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Energy quota trading is critical to both energy conservation and the achievement of dual-carbon goals. However, little remains to be done on total factor productivity and economic potential for Chinese industry. The purpose of this paper is to counterfactually model and explore the effects of energy quota trading on total factor productivity and economic potential in industrial sector. We propose a command-controlled DEA model and an energy quota trading DEA model through non-parametric linear programming based on two policy strategies of energy consumption control. We then assess total factor productivity and its growth sources for 35 two-digit industrial sector in China, as well as examine economic potential and optimal path forecasts for carbon peaking. The results show that total factor productivity and economic potential for energy quota trading are higher than those for command-controlled energy policy. Energy-saving effect can be achieved for energy quota trading because the total energy input is lower. Additionally, the sources of productivity growth differ in timing. The average economic potential and total factor productivity of different industries differ due to the heterogeneity of the production conditions and scale of development of individual market players. Moreover, the catch-up emission reduction path with an increase of 8% in output and 4.76% in energy consumption is the optimal carbon peaking path under energy quota trading. Our findings shed valuable light on China's efforts to expand energy quota trading for high-quality development in the context of carbon peaking. • Propose a command-controlled DEA model and an energy quota trading DEA model. • Assess TFP and its growth sources for industrial sector in China. • Compare the impact of different energy policies on economic potential and TFP. • Estimate the energy saving and emission reduction potential of energy quota trading. • Examine the optimal energy-saving and emission-reduction path forecasts for carbon peaking.. [ABSTRACT FROM AUTHOR]

The efficient removal of organic impurities in waste salts is significant for their recycling and environmental protection. The pyrolysis process of organic impurities in waste salt was investigated by thermogravimetric experiment to analyze the reaction kinetics characteristics. The results indicate that the pyrolysis reaction temperature range for the present waste salt is between 641.15 K and 726.22 K. The activation energy is 177.59 kJ mol−1, and the pre-exponential factor is 2.98 × 1011 s−1. Based on this, a numerical model was established to investigate the pyrolysis performance of organic impurities in practical tubular reactor. The effects of configuration and operating parameters on reaction performance and energy consumption were revealed. It was discovered that increasing the heating rate and wall temperature can accelerate pyrolysis rate but results in higher energy consumption. To balance reaction time and energy consumption, the heating rate of 15 K/min and heating temperature of 773.15 K are recommended. The content of organic impurities and bed porosity have minimal impact on the reaction rate and energy consumption, indicating that the tubular reactor has wide applicability in various working conditions. Additionally, reducing bed porosity can improve the processing rate and daily processing capacity. This paper provides a method to analyze the pyrolysis performance of organic impurities in waste salt, which is significant for designing and selecting working conditions of practical reactor for waste salt treatment. [Display omitted] • Pyrolysis kinetics characteristics of organic impurities were investigated. • Reaction temperature is between 641.15 K and 726.22 K. • Activation energy and pre-exponential factor are 177.59 kJ/mol and 2.98 × 1011 s−1. • Numerical model for pyrolysis process of organic impurities was established. • Effects of parameters on reaction rate and energy consumption were investigated. [ABSTRACT FROM AUTHOR]

Today, fog computing is an emerging technology to support resource-constrained Internet of Things (IoT) applications. The scalability, geographic distribution, and heterogeneity of edge computing nodes, as well as the diversity of users' expectations, have made optimal module placement, considering the maximum use of fog resources, a challenging optimization problem. This paper proposes a method based on distributed learning automata to reduce the search space by using the maximum capacity of fogs in a heterogeneous fog network. In this method, fog topology is mapped to a distributed learning automata. With the cooperation of the automaton in this DLA, the problem of module placement has been solved to reduce energy consumption and delay of applications. To evaluate the amount of energy consumption and the execution time of IoT applications, two single-objective cost functions for energy and delay and another single-objective function with simultaneous consideration of energy and delay have been used. The results indicate that the average efficiency of the proposed method is 15.99%, 18.21%, and 15.53%, respectively, compared to other methods. [ABSTRACT FROM AUTHOR]

Intermittent embedded devices and systems are widely used in various scenarios, but they often experience power failures due to unstable power supplies. Non-volatile memory (NVM) is gaining popularity in embedded systems due to its byte-addressability, low access latency, and high density. As a result, backups can be stored in NVM, and global data are directly manipulated in NVM in this architecture. However, NVM has limited write endurance and modifications to global data frequently occur which results in a significant number of writes. As a result, the extensive NVM accesses may lead to poor performance for intermittent programs. This paper presents an energy-efficient approach, called shadow-stack to minimize NVM write overhead while ensuring consistency. Furthermore, we propose a series of principles to assist programmers in rewriting the programs. And we prove the correctness of these principles using formal methods. Experimental results demonstrate that shadow-stack achieves an approximately 95.1%, 81.3%, and 76.6% improvement in NVM writes overhead compared with log-based, Coach, and Clank, respectively on average. Simultaneously, shadow-stack also significantly reduces energy and time consumption compared with log-based approach by up to 19.4 × and 6.87 × , Coach by up to 4.2 × and 2.46 × , and Clank by up to 3.4 × and 2.26 × respectively. [ABSTRACT FROM AUTHOR]

• Urgent sustainable alcohol production via a photoelectrochemical pathway is studied. • MXene-modified graphene enhances light absorption and expands graphene surface. • Synthesized nanohybrid enables efficient production of methanol and formic acid. • Synergy between MXene and graphene improves charge transfer and utilization. • Results hold promise for ambient alcohol production via methane conversion. In the pursuit of efficient ambient methane (CH 4) conversion, we introduce a novel MXene combining with a nitrogen-doped graphene (MX@NG) hybrid as a photoelectrocatalyst for CH 4 oxidation under ambient conditions with light and external bias. Through electrostatic assembly, graphene nanosheets are incorporated between MXene sheets, effectively preventing MXene nanosheet restacking and increasing interlayer spacing. This enhances ion diffusion of electrolyte, increasing access to electroactive sites. Using Ti 3 CN as MXene and nitrogen-doped graphene (NG) as catalyst supports, our research demonstrates the superior performance of the MX@NG composite over pure NG and MXene for methane oxidation. After a 4-hour reaction with a 2 × 2 cm2, 10 mg sample-loaded carbon paper, it produces 17 µmol/ml of CH 3 OH and 14.5 µmol/ml of HCOOH at 0.8 V vs. Ag/AgCl. This enhanced performance results from the synergy between MXene and graphene, improving charge transfer and promoting efficient utilization of photoexcited charges. Additionally, excellent electrical conductivity of NG facilitates the transfer of photo-generated electrons to surface reaction sites, promoting free radical formation. The incorporation of MXene extends visible light absorption and improves charge separation, thereby enhancing solar energy utilization. Overall, this study highlights the potential of MX@NG composite in solar light-based electrocatalytic methane oxidation. [ABSTRACT FROM AUTHOR]

Solar-driven CO 2 /H 2 O splitting via a two-step solar thermochemical cycle is a promising approach for fuel production and carbon neutrality to address the intermittent instability and low energy density of solar energy while taking advantage of its clean and nonpolluting nature. However, current experimental efficiencies are still below theoretical levels due to various limitations such as material properties and energy loss occurring in the reactor. The reactor, as an important component for commercialization, still faces various challenges including high reaction temperature, limited reactor material longevity, high temperature sealing of moving parts, and slow adsorption and desorption rate of porous structure lining. This paper reviews researches on solar thermochemical oxygen carriers and related reactors, with particular focus on analyzing reactor design criteria, modeling methods, energy efficiency and energy loss analysis. In addition, obtaining low partial pressure oxygen for reduction reactions, heat recovery methods, and synergies between the reactor and solar concentrating system play a significant role in further improving energy efficiency. This work demonstrates the feasibility of two-step solar thermochemical cycles in renewable energy technologies and provides guidance for future innovations in this field. [Display omitted] • A review of two-step solar thermochemical cycle systems including materials, reactors and solar concentrating systems. • The ideas of The idea of mutual selection of solar reactor and oxidied reduced material. • Energy loss and consumption analysis at the reactor level and reactor design considerations. [ABSTRACT FROM AUTHOR]

The paper deals with the energy transition, focusing on the decisions to be taken about the change in the energy conversion capacity of specified portion of the universe, such as industrial or domestic districts, regions, countries, etc. Conversion capacity is intended here as the set of all energy conversion and storage units characterized by type, size and number, and interacting with the natural gas and electricity grids. The goal is to find guidelines for new installations, obtained by simulations of a model able to describe properly the behavior of each energy conversion technology and the interaction with grids. The model is based on a traditional MILNP approach, but contains unique features in the literature, which allow to obtain results of general validity for applications to very different geographical areas, or to countries including very different regions that transmit energy with each other or export/import it from abroad. The main novelty of this work is to identify the best planning for the decarbonisation of energy demand sectors, according to the criterion of minimum cost, both at global and at regional level. The results applied to the case of Italy allow identifying clearly the energy demand sectors to which new installations should be applied first to minimize the cost for the total energy system, and the units that must be foreseen to satisfy these energy demands, i.e. a precise strategy for the energy transition towards a 100% renewable system. • A multi-regional model is built to find the most convenient energy transition path. • General guidelines for Western countries are derived from the Italian case study. • Replacement of boilers with heat pumps allows increasing RES share from 20 to 30%. • For RES share >60%, PtG is the low-carbon strategy for high temperature heat demand. • Transportation is the last energy sector that should be decarbonized. [ABSTRACT FROM AUTHOR]

This research paper presents an in-depth analysis of diesel–electric power systems in offshore Platform Supply Vessels (PSVs). The main contribution is the use of real data obtained from a PSV to produce and validate computational models subsequently used to accurately calculate fuel consumption and emissions, with representative load demands, considering different configurations of non-hybrid and hybrid power systems with the addition of lithium-ion batteries to provide a reliable analysis. Power dispatch optimization had a significant impact, achieving fuel and CO 2 reductions of around 7%–12% in non-hybrid systems, depending on the generator loading limits. Hybridization and battery integration further enhanced reductions, with up to 10% improvements in fuel consumption and CO 2 emissions. Particulate matter (PM) emissions and running hours were substantially reduced, but NO x varied. Validity checks accounted for biases, and the study provided reliable assessments of trends. The differences between monthly averages and robust approaches were minimal. Overall, the strategies presented achieved reductions of up to 15% in fuel consumption and CO 2 emissions, 20% in NO x emissions, and 68% in PM emissions for the complete mission. • Analyzing Platform Supply Vessel's power system with real data and computation models. • Power dispatch optimization yields significant fuel consumption and CO 2 reductions. • Battery integration further improves fuel efficiency and CO 2 reductions. • Particulate matter emissions and running hours reduce considerably, but NO x varies. • Validity checks account for biases providing a reliable assessment of trends. [ABSTRACT FROM AUTHOR]

Inequities in the built environment have long persisted in communities around the world. Prior studies have exposed that energy retrofit programs invest inequitably in residential buildings, leaving racial minorities with disproportionately less energy inefficient homes, regardless of income level. This inequity in energy performance and retrofit investments has yet to be studied for commercial and institutional buildings. Further research has shown that tree canopy coverage and vegetation is less dense in historically disadvantaged communities, contributing to a greater urban heat island effect and corresponding higher energy consumption for cooling compared to historically advantaged communities. Moreover, there is limited albeit emerging research that utilizes granular socioeconomic data with fine temporal scale energy consumption data to help identify energy inequalities among race and income groups. This paper examines the differential impact of energy efficiency retrofit installation on energy performance between advantaged vs. disadvantaged groups using data from U.S. public school buildings. Additionally, it studies how the effect of retrofits is compounded by the presence of vegetation surrounding the building. Utilizing hourly interval smart meter data from primary and secondary school buildings in California, the impact of retrofits are quantified using three energy metrics. Then the disparities in retrofit impact for advantaged vs. disadvantaged groups (as defined by three equity metrics) are evaluated using school-level demographic data. Lastly, regression on the percent change in monthly energy consumption from the pre-retrofit to post-retrofit period is used to quantify the incremental benefits of retrofits compounded by vegetation. Findings show that energy efficiency retrofits can improve energy performance and reduce energy inequities. Specifically, retrofits significantly increase energy efficiency and reduce the energy efficiency equity gap by up to 198%. Evidence shows that vegetation surrounding a building has a synergistic effect with mechanical retrofits for reducing energy consumption in respect to reduced cooling loads, even in cases where mechanical retrofits alone resulted in significant increases in energy consumption. Dense vegetation provides around 6% energy savings, similar in magnitude to savings from mechanical retrofit installation. This validates the effectiveness of retrofits for reducing energy consumption and improving energy equity, and demonstrates the ability of vegetation to provide additional energy savings beyond mechanical interventions. [ABSTRACT FROM AUTHOR]

Developing high-efficiency solar photothermal conversion and storage (SPCS) technology is significant in solving the imbalance between the supply and demand of solar energy utilization in time and space. Aiming at the current research status in the field of SPCS, this review thoroughly examines the phase change materials and substrates in SPCS systems. It elucidates the design principles and methods of SPCS integrated composites. Comparatively, it analyzes the parameters of various types of SPCS composites in terms of photothermal conversion, thermal conductivity, energy density, and cycling stability. Additionally, the review discusses the trade-offs between each parameter to achieve the most optimal effect of SPCS. By sorting out the current status of the application of SPCS technology in solar thermal/photovoltaic, aerospace, buildings, textile, and other industries, this analysis clarifies the requirements for various latent heat, phase change temperature, and other properties under different environmental conditions. Through a comprehensive discussion of SPCS technology, this paper accurately captures the development trend of efficiently and comprehensively utilizing solar energy by analyzing existing scientific problems. It identifies bottlenecks in SPCS technology and suggests future development directions that need focused attention. The insights gained from this analysis may provide a theoretical basis for designing strategies, enhancing performance, and promoting the application of SPCS. [Display omitted] • SPCS technology show high efficiency for energy utilization and management. • SPCS can solve the imbalance in the supply and demand of solar energy utilization. • The study of the design principles and methods of SPCS is important. • SPCS system can solve the energy crisis and environmental pollution. • SPCS systems have great potential for practical water treatment in the future. [ABSTRACT FROM AUTHOR]

In alloys, solute segregation at grain boundaries is classically attributed to three driving forces: a high solution enthalpy, a high size mismatch, and a high difference in interfacial energy. These effects are generally cast into a single scalar segregation energy and used to predict grain boundary solute enrichment or depletion. This approach neglects the physics of segregation at many competing grain boundary sites, and can also miss electronic effects that are energetically significant to the problem. In this paper, we demonstrate that such driving forces cannot explain, nor thus predict, segregation in some alloys. Using quantum-accurate segregation spectra that have recently become available for some polycrystalline alloys, we predict strong segregation for gold in aluminum, a solvent-solute combination that does not conform to classical driving forces. Our experiments confirm these predictions and reveal gold enrichment at grain boundaries that is two orders of magnitude over the bulk lattice solute concentration. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents an innovative study of a two-stage nonlinear dynamic system, providing insights into the occurrence of Large-Amplitude Long-Duration Vibration State (LALDVS) and outer isolated detached resonance curves (OIDRCs). The Lagrange equations and the incremental harmonic balance method (IHBM) are used to uncover the fundamental mechanism of OIDRCs and to discover two and three OIDRCs that have not been previously reported. Multiple OIDRCs could have significant practical implications in engineering, as they indicate the possibility of unpredictable and potentially hazardous vibrations under certain operating conditions. The existence of OIDRCs depends on the jump frequency point being a Neimark-Sacker bifurcation, and the resulting phase diagram provides vital insights into the stability and bifurcation behaviors of the system. This work also explores the effect of stiffness on the nonlinear response behavior of the system and identifies the range of stiffness values that lead to LALDVS. The correctness of the theoretical calculation results is validated by comparing them with vibration experiment and simulation calculations. These results have practical implications for engineering and energy harvesting research, including identifying potential hazards associated with vibration and designing systems that maximize energy harvesting efficiency. Overall, these findings are innovative and have significant implications for predicting potentially hazardous vibrations in engineering and the design of energy harvesting systems. • Innovative investigation on two-stage nonlinear dynamic system provides insight into large-amplitude and long-duration vibration state (LALDVS) and outer isolated detached resonance curves (OIDRCs). • Incremental harmonic balance method is used to uncover two/three OIDRCs, which is not previously reported. • Multiple OIDRCs could have significant practical implications, indicating potential hazardous vibrations. • Results identify stiffness range for LALDVS, with implications for energy harvesting. [ABSTRACT FROM AUTHOR]

The energy efficiency of the electric propulsion system in electric aircraft is significantly influenced by the propeller's efficiency, making the design of a high-efficiency propeller critical. Currently, the Blade Element Momentum Theory (BEMT) is widely used for propeller design. The BEMT method exhibits high efficiency but is limited by its low accuracy due to its inability to capture three-dimensional effects of rotating flow. On the other hand, the CFD simulation can enhance the accuracy of simulating complex rotating flows, but it is computationally expensive. To alleviate the contradiction between accuracy and design efficiency in propeller optimization design, this paper describes a Multi-Fidelity Neural Network (MFNN)-based optimization framework for the optimization design of electric aircraft propellers. This method is based on high-fidelity CFD numerical simulations for the optimization design of propellers. In order to enhance design efficiency and accuracy, it fuses low-fidelity BEMT knowledge, aiming to achieve higher model accuracy with fewer CFD simulations. Based on the MFNN, an adaptive sample-infilling approach is employed to update the model, and a teaching–learning-based optimization is used to build the surrogate-based optimization framework. The proposed method improves the cruise efficiency of the propeller from 82.3% (designed by BEMT) to 87.1% and shows advantages in optimization effectiveness and efficiency compared with single-fidelity optimization. [ABSTRACT FROM AUTHOR]

This article analyzes energy poverty in Latin America and the Caribbean and the importance of energy efficiency to address this issue. The paper also touches upon energy subsidies in Mexico and their negative environmental and social consequences. Then, the article studies the potentially detrimental billing practices of Mexico's state electricity company, which are provoked by the enterprise's growing past-due portfolio and losses, and the possible infringement of electricity consumers' rights. The authors propose three courses of action for consumers to protest the company's possibly perjudicial billing. Finally, the authors suggest that a distributed electricity system in Mexico could combat energy poverty and strengthen electricity users' rights. [ABSTRACT FROM AUTHOR]

Energy is the key driver of life. It is required for the growth, exaltation, modernization, and financial development of any country. The world is turning away from fossil fuels in favor of renewable energy sources in order to meet the rising demand for energy and protect the environment from pollution. Solar energy is becoming a significant source of energy among these new energy sources. The primary solar energy-using appliances are solar air heaters. However, the creation of a laminar sub-layer on the absorbing plate is the cause of its poor thermal efficiency, which is a cause for concern. This paper describes various techniques through which the efficiency of solar air heaters (SAH) is enhanced by disturbing the laminar sub-layer. This study is concentrated on four techniques artificial roughness, jet impingement, piezo-electric fan, and phase change material (PCM), which are explained in four sections. The first section gives a detailed description of artificial rib roughness, turbulators, and protrusions. The second section reviews the use of jet impingement in SAH. The third section introduces a new technique; a piezo-electric fan and describes its mechanism, working parameters, and possibilities for its use in SAH through the previous studies. The fourth section reviews the use of PCM material in SAH by the previous studies. The maximum thermal hydraulic performance factor (THPP) for non-circular ribs, ribs with a rectangular cross section, ribs with an equilateral cross section, and jet impinged SAH, respectively, has been observed to be 1.44–1.29, 2.19, 2.11, and 3.66. [ABSTRACT FROM AUTHOR]

Biomass remains the most important energy carrier of rural households in low- and middle-income countries, but its indoor combustion has grave impacts on human health and its extraction is associated with negative effects on ecosystems. Currently, robust and comprehensive data are lacking to trace biomass flows from ecosystems to consumption in households and quantify the related services. This impedes analyses of the social and environmental impacts of biomass use. By developing a source-to-service approach, this paper analyses the provision of domestic energy services in three villages in rural Ethiopia using a socio-metabolic perspective. We combine quantitative and qualitative methods to study the dynamics of domestic biomass use for energy and examine social and environmental implications. We find that the average household consumes 84 GJ/year of biomass (15 GJ per capita/year). Space heating, food and drinking water preparation combined require 86% of domestic energy. Improved cookstoves can reduce domestic energy use by 12%. Our results open new avenues for advancing the scientific understanding of rural energy systems dependent on biomass. These insights are essential to enhance research on sustainable energy systems in rural, bioenergy-dependent areas and may also prove useful in designing policies and innovations to improve provisioning of energy services. [Display omitted] • On average rural households in 3 studied villages in Ethiopia consume 84 GJ/yr of bioenergy • Space heating, food and drinking water preparation consume almost nine tenth of the total domestic energy • Family size, land and livestock assets are all significantly related with energy consumption • Adoption of improved cookstoves can be linked to a reduction in energy consumption of 12% • Combining MEFA and HANPP offers an avenue to analyse bioenergy systems from source to service [ABSTRACT FROM AUTHOR]

Increasing energy demands combined with local scarcities and rising prices make the valorisation of energy from domestic wastewater seen as a valuable resource. Chemically enhanced primary treatment (CEPT) enables an increased redirection of organic compounds into sludge in the primary stage of a wastewater treatment for a transformation into biogas (carbon capture). Traditionally used coagulants consist of metallic salts, but in the last two decades, the development of polymers, based on petroleum or synthesized from renewable sources such as plants, has been intensified. However, a direct comparison of the effectiveness of these products is missing. In this paper, we analysed data of peer-reviewed research from jar tests to full-scale studies, highlighting key parameters for successful carbon capture. More than 100 studies were identified, with a majority presenting results from tests under static conditions (jar tests), while data on full-scale applications are scarce. Overall, for TSS and COD, a clear correlation between inflow concentration and removal efficiency was found, irrespective of the product used. Comparison between the effectiveness of the different types of products is difficult, but bio-based coagulants need to be generally added in higher product concentrations for a considerable removal efficiency. While CEPT seems to increase the general sludge and biogas output, future studies should focus on harmonising laboratory analysis to make results comparable. Another important issue that should be addressed is the provision of experimental details, especially for full-scale trials, to enable for reliable conclusions. [Display omitted] • Studies on chemically enhanced primary treated domestic wastewater were evaluated. • Removal efficiencies for metal salts, petro- and bio-based coagulants were compared. • COD and TSS removal is strongly correlated to the inflow concentration. • Removal of COD increases up to 45% and TSS up to 30% compared to primary settling. • Future studies should focus on harmonising experimental conditions. [ABSTRACT FROM AUTHOR]

The present study explores net carbon-neutral road transport options in the EU27 in 2050 from a well-to-wheel (WtW) perspective. To this aim, three scenarios have been developed regarding the evolution of the road vehicle fleet composition in terms of the degree of electrification of powertrains, and technical measures such as vehicle efficiency improvements, transport flow, and transport volumes have been considered. The fleet scenarios are further combined with four scenarios deploying different mixes of alternative fuels in 2050, including electricity, e-fuels, liquid/gaseous advanced biofuels and remaining fossil fuel components. Two different electricity production pathways are considered, as well, one of them assuming fully renewable electricity production, and an alternative one including residual shares of nuclear and natural gas power plants plus carbon capture and storage. The Joint Research Centre's DIONE model was used for the scenario calculations. From a tank-to-wheel (TtW) perspective, each scenario reduces energy consumption, mainly due to powertrain electrification, with EU27 TtW road vehicle energy consumption in 2050 ranging from roughly 650 to 1650 TWh, which is 20%–53% of that of 2019. For the most strongly electrified fleet scenario with optimistic assumptions on measures to improve vehicle efficiency, transport flow and transport volumes and renewable electricity production, well-to-tank (WtT) energy consumption is around 110–160 TWh, depending on the fuel scenario. If a moderately electrified fleet is propelled with e-fuels, even when assuming fully renewable electricity and optimistic measures, the WtT energy consumption increases ten times or more, to 1300 TWh (2150 TWh with pessimistic measures). The results show that fleet electrification is the strongest lever for WtW transport energy consumption reduction among the options considered. Other efficiency measures contribute significantly to energy savings, but their benefit decreases with increasing fleet electrification. The production pathways of fuels make a substantial difference for WtW fleet energy requirement if the fleet is moderately electrified, in which case e-fuels require significant amounts of additional renewable electricity. Nevertheless, fuel production pathways become irrelevant from an energetic point of view under very ambitious electrification scenarios since fuel volumes become marginal. The economic feasibility and the societal acceptability of different pathways towards WtW carbon-neutrality of road transport is out of scope of this paper and requires further investigation. • The study examines well-to-wheel scenarios for 2050 carbon neutral road transport. • Electrification is the strongest driver in transport energy consumption reduction. • Vehicle efficiency and activity impacts decrease with fleet electrification. • With high fleet electrification fuel production barely impacts energy consumption. • E-fuels require particularly large amounts of additional renewable electricity. [ABSTRACT FROM AUTHOR]

Battery electric industrial vehicles (BEIVs) offer significant environmental protection and decarbonization potential for rapidly growing logistics and transportation sectors in China. However, the limited driving range remains a major obstacle to the widespread adoption of BEIVs. Eco-driving is an effective solution that can improve energy efficiency and extend the driving range of vehicles by controlling driving behavior and speed. Therefore, this paper introduces an energy efficient speed planning method for BEIVs, in which data-driven models are developed to estimate energy consumption. Real-world driving data collected from a BEIV are used to model the energy consumption by curve fitting, random forest, and LSTM. The speed planning process is considered a Markov Decision Process, which is solved using Dynamic Programming and Q-learning, respectively. The case study of a battery electric forklift shows that speed planning can reduce energy consumption by 13%–27% compared to time-optimal speed, while only increasing the driving time by 10%–20%. • Energy efficient speed planning methods for electric industrial vehicles are proposed. • The speed planning method uses data-driven vehicle energy consumption models. • The energy consumption models are developed using random forest and LSTM. • The speed planning problem is formulated as a Markov decision process. • The speed planning problem is solved by Dynamic Programming and Q-learning. [ABSTRACT FROM AUTHOR]

The interactions among chlorine (Cl), sulfur (S), and alkaline and alkaline-earth metals (AAEMs) are the critical factors that affect the formation, transformation, and deposition of ash during biomass and coal gasification and combustion. These issues cause slagging, agglomeration, corrosion, and other problems related to the safety performance of gasifiers and boilers. A better understanding of the determination methods, modes of occurrence, migration routes, and control technologies of Cl and S is the basis for clean and efficient utilization of biomass and coal. The paper summarizes the research work in this field, namely: (1) The modes of Cl and S occurrence in biomass and coal are introduced, mainly in different forms of inorganic and organic Cl/S compounds; (2) The principles, merits and demerits of quantitative determination methods for Cl and S are sorted out and compared; (3) The migration routes of Cl and S, mainly related to their modes of occurrence, reaction evolution, temperature, and atmosphere; and (4) The interactions among Cl, S, and AAEMs and its induced issues, especially the challenging problems related to ash, including alkali-induced slagging, silicate melt-induced slagging (ash fusion), agglomeration, and corrosion. Finally, ecological and technological significance of the Cl and S release is discussed. This knowledge is of great significance to effectively control the emission of Cl and S pollutants, stable operation of gasifiers and boilers, cleaner and more efficient utilization of coal, as well as to diminish the consumption of fossil energy and heighten the availability of renewable energy in such thermochemical conversion processes. • Migration routes of chlorine and sulfur are elucidated. • Interactions among chlorine, sulfur, and AAEMs are summarized. • Ecological and technical significance of chlorine and sulfur release is discussed. [ABSTRACT FROM AUTHOR]

The energy crisis caused by climate change is the most significant challenge of the 21st century. Single-objective and multi-objective optimization studies for buildings often only consider the building itself, without considering the impacts of climate change. However, in the future, energy-efficient design and appropriate operating parameters will be crucial considerations in the face of global climate change. This paper aims to discuss the impact of climate change in the humid-hot region over the past 60 years on the energy consumption of office buildings and propose countermeasures. Firstly, the meteorological data of Guangzhou from 1960 to 2020 is statistically analyzed, and four typical meteorological years and corresponding air conditioning design days are established. Secondly, Energyplus is used to simulate the building's energy consumption, and the energy consumption factors are analyzed for sensitivity using jEplus. Finally, a multi-objective optimization is performed using jEPlus + EA with the Non-dominated Sorting Genetic Algorithm (NSGA-II). The results found that climate change led to a significant increase in the duration of high outdoor temperatures during summer. Specifically, the daily dry-bulb temperature for air conditioning design increased by 0.4–0.7 °C, and due to climate change, the energy consumption of the office building increased by about 4.6 %. The main factor affecting the cooling energy consumption in summer was the indoor cooling set point (8.29%–30%), infiltration air volume (1.0%–8.6%), fresh air volume (3.2%–16.06%), the glazing solar transmission rate (1.25%–9.0%), building orientation (0%–1.43%), and relatively small influence of envelope insulation performance. The operation scheme determined by multi-objective optimization can reduce the cooling energy consumption of the building by 10.6%–16.8%, which can mitigate the rise in energy consumption resulting from climate change to a greater extent. [Display omitted] • Discuss the impact of climate change over the past 60 years on the energy consumption of office buildings. • There was a significant increase in the duration of high outdoor temperatures during the summer. • Multi-objective optimization is performed using jEPlus + EA. • Climate change caused energy consumption to increase by about 4.6 %. • Optimal solutions can reduce the cooling energy consumption of the building by 10.6%–16.8%. [ABSTRACT FROM AUTHOR]

Wastewater treatment plants (WWTPs) play a pivotal role in natural water recycling and safeguarding the water security of approximately 42% of the world's population, yet they confront challenges posed by escalating energy consumption and regional energy constraints. In this study, we developed an evaluation framework that integrated regional energy scarcity with the operation conditions of WWTPs to quantify their energy efficiencies by applying multiregional input–output (MRIO) analysis and data envelopment analysis (DEA). A case study using this framework was conducted on 3776 WWTPs in 30 provinces in China. The results showed that the average scarcity-based energy efficiency level of WWTPs in China was low, of which those in northern provinces were higher compared to southeastern provinces. Furthermore, only 24 WWTPs in China attained the highest efficiency levels, while the rest had different degrees of input redundancy, indicating an energy-saving potential of 9.05 × 109 kWh. This paper enables the development of optimal efficiency strategies for WWTPs in different provinces considering the potential energy scarcity risks caused by trading activities. • The DEA and MRIO analyses were combined for 3776 WWTPs in China. • Efficiency evaluation index was modified considering energy scarcity levels. • Scarcity-based energy efficiency assessment framework for WWTPs was developed. [ABSTRACT FROM AUTHOR]

A novel zero carbon emission combined power and hydrogen system is developed in this paper, which integrates power generation, hydrogen production, and energy storage. The novel combined system is composed of solar power tower subsystem, supercritical carbon dioxide recompression Brayton cycle subsystem, hydrogen gas turbine subsystem, hydrogen production subsystem and thermoelectric generator subsystem. Considering the solar instability, three operating modes are set for the combined system: pure hydrogen mode, solar‑hydrogen hybrid mode and pure solar energy mode. Comprehensive energy, exergy and economic analyses are performed for the combined system. The effects of five key parameters on the system performance are investigated. Taking the maximum exergy efficiency and the minimum levelized cost of energy as the objective function, the key parameters are optimized by the genetic algorithm. Results show that in pure hydrogen mode, the thermal and exergy efficiency of the combined system are 44.79% and 45.49%, respectively. Compared with pure hydrogen mode, the levelized cost of energy is reduced by 44.76% and 75.16% in solar‑hydrogen hybrid and pure solar energy mode. There is an optimal compressor pressure ratio to make the combined system have better thermodynamic and economic performance. Parameter optimization results show that for pure solar mode, the levelized cost of energy of the combined system is 75.4% and 56.3% lower than that of pure hydrogen mode, solar‑hydrogen hybrid mode respectively in the levelized cost of energy optimal design. The derived significant contributions of this work highlight strong potentials for combined system based on solar energy‑hydrogen complementary utilization. [Display omitted] • Development of a novel system for production power, hydrogen and energy storage. • Complementary power generation technology of solar energy and hydrogen is proposed. • Three operating modes have been proposed to reduce the impact of solar fluctuation. • Compared with pure hydrogen mode, the LCOE is reduced by 75.16% in pure solar mode. [ABSTRACT FROM AUTHOR]

This work investigates the design optimization of aggregated energy systems (multi-energy systems, microgrids, energy districts, etc.) with (N-1) -reliability requirements. The problem is formulated as a two-stage stochastic Mixed Integer Linear Program which optimizes design (first stage variables) and operation variables (second stage variables) simultaneously considering a set of typical and extreme days. The analysis proposes and compares different approaches to include the (N-1) reliability requirement in the optimization problem. Moreover, the paper proposes two effective decomposition algorithms to solve the large-scale Mixed Integer Linear Program suitable for design problems with and without (N-1) reliability requirements. Depending on the instance, such decomposition algorithms allow reducing the computational time by one or more orders of magnitude (from days to a few hours, in the worst cases tested in this work). The proposed methodology is tested to design the aggregated energy system for a real case study considering both a grid-connected and off-grid installation. Results indicate that the actual reliability of the design solutions depends by the profiles of energy demand and renewable production considered in the failure scenarios included in the design problem. Including N-1 reliability requirements causes an increase in the total annual cost in the range 15–20%, due to the increase in capital costs. • N-1 reliability is included in MILP for the optimal design of Aggregated Energy Systems. • Different approaches to include reliability constraints are proposed and compared. • Two decomposition algorithms allow reducing the computational time considerably. • Including N-1 reliability results in a 15–20% Total Annual Cost increase. [ABSTRACT FROM AUTHOR]

This paper studies the influence of temperature and of rainfall intensity and the effect of such variations on the treatment efficiencies and on the electrical consumptions in seven medium-large size Wastewater Treatment Plants (WWTPs) in Apulia in South Italy (Bari, Barletta, Brindisi, Lecce, Foggia, Andria and Taranto). It has been observed, in the considered WWTPs, a slight but clear increase of the incoming flow due to the increase in rainfall intensity, which results to an increase of the energy consumption per incoming volume. The impact of the climate change to the incoming flow, during the last five years (2016–2020), has been assessed indicating that an increase in rainfall intensity results to an increase of the WWTPs energy consumptions per wastewater treated volume. More specifically, for a specific WWTP (Lecce) it was found that the electrical consumption increases from 0.36 kw/m3 to 0.51 kw/m3 when the rainfall intensity was increased from 0.8 mm/min to 2.9 mm/min. Some adaption measures have been considered to upgrade the existing WWTP so to mitigate the energy increase and to limit the global effects of climate change. [Display omitted] • As Rain intensity rises, wastewater influx into WWTPs increases. • Rainfall intensity influences negatively electrical consumptions. • BOD removal is lower with increasing annual precipitations. • Temperature influences BOD, COD and SST removal efficiency. [ABSTRACT FROM AUTHOR]

The flexibility of power systems is critical to cope with the variability and uncertainty caused by the integration of renewable energy resources. Incorporating district cooling systems (DCSs) into power systems is an emerging approach to provide flexibility for renewable energy utilization. However, the existing models of DCS components, such as ice-storage cooling sources, cooling pipelines, and cold warehouses, are simplified, failing to fully utilize that flexibility, which brings difficulties for renewable energy utilization. Therefore, this paper proposed a coordinated operational strategy for an integrated power and cooling system combining detailed operating characteristics of DCSs to exploit the potential flexibility of DCSs for better integration of renewable energy. The primary cooling source components, including the chiller, ice storage tank, pump, and cooling tower, are established to model a detailed process of cooling generation and storage. The thermal inertial of district cooling networks (DCNs) is formulated as a quasi-dynamic model by considering dynamical temperature characteristics and transmission delay to exploit the virtual energy storage capacity of the cooling water pipeline. Moreover, the thermal inertia of the demand side, such as buildings and cold warehouses, is considered to utilize thermal storage capacity. The objective function of the proposed optimization model is to minimize the overall operating cost of the integrated power and cooling system. Numerical simulations show that the proposed method could reduce the operating cost by 15.4% and the wind curtailment rate by 86.2%, indicating better performance of the proposed method in improving economic benefits and reducing renewable energy curtailment. • We proposed a coordinated operational strategy combining flexible resources of DCSs. • We established a detailed model of cooling source including chiller, ice storage tank, pump, and cooling tower. • The thermal inertial of district cooling networks is formulated as a quasi-dynamic model. • The combined cooling and power system can reduce the costs and wind curtailment. [ABSTRACT FROM AUTHOR]

Acid gas enrichment (AGE) is a new method to increase the H 2 S conversion in conventional Clause process. In this paper effect of methyl diethanolamine (MDEA) flow rate, MDEA temperature, acid gas flow rate, and AGE tower pressure on the outlet H 2 S and CO 2 concentration and energy consumption have been studied. A novel procedure based on enthalpy of combustion in the reaction furnace and incinerator is conducted for energy consumption calculations. By changing MDEA and inlet acid gas flow rates, a maximum H 2 S concentration in the enriched stream could be attainable. Increasing MDEA temperature will decrease maximum H 2 S concentration and enriched acid flow rate; also, CO 2 concentration in off-gas will be decreased. By increasing AGE pressure, maximum H 2 S concentration in the enriched stream will be diminished. At higher MDEA flow rates, lower AGE pressures will bring higher H 2 S concentration in enriched acid gas stream. According to flame temperature simulation in the reaction furnace, energy consumption is directly related to H 2 S concentration in the sulfur recovery unit (SRU) feed stream. Results show that maximum H 2 S concentration in the enriched acid gas stream has the best conditions from energy point of view. At optimum conditions, H 2 S concentration in enriched acid gas is 62.45 mol%, which has been increased by 24.8%, compared with existing plant conditions, and energy consumption has been decreased by 25.05 GJ/h, and CO 2 concentration in off-gas is 94.94 mol%. • Study the effect of process variables on the AGE streams by ProTreat software. • Determine the relation between reaction furnace temperature and H 2 S concentration. • Generate an EES procedure for heat of combustion in reaction furnace. • Evaluate the fuel gas consumption and therefore energy consumption. • Multi-objective optimization based on the TOPSIS method. [ABSTRACT FROM AUTHOR]

A photovoltaic thermal collector (PV/T) is incorporated into the combined cooling, heating, and power (CCHP) system to decrease primary energy consumption, operation cost and carbon dioxide emission. Six CCHP scenarios based on three strategies are investigated for the energy utilization of the CCHP system. Also, a non-dominated sorting genetic algorithm III using competition crossover and opposition-based learning (NSGA-III-CO) is proposed for the six scenarios. The competition crossover determines the starting point of search in the decision space, and more potential regions can be exploited sufficiently. The opposition-based learning (OBL) carries out random searches and facilitates the convergence of the population. In addition, a constraint handling approach (CHA) is presented to decrease the constraint violations of the infeasible individuals and turn them into feasible individuals, and it guarantees the feasibility of the population. Experimental results suggest that each CCHP scenario with PV/T is more efficient than the one with photovoltaic panel (PV) and the one neglecting PV/T and PV, and it achieves the lowest primary energy consumption, operation cost and carbon dioxide emission. Also, NSGA-III-CO and the other three algorithms are applied to the six CCHP scenarios with PV/T, and it obtains the highest hypervolume and coverage rate for each scenario. [Display omitted] • This paper studies dispatch of combined cooling, heating, and power system (CCHP). • The CCHP involves photovoltaic thermal collector. • An improved NSGA-III is proposed for CCHP. • The improved NSGA-III has exhibited good comprehensive performance for CCHP. • A constraint handling approach is presented to satisfy the constraints of CCHP. [ABSTRACT FROM AUTHOR]

Nowadays, recycling end-of-life (EoL) products has emerged as a vital approach to address resource scarcity. Within the recycling process, disassembly plays a pivotal role and has garnered substantial attention from researchers. Disassembly sequence planning (DSP) is a crucial method to enhance disassembly efficiency. Among the various DSP models, selective disassembly sequence planning (SDSP) has gained prominence as a means to save time and reduce costs. It empowers operators to locate specific components or materials in real time, thereby boosting efficiency and minimising resource wastage. However, a notable research gap exists in the domain of SDSP, particularly in uncertain environments. To bridge this gap and render SDSP solutions more practical for real-world disassembly operations, this study adopts trapezoidal fuzzy numbers to represent uncertain information within the disassembly process and formulates a comprehensive SDSP model. In response to the intricate challenges posed by this problem, we propose a hybrid approach termed nondominated sorting genetic algorithm-II with simulated large neighborhood search (NSGA–II–SLNS). This innovative algorithm leverages the strengths of the nondominated sorting genetic algorithm-II (NSGA-II), simulated annealing algorithm (SA), and large neighborhood search (LNS). Additionally, we introduce several novel search operators into NSGA–II–SLNS, including a crossover and mutation strategy based on chaotic mapping, as well as a local search operator founded on the SA criterion and LNS. To assess the effectiveness of the proposed algorithm and model, extensive numerical case studies are conducted in this research. The outcomes contribute to the advancement of rapid, nearly optimal SDSP strategies in the face of uncertainty and ambiguity in problem settings. • This paper models an uncertain selective disassembly sequence planning problem with trapezoidal fuzzy numbers. • A model for disassembly that minimizes disassembly time and energy consumption and maximizes profit is developed. • A hybrid metaheuristic algorithm combining NSGA-Ⅱ, SA and LNS is applied to solve the model. • Real-world case studies and several stochastic cases validate the proposed model's feasibility in small and large scales. • The hybrid algorithm's superior performance is demonstrated through multi-objective metrics and comparison with popular literature algorithms. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) face challenges related to limited energy resources in sensor nodes, making network lifetime and energy consumption critical considerations. In this paper, we introduce a novel approach to extend network lifetime and reduce energy consumption in WSNs by optimizing network architecture selection. We explore various architecture options, including single-layer and multi-layer networks, to determine the most suitable configuration. Several studies have applied knowledge transfer in evolutionary multitasking optimization to maximize network lifetime, achieving state-of-the-art results for the problem. However, they still have some drawbacks, such as redundant representations, i.e., many genotypes represent the same phenotype, and invalid solution generation. To address these limitations, we propose a new evolutionary multitasking algorithm incorporating efficient encoding to represent tree-structure solutions (network architectures) to the problem. Additionally, we tailor new genetic operators, such as recombination and mutation, to meet the specific requirements of our proposed evolutionary multitasking algorithm. The standout feature of our proposal lies in its ability to consistently generate valid solutions for the problem, which significantly reduces redundancy within the genotype search space and effectively satisfies the problem constraints. Additionally, our multitask evolutionary algorithm facilitates the exploration of various network architectures, harnessing knowledge transfer to enhance optimal performance and reduce computation time compared to single-task methods. We conduct comprehensive experiments on diverse datasets and use statistical tests, e.g., the Wilcoxon signed rank test, to verify the performance of our proposed algorithms. The empirical results demonstrate that our proposed algorithm outperforms existing methods and achieves state-of-the-art results in terms of solution quality, convergence rate, and running time across a wide range of data instances. Specifically, our algorithm provides 49.54% better solution quality on average across all data types compared to the previous state-of-the-art method. [ABSTRACT FROM AUTHOR]

With the guidance of the advanced manufacturing philosophy, green scheduling and energy efficiency have received considerable attention from enterprises and countries. Meanwhile, distributed manufacturing is becoming widespread due to the exploration of the business. Thus, this paper investigates the energy-efficient scheduling of the distributed flexible job shop problem (EEDFJSP) with the goal of minimizing the makespan and total energy consumption (T E C). Considering the difficulty of simultaneously optimizing both objectives, a knowledge-guided bi-population evolutionary algorithm (KBEA) is proposed to address this issue. Firstly, a problem-specific initialization strategy based on a four-vector representation is presented, which corresponds to four sub-problems including factory assignment, operation sequence, machine assignment, and speed assignment. Secondly, five different types of evolutionary operators with adaption strategy is designed to guide the bi-population to complete efficient evolution. Thirdly, a knowledge-guided local search strategy is used to enhance the exploitation capability of the algorithm. Furthermore, an elaborately-designed energy-saving strategy based on knowledge is developed to further reduce energy consumption. Additionally, to verify the effectiveness of the proposed KBEA, extensive experiments are conducted to compare with other 7 comparison algorithms on 39 instances. Experimental results manifest that KBEA is superior to its competitors. [ABSTRACT FROM AUTHOR]

Joint operation optimization for virtual coupling of heavy-haul trains is hardly performed by locomotive engineers due to the increasing "harmonic" effects among train groups and environmental uncertainties. In this paper, an improved dynamic programming model is established based on the inherent Markov nature of train control decisions. Then, a new joint optimization principle for headway and energy savings is conducted simultaneously, and a connected locomotive engineering advisory analysis is performed for virtual coupling and quasi-mobile block. In view of the small fluctuations of control settings and the model's real-time performance, the constraint model for the speed limitations around the throat area has been developed. Simulation results based on real heavy-haul trains running data show that the proposed approach contributes a significant improvement in headway compression and energy efficiency. • The algorithm is based on real-line data from the Shuozhou-Huanghua Railway. • The algorithm demonstrates superior energy-saving and train stability. • Under the VC system, the theoretical achievable headway can be 43 s. [ABSTRACT FROM AUTHOR]

In the realm of economic scheduling and optimal operation within integrated energy systems, integrated energy load aggregation plays a pivotal role. Accurate prediction of integrated energy load aggregation enables the refined management of energy resources, thereby enhancing energy utilization efficiency. This paper merges the advantages of clustering algorithms, prediction algorithms and ensemble learning to propose a short-term prediction approach for integrated energy load aggregation. Firstly, the intrinsic distribution of energy consumption data is analyzed through Affinity Propagation (AP) clustering with multi-dimensional similarity. This analysis yields the division of all samples into clusters based on data characteristics. Subsequently, the Transfer entropy is harnessed to conduct correlation analysis within each cluster. For constructing predictive models for each individual cluster, a prediction algorithm based on the Feature-Temporal Attention-enhanced Bi-directional Simple Recurrent Unit (FTA-Bi-SRU) is employed. The Dynamic Weight Average (DWA)-based multi-task loss equilibrium and Multi-Term Adam (MTAdam) loss function are combined to enhance the efficiency of the forecasting model. Finally, a Light Gradient Boosting Machine (LightGBM) model with Bayesian optimization is employed to integrate the predictive outcomes of each individual cluster to generate the overall predictive outcomes for integrated energy load aggregation. The proposed methodology is experimented on real-world energy consumption data to ascertain the efficacy of the prediction method. The experimental results substantiate the superiority of the suggested forecasting approach. • This marks the inaugural discussion of load forecasting for integrated energy load aggregation is discussed. • A multi-dimensional similarity Affinity Propagation (AP) clustering algorithm is introduced to group multi-energy users. • A method based on transfer entropy for correlation analysis is presented. • An Feature-Temporal Attention based Bi-directional Simple Recurrent Unit (FTA-Bi-SRU) prediction model is introduced. • A Bayes-optimized Light Gradient Boosting Machine (BO-LightGBM) ensemble framework is proposed to enhance overall prediction. [ABSTRACT FROM AUTHOR]

• Mechanical response of Ti-6AlThe proposed structure can dissipate energy through both vibration and friction. • An analytical formula for the structure's energy consumption was provided. • The proposed metamaterial is self-recoverable and reusable. • The energy dissipation capacity can be changed without impacting self-recovery. Compared with traditional energy dissipation structures, negative stiffness metamaterials can repeatedly dissipate energy without incurring irreversible plastic deformation. The existing negative stiffness structures are generally multistable, and their energy dissipation mechanism is either through internal component vibration or friction. In this paper, a self-recoverable negative stiffness structure was proposed which can dissipate energy through both vibration and Coulomb friction. The structure consists of an external I-shaped structure and a buckle-like structure composed of a plug and cantilever beams inside. The proposed structure achieves monostable characteristics by utilizing the concept of self-contact. To investigate the mechanical properties, quasi-static loading-unloading experiments were performed and verified using finite element simulation. The energy dissipated in the proposed structure by vibration and friction was quantified theoretically. The influence of geometric parameters on energy dissipation was studied using simulation and analytical methods. This type of metamaterial has the potential to be employed in the production of multi-functional metamaterial structures such as deformable robots or reusable bumpers. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • Identifying recent developments in thermal-driven dehumidification technologies. • Evaluating critical characteristics including dehumidification efficiency, energy consumption and cost. • Assessing the desiccant properties for thermal driven dehumidification technologies. • Directing future research to address environmental concerns via innovative sustainable humidity control mechanisms. As we navigate an era marked by significant environmental concerns and technological advancements, the search for energy-efficient and ecological friendly solutions is more critical than ever. One area of growing significance is the control of indoor humidity levels, which not only impacts human comfort and health but also the structural integrity and air quality within buildings. The challenge is to control these levels while lessening energy consumption and environmental impact. A feasible solution lies in the use of thermal-driven dehumidifiers, a promising technology that offers a sustainable approach to humidity control. Thermal-driven dehumidifiers have emerged as promising solutions for humidity control in various applications, owing to their energy-saving and eco-friendly features. Thus, the focus of this review paper is to explore the most recent advancements and enhancements in thermal-driven dehumidification technologies. This includes systems based on solid desiccant, liquid desiccant, and thermoelectricity. The study aims to fill the existing literature gaps, providing a thorough analysis of the current state of these technologies, their performance indicators, and their potential for future development. The study also explores the suitability of these technologies for different ambient conditions and humidity loads. It addresses critical characteristics such as dehumidification efficiency, cooling capacity, regeneration heat, and energy consumption. Each technology is thoroughly examined, including its suitability for various ambient conditions and humidity load scenarios. Specific real-world examples are examined to highlight these technologies' practical importance and possible influence in tackling humidity management concerns. [ABSTRACT FROM AUTHOR]

Given the significant energy consumption and carbon emissions associated with buildings, there is increasing interest in improving sustainability of building operations to reduce impact on climate change. A common goal is to operate buildings as "net-zero" energy users in which all energy consumed by the building is balanced against renewable energy purchased from the grid or produced on site. To achieve net-zero status, many buildings will require significant retrofit so as to both reduce energy consumption in the absolute sense and provide the remainder without consuming fossil fuels. Thus, multi-year planning is required to ensure that goals can be met on time. In addition, due to the inherent uncertainty associated with energy consumption and generation, actually achieving net-zero energy use may require discretionary curtailment actions to be taken, and deciding whether such actions are necessary can be challenging. To address these challenges, we propose in this paper a design optimization and operational planning strategy to make the decisions needed to achieve sustainability goals in buildings. The strategy can be applied to schedule design changes over a long horizon to meet annual targets, and it can also be applied in closed loop on a shorter horizon to determine whether curtailment is needed to stay on track. We discuss the formulation of the optimization problem, solution methods, and modeling approaches for key parameters. Application of the strategy is illustrated via examples. Overall, this approach will help automate planning that is often done manually, allowing buildings to take a significant leap forward toward achieving their sustainability goals. • Achieving sustainability goals in buildings requires multi-year planning. • The required design and operational decisions can be made via optimization. • Key problem parameters can be obtained from data-driven modeling. • The strategy allows sustainability goals to be met at minimum cost. [ABSTRACT FROM AUTHOR]

• Peer-to-peer energy trading problem of multi-energy microgrids are investigated. • The concept of energy trading consistency is firstly proposed. • The off-design performance model of the energy conversion device is considered. • The decision-making problem is solved by multi-agent soft actor-critic approach. Multi-energy microgrid technology is an essential for addressing the diversification of energy demand and local consumption of renewable energy sources. Peer-to-peer energy trading has emerged as a promising paradigm for the design of a decentralized trading framework. Therefore, this paper investigated the external peer-to-peer energy trading problem and internal energy conversion problem of interconnected multi-energy microgrids. The concept of energy trading consistency to avoid unreasonable energy trading behavior is first proposed and an off-design performance model of the energy conversion device is considered to more accurately reflect the operating status of the device. The complex decision-making problem with significantly large high-dimensional data is formulated as a partially observable Markov decision process and solved using the proposed multi-agent deep reinforcement learning approach combining the centralized training decentralized execution framework and soft actor-critic algorithm. Finally, the effectiveness of the proposed method was verified through a case simulation. The simulation results showed that the proposed method can reduce the total cost compared with the rule-based method. [ABSTRACT FROM AUTHOR]

• A model for wind power system with sensor and actuator faults is established. • The distributed resilient multi-event triggering scheme (DRMETS) is derived. • The DRMETS is integrated into the fault-tolerant cooperative controller design. • The DRMETS based fault-tolerant cooperative control strategy is proposed. • It provides stronger reliability and higher wind energy utilization efficiency. In wind energy conversion power system, some sensor and actuator fall into abnormal condition which may lead to lower wind energy utilization rate and poor system performance or even system collapse. Meanwhile, with the increasing complexity of wind energy conversion power system, more device nodes compete for limited network communication resources, there are time-delays and packets dropout in data transmission, and these may reduce the efficiency of wind energy conversion. According to above consideration, the multi-event triggering mechanism based distributed resilient fault-tolerant cooperative control scheme for wind energy conversion power system is put forward. A novel model of the wind energy conversion power system is established when the sensor and actuator faults simultaneously. According to Lyapunov stability theory, the sufficient condition for system exponential mean-square stable is obtained. Different from previous fault-tolerant control method, the proposed control strategy considered the wind energy conversion power system with data transmission management under simultaneous stochastic sensor and actuator faults. The simulation demonstrates that the method proposed in this paper is effective and the multi-event triggering mechanism based distributed resilient fault-tolerant controller provides stronger reliability and higher wind energy utilization efficiency. [ABSTRACT FROM AUTHOR]

No matter in traditional hydraulic mechanical or newly numerical controlling system, pressure differential valve (PDV) always plays an important role in main fuel oil system and thrust augmentation fuel system. A throughout study on PDV is required to gain a more precise control and effective design for aero engine. Therefore, this paper presents a PDV and establishes the mathematical model to analyze its working characteristics, which shows that PDV performance is greatly affected by return oil inlet orifice and spring stiffness. The simulation and experiment are carried out to analyze the effect of PDV performance on system outlet volume flow rate and their results agree well with a maximum error of approximately 5 %. According to the study, it is found that there is a maximum increase of 9.99 % in system volume flow rate with pump rotating velocity from 300 to 900 rpm, but a maximum decrease of 3.4 % with outlet load pressure from 0.5 to 1.6 MPa. In addition, calculated curve slope reduces with return oil inlet orifice number increase and spring stiffness decrease, which means that PDV shows a faster response to working condition change and a better dynamic characteristic in controlling process. It is excepted that the research can be helpful in a further understanding of aero engine controlling system and a meaningful instruction on system optimization as well as fuel efficiency improvement. • A constant pressure differential valve and its controlling characteristics for aero engine is presented. • A maximum error of approximately 5% between simulation and experiment is gained. • System volume flow rate increases with pump rotating velocity increase but outlet load pressure decrease. • Large number of return oil inlet orifice and small spring stiffness is suggested for well PDV controlling quality. [ABSTRACT FROM AUTHOR]

Green and low carbon is an essential direction for the development of water treatment technology. Ozone catalysts prepared by the mixing method have advantages in terms of energy consumption and CO 2 emissions, but are considered to be insufficient in catalytic efficiency and stability. In this paper, an Mn–Cu–Ce/Al 2 O 3 (MCCA) catalyst was prepared by optimizing the preparation conditions of the mixing method and the types and ratios of active components. Taking petrochemical secondary effluent (PCSE) as the treatment object, the performance of the catalyst and the carbon emission in the preparation process were studied; and compared with the impregnation method. Results showed that compared with catalysts loaded with other components, the MCCA had a higher removal efficiency for TOC (43.04%) and COD (53.18%), which was basically equivalent to the impregnation method, and the treated effluent reached the expected concentration. MCCA promoted the decomposition rate of O 3 by ten times, and the main active species generated were found to be •OH and 1O. Similar to the catalytic ozonation by the catalyst prepared by the impregnation method, the adsorption sites and surface hydroxyl groups on the MCCA surface play a significant role in the degradation of pollutants. However, the carbon emission in the catalyst preparation process of the mixing method was 418.68 kg/ton, which was only 44% of the impregnation method (949.67 kg/ton). Under the global low-carbon transition, this study shows that the mixing method aligns more with the concept of green, clean, and efficient ozone catalyst preparation. [Display omitted] • The mixing method is a green ozone catalyst production process. • A ternary MCCA ozone catalyst was prepared by optimizing the mixing method. • The carbon emission of the mixing method is about 44% of the impregnation method. • The performance of MCCA is comparable to that prepared by impregnation method. [ABSTRACT FROM AUTHOR]

Currently, scarcity/security of clean water and energy resources are the most serious problems worldwide. Industries use large volume of ground water and a variety of chemicals to manufacture the products and discharge large volume of wastewater into environment, which causes severe impacts on environment and public health. Fossil fuels are considered as major energy resources for electricity and transportation sectors, which release large amount of CO 2 and micro/macro pollutants, leading to cause the global warming and public health hazards. Therefore, algae-bacterial consortium (A-BC) may be eco-friendly, cost-effective and sustainable alternative way to treat the industrial wastewaters (IWWs) with Bio-H 2 production. A-BC has potential to reduce the global warming and eutrophication. It also protects environment and public health as it converts toxic IWWs into non or less toxic (biomass). It also reduces 94%, 90% and 50% input costs of nutrients, freshwater and energy, respectively during IWWs treatment and Bio-H 2 production. Most importantly, it produce sustainable alternative (Bio-H 2) to replace use of fossil fuels and fill the world's energy demand in eco-friendly manner. Thus, this review paper provides a detailed knowledge on industrial wastewaters, their pollutants and toxic effects on water/soil/plant/humans and animals. It also provides an overview on A-BC, IWWs treatment, Bio-H 2 production, fermentation process and its enhancement methods. Further, various molecular and analytical techniques are also discussed to characterize the A-BC structure, interactions, metabolites and Bio-H 2 yield. The significance of A-BC, recent update, challenges and future prospects are also discussed. [Display omitted] • Scarcity and security of clean water and energy resources are the most critical today's problem. • IWWs are rich in color, nutrients, organic pollutants and heavy metals. • IWWs are highly toxic to human/animal/plant/soil/water and microbes. • A-BC is cost-effective and eco-friendly methods of IWWs treatment with Bio-H 2 production. • Bio-H 2 is a future fuel to fulfil the world's energy demand in eco-friendly manner. [ABSTRACT FROM AUTHOR]

Despite the growing interest, a thorough explanation of how geopolitical and energy security risks relate to carbon emissions is still missing. To fill this gap, this paper investigates the impact of these factors, both individually and jointly, on carbon dioxide emissions in European Union countries from 1990 to 2021, also accounting for the effects of per capita final energy consumption and real income. Empirical findings from the augmented mean group and novel dynamic common correlated effects mean group estimator reveal that the strongest long-term escalation in carbon dioxide emissions is attributable to final energy consumption, followed by energy security risk. Geopolitical risk, however, has a mild short-term mitigating effect on carbon emissions. In the long run, this effect is statistically insignificant. The joint effect of energy security and geopolitical risks indicates that they may reinforce each other and lead to even higher carbon dioxide emissions in the long run. Therefore, policies aimed at reducing carbon emissions need to address both risks simultaneously, while also considering the causal relationships observed between the variables under study. • Examined the individual, joint, and causal geopolitical and energy security risks effects on CO 2 emissions. • Energy consumption and energy security risks were identified as the main drivers of CO 2 emissions. • Energy security risk's effect on CO 2 emissions was escalating, gradual, and long-lasting. • Geopolitical risk's effect on CO 2 emissions was mild, short-term, and mitigating. • Found significant long-term joint effect of geopolitical and energy security risks. [ABSTRACT FROM AUTHOR]

Recirculation of hydrothermal carbonization (HTC) process water (PW) has been revealed as an effective strategy to promote the recovery of lost dissolved organics in the spent solvent. However, the role of operating parameters such as temperature, time and solid load during recirculation needs to be clarified and better investigated in order to achieve a successful PW valorization. In this paper, the effects of different reaction conditions during HTC with PW recirculation were studied by using orange peel waste as feedstock. The results showed that strong synergistic interactions occurred between the biomass and the recycled solvent, leading to a hydrochar mass yield increase between 0.5 and 11 wt% on a dry basis (d.b.), according to the reaction conditions. PW recirculation, especially at low conversion severity and solid load, increased hydrochar carbon content while drastically reduced the oxygen fraction. The reason was probably related to promoted dehydration and decarboxylation. As result, carbon and energy yields were respectively increased by 26.1 % and 27.0 %, on average. The energy balance outcomes showed that the energy recovery rose up to 8 times by recycling PW compared to phases with deionized water as HTC solvent. [Display omitted] • Hydrochar recovery enhancement is obtained by solvent recirculation. • Intense synergistic effects in hydrochar formation occur at low temperatures. • Dehydration and decarboxylation are promoted by solvent recycling. • Hydrochar carbon retention is increased by 6.2 % after recirculation at 180 °C. • Process water recirculation improves the conversion energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a three-maneuver strategy is presented to study the transfer from a cislunar L 2 halo orbit to a low lunar orbit (LLO). Both the cislunar L 2 point orbits and lunar orbits are generally considered as habitats for space vehicles of various missions, so a transfer strategy between these orbits that combines flexibility with minimal fuel consumption is worth considering for future complex space applications in the Earth-Moon system. In terms of the three-maneuver transfer method, the first maneuver is carried out along the direction of the unstable invariant manifold toward the moon, and each point in the halo orbit can be a departure position of the transfer. Then, the spacecraft is allowed to drift for a period of time, but in the meantime we are constantly searching the window for the second maneuver that towards the target LLO and figuring out the transfer trajectory for optimal maneuver cost. The third maneuver is a deceleration into the target LLO. The transfer procedure is first established in the circular restricted three-body problem (CRTBP) model, with a feasibility analysis of not constraining orbital inclination of LLOs. After that, the results of the transfer to both polar and equatorial LLOs in the CRTBP model are used as initial solutions for the design procedure of transfer trajectories in the ephemeris model. Finally, optimized results of transfer window of up to 180 days with corresponding overall maneuver costs in the ephemeris model are given for a quasi-halo orbit, which demonstrate the flexibility and reliability of the three-maneuver transfer strategy. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the implementation strategies of an explicit matrix inversion technique based on the recursive Gram matrix inversion update (RGMIU) algorithm. These strategies are explicitly chosen to optimize the design for high throughput dictated by the enhanced mobile broad band (eMBB) use case and by the low latency imposed by the ultra-reliable low-latency communications (URLLCs) use case. The RGMIU algorithm is recently proposed to implement the zero forcing (ZF) problem encountered in massive multiple-input multiple-output (MIMO) detection task. We therefore compare and analyse the performance in terms of symbol error rate (SER) against popular implicit and explicit methods such as optimized coordinate descent (OCD), Gauss-Seidel (GS) and Neumann series expansion (NSE) algorithms. To determine the optimal word length, a fixed-point analysis shows that 16 bits are enough to reach a floating-point precision. We, thereafter, use Vivado High-Level Synthesis tools and optimization directives to implement the RGMIU algorithm based on three strategies to infer key insights and design trade-offs from the resource's utilization, latency, throughput and energy efficiency. • Implementation strategies of an explicit matrix inversion technique. • Design optimization for enhanced mobile and ultrareliable low-latency use cases. • Recursive Gram matrix inversion update algorithm. • High-Level Synthesis tools and optimization directives for FPGA implementation. • Strategies to infer key insights and design trade-offs. [ABSTRACT FROM AUTHOR]

• The HR neuron model with one parameter's memristor is proposed and its hidden dynamics is studied. • The generalized Hamiltonian function is derived completely and reveals the mechanism of firing of neuron. • The positive effects of noise on electrical activities are found in the improved MHR neuron. It has been extensively studied to employ memristors to model the relationship between the electromagnetic field and the membrane potential, especially for the research of modeling and dynamical analyses of electrical activity using HR neurons with memristors. This paper proposes a novel 4D HR model with a threshold flux-controlled memristor (MHR), which describes the electromagnetic induction effect. The proposed 4D HR model retains the original HR properties and can describe the complex dynamics of neurons' electrical activities with fewer parameters than the existing models. Due to the particularity of the no equilibrium point of the MHR model, the hidden dynamics are found in the proposed MHR model. The generalized Hamiltonian function is fully derived for the MHR neuron model using Helmholtz's theorem. The simplest form of the Hamiltonian form is given by assigning special values. The average Hamiltonian energy and its bifurcation are employed to find the connection between energy and firing patterns. The band-limited white noise is also studied, and it is found that it positively influences the electrical activities in the proposed MHR system. [ABSTRACT FROM AUTHOR]

• A novel hardware configuration is proposed to reduce the throttle loss and overflow loss simultaneously. • The nonlinear model of the proposed system is established and the key parameters are analyzed. • A desired compensation approach based output feedback controller is proposed for the asymmetric hydraulic actuator. • The proposed system and controller can achieve the multi-objective task of position tracking and energy saving. Eliminating substantial energy dissipation and achieve precise tracking accuracy are both vital for the hydraulic actuated servo system, especially in the face of the global energy crisis and energy exhaustion. When an asymmetric hydraulic cylinder moves symmetrically back and forth, the pressure and flow in the two chambers differ a lot, resulting in huge energy loss. To reduce the throttle loss and overflow loss of the system simultaneously, a hydraulic servo system structure based on dual hydraulic accumulators is proposed in this paper. On the one hand, two hydraulic accumulators with different pressure levels can switch the supply pressure based on the motion direction of the hydraulic cylinder to reduce the throttle loss. On the other hand, the hydraulic accumulators can absorb the excess flow to reduce the overflow loss. In addition, an output feedback controller based on the desired compensation approach is proposed to guarantee the control performance. First, the pressure and velocity signals are replaced by their desired values to reformulate the state equation. Subsequently, the state variables and lumped disturbance are estimated by the low-pass filter-based observer. Compared with the conventional method, this method has the advantages of low noise interference, fewer control parameters and intuitive form. To validate the feasibility of the proposed strategy, the stability of the controller and the energy efficiency of the system are analyzed. The results indicate that the proposed method can significantly improve the system efficiency and ensure tracking accuracy simultaneously. [ABSTRACT FROM AUTHOR]

• Automatic MiC envelopes and layout generation by a novel SSG-MOO method. • Multi-objective optimization formulation for passive MiC design. • A pilot MiC study in Hong Kong produced 5 selected Pareto optima out of 625. • Up to 0.42% energy savings and 9.71% spatial daylight autonomy improvement against the baseline. • A multi-level analysis of results and design strategies for practitioners. Modular-integrated Construction (MiC) is an emerging construction technique promoted in the building sector for high productivity and low waste emission in the construction phase; yet, the standardized modules also bring new challenges, such as balancing passive energy efficiency and spatial daylight autonomy, to the operational phase. This paper proposes a Symmetric Skeleton Grammar-based Multi-Objective Optimization (SSG-MOO) method to formulate parametric MiC envelopes and detailed layout, with the two objective functions being energy efficiency and interior daylight performance in the operational phase. Pareto optima of the SSG-MOO, computed by the Non-dominated Sorting Genetic Algorithm II, are generally verified and analyzed in three levels, i.e., MOO's solution space, SSG layout, and MiC design parameters. A case study of MiC residential building in Hong Kong demonstrated the SSG-MOO method through five new passive MiC designs (i.e., spatial reorganization of three architectural modules, and parameter tuning of the envelops and corridors), achieving up to 0.42% energy savings and 9.71% spatial daylight autonomy improvement compared to the baseline design. The contribution of this paper is two-fold, including a novel and sound SSG-MOO formulation for parametric MiC designs, and offering time-efficient and evidence-based design tactics for MiC designers and industrial practitioners to push boundaries of MiC. [ABSTRACT FROM AUTHOR]

[Display omitted] • The LCA methods and findings of 41 papers on air-conditioning systems are examined. • Retrieved studies presented discrepancies in the LCA goals and scope definition. • The global warming potential (GWP) was the most evaluated indicator across papers. • Grid-connected electrical heat pumps generally had a higher life cycle GWP. • Improvements related to AC energy sources and other life cycle aspects are proposed. Air conditioning (AC) has become the fastest-growing energy end-use in buildings worldwide, and its adoption is expected to increase further due to various socioeconomic factors and climate change. Despite this, the decarbonization of space cooling is progressing slowly; hence, the emissions burden at each stage should be examined in detail. Given this context, it is crucial to assess the environmental performance of AC systems by considering a comprehensive approach like the Life Cycle Assessment (LCA). While several studies have already targeted the LCA of AC systems, a clear overview of those studies' methodologies, assumptions, and results is still lacking. Therefore, this systematic review examined the methods and key findings of 41 peer-reviewed articles. Although the results revealed discrepancies among the goals and scope, impact assessment methods, and indicators, most articles predominantly focused on the global warming potential (GWP) impact, and its reported quantification was further analyzed. It was observed that grid-connected vapor-compression heat pumps/chillers (conventional systems) presented higher GWP impacts than other AC systems powered by renewables, which in contrast, exhibited higher impacts in some toxicity and resource depletion-related indicators. However, the share of GWP showed different distributions, as the pre-operation emissions of solar-powered AC systems (either PV or thermal) were higher than those of conventional systems, mainly driven by reduced energy consumption usage. This review concludes with some recommendations for each stage of the life cycle. Since the operation phase generally showed the highest impact, more efforts should be devoted to accelerating the transition to a 100% clean electricity power system at a national scale. In contrast, the need to power AC systems with on-site renewables should be carefully assessed based on the context. [ABSTRACT FROM AUTHOR]

K-12 schools are the largest energy consumers in the public sector, with their HVAC energy consumption representing the largest portion of their total energy use. While transitioning these schools to grid-interactive HVAC system operation through advanced controls offers significant financial and environmental benefits, and model predictive control (MPC) has been identified as a promising solution to achieve that, very few MPCs are affordable and have been deployed in K-12 schools. This situation raises concerns about the unclear real-world benefits of MPC technology among facility managers and industries. To address this gap, this paper presents a low-cost MPC solution that requires minimal control infrastructure costs and a unique field demonstration at a K-12 school, conducted for both cooling and heating seasons. This work adopted a previously developed MPC and extended it for use in the school application. The MPC aims to coordinate multiple packaged units to eliminate unnecessary peaks and shift cooling or heating loads in response to grid signals based on load conditions, while maintaining thermostat temperatures within school-defined bounds. Throughout the field tests, the MPC achieved a 24% reduction in peak demand during the cooling season and shifted cooling or heating loads by up to 16% in response to the school's utility tariff, considering load conditions, while also allowing end-users to override thermostat setpoints. The paper also discusses the limitations of this study and future research directions for better performance of the MPC at K-12 schools. • First MPC demonstration in K-12 school buildings for peak demand reduction and load shifting. • Experimentally demonstrated a scalable MPC for multiple RTUs without hardware retrofit. • Hierarchical MPC is improved in consideration of practical challenges such as user's override and poor data resolutions. • Achieved 24% peak demand reduction and showed 16% of load shifting potential. • Share lessons learned during the demonstration of practical challenges. [ABSTRACT FROM AUTHOR]