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Research has revealed the existence of an energy-efficiency gap – the difference between optimal and actual energy end-use, suggesting that energy efficiency can be improved. Energy management (EnM) is a means for improving industrial energy efficiency. However, due to various barriers, the full potential of EnM is not realised. Several studies have addressed drivers and barriers to energy efficiency but few to EnM. This study aims to identify EnM practices, the most important perceived drivers and barriers for EnM, and relations among them in the energy-intensive Swedish pulp and paper industry (PPI), which has the longest experience internationally of practising EnM systems, and has worked according to the standards since 2004. Our results show that, altogether, the PPI works regularly and continuously with EnM, with a clear division of responsibilities. The highest maturity for EnM practices was for energy policy, followed by organization, investments, and performance measurement. The study also shows that communication between middle management and operations personnel has potential for improvement. The most important categories of drivers were economic, whereas for barriers they were organizational. Nevertheless, knowledge-related barriers and drivers were amongst the most important, suggesting that the absorptive capacity for energy issues could be improved. Image 1029 • Energy is managed continuously, with clear policies and responsibilities. • The main drivers are economical while main barriers are organizational. • Knowledge related barriers and drivers are amongst the next most important. • Employees' absorptive capacity for energy issues could be improved. • Link between energy policy and performance measurement could be improved. [ABSTRACT FROM AUTHOR]

Guiding industries to produce in an effective and environmentally sustainable manner has become a key issue for governments around the world. Over the past 15 years, series of regional and industrial development policies have been introduced by the Chinese government. This has influenced the geographical distribution and energy efficiency performance of China’s industries. This paper quantifies the influence of enterprises geographical distribution on energy efficiency improvement in the paper industry and aims at providing some helpful suggestions on industrial development to policy makers. The main results show that, firstly, the paper industry in eastern China shows obvious characteristic of agglomeration. The average location quotient is 1.2278. However, following the industrial and regional development policies, the characteristic of agglomeration is weakening. Secondly, only when agglomeration reaches a certain level (location quotient is above 0.5447) will industrial agglomeration positively impact on industrial energy efficiency improvement (a 1% increase in agglomeration will increase dynamic energy efficiency by at least 0.23%). Thirdly, the regional development policies carried out by the government slow the pace of energy efficiency improvement in China’s paper industry. The empirical results indicate that the government needs to consider regional characteristics and consciously guide industrial enterprises to concentrate in the dominant area. [ABSTRACT FROM AUTHOR]

China’s paper industry consumes a lot of energy and emits huge amounts of pollutions. The Chinese government over the past decades has done a lot to promote energy efficiency of the industry. This paper studies the impact of industrial polices to improve energy efficiency. The energy efficiency change is firstly studied under a framework of total factor efficiency. On this basis, different econometric models are built to discuss the detailed energy efficiency characteristics of the paper industry. The main results are as follow: (i) ownership structure did influence the energy efficiency, but the influence is different in different conditions; (ii) energy efficiency of the paper industry can be improved by economies of scale; (iii) market competition can to some extent overcome the motivation and supervision problem that often occur in state-owned companies; (iv) energy price cannot play the role of resource allocation well in China. Finally, based on our findings, some suggestions are provided. [ABSTRACT FROM AUTHOR]

The Swedish pulp and paper industry (PPI) must increase energy efficiency to remain competitive on the global market, which has experienced entries from countries with cheaper energy and raw material supplies. Interactions among variables for energy use, production, energy management, electricity price and firm characteristics (FC), in different types of mills, i.e., pulp, paper and integrated mills, in Sweden from 2006 to 2015 indicate that correlations among the studied variables were different in different types of mills. This difference between types of mills seemed to originate partly from varying accessibility to production residue that could be used for energy. For all types of mills, variation of electricity prices did not correlate significantly with energy efficiency during the study period. The studied FC were firm's age, number of employees, number of companies in company group, net sales and profit for the year. Energy efficiency was more affected by the variables characterizing energy and production compared to the variables representing FC. This study also suggested presence of possible discrepancies between FC that were perceived as barriers to energy management towards energy efficiency, according to previous studies, and what was shown by the data combining variables representing energy use, production and FC. [ABSTRACT FROM AUTHOR]

Abstract: The pulp and paper industry is one of the most energy-intensive industries worldwide. In 2007, it accounted for 5% of total global industrial energy consumption and 2% of direct industrial carbon dioxide (CO2) emissions. An energy audit is a primary step toward improving energy efficiency at the facility level. This paper describes a plant-wide energy audit aimed at identifying energy conservation and CO2 mitigation opportunities at a paper mill in Guangdong province, China. We describe the energy audit methods, relevant Chinese standards, methods of calculating energy and carbon indicators, baseline energy consumption and CO2 emissions of the audited paper mill, and nine energy-efficiency improvement opportunities identified by the audit. For each of the nine options, we evaluate the energy conservation and associated CO2 mitigation potential. The total technical energy conservation potential for these nine opportunities is 967.8terajoules (TJ), and the total CO2 mitigation potential is equal to 93,453tonnes CO2 annually, representing 14.4% and 14.7%, respectively, of the mill’s total energy consumption and CO2 emissions during the audit period. [Copyright &y& Elsevier]

Abstract: In this study the possibilities of making a cost-effective heat exchanger retrofit in a simulated thermo mechanical pulp and paper mill are analysed using advanced composite curves. The use of advanced composite curves is a fairly new method developed in Chalmers University, and they have not previously been used to analyse a mechanical pulp and paper mill. The simulated mill used as an example in this study has one TMP (thermo mechanical pulping) line, one paper machine, debarking and auxiliary systems. The results show that the advanced curves give valuable information on the system and provide guidance as to where to look for improvement potential. The relative costs of different improvements are in line with traditional knowledge of improvement costs. [Copyright &y& Elsevier]

The pulp and paper industry faces several challenges linked to climate and environmental impact, resource efficiency, rising energy prices, increased competition for biomass resources, and declining demand for traditional printed paper products. However, these challenges also offer strategic opportunities for the industry to develop into a competitive, resource-efficient, and low-carbon industry in line with a biobased economy. Against this background, this paper aims to analyse current energy strategies in the pulp and paper industry in Sweden. Specifically, the paper analyses how companies combine continuous process efficiency to reduce energy costs with activities that could be developed into new energy-related products to increase revenue. Most of the analysed companies work to various degrees with both these strategies, employing methods that include improving energy efficiency, energy security, and energy conversion, as well as developing a wide range of biobased energy products. However, our study indicates that there is an untapped potential associated with energy product development, and we conclude that energy efficiency measures can free up resources, enabling the development of new energy products. Finally, several potential managerial outcomes and implications are outlined. • The paper analyses the energy strategies in the pulp and paper industry in Sweden. • Energy strategies focus both to improve energy efficiency and develop new energy-products. • The companies' energy strategies combined exploitation and exploration. • A number of managerial implications are presented. [ABSTRACT FROM AUTHOR]

Abstract: The paper industry is, with about 6% of the total worldwide industrial energy use, an energy-intensive industry. The drying section is with approximately 50% the largest energy consumer in a paper mill, energy use in this section is mainly heat use. Several options to decrease heat use in conventional multi-cylinder drying sections are investigated, calculating the effect on energy use. Optimization measures include a) decreasing the amount of water evaporation by applying additives in higher consistencies and by lowering the water viscosity, b) decreasing the heat use of water evaporation by increasing the dew point temperature of the dryer and c) increasing the amount of heat recovery by using exhaust air to not only pre-heat the incoming air but also to increase process water temperatures. These could all be achieved by retrofitting and/or choosing different processing conditions in existing factories. The combined thermal heat saving potential due to the optimization actions is 1.3GJh/t paper (or 32% of the drying section’s heat use) as compared to the reference situation. [ABSTRACT FROM AUTHOR]

Abstract: The success of any process improvement study depends on the quality of the available data and the way in which the plant-specific characteristics are incorporated in the applied conceptual models; in the context of process integration studies these issues are directly related to the rules followed during the data extraction stage. Improving energy efficiency in a pulp and paper Kraft mill requires the identification of the most promising heat recovery network retrofit projects. In a retrofit analysis using pinch technology/process integration methods, only the process streams associated to the existing heat exchangers and some outlet streams (such as wastewater/effluent streams and vents) with high potential for heat recovery are usually included, while the energy exchanged through non-isothermal stream mixing (NIM) or direct heat transfer (DHT) is often assumed fixed and is not considered in the analysis. Relaxing this assumption requires extracting more data to represent the DHT design configuration that exists in the plant. However, different data extraction options can be considered to represent the DHT configuration depending on the associated process/operation constraints. This work describes a systematic procedure to extract and analyse the impacts of DHT on the overall energy efficiency of a Kraft process with a specific focus on mixing along the pulp line and in water tanks. [Copyright &y& Elsevier]

The adhesion of gases to the electrodes is a major reason for reductions in energy efficiency, and hinders the production of hydrogen in alkaline water electrolysis (AWE). The development of an effective solution to this problem will be crucial for the design and optimization of AWE. In this paper, we present a novel structure for an electrolyser in which baffles are inserted into the channel. The flow process of the electrolyte and the electrochemical reactions in the electrolyser with the new structure are simulated, and the effects of the added baffles on the distribution of the gas phase production and the efficiency of energy conversion are analysed. Several important structural parameters are considered, such as the angle, number and length of the baffles, and corresponding numerical calculations are performed. In order to carry out a comprehensive evaluation of the performance of each electrolyser, the energy efficiency of AWE is evaluated and the current effect, ohmic overvoltage effect and bubble overvoltage effect caused by gas adhesion are quantitatively investigated. The results show that the flow of the electrolyte can be divided into three phases when baffles are added to the channel of the electrolyser, and that the baffles play a crucial role in both the transition phase and the full development phase. The maximum reduction in gas coverage at the electrodes reaches 60% and the maximum increase in energy efficiency reaches more than 3.0% with the addition of baffles. The addition of baffles has significant effects on the current and ohmic overpotential, and the balance between the gas coverage and ohmic overpotential can allow the maximum energy efficiency of the AWE to be achieved. [Display omitted] • A novel design for an electrolyser with baffles optimises the electrolyte flow. • The mechanism of transport in an electrolyser with baffles is analysed. • The effects of the angle, number and location of baffles are studied. • The thermodynamics performance of electrolyser is evaluated. [ABSTRACT FROM AUTHOR]

To reduce energy use and achieve a low carbon production in pulp and paper industry (PPI), it is essential to improve energy efficiency in pulp and paper production. In this study, the energy savings from the PPI were estimated based on a techno-economic analysis by EAGER tool for PPI at the factory level. The structure and implement procedure were introduced initially. Two typical pulp and paper mills from China were determined to estimate the impact of selected energy efficiency technologies (EETs) on energy savings as the cases in the base year. One Factory (A) only produced paper products (i.e. corrugated paper and Kraft linerboard) while the other Factory (B) also produced market pulp besides sack paper. In totally 22 selected EETs were applied to assess their saving potential in Factory A, and 29 measures were applied in Factory B. The results show that 2290 TJ and 1179 TJ of final energy could be saved annually in Factory A and B, corresponding to reduction of 29.4% and 13.6% total energy consumption respectively for the case factories. The CO 2 reduction potential was 32.3% for Factory A compared with that of 16.6% in Factory B. It suggests that the pulp and paper mills should encouraged to use this method estimating energy saving potential, and improve their energy efficiency with suggested measures in order to reduce energy consumption and related carbon emissions further in the coming years. • Energy savings of pulp and paper production were estimated at the factory level. • A techno-economic evaluation tool was applied to access the energy savings and carbon emission reductions. • Two typical pulp and paper factories from China were investigated to estimate their energy savings in the base year. • It is useful to assess energy savings and select feasible EETs in achieving energy saving and emission reduction targets. [ABSTRACT FROM AUTHOR]

Under the dual pressure of energy consumption and environmental pollution, mankind hopes to develop clean and renewable alternative energy, and the rapid development of fuel cells meets people's demand for energy-efficient power systems. The emergence of portable micro energy systems represented by microfluidic fuel cells, such as paper-based microfluidic fuel cells, has greatly enriched the means of medical detection to better cope with the threat of disease transmission. In this work, the numerical simulation method is innovatively introduced to study the paper-based microfluidic fuel cells. Both transient and steady-state modes are employed to demonstrate the whole operation process of the paper-based microfluidic fuel cell. In addition, the different structural parameters, including electrode spacing, the distance between electrode and inlet, channel thickness, and electrode length, are also investigated their influence mechanisms on cell performance. Results show that the increase of most structural parameters decreases cell output power in different degrees. Even on the premise that increasing channel thickness has a positive impact on the output power, the fuel utilization still shows a downward trend. These conclusions provide theoretical support and reference for future optimization work and accelerate the development of microfluidic fuel cells. • Numerical simulation is employed to study paper-based microfluidic fuel cell. • The effects of structure parameters on cell performance are discussed. • Through fuel concentration distribution to reveal cell performance mechanism. • Fuel utilization is used to evaluate energy efficiency. [ABSTRACT FROM AUTHOR]

The paper describes a new method for determining the energy efficiency of the life cycles of wind farms by the aggregated data of energy costs. The rationale for the use of aggregated data to determine energy costs during the life cycle of the wind farms is given. The classification of wind turbines and wind farms elements by the parameters and technical characteristics of the elements with subsequent division into groups for which aggregated data of energy costs are determined is given. Within the framework of the method have been developed an algorithm for determining energy costs on the production of elements of wind turbines and wind farms and formulas for their calculation during the life cycle of wind farms. In order to test the method, energy cost was calculated during the life cycle for two wind farms with wind turbines that differ parameters and technical characteristics of the elements and their energy efficiency was determined. It has been shown that the use of a hydrogen storage unit as part of a wind turbine makes it possible to efficiently use energy during down periods and increase the efficiency of the installation by 25–30%. It is noteworthy that a wind-hydrogen farm allows not only to accumulate excess energy during lean periods, but also to save the resource of wind turbines. When the energy reserve in the hydrogen battery reaches close to full, part of the wind turbines of the wind-hydrogen farm can be automatically temporarily stopped in a given order. This technology can provide an extension of the warranty service life for each wind turbine. [ABSTRACT FROM AUTHOR]

The measures needed to limit global warming pose a particular challenge to current fossil fuel exporters, who must not only decarbonise their local energy systems, but also compensate for the expected decline in fossil fuel revenues. One possibility is seen in the export of green hydrogen. Using Algeria as a case study, this paper analyses how different levels of ambition in hydrogen exports, energy efficiency and fuel switching affect the cost-optimal expansion of the power sector for a given overall emissions reduction path. Despite falling costs for photovoltaics and wind turbines, the results indicate that in countries with very low natural gas prices, such as Algeria, a fully renewable electricity system by 2050 is unlikely without appropriate policy measures. The expansion of renewable energy should therefore start early, given the high annual growth rates required, which will be reinforced by additional green hydrogen exports. In parallel, energy efficiency is a key factor as it directly mitigates CO2 emissions from fossil fuels and reduces domestic electricity demand, which could instead be used for hydrogen production. Integrating electrolysers into the power system could potentially help to reduce specific costs through load shifting. Overall, it seems advisable to analyse hydrogen exports together with local decarbonisation in order to better understand their interactions and to reduce emissions as efficiently as possible. These results and the methodology could be transferred to other countries that want to become green hydrogen exporters in the future and are therefore a useful addition for researchers and policy makers. • The required strong capacity expansion for PV and wind is challenging, but feasible. • "Energy efficiency first" benefits both the local energy transition and H2 exports. • Fully renewable power systems require a suitable political framework. • Higher hydrogen exports might help to reduce the specific power system cost. • Fossil capacity expansion must be stopped to avoid lock-ins and higher future costs. [ABSTRACT FROM AUTHOR]

Separate sensible and latent cooling (SSLC) is a promising technology for enhancing the energy efficiency of space cooling systems. In this paper, we propose an SSLC configuration consisting of an indirect evaporative cooling (IEC) device and vapor compression cycle (VCC) for handling space sensible load, and a desiccant wheel for handling space latent load. The performance of the proposed configuration is investigated using physics-based or empirical models for each component. A year-round simulation study of the proposed SSLC system is performed for 12 different cities with diverse climatic conditions. This study considers electrical, thermal, or solar energy for desiccant wheel regeneration. Parameters governing the system performance such as the air flowrate ratio in the IEC and the DW, DW regeneration air temperature, and supply temperature are studied. The proposed SSLC configuration is superior to the conventional system in all cities. The enhancement of seasonal coefficient of performance (sCOP) ranges between 50 and 261 %, 67 and 387 %, and 60 and 2072 % with yearly electrical energy savings of 33 and 72 %, 40 and 79 %, and 38 and 95 % for electrical, thermal, and solar energy for regeneration of DW, respectively. [ABSTRACT FROM AUTHOR]

Considering the escalating global warming and its severe consequences, the exploitation of hydrogen, an eco-friendly energy carrier, is gaining great interest. This paper comprehensively reviews the most common hydrogen production, storage, transportation and utilization processes, while their effectiveness is explored from energy, water, economic and environmental perspectives. Their main characteristics, advantages, shortcomings and challenges are discussed as well. The research reveals that the hydrogen's water footprint, energy consumption, carbon emissions, and cost vary significantly depending on the specific technologies used throughout its life cycle, from production to end use. For instance, fossil fuel-based hydrogen production methods, such as steam methane reforming, offer elevated energy performance and cost-effectiveness, but have a significant carbon footprint. In contrast, water electrolysis is environmentally friendly, but requires more water and has lower energy efficiency. Besides, high-pressure containers are the preferred method for hydrogen storage, while liquid and solid storages are better suited for high-energy-density applications. Regarding hydrogen transportation, pipelines are effective over long distances, whereas tankers are convenient for short distances. Furthermore, fuel cells are more energy, carbon and water efficient when using hydrogen as fuel, but they are generally more expensive than internal combustion engines. [Display omitted] • Hydrogen production, storage, transportation and utilization methods are reviewed. • Their energy efficiency, water use, cost, and environmental impact are explored. • Key recommendations to boost the utilization of hydrogen systems are outlined. • Fossil fuel-based technologies are more energy-efficient and cost-effective. • Renewable-based processes are more environmentally friendly but water-intensive. [ABSTRACT FROM AUTHOR]

Regular Ship hull maintenance is an essential for sustainability. The maintenance work of ship hulls that involve human labor suffers from many shortcomings. Maintenance robots have been introduced for drydocks to eliminate these shortcomings. An energy-efficient Complete Coverage Path Planning (CCPP) is a crucial requirement from a ship hull maintenance robot. This paper proposes a novel energy-efficient CCPP method based on Glasius Bioinspired Neural Network (GBNN) for a ship hull inspection robot. The proposed method accounts for a comprehensive energy model for path planning. This energy model reflects the energy usage of a ship hull maintenance robot due to changes in direction, distance, and vertical position. Furthermore, the proposed method is effective for dynamic workspaces since it performs online path planning. These are the major contributions made to state of the art by the work proposed in this paper. The behavior and the performance of the proposed method have been compared against state of the art through simulations considering Hornbill, a multipurpose ship hull maintenance robot. The validation confirms the ability of the proposed in realizing a complete coverage of a given dynamic workspace. According to the statistical outcomes of the comparison, the performance of the proposed method significantly surpasses that of the state-of-the-art methods in terms of energy usage. Therefore, the proposed method contributes to the development of energy-efficient CCPP methods for a ship hull maintenance robot. • A novel coverage method based on Glasius Bioinspired Neural Network is proposed. • The proposed method is intended for a multipurpose ship hull maintenance robot. • A comprehensive energy model is utilized by the proposed method for path planning. • The proposed method is significantly efficient than the state of the art methods. • The paper contributes to the development of a ship hull maintenance robot. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of import competition on energy efficiency among firms in China. Using a difference-in-differences approach that exploits industry- and firm-level tariff reductions following China's accession to the WTO, we examine the effects of reduced output tariffs on coal intensity, measured as coal use per output, of Chinese manufacturing firms. Our findings indicate that higher levels of import competition lead to significant reductions in coal intensity, without negatively affecting firm production levels. We also find evidence that these effects are primarily driven by multi-product firms adjusting their product mix to prioritize energy-saving options. [ABSTRACT FROM AUTHOR]

• Combining hydrodynamic and acoustic cavitation significantly enhances the energy-efficient processing of cellulose fibers. • Achieving maximum efficiency requires extensive optimization through multiphysical simulations and experimental investigations to understand the interaction of physical phenomena. • Effective sonicator design requires tuning of sonotrodes, signals, structural geometry, fluid volumes, flow conditions, and nozzle design to achieve synchronized resonance amplification at high power. • Cavitation treatment gives a notably improved fiber properties at specific concentrations, temperatures when combined with proper acoustic and hydrodynamic cavitation intensities. The cellulose pulp refining process is crucial for achieving high-quality paper characteristics. This research aims to enhance energy efficiency while maintaining good fiber quality using hydrodynamic and acoustic cavitation (HAC). Experiments were conducted with an in-house developed flow-through sonicator combined with a novel Venturi nozzle for hydrodynamic cavitation. The Venturi design was determined by analytical modeling and verified by CFD simulation with multi-phase turbulence models to balance cavitation intensity and turbulence against the acoustic cavitation effect. Experimental evaluation of two batches of CTMP fibers, pre-processed in different ways, showed significant improvements in paper strength and fiber properties. The best results for Batch 1 (HC and LC) were obtained with 386 kWh/bdt for AC and 350 kWh/bdt for HC (60 °C, 2 % concentration). The tensile strength index increased by 26 %, and the TEA-index, related to freeness, increased by 55 %. HAC treatment (750 kWh/bdt, 70 °C, 1.5 % concentration) of the less refined Batch2 (HC) yielded results better than the Batch 1 reference. These findings confirm the energy-efficient potential of the sonicator concept compared to traditional industrial processes. The conclusion is that HAC-refining of softwood pulp requires a proper balance between hydrodynamic and acoustic cavitation intensities. Both fiber concentration by weight and temperature are critical for an energy-efficient process. [Display omitted] [ABSTRACT FROM AUTHOR]

The contemporary world faces several energy problems and struggles to balance sustainable energy and steady industrial development. In this context, the existing energy efficiency studies have not unfolded the mysteries surrounding the industrial units' energy-saving potential and the key determinants and impacts of the manufacturing units' energy efficiency levels. Therefore, there is a need to comprehensively study the existing energy efficiency studies and clarify the above. Considering two databases, Scopus and Web of Science, the study uses bibliometric analysis and a systematic literature review to analyze the current energy efficiency literature. The bibliometric analysis is based on 619 published papers and assesses the existing energy efficiency literature to ascertain the evolution of energy efficiency studies from 2001 to 2022. This investigation adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-2020) framework for a systematic review of ninety-three relevant papers from the existing energy efficiency literature. The systematic literature review (SLR) highlights twenty-seven theories discussed in several dimensions of the environmental and industrial linkages in the manufacturing sector. Subsequently, the study identifies energy efficiency measures and the associated methodological issues. Next, the study addresses the available set of determinants and the impacts of energy efficiency performance on manufacturing industries across the globe. It explores the potential research gap in the existing studies. Finally, the concluding remarks, discusses several policy implications, and suggests avenues for future research in the direction of theory, context, and methodology in energy efficiency domain. [Display omitted] • Bibliometric analysis and systematic literature review are performed to synthesize the current energy efficiency studies. • The study considers 619 energy efficiency papers from Scopus and Web of Science databases over the past 22 years. • The study uses the PRISMA-2020 framework and uses Bibliometrix in R (RStudio) and VOSViewer software for the analysis. • SLR considers 93 papers and identifies 27 existing theories, addresses determinants and impacts of energy efficiency. • The study identifies the research gap, presents several policy implications and provides the scope for future research. [ABSTRACT FROM AUTHOR]

This paper presents an energy efficiency improvement scheme for offshore wind farms. This scheme uses learning-based nonlinear model predictive control (NMPC) and does not require wake modeling. The main feature is that each wind turbine can independently complete all optimal control, without the need for wind farm wake model calculation and iterative optimization. Specifically, this paper studies the critical factors for increasing the active power of wind farms and reducing the structural load of wind turbines, and reconstructs the optimal control objectives of wind turbines. Furthermore, combined with the nonlinear model of wind turbines containing uncertainty, a nonlinear model predictive control algorithm based on deep neural networks was designed, which can achieve multi-objective optimal control at the wind turbine level. The simulation results show that the uncertainty estimation method can effectively improve the nonlinear control performance, achieve an increase in the active power of the wind farm without wake iterative optimization, and at the same time suppress the structural loads of all wind turbines. • Propose a solution to improve wind farm energy efficiency without requiring wake modeling. • A nonlinear model of wind turbines considering uncertainty was established. • A deep neural network was used for uncertainty estimation. • Nonlinear model predictive control is used to achieve optimal control of wind turbines. [ABSTRACT FROM AUTHOR]

The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

Black liquor (BL) is a by-product in the paper and pulp industry. Although it has good potential for providing energy as an industrial waste, BL's high moisture content limits its usability. In this study, an integrated system to effectively co-produce power and ammonia (NH 3) from BL is modeled and evaluated. The modeling and integration are conducted using the principles of exergy recovery and process integration to effectively circulate the energy/heat throughout the whole system. The developed system involves BL evaporation, gasification, syngas chemical looping (SCL), and NH 3 synthesis. During SCL process, H 2 , CO 2 , and N 2 -rich gas are produced consecutively in the oxidation, reduction, and combustion reactors, respectively. The designed system can achieve the total energy efficiency of ~50%. The result also suggests that N 2 -rich gas and pure H 2 produced during SCL can be used directly for NH 3 synthesis without any additional energy penalty. The additional step for CO 2 separation can also be avoided, affording a cleaner and more efficient system that provides complete carbon capture. • An efficient energy system of a H 2 production is proposed. • The system comprises evaporation, gasification, syngas chemical looping and NH 3 synthesis. • High energy efficiency in NH 3 production can be realized. • The designed system achieved about 50% of total energy efficiency. • The system has cleaner and more efficient system with 100% carbon capture. [ABSTRACT FROM AUTHOR]

• A bibliometric analysis of the relevant literature on tunnel lighting was conducted. • The literature is sorted by energy saving, visual comfort, and low carbon. • The relationship between energy saving and low carbon is discussed. • The relationship between energy saving and comfort is also discussed. • The optimization methods of tunnel light environment are summarized. A reasonable and applicative tunnel light environment is important to ensure driving safety. This review aims to contribute to this growing area of research by exploring the tunnel lighting, which expands from safety to visual comfort, energy saving, and low carbon This paper employs bibliometric method to a visual analysis of the relevant literature and summarizes current research efforts and development directions. Firstly, the literature is visually presented by CiteSpace to elaborate the state-of-art with the dimensions of the timeline graph, the keyword co-occurrence graph, and the institutions & countries co-occurrence graph. Afterwards the literature is reviewed from three major perspectives: energy saving, visual comfort, and low carbon. Current research is mainly on luminaire arrangement, auxiliary lighting and other measures to reduce energy consumption. Visual comfort is mainly improved by optimizing the light environment's physical quantities, e.g. luminance, color temperature, color rendering etc. Current measures to achieve low carbon targets include green energy, green photoconductivity, green luminaires and green operation. Energy saving, visual comfort and low carbon are interrelated and complementary. However, a systematic understanding of how tunnel lighting contributes to carbon emission is still lacking. There is still room for development in tunnel lighting to achieve full intelligent control and the goal of carbon neutrality. [ABSTRACT FROM AUTHOR]

This paper investigates conversion of a Nordic oil-heated townhouse into carbon-neutral by different energy efficiency (EE) improvements and an off-grid system including solar photovoltaics (PV), wind power, and battery and hydrogen energy storage systems (BESS and HESS). A heat-pump-based heating system including waste heat recovery (WHR) from the HESS and an off-grid electrical system are dimensioned for the building by applying models developed in MATLAB and Microsoft Excel to study the life cycle costs (LCC). The work uses a measured electrical load profile, and the heat generation of the new heating system and the power generation are simulated by commercial software. It is shown that the EE improvements and WHR from the HESS have a positive effect on the dimensioning of the off-grid system, and the LCC can be reduced by up to €2 million. With the EE improvements and WHR, the component dimensioning can be reduced by 22%–41% and 13%–51% on average, respectively. WHR can cover up to 57% of the building's annual heat demand, and full-power dimensioning of the heat pump is not reasonable when WHR is applied. Wind power was found to be very relevant in the Nordic conditions, reducing the LCC by 32%. [Display omitted] • Energy efficiency improvements notably reduce dimensioning of system components. • Waste heat from FC and electrolyzer covers up to 57% of the annual heat demand. • A full-power-dimensioned GSHP does not reduce the fuel cell dimensioning. • Seasonal storage on a small scale makes the system not economically viable. • Small-scale wind power is expensive, but it significantly reduces the system LCC. [ABSTRACT FROM AUTHOR]

To identify the influence of digital transformation on energy consumption, this paper introduces digitalization and energy elements into the multi-sector energy efficiency analysis model. Furthermore, this paper empirically tests the influence of digital transformation on energy efficiency from the micro level. The results show that the digital transformation indeed improves the enterprises energy efficiency, in which enterprises with higher energy efficiency are more affected. The improvement effect of digital transformation on enterprise energy efficiency continues to increase over time. In comparison, digital transformation shows more effective improvement effect on energy efficiency in enterprises that substantive transformation priority, with same group and maturity stage. Mechanism analysis shows that digital transformation can improve the energy efficiency of enterprises by reducing costs, increasing income, improving production efficiency and promoting technological innovation. In addition, the digital inclusive finance, government subsidies, intellectual property protection and service industry opening show positive regulatory effects. This paper proposes that, with the goal of peak carbon dioxide emissions and carbon neutrality, enterprises should actively increase digital investment and strengthen digital management of energy utilization. [Display omitted] • Digital transformation indeed improves the enterprises energy efficiency. • The promotion effect of digital transformation increases over time. • Digital transformation drives efficiency improvement and technological innovation. • Digital transformation contributes to cost reduction and benefit increase. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative decision support system for improving ship energy efficiency. It combines an engine optimization model and an artificial neural network (ANN). Real-time data regarding the ship and engine performance is obtained along a specific ship navigation, and an engine optimization model is built using Ricardo Wave software. This model is validated with high accuracy. Ship-specific parameters are derived, and fuel consumption is estimated using ANN models with different structures. The study identifies the most suitable ANN model with 1 hidden layer and 5 neurons based on error metric evaluation where the 0.99697 R2, 0.00035 RMSE, and 0.06470 MAPE scores are calculated. This approach offers a cost-effective solution for shipping companies to monitor critical engine parameters in real-time without investing in sensors or data collection systems. Thus, it contributes to dealing with the problem of the scarcity of analyzable data in the maritime literature, which is one of the significant issues in the papers related to energy efficiency, machine learning, and deep learning. It is an innovative approach since the parameters that are related to real-time operations rather than considering only instruction book information are aggregated, and the produced data is analyzed by an intelligent tool. • A system is proposed to control ship energy efficiency level by combining an optimization model and a neural network • A simulation model is created in the Ricardo Wave tool and the engine model is validated by actual data. • The ship fuel consumption is estimated by an artificial neural network based on the derived input parameters. • The prediction process is evaluated by error metrics to determine its success. [ABSTRACT FROM AUTHOR]

This study analyzes the impact of green finance and renewable energy on energy efficiency in E−7 economies. We analyzed the nonlinear and asymmetric effects of green finance and renewable energy on energy efficiency. We collected data from 1985 to 2017 and applied the NARDL and 2SLS methods. The results reveal that the differences in green finance development in the E−7 region are as follows: China (0.61), Brazil (0.55), India (0.53), Indonesia (0.49), Mexico (0.37), and Russia (0.39). In 2019, the Gini coefficient was the largest in Russia (0.57), followed by Turkey. Based on empirical findings, the largest barrier to green energy efficiency is insufficient private and governmental investment in the energy sector to improve access to power, to increase energy security and promote economic growth in an environmentally sustainable manner. The findings point to a promising but vulnerable future for renewable energy and energy efficiency deployment in E−7 countries. This paper emphasizes that green financing and renewable energy policy limitations must be addressed to realize the funding potential of energy efficiency in E−7 countries. • We have analyzed the barrieres for renewable energy and energy efficiency. • Non-linear and asymmetric effects is observed on energy efficiency. • We have applied NARDL model to examine the relationship among variables. [ABSTRACT FROM AUTHOR]

• A multi-parametric analysis study has been conducted for 96 building scenarios. • The impact of building construction on the energy and temperature performance is studied. • The effect of different phase change materials on temperature regulation is investigated. • Several configurations have been recommended for both building constructions. This paper presents a comparative study that will evaluate and optimize 96 building cases in a multi-parametric manner. This study is conducted by using the EnergyPlus simulation software with the integrated PCM hysteresis model. The studied building designs were carefully selected to have equivalent air volume, while taking into consideration the outer wall shape of the building to increase or decrease the outside sun and wind exposed surface as well as increasing the PCM area inside the buildings. Three different building forms were evaluated, the square form, the octagonal form which is considered as the building design with a reduced surface, and finally the innovative zigzag form which has a higher wall surface compared to the square form. Additionally, these three building forms, were evaluated at two different constructions (light-weight and heavy-weight), four different PCM types and four different HVAC scenarios. The main purpose of this study is to assess indoor air temperature fluctuation reduction, energy efficiency, and PCM activation. The results reveal an energy saving up to 13.1 % and a maximum total yearly temperature fluctuation reduction of 653 °C. The recommended building configuration was able to reach a maximum PCM activation of 26.63 %. [ABSTRACT FROM AUTHOR]

This paper investigates the sustainability of Tabriz City focusing on its decarbonization. Statistical, quantitative, and comparative research methods are used to analyze the total energy consumed, conversion of primary energy, final exergy, and losses analysis according to the renewable and carbon-based energies. Then, the renewable resource potentials of the city are simulated using Grasshopper plug-in-Rhino simulator, THERM, PVSyst photovoltaic software, and ArcMap. In terms of the socio-economic assessment, non-oil Per Capita GDP, emissions, and energy intensities are analyzed concerning the B2DS scenario by 2050. The per capita GDP of the city is 71% of the country's average value. Tabriz's real GDP growth averaged −1.11% per year, primary energy demand increased by an average of 6.1%, CO 2 emissions from energy use grew by an average of 6% annually and the emission intensity of the economy increased by a total of 52.02%, in the period from 2011 to 2019. Finally, it is shown that the city consumes 58050 GWh/year of different carbon-based resources that only produced 20110 GWh/year of useful work with 5.63 MT CO 2 emissions, while the same work could locally be supplied by 30000 GWh/year RE with a minimum rate of 0.18 MT CO 2 emissions. The city Landfill receives 1000 tons/day of MSW, but two-thirds of this amount can be recycled. Regarding different sectors, buildings produce half of the CO 2. The cement industry with dual pollutants (17%) is the most polluting factory. Carbon-based electricity (66%) is the most polluting form of energy. In terms of carbon sequestration requirements with trees, shifting from fossil fuels toward green energies, EE, and recycling MSW can plunge required trees from 81.9 to 1.16 million trees. This paper highlights the importance of electrification of transportation based on RE, energy performance of buildings, and all sectors. • Large city decarbonization strategies • Application of RE according to geographic and climatic factors. • Electrification of sectors based on RE locally. • MSW recycling and sequestration by trees for decarbonization regarding the LULC and NDVI analysis. • Conversion and storage efficiencies of carbon-based fuels vs RE. [ABSTRACT FROM AUTHOR]

• Overall energy efficiency (OEE) is a crucial factor in tractor-implement systems. • Response surface methodology (RSM) identifies the influence of factorial changes. • Higher correlation value associated with the subsoiler's OEE prediction model. • RSM model accurately predicted the OEE in current study. • Vertical load has a negligible effect on the tested parameters and OEE. The aim of this paper was to predict and optimize the overall energy efficiency (OEE) of a tractor-implement system in semi-deep tillage via response surface methodology (RSM) approach. The OEE was affected by two tillage tines (i.e., subsoiler and paraplow), four forward speeds (i.e., 1.8, 2.3, 2.9 and 3.5 km/h), three operating depths (i.e., 30, 40 and 50 cm) and vertical load imposed on tractor rear wheels at two levels (i.e., 225 kg and no-weight). All tests were replicated four times, forming 192 data points. Field test results revealed all variables were influential on the OEE except vertical load. An increment of speed and depth increased OEE. The RSM approach displayed 3D views with higher accuracy for the OEE change due to changing tine, speed, depth and vertical load relative to regression prediction models. Another feature of the RSM approach was the output graphs with many small pixels. Accordingly, input variables changes and their influences on the OEE are more locational and visible. Moreover, the RSM model accurately predicted the OEE as 35.216% with the desirability of 0.843 at the optimum state as paraplow tine, 30 cm depth, 2.07 km/h forward speed, and 0.01 kg vertical load. [ABSTRACT FROM AUTHOR]

Energy efficiency is crucial for the sustainable operation of industrial and urban sectors. However, practicing engineers seldom have access to open-source tools that can readily evaluate and compare scenarios. In this work, a novel web-based tool called ROSMOSE is developed and proposed for analyzing the energy efficiency of industrial and urban systems and comparing potential process integration options. This optimization framework computes and graphically represents the minimum energy requirements, the pinch temperatures, and the Grand and integrated Carnot composite curves of process systems. Results are used to design utility systems that meet energy demands, while minimizing a specified objective function, such as total cost or environmental impacts. The Quarto environment, in which ROSMOSE is built upon, allows the integration of various open-source software and tools, such as database handling software, process modeling suites, and optimization solvers, along with interactive data visualization and reporting tools. This paper discusses the application of ROSMOSE for the energy integration and total site optimization of a dairy process consisting of milk treatment, and cream and cheese production to demonstrate the tool features. Other integration options, such as the use of heat pumps or solar panels, and the production of soft drinks or biogas as value-added byproducts, are also proposed and evaluated. As a result, from 75 % to 90 % in fuel savings and up to 80 % CO 2 emissions reduction in the dairy plant are identified by optimizing the utility systems integration. Moreover, complete electrification via heat pumps eliminates any fossil fuel needs and it results in 85 % energy savings and up to 96 % CO 2 emissions reduction. The activation of renewable electricity sources, such as solar power, made plausible this fully electrified scenario. Finally, optimized waste management strategies led to in-house fuel production and net export of biogas to the grid, but they also reduce cheese yield by prioritizing the biogas production from whey upgrading process. In this way, decision-makers have access to a clear performance comparison for a list of system configurations to draw informed trade-offs. A similar approach could be adopted to aid decision-making for the design and operation of other industrial and urban energy systems using ROSMOSE. [Display omitted] • ROSMOSE is an open-source energy integration, optimization and reporting tool. • Pinch analysis and other assessments of decarbonization strategies can be performed. • The process integration and waste valorization of a dairy plant is used as case study. • Above 75 % energy saving is achieved by process integration and optimal utility selection. • 96 % reduction in CO 2 emissions and on-site biogenic energy sources are identified. [ABSTRACT FROM AUTHOR]

Deep Neural Networks (DNNs) are widely used in many application domains. However, they require a vast amount of computations and memory accesses to deliver outstanding accuracy. In this paper, we propose a scheme to predict whether the output of each ReLu activated neuron will be a zero or a positive number in order to skip the computation of those neurons that will likely output a zero. Our predictor, named Mixture-of-Rookies , combines two inexpensive components. The first one exploits the high linear correlation between binarized (1-bit) and full-precision (8-bit) dot products, whereas the second component clusters together neurons that tend to output zero at the same time. We propose a novel clustering scheme based on analysis of angles, as the sign of the dot product of two vectors depends on the cosine of the angle between them. We implement our hybrid zero output predictor on top of a state-of-the-art DNN accelerator. Experimental results show that our scheme introduces a small area overhead of 5.3% while achieving a speedup of 1.2x and reducing energy consumption by 16.5% on average for a set of diverse DNNs. [ABSTRACT FROM AUTHOR]

Energy performance indices are widely used methods for determining and assessing the efficiency of buildings in operation. However, methodologies for indices that compare heat consumption among buildings and at higher temporal granularity than yearly are needed. This makes it possible for consumers to compare themselves to others to encourage them to improve their energy efficiency. For a district heating system, it also provides the district heating company with an overview of consumers' energy efficiency (and consumers' efficiency must be comparable). This is critical due to the interconnectedness of district heating systems, as consumers can indirectly have negative impacts on each other's operations. Having an overview of the efficiency of consumers can provide useful information for the district heating companies to detect unusual consumption and possible faults. This paper therefore has created a framework for the comparison of heat consumption among buildings, by taking into account various contextualities to make comparisons possible. The framework has been tested on a case study, and its capabilities are showcased. • Leveraging building metadata to allow energy consumption comparison among buildings. • Creation of heating consumption performance indexes for use by end-consumers. • Enabling of higher temporal resolution performance indexes increasing usability. • Fault detection based on performance indexes. • Showcasing of methodology on real consumption data from a suburb in Denmark. [ABSTRACT FROM AUTHOR]

Previous design methodologies for spring-driven jumping robots focused on jump height optimisation for specific tasks. In doing so, numerous designs have been proposed including using nonlinear spring-linkages to increase the elastic energy storage and jump height. However, these systems can never achieve their theoretical maximum jump height due to taking off before the spring energy is fully released, resulting in an incomplete transfer of stored elastic energy to gravitational potential energy. This paper presents low-order models aimed at characterising the energy conversion during the acceleration phase of jumping. It also proposes practical solutions for increasing the energy efficiency of jumping robots. A dynamic analysis is conducted on a multibody system comprised of rotational links, which is experimentally validated using a physical demonstrator. The analysis reveals that inefficient energy conversion is attributed to inertial effects caused by rotational and unsprung masses. Since these masses cannot be entirely eliminated from a physical linkage, a practical approach to improving energy efficiency involves structural redesign to reduce structural mass and moments of inertia while maintaining compliance with structural strength and stiffness requirements. • Spring-driven jump robots exhibit incomplete elastic to kinetic energy conversion. • The inefficient energy transfer results from premature and delayed take-off. • Undesired take-offs are due to inertia effects from rotational and unsprung masses. • Mass reduction and higher stored energy have limited effects on energy efficiency. • Strategic mass positioning or structural optimisation increases energy efficiency. [ABSTRACT FROM AUTHOR]

[Display omitted] • A high-temperature superconductor (HTS) magnet system for the maglev application. • Magnet energy-robustness, -density, and -economy improved by 96.8%, 66.7%, and 52.5%. • High defect tolerance reaching 716 A/mm2 current density and 25 T magnetic field. • A practical solution for energy bottleneck in HTS magnets/maglevs development. The blooming ultrahigh-speed SC maglev (superconducting magnetically levitated train) prompts green travel worldwide. We utilized high-temperature superconductor (HTS) magnets and have developed an energy-economical SC maglev prototype (Energ. Convers. Manage. (291)117247). In this paper, as a continuation, we further improve and maximize the performance of its key part – the HTS magnets, especially in compact high magnetic field and energy density operating conditions, by innovating an HTS magnet technology named the Shunted Extreme-NI Concept. The operational energy-robustness, -density, and -economy of HTS magnets, compared to the conventional type, are improved by 96.8%, 66.7%, and 52.5%, respectively. For a full-scale maglev equipped with this technology, the estimated energy consumption per passenger kilometer can be further reduced to below 20% of that of airplanes under the same cruising speed of 1000 km/h. This work is dedicated to advancing the energy performances of HTS magnets, heading to the future sustainable maglev transportation. [ABSTRACT FROM AUTHOR]

Deep Reinforcement Learning (DRL) has emerged as a promising approach to address the trade-off between energy efficiency and indoor comfort in buildings, potentially outperforming conventional Rule-Based Controllers (RBC). This paper explores the real-world application of a Soft-Actor Critic (SAC) DRL controller in a building's Thermally Activated Building System (TABS), focusing on optimising energy consumption and maintaining comfortable indoor temperatures. Our approach involves pre-training the DRL agent using a simplified Resistance-Capacitance (RC) model calibrated with real building data. The study first benchmarks the DRL controller against three RBCs, two Proportional-Integral (PI) controllers and a Model Predictive Controller (MPC) in a simulated environment. In the simulation study, DRL reduces energy consumption by 15% to 50% and decreases temperature violations by 25% compared to RBCs, reducing also energy consumption and temperature violations compared to PI controllers by respectively 23% and 5%. Moreover, DRL achieves comparable performance in terms of temperature control but consuming 29% more energy than an ideal MPC. When implemented in a real building during a two-month cooling season, the DRL controller performances were compared with those of the best-performing RBC, enhancing indoor temperature control by 68% without increasing energy consumption. This research demonstrates an effective strategy for training and deploying DRL controllers in real building energy systems, highlighting the potential of DRL in practical energy management applications. [Display omitted] • Implementation of SAC as a continuous action thermostatic controller. • SAC pre-trained in simulation before the deployment in a real-world office. • Development of a benchmarking strategy to compare DRL and RBC in real deployment. • Reduced temperature violations by 68% maintaining the same energy consumption. • Discussing practical DRL controller deployment challenges. [ABSTRACT FROM AUTHOR]

Given the environmental impact of the construction industry, sustainable solutions are increasingly needed to minimize the life cycle impacts of buildings by means of reusing renewable industrial waste. Abundant forest and farm waste exist in center-south Chile, with great potential to be used as thermal insulation and, thus, improve the livability of houses located in an area facing air pollution and energy poverty issues. This paper discusses two types of insulation produced and tested jointly with Germany-based Fraunhofer Institute for Wood Research (WKI)– using Chilean agroforestry waste: First, a flakeboard made of wood veneer chips; and second, a flexible substrate and a pressure resistant board made of wheat and oat straw. The main goal of this research is to assess, by means of digital modeling and experimental tests, the thermal characteristics and the resistance to humidity of these new insulation materials and their performance within construction solutions usually used in Chile. The benchmarks used were the most demanding national residential energy efficiency standard implemented to address air pollution and energy poverty, and the most demanding international standard, both for walls and roofing. The results of both insulation materials was promising, as they met the thermal performance and resistance to humidity requirements and show technical feasibility for the insulation market; in addition, they share the potential to be used in different types of buildings and construction solutions both in Chile and abroad, promoting a sustainable approach starting from the raw materials and the manufacturing process, through the buildings' operations. [Display omitted] • Agroforestry waste creates competitive insulation for local and global markets. • Wheat, oat straw, and wood veneer insulation show adequate thermal conductivity. • Renewable insulation material outperforms fossil-based in humidity control. [ABSTRACT FROM AUTHOR]

• The maximum treatment capacity of 1167.44 g is achieved in Mode 1 systems. • Optimization of operating conditions enhances energy efficiency and reduces costs. • Systems in Mode 2 exhibit higher energy efficiency but a lower COD removal value. • Switching modes maximize energy utilization across different application scenarios. A multi-stage reverse electrodialysis reactor (MSREDR) wastewater treatment system with an independent strategy is researched theoretically in this paper. The influences of operating conditions on the system performance and levelized costs are explored. Results illustrate that reducing the number of membrane pairs not only enhances the system performance but also decreases the levelized cost of wastewater treatment. A high concentration of concentrated solution facilitates enhancing the total COD removal value and reducing the levelized cost, but it adversely affects the energy conversion efficiency of the system. The optimal total COD removal value and energy efficiency are respectively 1164.77 g and 3.58 % under the given conditions. However, when the wastewater discharge rate from the plant is lower than the maximum treatment capacity, it transitions from a wastewater treatment mode to a combined power generation and wastewater treatment mode. Compared to the system operating in the wastewater treatment mode, the system operating in the combined power generation and wastewater treatment mode exhibits higher effective energy and energy efficiency but a lower COD removal value. This research successfully achieves the flexible adjustment of operating modes based on real-world applications, thereby maximizing energy efficiency and establishing a theoretical foundation for practical implementations. [ABSTRACT FROM AUTHOR]

The energy of Wireless Sensor Network (WSN) in edge network is limited. In order to save energy, data bundling is proposed to reduce the energy consumed in setting up connection links by joining several packets into a single unit. However, although current data bundling schemes can reduce energy consumption, they will increase the delay of data routing, so it makes the multi-hop end to end delay a lot in WSN. Also, there is no appropriate solution to tackle the delayed growing problem caused by data bundling. And the energy consumption of the Acknowledgment (ACK) transmission is not comprehensively considered either. Regarding to those issues of previous researches, in this paper, a Data and ACK Bundling Scheme (DAB) is proposed to aggregate several data or ACK packets into a single unit to reduce the energy consumption as well as the end to end delay of the WSN. In the proposed DAB scheme, not only multiple packets but also ACK bundling into a single unit can reduce energy consumption. More importantly, the parameters of bundling technology are optimized from a global perspective in DAB scheme, so that WSN can not only improve the network lifetime, but also reduce the end to end delay which is impossible in previous studies. The theoretical analysis and experimental results illustrate that, while ensuring the reliability of data collection, our DAB scheme improves the energy efficiency by 29.03 % and reduces the delay by 22.29 % when compared with our traditional data collection scheme. [ABSTRACT FROM AUTHOR]

The average electricity consumption per capita in China still has significant room for growth. Under the current rigid residential electricity pricing, it is very important to study how factors other than price can promote electricity conservation among residents. This paper uses micro-survey data of 2018 and 2020 to analyze energy efficiency and the rebound effect through a stochastic energy demand frontier model, as well as to examine the role of news propaganda in this context. The research conclusions are as follows: (1) News propaganda promoting electricity saving can weaken the rebound effect of electricity use, which can lead to better electricity savings. (2) The energy efficiency of residential electricity has improved significantly compared to previous years, and the rebound effect has decreased. (3) Electricity prices have a negative impact on electricity consumption, while income, household size, home area, and extreme temperatures have a positive impact. Based on these conclusions, we propose policy recommendations to address the current inflexibility in residential electricity pricing and to improve the efficiency of electricity use among residents. • News propaganda promoting electricity saving can weaken the rebound effect. • The energy efficiency of residential electricity has improved. • The rebound effect of residential electricity has decreased. • News propaganda can be a complement to the inflexibility of pricing policy. [ABSTRACT FROM AUTHOR]

Using the unique micro-data of industrial enterprises' Nitrogen oxides (NOx) and Sulfur oxides (SOx) emissions covering 58,725 enterprise-year observations, this paper investigates how digitalization affect energy efficiency. The empirical results show that regional digitalization can significantly promote the improvement of energy efficiency of industrial enterprises. Specifically, the result of PSM-DID method indicates that the impact of digitalization on energy efficiency is significantly enhanced by the "National innovative pilot cities" policy, while the dynamic result validates the continuity of industrial enterprises' energy efficiency. Moreover, regional digitalization has superior impact on enterprises within eastern region enterprises, the manufacturing sector, and with elevated liquidity and profitability levels. Finally, moderating effect suggests that technological innovation expand the positive impact of digitalization on enterprises' energy efficiency. This study confirms regional digitalization can promote the energy efficiency of Chinese industrial enterprises, providing the policy suggestions to the energy transition in China. • Measure the regional digitalization level of Prefecture-level city and the energy efficiency at Industrial enterprise-level. • Findings demonstrated that digitalization increases energy efficiency. • The impact of digitalization on energy efficiency can be enhanced by the "National innovative pilot cities" policy. • Technological innovation expands the positive impact of digitalization on enterprises' energy efficiency. [ABSTRACT FROM AUTHOR]

Clustering in the Internet of Things (IoT) involves organizing devices into groups to streamline network management and optimize resource utilization, including Internet connections, energy usage, coverage, quality of service, and connectivity. DCOPA (A Distributed Clustering Based on Objects Performances Aggregation for Hierarchical Communications in IoT Applications) is a recent distributed clustering protocol based on a timer for cluster formation where the election of Cluster Heads (CHs) is modeled as a multicriteria problem. In this paper, three contributions are presented. Firstly, the DCOPA protocol is analyzed with a focus on its multi-criteria aggregation function T(i) which directly contributes to the election of the CHs and the formation of the network's clusters. This is then followed by an in-depth analysis of the impact and the variation of the weights assigned to the two aggregated criteria which are the energy and the distance from the base station. A verification of the scalability, load balancing and distribution of the clusters and CHs will follow. Secondly, a new formal notation for the performance analysis, specifically focusing on the mortality and lifetime based on the Vector of Performance Indicators (VPI), will be introduced for IoT. As a third contribution, a revised version of DCOPA is introduced called ADCOPA (Adaptive DCOPA Using Dynamic Weighting for Vector of Performances Indicators Optimization of IoT Networks). ADCOPA is based on a new property which is the dynamicity or the variability of the weights of the criteria used in the election function of CHs. The simulation results show that the ADCOPA algorithm, which dynamically adjusts the weights of the criteria during the network's lifetime, outperforms the DCOPA algorithm. The latter uses static weights for the criteria that remain unchanged for the entire lifetime of the network. This confirms that the ability to dynamically adjust the weighting of the criteria is an important factor in achieving better performance. • In-depth performance analysis of the DCOPA protocol. • The impact of varying the criteria weights on the DCOPA lifetime parameters. • A novel formal notation for analyzing the performance of an IoT network. • A novel Adaptive DCOPA protocol that uses dynamic weighting is proposed. • Simulation results show that the ADCOPA protocol performs better. [ABSTRACT FROM AUTHOR]

In recent years, the severe environmental degradation and high levels of fossil fuel consumption linked to conventional ship energy systems have drawn attention to the advancement of alternative ship energy systems. Consequently, ship energy systems based on the use of an electrical microgrid are coming to the fore as an increasingly popular alternative solution. However, managing the energy flows within a shipboard microgrid can be highly challenging due to the multiple energy sources (including renewable energy sources) and types of loads involved. Additionally, the integration of an energy storage system has been identified as an effective solution for improving the reliability of shipboard power systems, pointing out the important role of energy storage systems in maritime microgrids and their potential to enhance the energy management process. As such, effective energy management strategies are necessary to optimize performance and ensure that the vessel's energy demands are met while minimizing fuel consumption and reducing cost and environmental impacts. This paper presents a comprehensive review of such strategies and methods recently presented in the literature associated with energy management in shipboard microgrids integrating energy storage systems and examine the different techniques that can be utilized to achieve optimal system performance. Moreover, the paper also sheds light on the future challenges and perspectives imposed by the modern shipboard microgrids operation on the energy/power management of these systems. The aim of this paper is to provide a helpful guideline for researchers and engineers conducting research in the field of electrically powered ships. [Display omitted] • A comprehensive review of EMS for electrical and hybrid ship is presented. • A comprehensive review of ESS which can be used in such ships is presented. • Technologies of ESS and their suitability to ships are discussed and commented. • Future challenges and perspectives imposed by modern SMG operation are presented. [ABSTRACT FROM AUTHOR]

Energy efficiency has emerged as one of the most essential design parameters in contemporary computing system design. Approximate computing is a new computing paradigm to achieve energy efficiency by trading-off energy/area/latency improvements with accuracy for error-resilient applications. This paper proposes R econfigurable E nergy-efficient A pproximate D ivider (READ) that achieves several energy–quality configurable modes using fixed restoring array divider architecture. Conventional approximate binary dividers require various divider hardware configurations to achieve distinct energy–quality trade-off points, which decreases the hardware flexibility, especially for modern embedded systems. READ accomplishes energy efficiency while meeting the dynamically varying accuracy requirements of the targeted application. READ uses reconfigurable subtractor cells that can work in either accurate or approximate mode using a subtractor cell controller logic. The paper also introduces the design of overflow detector using minimal hardware resources. A comprehensive accuracy and hardware evaluation on CMOS 45-nm technology node are performed for the proposed dividers as well as other state-of-the- art divider designs. Compared to the accurate 16 ∕ 8 divider design, the proposed divider shows an improvement of 49% in terms of energy efficiency and is 1.26x faster, while introducing minimal errors. The proposed divider design is demonstrated for its efficacy in image processing tasks and shows nominal effect on the output quality. • A fixed restoring array based approximate divider is proposed and presented with detailed error and circuit analysis for CMOS-45 nm technology node. • The approximate divider presented is reconfigurable in runtime which enables accuracy configurability by varying the design parameter. • Reconfigurable half and full subtractor cells are designed and proposed which achieves dual mode of operation — accurate and exact with energy savings. • Overflow detector circuit for divider design is proposed and presented which is scalable and hardware efficient. • The approximate divider designs are evaluated for image processing applications and found to be satisfactory in terms of Quality of Services (QoS). [ABSTRACT FROM AUTHOR]

Abstract The annual market demand for disposable baby diapers exceeds 20 billion units (690 kt) in the European Union (EU). Almost 40,000 disposable diapers are used every minute, producing 1.3 t/min (dry weight) of waste. Based on the estimates in this work, this requires 90.3 PJ of primary energy and emits 2.7 Mt CO 2 eq. annually. In an attempt to reduce resource, waste and environmental impacts associated with diapers, this paper focuses on novel design and manufacture of disposable baby diapers. This involves using optimised absorbent core and novel bonding technologies to replace conventional gluing of diaper materials. Life cycle assessment has been performed to compare the environmental performance of these new diapers, here termed "glueless", with standard designs. The annual resource consumption and environmental impacts of glueless diapers have been estimated at different production levels: a single production platform, an industrial plant, a country (Italy) and the EU, taking into account different production volumes, electricity mixes and waste management options. Different market penetration scenarios have been considered to determine potential environmental implications at the EU level by 2020. The results reveal that glueless diapers reduce the consumption of raw materials by 23% (9.2 g/diaper), primary energy demand by 25% (752 kJ/diaper) and global warming potential by 10% (10.4 g CO 2 eq./diaper) compared to the standard diapers. They also have more than 50% lower eutrophication, ozone depletion and human and eco-toxicity. Up to 16 PJ primary energy and 191,000 t CO 2 eq. would be saved annually in the EU by using glueless diapers. The cumulative savings would amount to 47 PJ and 566,000 t CO 2 eq. by 2020. The annual consumption of raw materials at the EU level would be reduced by 196,000 t with cumulative savings of up to 584,000 t by 2020, also reducing the equivalent amount of waste from the disposal of diapers. Consequently, glueless diapers can contribute to meeting the energy, climate and resource efficiency goals specified in the EU 2020 strategy. Graphical abstract Image 1 Highlights • Novel design and manufacture of "glueless" disposable baby diapers is considered. • Glueless diapers save 23% of materials and 25% of energy relative to standard type. • Most life cycle impacts are reduced by >50% and global warming potential by 10%. • Up to 584,000 t of waste and 566,000 t of CO 2 eq. can be saved by 2020. • Glueless diapers can help meet the EU energy, resource and climate change targets. [ABSTRACT FROM AUTHOR]

This study aims to unveil the heterogeneous impacts of import product diversification and institutional factors for achieving energy efficiency in Organization for Economic Co-operation and Development (OECD) countries. In doing so, the study employs robust econometric techniques such as Fully modified ordinary least squares (FMOLS) co-integration, pooled Ordinary Least Squares (OLS), Feasible Generalized Least Squares (FGLS), pool mean group regressions, fixed effects, and random effects for panel data from 1990 to 2015 for selected OECD countries. The detailed empirical outcomes suggest that import product diversification is conducive to reducing the energy and carbon intensity (improvement in energy efficiency) in OECD countries. The empirical conclusions provide various guidelines to achieve cleaner and greener growth and align with various Sustainable Development Goals (SDG 7: Affordable and clean energy, SDG 9: Industry, innovation, and infrastructure, and SDG 13: Climate action) of OECD countries. The paper elaborates fruitful policy suggestions regarding the diversification of imports and energy use-carbon emission-nexus for the OECD member nations. Based on the findings, policymakers and environmental scientists should strengthen the trade-energy and import portfolio policies to attain energy efficiency. [ABSTRACT FROM AUTHOR]

Accurate depth estimation from monocular images is critical for various applications such as robotics, augmented reality, and autonomous navigation. However, achieving high accuracy while maintaining computational efficiency is a major challenge, particularly for resource-constrained devices. In this paper, we present LightDepth , an approach that leverages curriculum learning to estimate depth efficiently while taking into account resource constraints. It modifies the ground truth sparse depth maps from the KITTI dataset by resizing them to 31 extents during training to reduce sparsity and control complexity. The resulting model achieves comparable accuracy to state-of-the-art large models while outperforming them in response time by 71%. Our approach outperforms resource-efficient models regarding depth accuracy (measured by RMSE), achieving a 56% improvement. LightDepth is designed to be fast and resource-efficient, making it suitable for deployment in resource-constrained devices. It also balances the trade-off between accuracy and resource efficiency. All codes are available online at https://github.com/fatemehkarimii/lightdepth. [ABSTRACT FROM AUTHOR]

In both practice and literature, there is a lack of a concept for a supra-regional district heating network that efficiently transports heat from renewable and industrial waste heat sources to multiple heat sinks and regional district heating networks, like the high-voltage electricity transmission network in the electricity sector. This paper addresses this gap by presenting a novel method for the basic design of a supra-regional district heating network and its evaluation, along with key performance indicators for assessment. The method highlights essential data requirements and the derivation process necessary to enable the integration of such a heating network. Additionally, it describes how a basic design of such a network can be made feasible and evaluated. This method is then applied to a case study to demonstrate its implementation for a real-world application. Furthermore, this case study aims to either demonstrate or provisionally disprove the general feasibility of a supra-regional district heating network. The results indicate that the implementation of such a network has a positive impact on the CO 2 balance and primary energy demand. The case study further demonstrates the technical feasibility of such a network, showing that a high linear heat density can be achieved through integration and that temperature levels within the network can be maintained adequately. This study confirmed that the developed method can effectively assess whether further investigations into implementing a supra-regional district heating network in a specific region are warranted. Additionally, the method offers a guideline on how to initially design such a network. • Industrial Waste Heat • Load Flow Calculation • District Heating • Energy Efficiency [ABSTRACT FROM AUTHOR]

As Moore's law approaches its limit, conventional computing methods face energy efficiency challenges, prompting the exploration of alternative information processing methods like spiking neural networks (SNNs). Neurons, the fundamental unit of the nervous system, are crucial for efficiently implementing SNNs in hardware, requiring energy and area-efficient design. This paper presents novel circuit designs for the leaky integrate-and-fire (LIF) neuron model at 7 nm FinFET technology, including 8T FinFET LIF Neuron 1, 7T FinFET LIF Neuron 2, 7T FinFET LIF Neuron 3 and 6T FinFET LIF Neuron 4. In the proposed configurations of LIF neuron, the energy per spike is reduced by 95.80 %, 95.89 %, 95.89 %, 96.76 %, and 96.65 % in the 8T FinFET LIF neuron 1 (case 1), 8T FinFET LIF neuron 1 (case 2), 7T FinFET LIF Neuron 2, 7T FinFET LIF Neuron 3, and 6T FinFET LIF Neuron 4, respectively, compared to the lowest energy per spike reported in the literature, with the minimum known energy per spike being 0.67 fJ. The proposed FinFET neurons offer attractive hardware acceleration (spiking frequency in the MHz range), minimal energy per spike (fJ range), simplicity, and area efficiency, making them more biologically plausible due to their adaptable spiking frequency. [ABSTRACT FROM AUTHOR]