This study presents the electrification plan of a school bus (SB) fleet and examines its potential in vehicle-to-grid (V2G) applications. The data collected includes the efficiency of a 120 kW EV charger, energy consumption of a 40-foot electric school bus (ESB), and a diesel bus operating on the same route. The energy consumption data of the ESB and diesel school bus (DSB) were processed to derive the yearly average distance-specific energy consumption of 0.37 mile/kWh (0.60 km/kWh) grid electricity and 5.55 MPG (2.36 km/L), respectively. The energy consumption ratio of the ESB over the DSB is 14.92 kWh/gallon (3.94 kWh/L) diesel. Based on the CO2 intensity, 1.956 lb/kWh (0.887 kg/kWh) of electricity produced in WV and that of diesel fuel, the distance-specific CO2 emissions of the ESB were 5.38 lb/mile (1.52 kg/km), which are higher than the 4.08 lb/mile (1.15 kg/km) from the diesel bus operating on the same route. This study also presents the V2G potential of the proposed electrical school bus fleet. Based on the estimated grid-to-vehicle battery (G2VB) efficiency of 92% and vehicle battery-to-grid (VB2G) efficiency of 92%, the grid-vehicle battery-grid (G2VB2G) efficiency is 84.64%. The application of V2G technology is associated with a loss of electricity. Based on the 20% to 80% battery charge, and the estimated 92% VB2G efficiency, the proposed ESB fleet has the potential to provide 14,929 kWh electricity, 55.2% of the ESB fleet battery capacity. The increased cost associated with the implementation of the proposed V2G is about USD 7.5 million, a 400% increase compared to the charger satisfying the operation of ESBs when V2G is not used. The V2G application also is expected to increase the charging cycles, which raises concerns about battery degradation and its replacement during SB service lifetime. Accordingly, more research work is needed to address the increased cost and grid capacity demand, and battery degradation associated with V2G applications.

The dairy processing industries are energy-intensive because of the high level of thermal and refrigeration processes. In this study, a large-scale dairy processing unit in South India has been analyzed thermodynamically and optimized by applying integrated energy and exergy analysis along with heat exchanger network (HEN) optimization. The operational data of one year of a plant manufacturing milk, curd, butter and ghee was analysed. Estimation of theoretical electrical and thermal energy requirements was carried out and compared with the actual plant energy consumption. The exergy analysis was used to identify the thermodynamic irreversibilities and the thermo-economic losses, and pinch analysis was used to identify the potential of maximum heat recovery and to design an optimum HEN configuration. The outcomes reveal that the plant has a mean electrical efficiency of around 41%, a thermal efficiency of 52%, and the overall exergy efficiency is 54%–56%, which shows that the plant has high thermodynamic losses. A pinch analysis was performed, and a pinch temperature of 67°C and significant heat recovery opportunities were identified. Two HEN configurations were compared: one large plate heat exchanger and a two-stage compact system. The optimized two-stage design had an overall heat transfer coefficient that is 48% higher than the single-exchanger design and needed 49% of the heat transfer area. The proposed system can recover 0.68 MW of thermal energy, which translates to an annual reduction in biomass fuel consumption of 2929 t and electrical energy consumption of 1.52 GWh, equating to an estimated annual savings cost of ₹ 3.41 crores (or about 28% of the total energy costs). The proposed thermodynamic optimization framework is a viable and cost-effective method to enhance energy efficiency and sustainability in energy-intensive dairy processing industries.

Metal additive manufacturing (AM) processes are often energy-intensive because of the use of high-energy heat sources. Predicting energy consumption accurately is critical for optimizing AM process parameters and minimizing environmental impact. Traditional machine learning models that predict energy consumption in metal AM processes are usually not generalizable when the material or process condition varies. To address this issue, we introduce an incremental learning-integrated transfer learning (TL) approach to predict energy consumption in the directed energy deposition (DED) process. Using a small dataset collected from 20 samples fabricated with CoCrMo or IN718, we conduct three TL tasks with varying process conditions. The incremental learning approach is integrated into the source domain pre-training step to learn knowledge from small datasets more efficiently. We evaluate the performance of the extreme gradient boosting (XGBoost), long short-term memory (LSTM), temporal convolutional networks (TCN), and transformer models. The TCN model achieves the best predictive performance with a mean absolute percentage error of 4.65%, a root mean squared error of 0.28, and a coefficient of determination of 0.92. The incremental learning-integrated TL framework achieves excellent predictive performance and generalizability with small volumes of data.

This study introduces a comprehensive decision support tool to assess and enhance energy efficiency in manufacturing facilities, with a particular focus on Small and Medium-Sized Enterprises (SMEs). The tool integrates multi-criteria decision-making through the TOPSIS method with a fuzzy logic advisory system, enabling SMEs to systematically measure and improve their energy performance across both thermal and electrical domains. Based on an analysis of 100 energy assessments in Pennsylvania, Ohio, Virginia, and West Virginia, the study identifies 27 critical Energy Efficiency Practices (EEP). The fuzzy rules, developed and validated by industry experts, are structured into two advisory sub-systems addressing the specific characteristics of natural gas and electrical energy usage. Key findings reveal a positive correlation between the number of energy efficiency practices implemented and the number of full-time employees, suggesting that facilities with larger workforces tend to adopt more efficiency practices. Additionally, electricity was found to have a significantly greater impact on the Energy Efficiency Index (EEI) than natural gas, with a 42 % higher contribution margin. The fuzzy logic-based advisory system provides practical scenarios for facilities to explore optimal energy efficiency improvements, tailored to meet specific operational constraints. By adopting this tool, manufacturing facilities can strategically enhance their energy practices, thereby advancing their sustainability initiatives and operational efficiency.

The Paris Agreement’s pressing global mandate to limit global warming to 1.5 degrees Celsius above pre-industrial levels by 2030 has placed immense pressure on energy-consuming industries and businesses to deploy robust, advanced, and accurate monitoring and tracking of carbon footprints. This critical issue is examined through a systematic review of English-language studies (2015–2024) retrieved from three leading databases: Scopus (n = 1528), Web of Science (n = 1152), and GreenFILE (n = 271). The selected literature collectively highlights key carbon footprint tracking methods. The resulting dataset is subjected to bibliometric and scientometric analysis after refinement through deduplication and screening, based on the PRISMA framework. Methodologically, the analysis integrated the following: (1) evaluating long-term trends via the Mann–Kendall and Hurst exponent tests; (2) exploring keywords and country-based contributions using VOSviewer (v1.6.20); (3) applying Bradford’s law of scattering and Leimkuhler’s model; and (4) investigating authorship patterns and networks through Biblioshiny (v4.3.0). Further, based on eligibility criteria, 35 papers were comprehensively reviewed to investigate the emerging carbon footprint tracking technologies such as life cycle assessment (LCA), machine learning (ML), artificial intelligence (AI), blockchain, and data analytics. This study identified three main challenges: (a) lack of industry-wide standards and approaches; (b) real-time tracking of dynamic emissions using LCA; and (c) need for robust frameworks for interoperability of these technologies. Overall, our systematic review identifies the current state and trends of technologies and tools used in carbon emissions tracking in cross-sectors such as industries, buildings, construction, and transportation and provides valuable insights for industry practitioners, researchers, and policymakers to develop uniform, integrated, scalable, and compliant carbon tracking systems and support the global shift to a low-carbon and sustainable economy.

As structures age, air leaks naturally form and can remain undetected for years, resulting in increased utility bills and, in severe cases, structural damage. The traditional method to determine if a structure has developed these leaks is through an energy audit, including blower door testing, which is costly and disturbs normal use of the building. A numerical approach to indicate the presence of air leaks, for instance, a simple mathematic formula requiring only simple building information available to any layman, would be of great value. Within this paper, a formula was developed for climate Zone 5 regions using multiple linear regression models to infer the presence of air leaks using only four input variables. To validate the model, this framework was applied to a series of 700–770 square foot (65.03–71.54 m2) apartment units in Morgantown, West Virginia, USA. The model was determined to be able to accurately estimate the energy consumption of a given unit within this size range with 20% accuracy, which can then be used to ascertain sub-optimal, and thus unsustainable, consumption of energy. This framework can be applied in additional climate zones to create a more robust and generally applicable formula.

This paper presents a performance evaluation of a 140 kW solar array installed on the rooftop of the Mountain Line Transit Authority (MLTA) building in Morgantown, West Virginia (WV), USA, covering the period from 2013 to 2024. The grid-connected photovoltaic (PV) system consists of 572 polycrystalline PV modules, each rated at 245 watts. The study examines key performance parameters, including annual electricity production, average daily and annual capacity utilization hours (CUH), current array efficiency, and performance degradation. Monthly ambient temperature and global tilted irradiance (GTI) data were obtained from the NASA POWER website. During the assessment, observations were made regarding the tilt angles of the panels and corrosion of metal parts. From 2013 to 2024, the total electricity production was 1588 MWh, with an average annual output of 132 MWh. Over this 12-year period, the CO2 emissions reduction attributed to the solar array is estimated at 1,413,497 kg, or approximately 117,791 kg/year, compared to emissions from coal-fired power plants in WV. The average daily CUH was found to be 2.93 h, while the current PV array efficiency in April 2024 was 10.70%, with a maximum efficiency of 14.30% observed at 2:00 PM. Additionally, an analysis of annual average performance degradation indicated a 2.28% decline from 2013 to 2016, followed by a much lower degradation of 0.17% from 2017 to 2023, as electricity production data were unavailable for most summer months of 2024.

Commercial buildings consume significant energy in the United States and exhibit high potential for energy use reduction through retrofits. Benchmarking and energy simulation are well established tools in the industry to identify potential improvements and measure performance. Analysis to identify most sensitive retrofit parameters to energy performance can optimize investment and available energy savings. Presented study demonstrates methodology using a static model to determine sensitivity of building design and retrofit parameters with respect to energy performance. Calibrated simulation energy models (eQUEST) of two distribution centers (A, B) are presented. A fractional factorial analysis is conducted on retrofit parameters of efficiency measures targeting the highest energy consumers, and the results are benchmarked using Energy Star (R) Portfolio Manager. A custom Microsoft Excel (R) based simulation model is created to simulate occupancy levels, lighting, plug loads, and other equipment used in various spaces throughout the day. For Building A, efficient lighting was the most influential parameter for energy savings, carbon savings and benchmarking score; whereas, for Building B, HVAC efficiency was most influential for energy and demand controlled ventilation and economizers was most influential for benchmarking score. While retrofit projects can save energy and carbon emissions, variation in source-site ratios and state grid emissions, benchmarking scores may not always reflect equivalent improvement. State grid emissions factors, natural gas composition are difficult to model and hence not considered in this study. The synergistic analysis presented, emphasizes the importance of benchmarking and efficiency retrofits in promoting sustainable building practices to reduce energy consumption.

An eQUEST Based Building Energy Modeling Analysis for Energy Efficiency of Buildings

Industrial process heating furnace operations consume considerable energy in the U.S. manufacturing sector, making it crucial to identify energy efficient strategies due to the growing need to minimize energy usage and emissions. It is important to identify the potential impact of these factors to enable process engineers to operate process heating systems at the maximum possible efficiency. This study examines and identifies the key impact factors that influence the efficiency of process heating systems using MEASUR (v1.4.0), the DOE software tools such as the insulation effectiveness, the burner stoichiometry, cooling medium, thermal storage, and atmospheric gases. Data from a two-fuel-fired heat treatment furnace and an electric arc furnace (EAF) for steelmaking were employed to establish the baseline heat balance models in MEASUR. The fractional factorial design experiment was developed with two-level parameter values and energy efficiency strategies for the heat input into industrial furnaces. The three most significant parameters for the heat input for a fuel-fired industrial furnace, Industrial Furnace A, are excess air percentage or the oxygen percentage in flue gas (OF), average surface temperature (ST), and combustion air temperature (CT). Similarly, for an electric industrial furnace, Industrial Furnace B, the parameters are charge temperature (CHT), average surface temperature (ST), and time open (TO). A comparative analysis was carried out for the fuel-fired and equivalent electric resistance furnaces to identify the prospect of electrification of industrial furnaces relying upon fossil fuels. The study aims to assist industries and designers in making informed decisions regarding industrial furnace upgrades, process optimization, and maintenance investments, resulting in substantial energy and cost savings, and a reduced environmental impact.

Energy visualization systems provide information about use in real-time to assist users with energy efficiency. In this study, three energy visualization dashboards for small businesses were developed and tested. Performance measurement, NASA Task Load Index (TLX) workload assessment, and posttest survey were used to conduct the usability testing. Compared with the dashboards that were designed using line charts and tables, a dashboard designed using visuals (e.g., gauges, pie charts, and flashing lights) produced quicker response time, lower mental and temporal demand and effort ratings, and higher ratings of engagement, interest, and trustworthiness.

Decarbonizing fossil-fuel usage is crucial in mitigating the impacts of climate change. The burning of fossil fuels in boilers during industrial process heating is one of the major sources of CO2 in the industry. Electrification is a promising solution for decarbonizing these boilers, as it enables renewable energy sources to generate electricity, which can then be used to power the electric boilers. This research develops a user-driven simulation model with realistic data and potential temperature data for a location to estimate boilers’ current energy and fuel usage and determine the equivalent electrical boiler capacity and energy usage. A simulation model is developed using the Visual Basic Application (VBA)® and takes factors such as current boiler capacity, steam temperature and pressure, condensate, makeup water, blowdown, surface area, and flue gas information as input. Random numbers generate the hourly temperature variation for a year for discrete-event Monte Carlo Simulation. The simulation generates the hourly firing factor, energy usage, fuel usage, and CO2 emissions of boilers for a whole year, and the result compares fossil-fuel and electrical boilers. The simulated data are validated using real system data, and sensitivity analysis of the model is performed by varying the input data.

Industrial applications that require steam for their end-use generally utilize steam boilers that are typically oversized, citing operations flexibility.Similarly, gas turbine-based power plants corroborate a gas turbine system that may eventually relieve the usable exhaust into the atmosphere.This study explores the economic and technical feasibility of a topping cycle combined heat and power (CHP) system.It does so by leveraging a partially loaded boiler or gas turbine by increasing its unused load to generate steam and heat for subsequent usage.To this end, a decision support tool (COGENTEC) was developed, which emulates a given facility's boiler or gas-turbine system, and its operational parameters with the application of steam turbines.The tool provides necessary insights into the most appropriate parameters that enable a CHP system to be technically and economically advantageous.Based on input variables such as boiler-rated capacity, steam pressure, steam temperature, and existing boiler load, among others, COGENTEC designs a topping cycle CHP system to inform a user whether this system is feasible in their facility or not.If applicable, the tool assists the user to realize the point of break-even (fuel cost incurred and cost savings) at the desired steam flow rate.It also conducts sensitivity analyses between energy usage, cost savings, and payback on the investment of the operating parameters to understand the relationship between relevant variables.By utilizing parameters from a pulp and paper manufacturing facility, the research determines that the fuel cost, electricity cost, and steam flow rate are the most important parameters for the feasibility of the system with a desirable payback on the investment.

Building Energy Management Systems (BEMS) are computer-based systems that aid in managing, controlling, and monitoring the building technical services and energy consumption by equipment used in the building. The effectiveness of BEMS is dependent upon numerous factors, among which the operational characteristics of the building and the BEMS control parameters also play an essential role. This research develops a user-driven simulation tool where users can input the building parameters and BEMS controls to determine the effectiveness of their BEMS. The simulation tool gives the user the flexibility to understand the potential energy savings by employing specific BEMS control and help in making intelligent decisions. The simulation is developed using Visual Basic Application (VBA) in Microsoft Excel, based on discrete-event Monte Carlo Simulation (MCS). The simulation works by initially calculating the energy required for space cooling and heating based on current building parameters input by the user in the model. Further, during the second simulation, the user selects all the BEMS controls and improved building envelope to determine the energy required for space cooling and heating during that case. The model compares the energy consumption from the first simulation and the second simulation. Then the simulation model will provide the rating of the effectiveness of BEMS on a continuous scale of 1 to 5 (1 being poor effectiveness and 5 being excellent effectiveness of BEMS). This work is intended to facilitate building owner/energy managers to analyze the building energy performance concerning the efficacy of their energy management system.

This research focused primarily on self-fastening characteristics, standardisation of parts and minimal use of fasteners. The work investigated the significance of synergistic design for manufacturing techniques (DFMTs), with their inherent machine element systems (MES) and machining parameters (MPs) to evaluate which DFMT has the most influence on cost reduction and increasing throughput and under which circumstances. In sum, this research applies DFMT to product design. For each DFMT and associated MES and MP, process planning was used effectively with computer aided process planning (CAPP) tools to enhance the evaluation impact of the dialogue between the design and manufacturing functions. A systematic algorithm was inherently developed and incorporated into the software tools used herein. Generative process planning software is used to measure and analyse sensitivity in plan effectiveness. The final results showed a significant improvement in cost reduction and production rates. DFMTs 1 and 2 have the main influence on the systematic algorithm.

This paper addresses the question “Is energy that different from labor?” from the perspective of efficiency. It presents a novel statistical analysis for the auto assembly industry in North America to examine the determinants of relative energy intensity, and contrasts this with a similar analysis of the determinants of another important factor of production, labor intensity. The data used combine two non-public sources of data previously used to separately study key performance indicators (KPIs) for energy and labor intensity. The study found these two KPIs are statistically correlated (the correlation coefficient is 0.67) and the relationship is one-to-one. The paper identifies 11 factors that may influence both energy and labor intensity KPIs. The study then contrasts which of the empirical factors the two KPIs’ share and how they differ. Two novel statistical methods, Huber estimators and Multiple M-estimators, combined with regularized algorithms, are identified as the preferred methods for robust statistical models to estimate energy intensity. Based on our analysis, the underlying determinants of energy efficiency and labor productivity are quite similar. This implies that strategies to improve energy may have spillover benefits to labor, and vice versa. The study shows vehicle variety, car model types, and launch of a new vehicle penalize both energy and labor intensity, while flexible manufacturing, production volume, and year of production improve both energy and labor intensity. In addition, the study found that the plants that produce small cars are more energy-efficient and productive compared to plants that produce large vehicles. Moreover, in a given functional unit, i.e., on a per-unit basis, Japanese plants are more energy-efficient and productive compared to American plants. Plant managers can use the proposed data-driven approach to make the right decisions about the energy efficiency targets and improve plants’ energy efficiency up to 38% using hybrid regression methods, mathematical modeling, plants’ resources, and constraints.

The objective of this research study is to develop a set of expert systems that can aid metal manufacturing facilities in selecting binder jetting, direct metal laser sintering, or CNC machining based on viable products, processes, system parameters, and inherent sustainability aspects. For the purposes of this study, cost-effectiveness, energy, and auxiliary material usage efficiency were considered the key indicators of manufacturing process sustainability. The expert systems were developed using the knowledge automation software Exsys Corvid®V6.1.3. The programs were verified by analyzing and comparing the sustainability impacts of binder jetting and CNC machining during the fabrication of a stainless steel 316L component. According to the results of this study, binder jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC machining, considering the fabrication of components feasible for each technology.