Measurement: Energy最新文献

Despite the environmental advantages of biofuel blends, detailed studies on emissions of polycyclic aromatic hydrocarbons (PAHs), n-alkanes, and particle-bound carbon from diesel engines, particularly when fueled with biodiesel, remain limited. This study addresses these gaps by analyzing biodiesel blends from neem, linseed, and jatropha oils produced via mechanical extraction and assessing their impact on volatile organic compounds and particle-bound carbon emissions in diesel engine. Economic evaluations of production costs, engine modifications, and payback periods are also conducted. The result shows that jatropha biodiesel exhibits a calorific value of 35.7 MJ/kg, while neem biodiesel shows superior oxidative stability due to its low iodine value. Additionally, linseed biodiesel displays favorable cold flow properties due to its high density and cetane value. Compared to D100, the N10 and N30 blend notably reduced high molecular weight PAH emissions by 10.7 % and 38.4 %, respectively, with the N30 blend achieving a remarkable 76 % reduction in formaldehyde emissions. Conversely, the J10 blend increased specific PAHs, while the J30 blend reduced PAHs by 21.3 %. Both L10 and L30 blends showed reduced naphthalene emissions, with the J30 blend notably reducing elemental carbon (EC) by 31.4 %, although organic carbon (OC) slightly increased. In contrast, the N30 blend decreased both EC and OC emissions, demonstrating a dose-dependent relationship between biodiesel concentration and emissions reduction. Overall, Jatropha biodiesel blends offer the best balance of economic efficiency and emission reductions, resulting in shorter payback periods and lower carcinogenic risks. Neem and linseed blends also provide environmental benefits but with varying economic implications, highlighting the trade-offs between production costs and long-term sustainability.

Efficient energy utilization in buildings is crucial for sustainability. This work proposes an intelligent system that leverages computer vision techniques and CCTV images to assess indoor lighting energy usage based on occupancy, artificial lighting, and daylight conditions. Object detection models - You Only Look Once (YOLO) version 3 (v3) and v8 are employed to detect people, lights, and windows, achieving promising accuracies of 94.9 ​%, 73.3 ​%, and 98.7 ​%, respectively. The system categorizes scenarios as energy-efficient, wasteful, or neutral by integrating these outputs, highlighting opportunities for improving efficiency by harmonizing lighting infrastructure with occupancy and daylight exposure. Performance analyses, including training and validation metrics, are presented. This study demonstrates the potential of computer vision and AI for optimizing energy utilization, enabling sustainable building operation and promoting energy-positive occupant behaviors through sensor-driven methodologies.

Benzoic acid reference material is indispensable for bomb calorimeter instrument calibration and validation of gross calorific value (GCV) analysis of any substance. In this work, we demonstrated the preparation of benzoic acid reference material through a homogeneity study, round-robin analysis, and stability study. Two-factor analysis of variance (ANOVA) test for gross calorific value in the randomly selected sub-samples of benzoic acid exhibits a lower F_TS value than the F_crit value, indicating that the samples are sufficiently homogeneous. The calculated uncertainty of between-bottle (u_bb) and uncertainty of homogeneity (u_hom) for GCV of benzoic acid in the sub-samples were found as 1.82 and 4.42 ​cal/g respectively. We found that the observed homogeneity (u_{hom (Finding)}) value is lower than the assumed homogeneity (u_{hom (Assume)}) value for the prepared benzoic acid reference material. Overall observations confirm that the sub-samples are sufficiently homogeneous. Moreover, the round-robin analysis/or inter-laboratory comparison analysis was conducted to assign the gross calorific value and determine the characterization uncertainty. The seventh order of Grubbs' analysis was done using robust estimator Alogoritm A to assign the GCV of benzoic acid. Finally, the measurement uncertainty of the assigned GCV of benzoic acid was calculated with the combined uncertainties from various sources.

This study evaluated three approaches for characterizing voltage relaxation in lithium-ion batteries: voltage vs. time, the derivative of voltage vs. time, and the second derivative of voltage vs. time. The first two are well-established approaches, whereas the third was never investigated to our knowledge. To assess the potential of each approach, characterizations were performed on data with various depth-of-discharges, regimes, state of healths, temperatures, and chemistries. Findings indicate that the established approaches do not comprehensively characterize voltage relaxation whereas the novel approach demonstrated promise in providing a quantitative feature to compare relaxation behaviors. However, it was found to have severe limitations in its application due to its lack of consistency between chemistry, rates, and temperatures, reliance on heavy filtering, and inability to identify trends in capacity loss, all preventing any potential for widespread application.

The need for new and improved diagnostic tools becomes paramount for Li-ion batteries in order to ensure their optimum performance along with their longevity and safety and there remain aspirational targets for Li-ion batteries regarding fast-charging, energy density, safety and lifetime that must be met to achieve further growth. The study of degradation mechanisms (such as electrolyte degradation, thermal runaway, gassing and cycling aging) at the cell level contributes significantly to the understanding of the aging phenomena and will enable improvements for future generations of LIBs. Among various indicators, the colour change of the electrolyte within Li-ion cells presents a largely unexplored avenue for assessing state of health and predicting early signs of degradation. This research proposes a new methodology for the development of an innovative optical sensor technology and its incorporation into a pouch cell. The sensor comprises of a photodiode and RGB LED, mounted on a single flexible PCB, capable of the real-time detection of electrolyte colour changes inside Li-ion pouch cells. Previous studies have identified electrolyte colour change as a potential marker for battery condition through various invasive testing methods; however, the proposed optical technique represents a step change in in-situ real-time diagnostics. This study elaborates on the process of developing and incorporating the sensors precisely into pouch cells and seeks to demonstrate the capability of these sensors to accurately detect alterations in the colour of the electrolyte as the cell ages, without having adverse effects on the cell's performance and offers the potential for direct correlation of electrolyte change with battery state of health.

Global warming concerns, along with international agreements and regulations, reflect a broader effort to change the public's high energy demand in recent years. Smart public lighting systems are gaining popularity due to their energy-saving capabilities, reduction in carbon dioxide emissions, and improved public comfort. However, transitioning to smart public lighting requires careful planning and multiple stages. This is not only to accommodate public behavior, revise scenarios, and test citizen acceptance but also due to the necessary infrastructure investments. Smart public lighting incorporates new technologies, often with a breakeven point that takes several years to reach. To promote the widespread adoption of smart public lighting, it is essential to produce relatively expensive components in large quantities and explore cost-effective solutions. This research focuses on investigating a cost-effective photoelectric sensor for smart public purposes. The primary originality of this study lies in identifying a cost-effective photoelectric sensor that can replace technically equivalent but more expensive sensor solutions for indoor and outdoor lighting control purposes.

The state characterization inside the lithium-ion battery during charge/discharge cycling is extremely crucial for understanding the electrochemical reaction mechanism. However, current methods exhibit a challenge to overcome the specific battery environment obstacles, including strong redox properties, strong electromagnetic interference, and fast reaction processes. Hence, more efforts are still needed to monitor the actual state inside the battery accurately and reliably. To address this issue, we designed and developed a compact two-cavity cascade fiber-optic Fabry-Perot interferometer (FPI) sensor that can be safely implanted in batteries to measure internal temperature and pressure simultaneously. With its high pressure and temperature sensitivity of 26.6 ​nm/kPa and 107 ​nm/°C, this sensor exhibits an ultra-low cross-sensitivity of −40 ​Pa/°C. During charge/discharge cycling tests, regular cyclic pressure and temperature signals are obtained at various rates cycling in real-time and in situ, revealing details about the actual state characterization inside the battery. From the experiment results, the pressure inside the battery is divided into reversible changes caused by respiration effects and irreversible changes caused by trace gas production. Furthermore, the FPI sensor provides a more precise temperature than thermocouples that measure the surface temperature of the battery, reflecting the internal/external temperature difference to a maximum of 3.5 ​°C at 1 ​C rate cycling. This operando FPI sensor provides a valuable technological tool for battery performance testing and safety monitoring.

Most metal industries use reheating furnaces (RHF) for their finishing operations. This RHF is highly energy-consuming equipment that heats the material inside the chamber for rolling or shaping using the by-product gases, natural gas, or oil as fuel. It is necessary to minimize or optimize the fuel consumption to the extent possible. By analyzing the plant operating data, plant measurements, and energy balance calculation, this work aims to determine the potential for decreasing the fuel consumption of a billet reheating furnace. Predictions are made by modeling operating data to reveal the hidden problems and uncover underlying issues. The study results in increasing productivity by 11 ​% while oil consumption was reduced by 14 ​%. These actions significantly decreased carbon emissions considerably and generated significant cost savings.

Naval shipboard power systems (SPS) are rapidly embracing electrification, resulting in loads that generate pulsation currents and encounter substantial transients. However, conventional time-based features alone are inadequate for effectively monitoring and safeguarding these loads against faults. This highlights the critical requirement for advanced machine learning based methods to discern and differentiate between the various transient stages within the load profile. In this paper, we propose a Wavelet Graph Neural Network (WGNN) model for non-intrusive fault detection in SPS. The fault detection system leverages the dynamic model of the SPS to train and test performance with varying fault scenarios. The underlying structure and the interdependence among component states in the SPS network are effectively captured using the WGNN model, resulting in accuracies over 99% for intrusive fault detection and 97% for non-intrusive fault detection. The developed WGNN model has also shown to be robust in the presence of pulse loads and noise, achieving an accuracy of over 95%. At the end, a real-time simulation of the proposed method is validated on a hardware-in-the-loop system, guaranteeing the high fidelity and low latency of the proposed approach. These findings validate the effectiveness of the proposed WGNN model for fault detection and real-world applications in SPS.

Multiport power electronics converters enable interfacing of multiple sources and loads within a renewable-rich dc microgrid. The system evaluation of these microgrids such as dynamic performance of components, stability analysis are often evaluated using hardware-in-loop (HIL) approach for different real time conditions. The digital twin of device under test (DUT) is realized within real time simulator using voltage and current sensor measurements in the HIL-based testing approach. Current sensing systems for multiport power converter systems require transducers to be connected in path of current with restrictions on sensor bandwidth, auxiliary circuit overhead requirements for biasing and signal conditioning. This paper addresses the development of high frequency current sensing method using shunt-type measurements with reduced auxiliary circuit overheads. The proposed method provides a digital estimate of inductor current which can be implemented in an embedded processor.