensemble achieved superior performance with an MAE of 118.4 kWh, an RMSE of 164.2 kWh, an MAPE of 3.98%, and an R2 of 0.952, representing 16.8% improvement over individual models. SHAP analysis provided model interpretability, identifying temperature, historical consumption, and temporal features as the primary drivers of efficiency. The framework demonstrated robust performance under data quality degradation and successfully generalized across diverse network configurations. Field implementation yielded an 8.3% reduction in distribution losses (95% CI: 7.2%–9.4%, p < 0.0001), 34% decrease in transformer failure rates (95% CI: 28%–40%, p = 0.003), and 12%–15% operational cost reduction. The framework’s ability to provide accurate predictions from 15 min to 24 h ahead while maintaining computational efficiency enables proactive distribution network management, supporting the transition toward efficient and sustainable power systems.

Prediction and management of ship resistance in realistic sea conditions can play a significant role in reducing fuel consumption and carbon emissions. It usually relies on physical model testing and empirical formulas, both facing limitations in early-stage design and in adapting to operational variabilities like weather. Building on our ongoing study where a physics-based framework for added resistance calculation was developed, this paper presents a multi-branch Artificial Neural Network (MbANN) designed to decompose added resistance into distinct contributions from wind, waves, and currents, using only total measured resistance as supervision. The model was trained on operational data during a case-study voyage and evaluated across 14 input arrangements. Results show that the ANN achieved high accuracy in predicting added resistance and its components, as well as particularly robust performance for current resistance. Predictions of wind and wave resistance were less consistent but showed promising results. MbANN showed solid capability of decomposing total added resistance to its physical subcomponents, thus offering flexibility in handling complex environmental interactions. These findings highlight the potential of MbANN-based decomposition to complement hydrodynamic models for operational energy efficiency assessment and compliance with EEXI and CII requirements.

The accurate prediction of heating and cooling loads is a critical prerequisite for designing energy-efficient buildings and reducing their environmental footprint. This study presents a comprehensive comparative analysis of multiple regression models for estimating the energy efficiency of residential buildings based on their architectural parameters. Using the Energy Efficiency dataset, we evaluated the performance of seven distinct modelling approaches: Linear Regression, Decision Tree, Random Forest, Support Vector Regression with a Radial Basis Function kernel, K-Nearest Neighbours, Multi-Layer Perceptron, and Deep Neural Networks. Models were rigorously assessed using Root Mean Square Error, Mean Absolute Error, and the coefficient of determination (R²). The results demonstrate that non-linear machine learning methods significantly outperform traditional linear models. Specifically, the Random Forest and Support Vector Regression models achieved superior predictive accuracy, with RMSE values as low as 0.46 for heating load and 1.53 for cooling load, and R² scores exceeding 0.97. Furthermore, feature importance analysis identified Overall Height and Relative Compactness as the most influential parameters for heating and cooling load predictions, respectively, providing actionable insights for architectural design. This research shows that advanced machine learning models, particularly Random Forest and Support Vector Regression, offer a robust and accurate framework for building energy assessment.