Energy Efficiency Of System Research Articles

Energy characteristics of multi-chiller load distribution algorithms in a large office building

This study evaluates the energy efficiency of multi-chiller systems in large office buildings, focusing on their optimization across various climate zones as defined by ASHRAE. Using EnergyPlus for simulations, the research examines five different load distribution algorithms in multi-chiller systems that range from one to ten chillers, aiming to understand their effectiveness in 15 distinct climate zones. The primary objectives of the study include identifying the energy efficiency of multi-chiller systems in each climate zone, determining the appropriate number of chillers for each zone, and evaluating the performance of the load distribution algorithms. Based on the U.S. Department of Energy’s commercial building model, the results suggest that multi-chiller systems can significantly reduce cooling energy consumption in various climates. Among the algorithms evaluated, the Sequential Uniform Part Load Ratio (SUPLR) algorithm demonstrates notable efficiency, especially in the 4A climate zone (Baltimore), where it achieves substantial energy savings. Applying the SUPLR algorithm in a multi-chiller setup with four chillers in this zone leads to an estimated 24.5 % reduction in energy usage, equivalent to 183 MW annually. The research indicates that a range of 3 to 5 chillers is typically optimal for most climate zones. In-depth analysis in the 4A climate zone highlights the importance of minimizing operation hours at low Part Load Ratios (PLR) to ensure that chillers operate at a high Coefficient of Performance (COP). This strategy underscores the potential of well-designed multi-chiller systems to reduce cooling energy demand, particularly in climates with transitional seasons. This study provides an overview of the energy-saving potential of multi-chiller systems, applicable across a variety of climatic scenarios.

ROSMOSE: A web-based decision support tool for the design and optimization of industrial and urban energy systems

Energy efficiency is crucial for the sustainable operation of industrial and urban sectors. However, practicing engineers seldom have access to open-source tools that can readily evaluate and compare scenarios. In this work, a novel web-based tool called ROSMOSE is developed and proposed for analyzing the energy efficiency of industrial and urban systems and comparing potential process integration options. This optimization framework computes and graphically represents the minimum energy requirements, the pinch temperatures, and the Grand and integrated Carnot composite curves of process systems. Results are used to design utility systems that meet energy demands, while minimizing a specified objective function, such as total cost or environmental impacts. The Quarto environment, in which ROSMOSE is built upon, allows the integration of various open-source software and tools, such as database handling software, process modeling suites, and optimization solvers, along with interactive data visualization and reporting tools. This paper discusses the application of ROSMOSE for the energy integration and total site optimization of a dairy process consisting of milk treatment, and cream and cheese production to demonstrate the tool features. Other integration options, such as the use of heat pumps or solar panels, and the production of soft drinks or biogas as value-added byproducts, are also proposed and evaluated. As a result, from 75 % to 90 % in fuel savings and up to 80 % CO2 emissions reduction in the dairy plant are identified by optimizing the utility systems integration. Moreover, complete electrification via heat pumps eliminates any fossil fuel needs and it results in 85 % energy savings and up to 96 % CO2 emissions reduction. The activation of renewable electricity sources, such as solar power, made plausible this fully electrified scenario. Finally, optimized waste management strategies led to in-house fuel production and net export of biogas to the grid, but they also reduce cheese yield by prioritizing the biogas production from whey upgrading process. In this way, decision-makers have access to a clear performance comparison for a list of system configurations to draw informed trade-offs. A similar approach could be adopted to aid decision-making for the design and operation of other industrial and urban energy systems using ROSMOSE.

Enhancing photovoltaic solar panel performance with integration of PCM-based spectral filter and self-cleaning coating

This research investigates the enhancement of photovoltaic (PV) solar panel performance through the application of a paraffin-based spectral splitter. The study aims to improve electrical efficiency and thermal management while mitigating dust deposition effects. A paraffin (RT25) spectral filter is placed above the glass layer of the PV panel, coupled with a ZnO-water nanofluid-filled mini-channel for cooling at the panel's bottom. Optical properties, derived from previous experimental work, are incorporated into simulations. Self-cleaning SiO2 nanoparticle coating is proposed to improve transmissivity of upper glass. Fresnel lens is used to intensify irradiation at two concentration ratios (CR). Three-dimensional simulations integrating source terms based on optical analysis were conducted. Key findings include a decrease in panel temperature (TPV) due to partial irradiation absorption by the spectral filter, leading to lower heat flux to the cooling zone. The spectral filter increased electrical efficiency (ηPV) by 11.28 % and 38.63 % at 20 and 40 min, respectively. The presence of the filter and coatings significantly improved temperature uniformity, with improvements of 68.77 % and 50.35 % at 10 and 40 min, respectively. Increasing CR resulted in a 2.95 % and 83.31 % rise in filter temperature (TPCM) and liquid fraction (LF), with a minor 1.56 % decrease in efficiency. At CR = 2, in the presence of the spectral filter, ηPV decreases by 38.62 %, accounting for dust effects, while it increases by 21.83 % with adding coating. The research highlights the potential of spectral filters and innovative coatings to enhance PV panel performance, offering a novel approach to sustainable and efficient solar energy systems.

Modeling, prediction and analysis of natural gas consumption in China using a novel dynamic nonlinear multivariable grey delay model

Accurately predicting natural gas consumption is essential for developing a clean, low-carbon, safe, and efficient energy system. Therefore, this paper integrates the idea of the extended logistic model into multivariable grey prediction theory. Considering the delay effect and nonlinear characteristics of the system, a novel dynamic nonlinear multivariate grey delay prediction model is constructed. Meanwhile, the algorithmic framework for solving the parameters of the new model is provided to identify and optimize the parameters of the time-delay effect of the model. The genetic algorithm was identified as the best matching optimization algorithm for the new model. Furthermore, the new model is applied to fitting and forecasting China's natural gas consumption, and the validity of the model is fully verified. Compared with other forecasting models, the new model has a lower average relative error in the fitting and forecasting stage, and, in particular, achieves a better forecasting effect with the lowest MAPEpre value (0.001 % for one-step ahead, 0.005 % for two-step ahead, and 0.746 % for three-step ahead). Moreover, the model is employed to forecast China's natural gas consumption for the next five years, providing a robust foundation for government policies related to the supply of natural gas.

Exploring Recent Developments in Graphene-Based Cathode Materials for Fuel Cell Applications: A Comprehensive Overview.

Fuel cells are at the forefront of modern energy research, with graphene-based materials emerging as key enhancers of performance. This overview explores recent advancements in graphene-based cathode materials for fuel cell applications. Graphene's large surface area and excellent electrical conductivity and mechanical strength make it ideal for use in different solid oxide fuel cells (SOFCs) as well as proton exchange membrane fuel cells (PEMFCs). This review covers various forms of graphene, including graphene oxide (GO), reduced graphene oxide (rGO), and doped graphene, highlighting their unique attributes and catalytic contributions. It also examines the effects of structural modifications, doping, and functional group integrations on the electrochemical properties and durability of graphene-based cathodes. Additionally, we address the thermal stability challenges of graphene derivatives at high SOFC operating temperatures, suggesting potential solutions and future research directions. This analysis underscores the transformative potential of graphene-based materials in advancing fuel cell technology, aiming for more efficient, cost-effective, and durable energy systems.

Developing a sustainable semiconductor hetero-system with Gd-Ho-Dy tri-doping in ZrO2 for enhanced performance and efficiency in advanced energy systems

This work discusses the designing and applications of the novel rare earth tri-doped zirconia i.e. [Gd-Ho-Dy]:ZrO2 semiconductor. [Gd-Ho-Dy]:ZrO2 as a spacer layer improved the efficiency of the perovskite solar cell reaching up to 14.06% at the ambient conditions. This material is stable for an extended duration without alterations in the electrochemical output achieving a higher specific capacitance of 237.5 F g−1. The O2 and H2 production potential of this material is evaluated for electro-catalysis of water. [Gd-Ho-Dy]:ZrO2 semiconductor is an effective electro-catalyst for oxygen and hydrogen production with the comparatively lower Tafel slope values of 271.3 and 125.9 mV dec−1, respectively. The lower Tafel slope value for hydrogen generation shows the effectiveness of the prepared electro-catalyst for hydrogen evolution reaction (HER). The augmented electrochemical performance and stability of the developed semiconductor shows its practical scale employment in different energy related applications.

Recent advancements in carbon-based materials for resistive switching applications

In the dynamic field of microelectronics, there is a notable trend towards leveraging carbon materials, favored for their ease of synthesis, biocompatibility, and abundance. This trend is particularly evident in the development of memristor devices, which benefit from the unique electronic properties of carbon, leading to enhanced device performance. The appeal of carbon materials lies in their ability to offer distinctive resistive switching (RS) mechanisms, sparking significant interest among researchers. This article aims to provide an insightful overview of the advancements in carbon-based memristive devices, focusing on the resistive switching mechanisms enabled by carbon materials. It delves into the various classes of carbon-based memristor devices, ranging from zero-dimensional (0D) to three-dimensional (3D) structures, each with its unique advantages and applications. Additionally, the discussion extends to innovative next-generation memristive devices, including those designed for health monitoring and skin-adhesive, self-powered applications. Moreover, the article touches upon the synthesis techniques and functionalization strategies that are crucial for optimizing the performance of carbon-based memristors. It also outlines the future opportunities in this rapidly advancing field, highlighting the potential for further research and development towards energy-efficient, compact, and bio-integrated memristive systems.

Revolutionizing smart grid-ready management systems: A holistic framework for optimal grid reliability

Existing energy management systems are becoming increasingly insecure and inefficient due to the rapid adoption of smart grid technology. Current research indicates that effectively managing dynamic energy flows, adjusting to changing needs, and protecting against new cyber threats remain significant challenges for the smart grid system. An advanced and comprehensive plan for managing smart grids is therefore required, capable of addressing these delicate and multifaceted problems. The proposed framework addresses these challenges through unifying several key aspects, it includes an advanced data acquisition system that captures real-time data from various grid sources, enabling comprehensive energy monitoring and dynamic flow analysis. By integrating predictive algorithms, the framework provides precise energy demand forecasting, which is essential for adaptive grid management. A significant contribution is the incorporation of an AI-based module for diagnostics and prognostics, which leverages machine learning techniques to shift from reactive to proactive maintenance strategies. The optimal power flow (OPF) optimization module represents a central component of the framework. It employs advanced computational methods to ensure efficient and cost-effective power distribution, particularly in grids incorporating renewable energy sources. Additionally, the architectural framework is strengthened by a robust cybersecurity module designed to safeguard against a wide range of cyber threats, maintaining the integrity of both operational and consumer data. This paper also addresses practical implementation challenges such as compatibility with existing infrastructure, investment costs, and the need for specialized training. This solution represents a new benchmark for smart grid operations, ensuring more sustainable and efficient energy systems.

Low‐power multibit delta–sigma modulator based on passive and unattenuated summation scheme

AbstractA low‐power multibit delta–sigma modulator (DSM) based on a passive and unattenuated summation scheme is proposed. The summation circuit achieves multiplication of the voltage signal carried on the summation capacitor through a bidirectional sampling technique, thereby compensating for the inherent attenuation caused by passive summation, which eliminates the need for an active operational transconductance amplifier (OTA) to achieve perfect summation. Here, a second‐order DSM based on a 4‐bit first‐order passive noise‐shaping (NS) SAR quantizer is employed to satisfy the SNDR requirements exceeding 100 dB, which validates the reliability of the summation scheme. In addition, a cascoded floating inverter amplifier (FIA) is used as the core OTA to further improve system energy efficiency. Simulation results demonstrate that at a supply voltage of 1.2 V and a bandwidth of 20 kHz, the SNDR reaches 102.62 dB, power consumption is only 148.32 μW, and the Schreier figure‐of‐merit (FoM) of the SNDR is 183.92 dB. The results demonstrate that the proposed DSM has considerable potential for widespread application in the audio domain.

A secured and energy-efficient system for patient e-healthcare monitoring using the Internet of Medical Things (IoMT)

Introduction: The Internet of Things (IoT) is gaining popularity in several industries owing to the autonomous and low-cost functioning of its sensors. In medical and healthcare usage, IoT gadgets provide an environment to detect patients' medical problems, such as blood volume, oxygen concentration, pulse, temperatures, etc. and take emergency action as necessary. The problem of imbalanced energy usage across biosensor nodes slows down the transmission of patient data to distant centres and has a detrimental effect on the health industry. In addition, the patient's sensitive information is sent through the insecure Internet and is exposed to potential threats. For clinical uses, information privacy and stability against hostile traffic constitute a further research challenge. Methods: This article proposes a Secured and Energy-Efficient System (SEES-IoMT) e-healthcare utilizing the Internet of Medical Things (IoMT) monitoring, the main goal of which is to reduce the connectivity cost and energy usage between sensing devices while feasibly forwarding the medical data. SEES-IoMT also guarantees the clinical data of the patients against unverified and malevolent nodes to enhance the privacy and security of the system.Result and Discussion: In consideration of the memory and power limitations of healthcare IoT gadgets, this approach is designed to be very lightweight. A thorough examination of this system's safety is performed to demonstrate its reliability.Conclusion: In terms of computing speed and security, the research compares SEES-IoMT to relevant methods in the IoT medical environment to demonstrate its applicability and resilience.

Energy efficiency and greenhouse gas emission reduction potential of solar PV and biomass-based systems for a food processing plant

The industrial sector is one of the most energy-intensive sectors, with a share of nearly 38 % (156 EJ) of all energy used globally in 2020. Historical energy demand and consumption data from a poultry processing plant have been analyzed to lower greenhouse gas emissions (GHG) and reliance on traditional energy sources. The study includes energy, economic, and emissions analyses for (a) solar photovoltaic (PV) systems, (b) biomass fuels, and (c) coal-fired with wood. Fixed, single, and two-axis tracking solar PV systems that are company-owned and funded by banks are considered. The historical energy consumption data of the plant shows that 52.36 % of energy demand is met by electrical energy and 47.63 % by thermal energy. The yearly electricity demand is 9,938 MWh, costs $1,192,560, and emits 5117.6 tCO2. Thermal energy demand is based on an annual use of 898 tonnes of coal and 71.6 tonnes of liquefied petroleum gas (LPG), which costs $ 143,681 and emits 2977.7 tonnes of carbon dioxide (tCO2). Feasibility of 1.3 MW, fixed axis company-owned, solar PV plant shows an annual electricity production of 1876 MWh, GHG emission reduction of 965.9 tCO2 with a payback period of 3.4 years. Biomass system delivers yearly fuel cost savings of $25,724, reducing 2784 tCO2 with a 1.9-year payback period. Co-firing biomass offers a 3.58 % fuel cost saving and 557 tCO2 with a 0.93-year payback period. It concluded that the wood fuel feedstock biomass option is most suitable renewable energy resource for such large-scale poultry facilities.

Energy Efficiency in Public Lighting Systems Friendly to the Environment and Protected Areas

Solid-state lighting technology, such as LED devices, is critical to improving energy efficiency in street lighting systems. In Ecuador, government policies have established the obligation to exclusively use LED systems starting in 2023, except in special projects. Ecuador, known for its vast biodiversity, protects its national parks, which are rich in flora, fauna and natural resources, through international institutions and agreements such as UNESCO, CBD and CITES. Although reducing electrical consumption usually measures energy efficiency, this article goes further. It considers aspects such as the correlated color temperature in the lighting design of protected areas, light pollution and the decrease in energy quality due to harmonic distortion. Measurements of the electromagnetic spectrum of the light sources were made in an area in the Galápagos National Park of Ecuador, revealing highly correlated color temperatures that can affect ecosystem cycles. In addition, the investigation detected levels of light pollution increasing the night sky brightness and a notable presence of harmonic distortion in the electrical grid. Using simulations to predict the behavior of these variables offers an efficient option to help preserve protected environments and the quality of energy supply while promoting energy savings.

Artificial Intelligence for Sustainable Energy Transition: Optimising Renewable Energy Integration and Management

As climate change and long-term energy security drive the global energy sector towards renewable resources, powerful tools are required to optimise integration and management. A novel framework is proposed for effectively utilising Artificial Intelligence (AI) in the renewable energy landscape. AI algorithms can analyse weather patterns, historical generation data, and environmental factors to predict renewable energy output. Energy dispatch is optimised, grid integration is improved, and energy storage requirements are reduced. A system powered by artificial intelligence also significantly reduces downtime, optimises maintenance schedules, and minimises operational costs in wind turbines, solar panels, and other renewable infrastructure. AI can also optimise energy flows, reduce grid instability, and ensure efficient resource utilisation within the smart grid by dynamically managing renewable sources, energy storage systems, and demand profiles. Furthermore, AI-driven spatial analysis and resource mapping can identify optimal locations for renewable installations, considering factors like wind speed, solar irradiance, and environmental constraints. This paper presents two AI frameworks, one for solar energy and one for wind energy, to demonstrate possible applications. They both utilise comprehensive data acquisition, including real-time sensor data and external factors like weather forecasts and historical generation patterns. AI algorithms use these combined data to perform critical tasks such as predictive maintenance, minimising downtime, and maximising efficiency. Power output forecasting enables real-time adjustments based on weather, and optimal site selection maximises energy production. AI is used for proactive issue identification, accurate power output forecasting based on wind conditions, grid demand, storage capacity, dynamic load optimisation for maximum energy efficiency, and wind farm site selection. Integrating these tailored AI frameworks with solar and wind energy can achieve significant benefits such as increased efficiency, reduced operational costs, and seamless grid integration. In addition to analysing the challenges and opportunities associated with this AI integration, the paper explores infrastructure development, ethical considerations, and data acquisition. A second benefit of the research methodology is that it highlights how these tailored AI frameworks can optimise the integration of solar and wind renewable energy sources, providing valuable insights for researchers, practitioners, and policymakers who wish to use AI to create a more sustainable and efficient energy system. Keyword: Artificial Intelligence, renewable energy, climate change.

Performance analysis of residential hybrid cooling and heating system operating under low solar radiation

Renewable energy sources are becoming increasingly important due to rising energy consumption and environmental pollution caused by greenhouse gases. In the study, a system was designed to provide year-round air conditioning in a residential located in regions with low solar radiation. This design consists of a single-stage absorption cooling system to meet the cooling need in summer and a solar energy-supported natural gas combi boiler heating system to meet the heating need in winter. The designed system was subjected to a comprehensive analysis, taking into account the integration of four different collector types: flat plate collector, evacuated tube collector, compound parabolic collector and parabolic trough collector. A thermal energy storage unit is integrated into the system to increase its efficiency. In the study, the feasibility and efficiency of the solar energy system recommended for residences in low-radiation regions were investigated. Appropriate system conditions were determined by evaluating economic and environmental aspects. In the study, a maximum net present value (NPV) of 3.941.49 US$ and 4 % exercise efficiency were calculated for the system designed using ETC collectors at 80°C generator and 6°C evaporator temperature. At the same time, the hybrid system can prevent annual carbon dioxide emissions of 800.28 kg compared to coal-fired systems and 62.42 kg annually compared to natural gas systems. These results show that the designed system is economically feasible and contributes positively to environmental sustainability.

Performance assessment of large-scale rooftop solar PV system: a case study in a Malaysian Public University

Adopting rooftop solar PV systems in various domestic and non-domestic sectors (including commercial, industrial, and agricultural) exhibits their commitment to green energy ventures. This study intends to evaluate the effectiveness of a grid-connected solar system that has been installed so far: a 6.9 MWp photovoltaic (PV) system implemented at University Tun Hussein Onn (UTHM) in Batu Pahat, Johor. This green energy system was installed as a part of the Self-Consumption (SELCO) program utilizing a Supply Agreement for Renewable Energy (SARE) contract. To assess the mounted energy system's efficiency, performance monitoring was carried out between December 2021 and November 2022. By evaluating the enactment of the 6.9 MWp solar system at UTHM, this present work has attempted to compile the achievement of implementation of self-consumption in terms of performance analysis and financial benefits to consumer. Based on International Electrotechnical Commission (IEC) 61724 standard, this study also investigates several performance characteristics of the PV system, such as final yield, clearness index (CI), array yield, PV module efficiency, inverter efficiency, reference yield, and capacity factor (CF) performance ratio (PR). Moreover, an assessment of Carbon Dioxide (CO2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2})$$\\end{document} avoidance was also conducted to quantify the amount of CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2}$$\\end{document} reduction achieved. In addition, the self-consumption ratio and self-sufficiency ratio pattern for solar power plant in UTHM has been assessed. The monthly clearness index at the project location from December 2021 to November 2022 ranges from 0.63 to 0.66. The PV system was monitored throughout this time and generated 8529 MWh of energy. The installed PV system has shown an efficiency of 11.86%, a final yield of 108.28%, and a PR of 0.78, respectively. Further, the installed 6.9 MWp PV system at UTHM will cut CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2}$$\\end{document} emissions by 5450 tons annually. The 6.9 MWp solar PV system is also estimated to reduce 25.1 RM mil in 25 years. Making the system larger to 6.9 MWp would reduce the consumption from the grid during the week, makes it possible to achieve self-consumption levels of up to 100% and self-sufficiency of this solar plant is relatively low ranges from 22 to 35%. The research finding bridges the gap between policy makers, investors, and the public regarding acceptance of the large-scale Self-Consumption (SELCO) and strengthen the strategic collaborations between solar service provider and educational bodies to support of the government’s agenda to achieve carbon emission intensity reduction in Malaysia.Graphical

Efficient and sustainable power propulsion for all-electric ships: An integrated methanol-fueled SOFC-sCO2 system

To address marine pollution caused by fuel usage and reduce carbon emissions in ships, the development of alternative fuel electric propulsion ship power systems presents a promising solution. In this study, an integrated Methanol-fueled SOFC-sCO2 combined power system is proposed for all-electric ships. The system efficiently utilizes waste heat from SOFCs by integrating it into a partial heating sCO2 power cycle to generate additional power. Through the utilization of a thermodynamic model, parametric sensitivity analysis and multi-objective optimization are conducted to maximize the system's energy efficiency while reducing investment and maintenance costs. Comprehensive energy, exergy, and economic analyses are performed on the optimized system. The achieved energy efficiency and exergy efficiency are 66.10 % and 57.74 % respectively, with an investment and maintenance cost of 16.96 $/h and a payback period of 2.2 years. When applied to container ships and compared with other ship power systems, the proposed system demonstrates the highest energy efficiency, delivering an impressive output power of 65,092 kW. The energy efficiency design index (EEDI) of the system is moderate, reaching 5.94. Despite the relatively high fuel cost, the proposed system offers a simpler design compared to existing SOFC-sCO2 systems and is only slightly more complex than integrated systems like SOFC-GT.

Ensemble Random Forest-based Gradient Optimization based Energy Efficient Video Processing System for Smart Traffic Surveillance System

Deep learning solutions in big data applications can benefit cloud centres and can also lead to network communication overhead. Typically, data collected from traffic are sent to the traffic management centre for analysis. However, this process can worsen the network route to the traffic management centre. A two-tier mechanism has been developed to address this issue, which performs vehicle speed estimation and traffic congestion detection for efficient traffic management. The real-time traffic video data are captured and the video frames are initially processed through a foreground extraction process, which extracts the temporarily stopped vehicles on the road by removing background pixels from the frames. The video frames are then wrapped in an up-down view to remove the influence of the observation angle. The traffic congestion is then detected accurately based on the traffic characteristics using the proposed Ensemble Random Forest-based Gradient Optimization (ERF-GO) algorithm. The generalization error occurs when learning complex features on frames is minimized using a gradient-based optimization (GO) algorithm. Finally, the learned information on traffic conditions is forwarded to the cloud and edge computing environments based on network connection speed. The efficiency of the proposed ERF-GO is investigated in terms of performance metrics, namely root mean square error, speed detection error, execution time, computational cost, accuracy, latency, workload balance, precision, recall, f-measure, and congestion detection error rate. The analytic result displays that the proposed ERF-GO algorithm attains a greater accuracy rate of about 98.65% in detecting traffic congestion which is comparably higher than state-of-the-art methods.

New procedure for designing an innovative biomass-solar-wind polygeneration system for sustainable production of power, freshwater, and ammonia using 6E analyses, carbon footprint, water footprint, and multi-objective optimization

The correct management of renewable polygeneration systems is a highly effective approach that can not only help expand renewable energy systems but also ensure sustainable production. In the present work, a new procedure for designing renewable polygeneration systems is introduced. In this procedure, the management of polygeneration systems is discussed from various aspects, including climatic change, techno-economic and environmental conditions, layout design, ecological sustainability, and the sociological approach to polygeneration systems in a practical form. As a case study, an innovative biomass-solar-wind polygeneration system to produce power, ammonia, and freshwater for Ahvaz in Iran was designed and evaluated according to the developed procedure. The integration of multi-effect distillation and humidification-dehumidification desalination has been employed for freshwater production. For ammonia production, syngas produced in the gasification unit and nitrogen obtained from the air separation unit were utilized. Power production has been achieved using a combination of the hydrogen-ammonia Brayton cycle, the supercritical carbon dioxide cycle, and the organic Rankine cycle. The designed system was evaluated using energy, exergy, exergoeconomic, exergoenvironmental, emergoeconomic, emergoenvironmental, carbon footprint, and water footprint analyses. The layout design of the proposed system and the sociological study of the system from a systemic approach have enhanced the visibility of its application. Additionally, the investigation of biomass fuel variations and combinations according to climate, along with optimization and dynamic analyses, are key measures that allow for a more comprehensive evaluation of the system from various management perspectives. The results show that the proposed system is capable of producing 523.34 m3/day freshwater, 10.1 MW of power, and storing 17.75 ton/day of ammonia in optimal state. The energy and exergy efficiency of the polygeneration system in the optimal state is 29.97% and 13.12%. The total cost rate and total environmental impact rate of the entire system in this case are equal to 0.48 $/s and 19.79 mPts/s, respectively. Meanwhile, the defined renewable system has an ecological sustainability index of 1.76.

Assessing and auditing water transport systems by applying the energy equations

ABSTRACT In improving the energy efficiency of water transport systems, two critical stages are involved: assessment (to understand the system's operation and identify potential energy savings) and auditing (to locate and break down the energy losses). Both stages are based on energy balances, which can be conducted using either the extended Bernoulli equation or the energy integral equation. Both equations can be applied, but depending on the system, data availability, and the kind of study to be performed, one is preferable over the other. This paper analyses, applies and compares both equations, with a particular focus on the less commonly used energy integral equation in the hydraulic field. This more general equation includes thermal and transient effects and it is more suitable for analyzing complex systems. In contrast, the extended Bernoulli equation, while simpler to apply, can lead to the loss of relevant information, such as the evaluation of the topographic energy. The main objective of this work is to bridge the gap between these two fundamental energy equations and recommend the most appropriate one for hydraulic problems. Real examples are presented to show their differences and validate our recommendations.

Interfacial effects in Ni(OH)2/MnO@Ni aerogel heterostructures promote highly efficient electrooxidation of ethylene glycol to formate and hydrogen

The sluggish oxygen evolution reaction (OER: 4OH− → O2 + 2H2O + 4e−, E = 1.23 V vs RHE) retards the large-scale hydrogen production. Replacing OER with low-potential ethylene glycol oxidation reaction (EGOR) to drive water splitting is a promising approach to solve the high energy consumption in hydrogen production. Herein, we report an energy-efficient electrocatalytic water splitting system by using a heterostructure Ni(OH)2/MnO aerogel catalyst (NiMn AG) that replaces OER with ethylene glycol oxidation reaction (EGOR), resulting in the co-production of value-added formate and green hydrogen. Owing to the optimized electron distribution on the heterostructure interface, the NiMn AG catalyst exhibits excellent EGOR catalytic performance, with an overpotential of only 290 mV at 100 mA cm−2. The open-circuit potential of the reaction is reduced to 0.86 V for EGOR, rendering an earlier occurrence of electron transfer. The ion chromatography (IC) result demonstrates a highly selectivity (96%) for formate in EGOR. The density functional theory (DFT) calculations show that the interfacial effects between Ni(OH)2 and MnO optimizes the surface energy states of NiMn AG, improving the adsorption efficiency of the reaction intermediates for EGOR and further promoting the efficient cleavage of C–C to form formate. Additionally, the incorporation of MnO stabilizes the overall energy state during violent bond-breaking processes. This work sheds light on a new methodology to design bimetallic catalysts assisted by EGOR for decoupled green hydrogen production.