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Hydrogen is a kind of clean renewable energy with great development potential. However, traditional electrocatalytic water splitting methods often require high energy consumption and expensive catalysts, which limits their practical application. Transition metal selenides (TMSes), as a new type of hydrogen production materials from electrolytic water, have been the focus of research in recent years. The basic principle of electrochemical water cracking and the preparation of TMSes are reviewed in this paper. The electrocatalysts for hydrogen evolution, oxygen evolution and integral water cracking based on TMSes, including single metal selenides, binary metal selenides, and multinary mixed metal selenides, are discussed. Finally, the future research direction and the development prospect of TMSes electrocatalysts are prospected. • Eley-Rideal and Langmuir-Hinshelwood mechanisms were used to analyze the OER reaction mechanism. • The production and properties of transition metal selenides in recent years are introduced in detail. • In this paper, transition metal selenides are introduced from single TMSes, binary TMSes and multinary mixed TMSes. • The physical properties and electronic structures of transition metal selenides are described in detail. [ABSTRACT FROM AUTHOR]

Along with the rapid economic development and social progress, the problem of energy and environment is becoming more and more outstanding. Therefore, it is very important to use new energy saving techniques to increase energy consumption and reduce energy consumption. In this paper, we have developed a hot water system, which is based on solar energy technology, gas-source heat pump, and phase change energy storage technology. The system was designed and constructed, and its performance was tested in various control conditions, with an emphasis on the analysis of the influence of phase change heat supply on heat storage properties. In this paper, the effect of the heat storage property is analyzed. Studies have shown that compared to a conventional tank that does not have a phase change heat storage device, the heat storage tanks equipped with phase change energy storage devices have longer discharge time, higher discharge temperature, better temperature, and better operation efficiency. In addition, the data show that flow rate control is also an important factor in optimizing the thermal storage characteristics of the system. [ABSTRACT FROM AUTHOR]

In the stochastic and dynamic edge-cloud collaborative environment, the computing resources of the host are limited, and the resource requirements of computing tasks are random and changeable. Therefore, how to efficiently schedule dynamic tasks and improve system performance becomes challenging. The scheduling algorithm based on deep reinforcement learning optimizes the delay and energy consumption of the system by dynamically interacting with the environment, which can solve the problem of dynamic and changeable environment to a certain extent, but there are still problems such as poor model adaptability, low training efficiency, and unbalanced system load. In this paper, aiming at optimizing the average response time of task scheduling and the average energy consumption of the system, a multi-objective task scheduling model is designed, and a task scheduling policy optimization algorithm based on improved asynchronous advantage actor-critic (A3C) is proposed. The residual convolutional neural network (RCNN) improves the network structure of A3C, using asynchronous multi-threaded training methods to interact with the edge-cloud collaborative environment and capturing the random dynamic characteristics of resources required for computing tasks and heterogeneous edge-cloud hosts resource change characteristics to better adapt to random dynamic environments, and it can quickly updating network parameters, improving training speed, and make full use of host resources to solve system load imbalance problems. Simulation results show that the scheduling algorithm proposed in this paper can effectively reduce task response time and system energy consumption in an edge-cloud collaborative environment. [ABSTRACT FROM AUTHOR]

Mining production, being one of the most energy-intensive industries globally, consumes substantial amounts of fossil fuels and contributes to extensive carbon emissions worldwide. The trend toward electrification and advanced developments in battery technology have shifted attention from diesel power to battery alternatives. These alternatives are appealing, as they contribute to decarbonisation efforts when compared to conventional diesel trucks. This paper presents a comprehensive review of recent technological advancements in powertrains for Mining Haulage Truck (MHT). It also compares these configurations based on mining system-level considerations to assess their future potential. The evaluated configurations include Diesel-Electric Truck (DET), Trolley Assist Truck (TAT), Battery-only Truck (BOT), Battery Trolley with Dynamic charging truck (BT-D), and Battery Trolley with Stationary charging truck (BT-S). According to the analysis, the energy demand for on-board diesel or battery power (excluding trolley power) in these alternative options is as follows: DET—681 kWh, BOT—645 kWh, TAT—511 kWh, BT-S—471 kWh, and BT-D—466 kWh. The paper also illustrates the theory of battery size design based on the current battery technology, battery material selection, battery package design, and battery size selection methods. In the case of tailored battery size selection, BOT, BT-D, and BT-S configurations require LiFePO4 (LFP) battery masses of 25 tonnes, 18 tonnes, and 18 tonnes, respectively. Based on a techno-economic assessment of battery MHT alternatives with a future perspective, it has been determined that BT-D requires the lowest amount of on-board battery energy. Furthermore, over a span of 20 years, BT-S has demonstrated the lowest on-board battery cost. [ABSTRACT FROM AUTHOR]

This paper introduces a groundbreaking approach to demand response management, aiming to empower consumers through innovative strategies. The key contribution is the concept of "acquiring flexibility rights", wherein consumers engage with power aggregators to curtail energy usage during peak-load periods, receiving incentives in return. A flexibility right coefficient is introduced, allowing consumers to tailor their participation in demand response programs, ensuring their well-being. Additionally, a lighting intensity control system is developed to enhance residential lighting network efficiency. The study demonstrates that high-energy consumers, adopting a satisfaction factor of 10, can achieve over 61% in electricity cost savings by combining the lighting control system and active participation in demand response programs. This not only reduces expenses but also generates income through the sale of flexibility rights. Conversely, low-energy consumers can fully offset their expenses and accumulate over USD 33 in earnings through the installation of solar panels. This paper formulates an optimization problem considering flexibility rights, lighting control, and time-of-use tariff rates. An algorithm is proposed for a distributed solution, and a sensitivity analysis is conducted for evaluation. The proposed method showcases significant benefits, including cost savings and income generation for consumers, while contributing to grid stability and reduced blackout occurrences. Real data from a residential district in Tehran validates the method's effectiveness. This study concludes that this approach holds promise for demand response management in smart grids, emphasizing the importance of consumer empowerment and sustainable energy practices. [ABSTRACT FROM AUTHOR]

In the European Union, increasing attention is already being paid not only to the development of renewable energy sources, but also to the establishment of solutions to achieve local energy self-sufficiency while increasing the role of citizens in managing the energy they generate. This approach is expected both to have a positive impact on the environment and the reduction of greenhouse gas emissions, and to enhance energy security—both in economic and civic terms by, i.a., combating energy poverty. The development of local energy communities promoted in the EU is supported i.a. by energy cooperatives. These contribute to the efficient harnessing of renewable energy potential in rural and urban-rural areas, and have been developing in Poland for several years now. In their previous studies, the authors of this research paper attempted to verify the generation (number, type and capacity of installed sources) and consumption (energy demand) configurations in which an energy cooperative would be a viable solution for prosumers who might establish it. However, over the past few years, the conditions for prosumers and the method of their accounting with the electricity seller have changed radically in Poland (shift from net metering to net billing). This situation has opened up space for further research and encouraged the authors to revisit the problem of analyzing the viability of establishing energy cooperatives in relation to the rules of operation of individual prosumers. This research was carried out for three scenarios, and the horizon of the analyses conducted and described extends to 2045. The comparative analysis included energy consumers without their own generation sources, prosumers with a photovoltaic generation installation covered by the net billing model, as well as a scenario involving prosumers' cooperation within an energy cooperative, which by law is settled in the net metering model. Conclusions from the research and simulations made it possible to confirm the claim that, despite changes in the rules of prosumer billing, developing energy independence in the energy community formula results in a significant reduction in the cost of purchasing electricity (even several times lower purchase costs in the timeframe analyzed) and can lead to a reduction in the payback time of investments in generation sources even by a factor of two. The results presented in this research paper open up space for further research. The outcomes allow us to assume that energy cooperatives—in the organizational and institutional model in Poland—are a good tool for reducing the phenomenon of energy poverty on a local scale. [ABSTRACT FROM AUTHOR]

Cities play a pivotal role in achieving worldwide carbon neutrality due to their significant contribution to global energy consumption and carbon emissions. Therefore, planning effective strategies and guiding evidence-based policymaking at the city scale becomes even more crucial. Composite indices serve as a valuable tool for monitoring urban energy transition trends. This paper aims to present a novel approach, robust and flexible even under conditions of data scarcity, for tracking the energy transition trend of a city by means of a composite index (UETI). The Turin case study is introduced to test the applicability of the proposed approach. Additionally, to demonstrate the robustness of the composite index framework, the paper includes the findings of correlation and sensitivity analyses. This study reveals a significant improvement in Turin's environmental and energy domains, while the socio-economic domain shows more modest improvement. Furthermore, the study highlights the need to address the shortage of urban data to enhance the accuracy and reliability of metric-based frameworks and to extend the assessment to a larger sample of cities. [ABSTRACT FROM AUTHOR]

AbstractDrainage is a common practice in geotechnical engineering concerning dredged marine soils. Current drainage techniques, including surcharge preloading, vacuum preloading, and combined vacuum-surcharge preloading, have been proven to be effective in soft soil treatment, but are also criticized for their high energy consumption. This paper made a brief review on existing drainage techniques and proposed some prospects for the next-generation techniques in response to the public concern of sustainability. It is found that all conventional preloading techniques have been well studied from tests to modeling, and improved vacuum preloading tends to be used in combination with other techniques. Drainage techniques with lower energy consumption can be realized either by using renewable energy or designing biomimetic devices. The paper is expected to provide a comprehensive while concise report on recent advances in drainage techniques for dredged marine soils and in the meanwhile give an insight into the further development towards a more sustainable future. [ABSTRACT FROM AUTHOR]

Integrating hybrid energy storage system (HESS) consisting of battery and supercapacitor provides balance over the power generation and load demand, ensuring the system's stability under transient conditions. Along with battery storage, to control its rate of charging/discharging a rate limiter (RTL) is usually employed. However, the impact of the RTL on battery storage efficiency and system performance has not been explored in the existing literature. This paper investigates the performance of a grid‐tied photovoltaic (PV) system with HESS under dynamic scenarios with different RTL values applied to the battery storage. The paper explores the simulation analysis considering transient, linear, and steady‐state conditions with varying PV generation and load demand. The simulation study shows that a lower RTL value may slow down the battery response, leading to inefficient utilization of battery storage. Alternatively, a very high RTL value will not appropriately regulate the battery current, which may lead to frequent charging and discharging and deterioration of battery life. For the proposed system, RTL with a value of 100 A/s seems appropriate, providing 1.29 Wh of energy through battery storage with an energy efficiency of 91.81% for the simulated time. The DC voltage is efficiently maintained at 400 V with a ripple factor of 0.45%. [ABSTRACT FROM AUTHOR]

The deregulation of the electricity market has been accompanied by the growing utilization of unpredictable renewable energy sources (RESs) such as solar, wind, and hydropower plants. Additionally, advancements in energy storage technologies and new energy demands have further contributed to this trend. As a result, the planning and operation of power systems are now surrounded by a higher level of uncertainty. In order to ensure the proper operation of power systems integrated with RESs, modern power systems are equipped with specific vital tools such as optimal power flow (OPF), which regulates generation and demand to achieve specific objectives. Hence, this paper conducts a comprehensive review of recently published research articles focusing on various solution strategies to address OPF problems in the presence of stochastic RESs and power demand. The review encompasses diverse solution methodologies, objective functions, constraints, and distinct techniques to simulate the stochastic behavior of RESs and dynamic loads. Additionally, the paper explores fundamental challenges, identifies critical research gaps, and highlights unexplored areas pertaining to optimal power system operation in the future. This review is essential for system operators who need to assess and pre-plan flexibility competency for their power systems to ensure practical and cost-effective operation under high RESs penetration. [ABSTRACT FROM AUTHOR]

This paper introduces a novel design for a solar photovoltaic/thermal (PV/T) air collector that utilizes staggered fins. The collector features double-layer flow channels in the same direction, with the upper channel equipped with transparent wave plates, and the lower channel fitted with staggered fins. The paper includes a calculation scheme for wave plate heat balance. The numerical model of the collector is established and verified by experimental results. A numerical model is used to analyze the height combination of the upper and lower runner and the fin arrangement of the lower runner. The results indicate that, under the given environmental parameters, as the PV panel moves down, the overall air heat transfer capacity decreases. When the height combination of the upper/lower runner is 23 mm/105 mm, the maximum value is 53.741%. With the increase of fin spacing, the heat transfer capacity of the lower runner decreases. With the increase of the number of fin rows, the heat transfer capacity of the lower runner increases. When the equivalent diameter of the rectangular channel and the number of fin rows were 70 mm and 3 rows, respectively, the maximum solar energy comprehensive utilization efficiency was 56.261%. [ABSTRACT FROM AUTHOR]

The concerns over greenhouse gas emissions, environmental impacts, climate change, and sustainability continue to grow. As a result of countermeasures, many modern gas turbine power plants and combined cycle power plants are considering to use hydrogen as a clean fuel alternative to fossil fuels in the power plant industry. We assessed the implications of such transition from natural gas to hydrogen as fuel in a gas turbine power plant's balance of plant (BOP) equipment. Using the DWSIM process simulation software and the methodology of compression power changes against different gas compositions, the impact of blending hydrogen with natural gas on temperature differentials, energy consumption, adiabatic efficiency, compression power, and economic implications in gas turbine power plants were examined in this paper. We discovered, through analysis, that there was not a noticeable boost in compression power or energy consumption when 50% hydrogen and 50% natural gas were blended. Similarly, there was no discernible difference in temperature differentials or adiabatic efficiency when 30% hydrogen and 70% natural gas were blended. Moreover, mixing 50% hydrogen and 50% natural gas did not result in a noticeable cost climb. In addition, the techno-economic analysis presented in this paper offered valuable insights for power plant engineers, power generation companies, investors in energy sectors, and policymakers, highlighting the nature of the fuel shift and its implications on the economy and technology. [ABSTRACT FROM AUTHOR]

In order to cope with the problems of energy shortage and environmental pollution, carbon emissions need to be reduced and so the structure of the power grid is constantly being optimized. Traditional centralized power networks are not as capable of controlling and distributing non-renewable energy as distributed power grids. Therefore, the optimal dispatch of microgrids faces increasing challenges. This paper proposes a multi-strategy fusion slime mould algorithm (MFSMA) to tackle the microgrid optimal dispatching problem. Traditional swarm intelligence algorithms suffer from slow convergence, low efficiency, and the risk of falling into local optima. The MFSMA employs reverse learning to enlarge the search space and avoid local optima to overcome these challenges. Furthermore, adaptive parameters ensure a thorough search during the algorithm iterations. The focus is on exploring the solution space in the early stages of the algorithm, while convergence is accelerated during the later stages to ensure efficiency and accuracy. The salp swarm algorithm's search mode is also incorporated to expedite convergence. MFSMA and other algorithms are compared on the benchmark functions, and the test showed that the effect of MFSMA is better. Simulation results demonstrate the superior performance of the MFSMA for function optimization, particularly in solving the 24 h microgrid optimal scheduling problem. This problem considers multiple energy sources such as wind turbines, photovoltaics, and energy storage. A microgrid model based on the MFSMA is established in this paper. Simulation of the proposed algorithm reveals its ability to enhance energy utilization efficiency, reduce total network costs, and minimize environmental pollution. The contributions of this paper are as follows: (1) A comprehensive microgrid dispatch model is proposed. (2) Environmental costs, operation and maintenance costs are taken into consideration. (3) Two modes of grid-tied operation and island operation are considered. (4) This paper uses a multi-strategy optimized slime mould algorithm to optimize scheduling, and the algorithm has excellent results. [ABSTRACT FROM AUTHOR]

Ride comfort is an important indicator to evaluate the dynamic performance of automotive vehicles. One method for improving ride comfort is to incorporate an active actuator into the passive suspension. However, the improvement is closely linked with the required actuation power and force. Increased actuation requirements may lead to higher energy consumption and a larger actuator size. To enhance the trade-off between ride comfort and active actuation requirements, a new design approach for searching for the optimal passive-active-combined suspension across many different mechanical component layouts incorporating an inerter is proposed in this paper. Compared with traditional designs where the suspension is limited to a few special layouts, via this approach the optimal passive part among all network possibilities with pre-determined numbers of each element type (springs, dampers and inerters), and optimal controller parameters of the parallel active actuator can be identified. Considering a quarter-car model, with a benchmark combined active-passive suspension in which the passive part is a spring-damper, the optimal inerter-based suspension can reduce the active force by more than 48% and regenerate 6 W more average power in the active part while achieving the same ride comfort. Note that in this case, two constraints are always considered: the road-holding ability (as indicated by the dynamic tyre load) and suspension travel of the identified suspension will not be worse than those of the benchmark suspension. The optimal trade-off obtained with this approach serves as a powerful tool in the automotive suspension design as it provides guidance on the following three aspects: the optimal ride comfort, the minimum power consumption and the feasibility of specific actuation requirements, where the second point is obtained in conjunction with the power conservation theorem proposed in this paper which proves that the total power consumed by the passive and active parts will not vary with changes in the passive part and active controller parameters. [ABSTRACT FROM AUTHOR]

This paper investigates a generalized Camassa–Holm equation with cubic nonlinearities (alias the mCH-Novikov equation), which is a generalization of some special equations. The mCH-Novikov equation possesses well-known peaked solitary waves that are called peakons. The peakons were proved orbital stable by Chen et al. in [Stability of peaked solitary waves for a class of cubic quasilinear shallow-water equations. Int Math Res Not. 2022;1–33]. We mainly prove the orbital stability of the multi-peakons in the mCH-Novikov equation. In this paper, it is proved that the sum of N fully decoupled peaks is orbitally stable in the energy space by using energy argument, combining the orbital stability of single peakons and local monotonicity of the method. [ABSTRACT FROM AUTHOR]

This paper focuses on optimizing the Zuker RNA folding algorithm, a bioinformatics task with non-serial polyadic dynamic programming and non-uniform loop dependencies. The intricate dependence pattern is represented using affine formulas, enabling the automatic application of tiling strategies via the polyhedral method. Three source-to-source compilers—PLUTO, TRACO, and DAPT—are employed, utilizing techniques such as affine transformations, the transitive closure of dependence relation graphs, and space–time tiling to generate cache-efficient codes, respectively. A dedicated transpose code technique for non-serial polyadic dynamic programming codes is also examined. The study evaluates the performance of these optimized codes for speed-up and scalability on multi-core machines and explores energy efficiency using RAPL. The paper provides insights into related approaches and outlines future research directions within the context of bioinformatics algorithm optimization. [ABSTRACT FROM AUTHOR]

• Innovative passive cooling system to reduce refrigerator energy consumptions. • Use of external air for condenser cooling enables a lower temperature heat source. • The heat from the condenser drives natural airflow circulation of the external air. • CFD analysis optimize device geometry and heat transfer. • Experimental test demonstrates significant energy savings of 36.0 % ± 5.2 %. The paper presents a solution aimed at enhancing the cooling efficiency of domestic refrigerators. The proposed approach involves a passive cooling system designed to leverage the colder external air during winter for the cooling of the fridge condenser. Indeed, the use of a colder external source helps to increase the efficiency of the refrigerator. The system provides a connection between the rear section of the refrigerator and the external environment via two ducts. The heat dissipated by the condenser induces the natural circulation of outdoor cooling air. The study includes experimental and CFD analysis. The experimental results shows that the configuration with the cooling system achieves higher useful effects and lower power consumption compared to the traditional setup, yielding a reduction in energy consumption of 36.0 % ± 5.2 % when operating with an external air temperature of 0 °C. The CFD model is validated through experimental data, and simulations are conducted to optimize the sizing and design of the system components. Parametric analysis indicates that duct diameters of 12 cm and a box depth of 15 cm behind the fridge are required to optimize heat transfer and aid natural circulation. In summary, the paper offers a suitable solution to increase the fridge efficiency in cold climatic locations. [ABSTRACT FROM AUTHOR]

Thermal power plants are pivotal in meeting global energy demands, yet enhancing their efficiency and sustainability remains an enduring challenge. While previous studies have scrutinized energy and exergy analyses of distinct plant components, there's a scarcity of comprehensive reviews integrating findings across diverse plant types. This paper bridges this gap by presenting a comprehensive synthesis of recent advancements in energy and exergy studies across coal, gas, biomass, oil, and combined cycle plants. The review focuses on critical aspects: optimizing operations through modeling and advanced controls, economic evaluations encompassing costs and revenue, and assessing environmental impacts such as emissions and water use. Achieving a balance between performance, cost-effectiveness, and environmental responsibility is crucial for sustainable thermal power generation worldwide. It requires an integrated approach that considers technical, economic, and environmental factors to ensure efficiency, profitability, and minimal adverse effects on health and climate. Key findings emphasize that, in most cases, the boiler emerges as the primary source of exergy destruction, accounting for over 50% of losses across varied plant configurations. Turbines and condensers also significantly contribute to energy losses. Supercritical and ultra-supercritical power plants exhibit higher efficiencies compared to subcritical counterparts. Integrating waste-to-energy technologies with coal plants holds promise, offering efficiency improvements and reduced environmental impact. Optimizing parameters such as pressure and temperature, along with component advancements, shows potential in curbing losses. These optimization endeavors have showcased a notable up to 6% enhancement in exergy efficiency. This review underscores the critical role of ongoing thermodynamic modeling and assessments in steering towards more sustainable thermal power generation. In summary, this paper delivers valuable insights into performance benchmarks and delineates effective strategies for augmenting thermal power plant efficiency through exhaustive energy and exergy analyses. [Display omitted] • Exergy analysis provides a broader perspective than energy analysis alone. • Boiler subsystem is a major source of exergy destruction (52-85% of total losses). • Supercritical and ultra-supercritical plants display higher efficiencies than subcritical ones. • Optimization of parameters like temperature and pressure can boost exergy efficiency by up to 6%. • Advanced combined cycles achieved over 50% efficiency, indicating potential for lower CO2/kWh. [ABSTRACT FROM AUTHOR]

Traditional buildings have a central role to play in Europe's plans to be the first climate-neutral continent by 2050, with the building sector being the single largest energy consumer in the EU with 40 per cent of total energy use. Seventy-five per cent of the existing housing stock is energy inefficient with minute numbers renovated each year. Existing buildings offer huge potential for energy use reduction. Research has shown that a historic retrofit can emit less carbon compared to demolishment and rebuilding. Reductions in carbon emissions of up to 62 per cent were observed across a body of case studies by Historic England, including a 95 per cent reduction in the mews case study described in this paper. Moreover, carefully managed thermal retrofits, taking a 'whole house approach', have many benefits, including improved indoor comfort and air quality as well as reduced operational costs. These are factors which are fundamental in ensuring the continued use of historic constructions while improving their standards of preservation and durability. Thermally upgrading traditional building is still a much-debated topic. Inappropriate thermal interventions with unsuitable materials and finishes can have unforeseen consequences, resulting in building damage and a reduction in internal comfort, health and well-being. This paper aims to contribute to this discussion by providing an overview of key considerations when thermally upgrading the walls of traditional buildings while preserving unique characteristics and functionality. Advantages of adopting thermal mortars such as cork lime are put forward, and their potential efficacy is discussed, concluding with a case study focusing on the deep retrofit of a London mews within a conservation area. [ABSTRACT FROM AUTHOR]

This paper reviews evidence on how the fast growth in renewable energy technologies can trigger social tipping dynamics that potentially accelerate a system-wide energy transition. It does so by reviewing a variety of literature across several disciplines addressing socio-technical dimensions of energy transitions. The tipping dynamics in wind and solar power create potential for cascading effects to energy demand sectors, including household energy demand. These most likely start with shift actions and adoption of household-scale batteries and heat pumps. Key enablers are strong regulations incentivising reductions in demand and setting minimum efficiency levels for buildings and appliances. While there is evidence of spillovers to more environmentally friendly behaviour, the extent of these and the key leverage points to bring them about present a knowledge gap. Moreover, these behavioural feedback loops require strong additional policy support to "make them stick". Understanding the economic and social tipping dynamics in a system can empower decision-makers, fostering realistic energy transition policies. This paper highlights energy communities as a promising niche for leveraging tipping dynamics. Ultimately, bridging the gap between these tipping dynamics and institutional reforms is crucial for unlocking the full potential of sustainable energy systems. [ABSTRACT FROM AUTHOR]

The paper offers innovative cooking utensil designs for remote, isolated, and even peri-urban communities at a low price, with high reliability and simple construction. It can alleviate energy poverty and improve food security. This utensil uses only local solar energy directly and allows comfortable indoor cooking. This paper provides the design principles of a solar cooker/frying pan or generic heater, based on a PV panel or a plurality of them, which are directly connected to a plurality of Positive Thermal Coefficient (PTC) resistors to match the power. PTCs are nowadays produced in massive quantities and are widely available at low cost. The proposed device does not require an electronic controller or a battery for its operation. The aim is for family use, although the design can be easily scaled to a larger size or power, maintaining its simplicity. Electric heating inside or attached to the cooking pot, plus the temperature self-limiting effect of PTCs, allows for thermally insulating the cooking pot from its outside using ordinary materials. Insulation enhances energy efficiency during cooking and keeps cooked food warm for a long time. Clean development would receive a significant impulse with its application. A simple mathematical model describes its functioning and states guidelines for adequate design. Its results indicate a successful proof of concept and high efficiency both for water and oil as representatives of cooking. [ABSTRACT FROM AUTHOR]

This paper presents the process of creating a model for electric vehicle (EV) energy consumption, enabling the rapid generation of results and the creation of energy maps. The most robust validation indicators were exhibited by an artificial intelligence method, specifically neural networks. Within this framework, two predictive models for EV energy consumption were developed for winter and summer conditions, based on actual driving cycles. These models hold particular significance for microscale road analyses. The resultant model, for test data in summer conditions, demonstrates validation indicators of an R2 of 86% and an MSE of 1.4, while, for winter conditions, its values are 89% and 2.8, respectively, confirming its high precision. The paper also presents exemplary applications of the developed models, utilizing both real and simulated microscale data. The results obtained and the presented methodology can be especially advantageous for decision makers in the management of city roads and infrastructure planners, aiding both cognitive understanding and the better planning of charging infrastructure networks. [ABSTRACT FROM AUTHOR]

The primary contributors to elevated line losses in low-voltage distribution networks are three-phase load imbalances and variations in load peak–valley differentials. The conventional manual phase sequence adjustment fails to capitalize on the temporal characteristics of the load, and the proliferation of smart homes has opened up new scheduling possibilities for managing the load. Consequently, this paper introduces a loss-reduction method for low-voltage distribution networks that leverages load-timing characteristics and adjustment capabilities. This method combines dynamic and static methods to reduce energy consumption from different time scales. To commence, this paper introduced a hierarchical fuzzy C-means algorithm (H-FCM), taking into account the distance and similarity of load curves. Subsequently, a phase sequence adjustment method, grounded in load-timing characteristics, was developed. The typical user load curve, derived from the classification of user loads, serves as the foundation for constructing a long-term commutation model, therefore mitigating the impact of load fluctuations on artificial commutation. Following this, this paper addressed the interruptible and transferable characteristics of various smart homes. This paper proposed a multi-objective transferable load (TL) optimal timing task adjustment model and a peak-shaving control strategy specifically designed for maximum sustainable power reduction of temperature-controlled loads (TCL). These strategies aim to achieve real-time load adjustment, correct static commutation errors, and reduce peak-to-valley differences. Finally, a simulation verification model was established in MATLAB (R2022a). The results show that the proposed method mainly solves the problems of three-phase imbalance and large load peak–valley difference in low-voltage distribution networks and reduces the line loss of low-voltage distribution networks through manual commutation and load adjustment. [ABSTRACT FROM AUTHOR]

This paper presents the design of and transient time simulations for a four-pole magnetic bearing with permanent magnets. The usage of permanent magnets reduces the consumption of electric energy in comparison to a traditional active magnetic bearing. Permanent magnets are installed in the yoke of the stator core to limit the cross-coupling of the magnetic flux generated by the windings. The first part of this paper presents the design of the magnetic bearing and its finite-element model, while the second part describes the field-circuit indirectly coupled finite-element model for the transient time simulation. The presented simulation model was used to calculate the transient response for the rotor lifting from the starting position, the step change in the rotor position and the change in the rotor position under an external impact force applied along the y-axis. [ABSTRACT FROM AUTHOR]

In order to meet the hydrogen refueling demand of fuel cell vehicles and reduce the cost of HRS operation process, it is necessary to study the hydrogen refueling process of cascade hydrogen storage system of hydrogen refueling station. In this paper, a thermodynamic model of the hydrogen refueling process for fuel cell vehicles is established, and the effect of the variation of these thermodynamic parameters on the specific energy consumption and utilization rate of the hydrogen refueling process is investigated in terms of the pressure ratio and capacity of the hydrogen storage tank, which is the core of the hydrogen refueling station, consisting of multiple hydrogen storage tanks. The results show that the specific energy consumption and hydrogen utilization of the hydrogen refueling station decreases as the ratio of the nominal pressure of the medium-pressure stage to the nominal pressure of the high-pressure stage increases over the course of the pressure ratio change. In addition, the capacity variation is only slightly changed as a result of the pressure ratio variation. Therefore, the lowest ratio of specific energy consumption can be found by fixing the utilization rate and by changing the pressure ratio. For example, in this paper, if the total mass of hydrogen in the station is 600 kg and the hydrogen utilization rate is required to be not less than 12 %, the optimal pressures for each level are 50.2 MPa, 65 MPa and 86.7 MPa. • A thermodynamic model of the hydrogen filling process for fuel cell vehicles is established. • The pressure ratio and capacity of cascade hydrogen storage systems were analyzed. • Specific energy consumption and hydrogen utilization are used as evaluation indicators. • The optimal pressures for each level are 50.2 MPa, 65 MPa and 86.7 MPa. [ABSTRACT FROM AUTHOR]

Microwave‐driven catalytic processes have attracted the attention of many researchers and industries as these processes are powerful strategies for use in industrial electrification. However, undetectable hot spots inside the catalyst bed can be misleading in terms of the deceptive effects of microwaves on acceleration of catalytic reactions because of the lack of physicochemical knowledge about these processes. In this paper, the hot spots temperatures were estimated based on catalytic ammonia (NH3) synthesis/decomposition equilibrium. The reaction temperature estimated from the chemical equilibrium was more than 100 °C higher than the catalyst bed surface temperature measured with an infrared thermometer under microwave irradiation. Assuming a formation of very steep local high temperature fields, rigorous kinetic analysis led to an estimate that the active site fraction which reaches the high temperature was only about 10 % of total sites. Compared with a resistivity heating furnace, the energy input was reduced by more than 85 % under microwave irradiation. The temperature estimated by the chemical equilibrium was almost corresponding to the temperature estimated by an oscillation of extended X‐ray absorption fine structure recorded under microwave irradiation. This paper presents a proposal for a method to estimate hot spots based on thermodynamic equilibrium, yielding a quantitative evaluation of energy consumption reduction caused by microwaves. [ABSTRACT FROM AUTHOR]

In this paper, a user thermal comfort criterion based on predicted mean vote (PMV) values is introduced to realize the optimal operation of an improved energy hub (IEH) while considering thermal inertia and user thermal behavior. A three-layer optimization model based on user thermal comfort is constructed which fully considers user thermal comfort demand, IEH operating costs, and energy network constraints. Moreover, since IEH optimization considering user thermal comfort is a multi-objective bilevel optimization (MNBO) problem, this paper proposes an improved multilayer nested quantum genetic algorithm (IMNQGA) to solve it. Finally, the effectiveness of the proposed optimization model and algorithm is verified through the analysis of the four modes. The examples show that the proposed optimal control method can reduce the system's operating costs and improve energy efficiency while satisfying user thermal comfort demand. [ABSTRACT FROM AUTHOR]

The global energy crisis, which began in 2021 due to the extraordinary economic recovery after the pandemic and intensified after Russia's invasion of Ukraine in February 2022, has changed the conditions of energy management, paying more attention to energy efficiency. Natural gas prices have reached record levels and, consequently, so have electricity prices in some markets. Oil prices have reached their highest level since 2008. Higher energy prices have contributed to sharply increased inflation. Households are again becoming interested in buying coal as a source of heat. High energy and gas prices have pushed many families into poverty and forced some factories to cut production or even close. They have also slowed economic growth to the point where some countries are heading for a serious recession. Paradoxically, the negative effects of the energy crisis may accelerate the introduction of cleaner, sustainable, renewable energy such as wind and solar energy. The energy crisis is comparable to the oil crisis of the 1970s, when it contributed to significant advances in energy efficiency. The current crisis has highlighted the importance of investments in renewable energy resources and initiated the process of integrating regional markets, developing energy efficiency and promoting renewable energies. The aim of this article is to comprehensively explore the complex relationship between energy awareness, consumption patterns, and energy efficiency, with a focus on both individual consumers and industries, during the global energy crisis. This paper is based on a literature review, overarching policy documents, energy reports, and other secondary documents. The primary research method was the systematic literature review method, based on which the impact of the global energy crisis on energy efficiency was evaluated. This study emphasizes the diverse influences on energy awareness, ranging from economic factors to consumer preferences and environmental consciousness. The findings of the paper underscore the significant responsibility of industries in contributing to energy-saving efforts and the active role of consumers in the energy market. The responsibility of industries in contributing to energy efficiency is highlighted, with a call for a comprehensive approach that integrates energy-saving criteria into product development and corporate social responsibility. [ABSTRACT FROM AUTHOR]

Before the COVID-19 pandemic, ventilation in buildings was not always given its due importance. The World Health Organization has highlighted the important role of air exchange with the outdoors in improving the air quality in buildings; buildings should, therefore, be equipped with mechanical ventilation or adequate air conditioning systems. This paper aims to investigate different retrofit solutions for air conditioning, evaluating them in terms of energy consumption and cost and the impact of increased outdoor air exchange rates on countering the propagation of COVID-19; the latter is the main novelty of the paper. As a case study, we take an existing office building located in Central Italy that was previously not equipped with a mechanical ventilation system (a system with primary air was introduced during the study). The energy analysis was conducted using dynamic simulation software after validation through energy bills; energy and economic analyses were conducted considering different external-air exchange rates. An optimal number of outdoor air changes was found to mitigate the risk of COVID-19 infection, a finding in line with the international literature. The increase in air changes with outdoor air leads to a rise in energy consumption and costs. These values were evaluated for different air conditioning systems and operational schedules. These drawbacks can be made less significant by combining interventions in the system with energy-efficiency measures applied to the building envelope. [ABSTRACT FROM AUTHOR]

In the era of continuous development in Internet of Things (IoT) technology, smart services are penetrating various facets of societal life, leading to a growing demand for interconnected devices. Many contemporary devices are no longer mere data producers but also consumers of data. As a result, massive amounts of data are transmitted to the cloud, but the latency generated in edge-to-cloud communication is unacceptable for many tasks. In response to this, this paper introduces a novel contribution—a layered computing network built on the principles of fog computing, accompanied by a newly devised algorithm designed to optimize user tasks and allocate computing resources within rechargeable networks. The proposed algorithm, a synergy of Lyapunov-based, dynamic Long Short-Term Memory (LSTM) networks, and Particle Swarm Optimization (PSO), allows for predictive task allocation. The fog servers dynamically train LSTM networks to effectively forecast the data features of user tasks, facilitating proper unload decisions based on task priorities. In response to the challenge of slower hardware upgrades in edge devices compared to user demands, the algorithm optimizes the utilization of low-power devices and addresses performance limitations. Additionally, this paper considers the unique characteristics of rechargeable networks, where computing nodes acquire energy through charging. Utilizing Lyapunov functions for dynamic resource control enables nodes with abundant resources to maximize their potential, significantly reducing energy consumption and enhancing overall performance. The simulation results demonstrate that our algorithm surpasses traditional methods in terms of energy efficiency and resource allocation optimization. Despite the limitations of prediction accuracy in Fog Servers (FS), the proposed results significantly promote overall performance. The proposed approach improves the efficiency and the user experience of Internet of Things systems in terms of latency and energy consumption. [ABSTRACT FROM AUTHOR]

Internet of things establishes communication among heterogeneous devices. IoT network is low power and lossy network known as LLN. The components in LLN use low power for its operations. The Internet Engineering Task Force (IETF) has defined routing protocol for standardized LLN, i.e., routing protocol for low-power and lossy networks (RPL). One of the major challenges in RPL is efficient conservation of node energy to improve the life of the LLN network. In the RPL network, most of the energy is consumed while regulating and controlling the packets rather than transmission. The algorithm used for regulating and controlling packet in RPL is called trickle timer algorithm. Hence to improve the lifetime of network it is essential to modify the existing trickle timer algorithm. In this paper, we have proposed a new algorithm called EE-trickle. The performance of EE-trickle is compared with existing trickle using the simulator Cooja and using open test bed of future Internet of things lab. From the experiments, it is identified that EE-trickle provides better PDR along with less energy consumption than the existing trickle. Hence, the paper helps the future researchers who work on energy consumption in RPL to make use of EE-trickle in their experiment rather than existing trickle. [ABSTRACT FROM AUTHOR]

Hybrid renewable energy systems (HRES) are gaining high interest in supplying electric energy for remote communities. Energy storage systems (ESS) are utilized by green autonomous HRESs to accommodate the variability of renewable resources such as wind and solar energy systems. The lack of any traditional energy source is adding a great reliability challenge which should be compensated using expensive ESS. This challenge can be avoided by using a pumped hydro energy storage system (PHES) in harmony with batteries. The PHES is an excellent option to be used in NEOM city due to the perfect topographical characteristic of this site. The minimum cost of energy and the highest reliability is used as an objective for sizing the proposed entire green HRES. Using smart grid principles (SGP) and demand‐side management (DSM) in the design and operation stages will minimize system size and cost, which can result in a significant reduction in consumer bills. As a result, this paper introduces an innovative DSM based on a dynamic tariff. The suggested DSM technique was developed utilizing a unique fuzzy logic that takes into account the present and day‐ahead ESS situations to intelligently determine the ideal tariff for the lowest cost and maximum reliability of the HRES. This paper introduces a modified grey wolf optimization (MGWO) technique to shorten convergence time while preserving the best accuracy. The suggested MGWO is assessed against 10 swarm optimization techniques. The payback period of the project is 7 years. The findings acquired from this unique program demonstrated its superiority, with conversion times reduced by 22% to 80% when compared to previous optimization procedures. Furthermore, as compared to the flat rate pricing tariff, the usage of the dynamic tariff lowered the LCOE by 53%. [ABSTRACT FROM AUTHOR]

In addition to pressure to improve energy efficiency, the maritime industry may soon face demands to reduce underwater noise from shipping. This is illustrated by a large number of studies being reviewed by UN panels to bring clarity to the subject. In this paper, the studies relevant to shipping and shipbuilding are reviewed. From this review the progress of the consultative process, possible shortcomings, and potential collaborative actions beneficial to the marine industry are identified. Key revelations include inconsistencies in the way the scientific community represents shipping noise and areas where more focus is needed to ensure resilience. Furthermore, several future research focus topics are suggested in order to address these issues. This paper does not argue for or against underwater noise being viewed as harmful to marine life; it only serves to review the current consultative process and how the maritime industry could benefit from participating. [ABSTRACT FROM AUTHOR]

This paper presents an artificial intelligence deep learning model for an energy-aware task scheduling algorithm based on learning automata (LA) in the Fog Computing (FC) Applications. FC is a distributed computing model that serves as an intermediate layer between the cloud and Internet of Things (IoT) to improve the quality of service. The IoT is the closest model to the wireless sensor network (WSN). One of its important applications is to create a global approach to health care system infrastructure development that reflects recent advances in WSN. The most influential factor in energy consumption is task scheduling. In this paper, the issue of reducing energy consumption is investigated as an important challenge in the fog environment. Also, an algorithm is presented to solve the task scheduling problem based on LA and measure the makespan (MK) and cost parameters. Then, a new artificial neural network deep model is proposed, based on the presented LA task scheduling fog computing algorithm. The proposed neural model can predict the relation among MK, energy and cost parameters versus VM length for the first time. The proposed model results show that all of the desired parameters can be predicted with high precision. [ABSTRACT FROM AUTHOR]

In the wake of the COVID-19 pandemic, prioritizing indoor air quality has emerged as a crucial measure for preventing infections. Effective ventilation is vital in mitigating airborne pathogen transmission and maintaining a healthy indoor environment by diluting and removing infectious particles from enclosed spaces. However, increasing the supply of pathogen-free air to enhance infection control can lead to a rise in energy consumption. Nevertheless, evaluating the overall efficacy of ventilation-based infection prevention strategies while considering their energy requirements has posed challenges. This scientific paper introduces the ICEE (Infection Control's Energy Efficiency) index, a newly developed simple integrated index to assess the effectiveness of ventilation strategies in reducing infection risks while accounting for associated energy demands. The paper reviews the current understanding of ventilation strategies, their impact on infection prevention, and their corresponding energy consumption. By employing a straightforward analytical approach, this metric offers a comprehensive framework to optimize ventilation systems for both infection prevention and energy efficiency. To quantify infection risk, a simplified equation model is utilized, incorporating factors such as ventilation effectiveness and filter efficiency, in case of recirculation. Energy demand is determined using approximations and relevant values from existing literature. Reference cases are defined, distinguishing between natural and mechanically ventilated scenarios, as these reference situations influence the energy-related effects of any implemented measures. The paper outlines the methodology employed to develop the index and illustrates its applicability through exemplary measures. The proposed index yields valuable insights for the design, operation, and retrofitting of ventilation systems, enabling informed decision-making towards fostering a healthier and more sustainable built environment. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a wireline serial link transceiver for on‐package die‐to‐die links based on 12‐nm fin field‐effect transistor (FinFET) technology. The link is crucial for improving the computational performance of larger systems built from chiplets. To achieve high pin/energy efficiency and prevent traditional single‐ended signaling disadvantages, the transceiver adopts single‐ended ground‐reference signaling (GRS) to transfer information. To precompensate for the frequency‐dependent loss, retain the advantages of GRS, and avoid the disadvantages of traditional AC‐coupled equalizer, the transmitter in the transceiver adopts a DC‐coupled switched‐capacitor charge pump equalizer that can transmit GRS and its equilibrium value can be adjusted according to the channel attenuation. This paper proposes a method for timing de‐skew using digital control delay lines to eliminate the timing skew between data and the clock. In addition, this paper presents a duty cycle corrector to avoid duty cycle distortion, with an adjustment range of 44.5% to 52% and a power dissipation of only 30 μW. The transceiver has an area of 1.016 × 0.676 mm2, including one clock lane link and four data lane links. At a nominal 0.8‐V power supply voltage, the aggregate eye‐opening of the measured 25 Gb/s data on an organic substrate channel with an attenuation of −2.2 dB over 6 mm is 0.675 UI, with a bit error rate (BER) of less than 10−12 and an energy efficiency of 1.01 pJ/bit. [ABSTRACT FROM AUTHOR]

In Intelligent Transport Systems (ITSs), Internet of Vehicles (IoV) communications and computation offloading technology have been introduced to assist with the burdensome sensing task processing, thus prompting a new design paradigm called mobile sensing–communication–computation (MSCC) synergy. Most researchers have focused on offloading strategy design to reduce energy consumption or execution costs, but ignore the intrinsic characteristics of tasks, which may lead to poor performance. This paper studies the offloading strategy of vehicle MSCC tasks represented by a Directed Acyclic Graph (DAG) structure. According to the DAG dependency of the subtasks, this paper proposes a computation offloading strategy to optimize energy consumption under time constraints. An energy consumption model for task execution is established. Then, the Simulated Annealing and Tabu Search hybrid optimization algorithm (ST-HO) is designed to solve the problem of minimizing the energy consumption. Crucially, this research integrates the concept of symmetry into the typical DAG structure of MSCC tasks, ensuring the integrity and efficiency of task execution in ITS. The simulation results show that ST-HO reduces energy consumption by at least 5.58% compared to the conventional algorithm. Particularly, the convergence speed of ST-HO is improved by 52.63% when the replication strategy of symmetric task is considered. [ABSTRACT FROM AUTHOR]

This paper presents a three-port isolated hybrid converter (3PIHC) with extended phase-shift modulation (EPM) to reduce voltage and current stress in the converter for DC microgrid applications. A high-frequency transformer with three independent windings is employed for isolating three ports in the converter. A wide variation in voltage transformation ratio is one of the main concern in DC microgrid, which need to be addressed for effective power conversion and utilization of the energy generation systems. Traditional phase-shift modulation (TPM) in 3PIHC leads to high conduction and core losses in the converter under wide variation in voltage transformation ratio. This paper proposes an extended phase-shift modulation in 3PIHC for different sequence of voltage transformation ratios between port 1 and port 2 (m 12) and between port 1 and port 3 (m 13) , which is a cost-effective solution for reducing losses in the converter. The peak current at all ports and power equations in 3PIHC are deduced, and the computational method for obtaining the phase-shift values with minimum current stress is explained in detail. A comparative evaluation study proves that the proposed three-port converter with EPM outperforms to TPM in the aspects like low current stress, reduced semiconductor switching losses and transformer losses for wide variation in voltage transformation ratios. A hardware prototype of the proposed converter is built, and the results are evaluated and presented. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to assess the impact of electrifying the environmental control system (ECS) and ice protection system (IPS), the primary pneumatic system consumers in a conventional commercial transport aircraft, on aircraft weight, range, and fuel consumption. Design/methodology/approach: The case study was carried out on Airbus A321-200 aircraft. Design, modelling and analysis processes were carried out on Pacelab SysArc software. Conventional and electrical ECS and IPS architectures were modelled and analysed considering different temperature profiles. Findings: The simulation results have shown that the aircraft model with ±270 VDC ECS and IPS architecture is lighter, has a more extended range and has less relative fuel consumption. In addition, the simulation results showed that the maximum range and relative fuel economy of all three aircraft models increased slightly as the temperature increased. Practical implications: Considering the findings in this paper, it is seen that the electrification of the conventional pneumatic system in aircraft has positive contributions in terms of weight, power consumption and fuel consumption. Social implications: The positive contributions in terms of weight, power consumption and fuel consumption in aircraft will be direct environmental and economic contributions. Originality/value: Apart from the conventional ECS and IPS of the aircraft, two electrical architectures, 230 VAC and ±270 VDC, were modelled and analysed. To see the effects of the three models created in different temperature profiles, analyses were done for cold day, ISA standard day and hot day temperature profiles. [ABSTRACT FROM AUTHOR]

The object of the research of the article is transport system of a city. The paper reviews the necessity to assess the greenhouse emissions in the city and proposes the methods for greenhouse emissions inventory of the urban transport system. The proposed approach is aligned with guidelines for the development of Sustainable Energy and Climate Action Plan (SECAP) of Mayor Covenants of European Union (EU). The methodologies outlined in the paper allow to estimate annual greenhouse emissions from transport sector. The SECAP defines transport sectors based on ownership and functioning as following: municipal transport fleet, public transport, and private and commercial transport. The paper proposes the methodology to estimate direct and indirect emissions in each of the described sector based on the information that is typicaly available to municipalities in Ukraine. The assessment is conducted on disaggregated level for different fuel types (diesel, petroleum, natural gas, biofuel, electricity, etc.) and separately for each fleet type (buses, trucks, passenger vehicles, specialized machinery). Total CO2 emissions are then estimated by multiplying the amount of fuel consumed by the emission factor for each fuel type and vehicle type. Information of fuel consumption is estimated based on annual milage and estimated based on available data and number of assumptions proposed in the paper. The proposed methodology for greenhouse emissions inventory for the transport sector allows to analyze available data, recommendations for data collection and a methodology for determining CO2 emissions from the operation of the transport system with sufficient accuracy of calculations. Based on these results, it is possible to forecast changes in energy consumption and emissions in the transport sector as a result of various interventions. For example, in Zhytomyr city the results of inventory were used to develop a set of measures, which include updating the rolling stock of electric transport; increasing the energy efficiency of the power grids of the transport system, developing cycling infrastructure. [ABSTRACT FROM AUTHOR]

Since the power generation structures and load characteristics in each province in China are quite different, the distribution of peak regulating resources and demands are extremely imbalance. Restricted by a low power marketization degree, peak regulating resource shortages, and transmission channel blocks, the efficient utilization of new energy is facing greater pressures. In order to improve the mutual aid in regional power grids and to obtain more precise simulation results, this paper proposes a two-layer optimization dispatching model, considering the mutual aid of peak regulation resources between each province. It determines the optimal startup mode and the units' power output in each province and obtains the power output arrangements for all the units and the technical and economic indicators. The model and the solution method are original and innovative. And it effectively solved the unequal distribution problem between the peak regulating demands and resources of each provincial power grid. Finally, taking an actual regional power grid in China as an example, the simulation results show that the proposed model can significantly improve the utilization rate of new energy, which verifies the effectiveness and feasibility of the proposed model and methods presented in this paper. [ABSTRACT FROM AUTHOR]

Abstract: With the final aim of reducing the energy consumption and increase the methane production at Swedish pulp and paper mills, the methane potential of 62 wastewater effluents from 10 processes at seven pulp and/or paper mills (A–G) was determined in anaerobic batch digestion assays. This mapping is a first step towards an energy efficient and more sustainable utilization of the effluents by anaerobic digestion, and will be followed up by tests in lab-scale and pilot-scale reactors. Five of the mills produce kraft pulp (KP), one thermo-mechanical pulp (TMP), two chemical thermo-mechanical pulp (CTMP) and two neutral sulfite semi-chemical (NSSC) pulp. Both elementary and total chlorine free (ECF and TCF, respectively) bleaching processes were included. The effluents included material from wood rooms, cooking and oxygen delignification, bleaching (often both acid- and alkali effluents), drying and paper/board machinery as well as total effluents before and after sedimentation. The results from the screening showed a large variation in methane yields (percent of theoretical methane potential assuming 940NmL CH4 per g TOC) among the effluents. For the KP-mills, methane yields above 50% were obtained for the cooking effluents from mills D and F, paper machine wastewater from mill D, condensate streams from mills B, E and F and the composite pre-sedimentation effluent from mill D. The acidic ECF-effluents were shown to be the most toxic to the AD-flora and also seemed to have a negative effect on the yields of composite effluents downstream while three of the alkaline ECF-bleaching effluents gave positive methane yields. ECF bleaching streams gave higher methane yields when hardwood was processed. All TCF-bleaching effluents at the KP mills gave similar degradation patterns with final yields of 10–15% of the theoretical methane potential for four of the five effluents. The composite effluents from the two NSSC-processes gave methane yields of 60% of the theoretical potential. The TMP mill (A) gave the best average yield with all six effluents ranging 40–65% of the theoretical potential. The three samples from the CTMP process at mill B showed potentials around 40% while three of the six effluents at mill G (CTMP) yielded 45–50%. [Copyright &y& Elsevier]

[Display omitted] • Novel method to estimate total technical potential for improving energy efficiency. • Bottom-up sector energy model used to characterize energy demands and opportunities. • Technology-explicit analysis framework used to estimate integrated energy savings. • Application to Canada's pulp & paper sector indicates major savings are possible. • Technical potential to reduce natural gas use by 95% and net electricity by 54% Industrial energy efficiency has long been recognized as a critical tool for energy management and emissions reduction and is increasingly considered to be an energy resource in its own right. However, the complexity of most industrial sectors creates barriers for modellers, companies, and policymakers in understanding and achieving the overall potential for efficiency-driven energy savings. To address this gap, we have developed a novel method that combines disaggregated bottom-up sector energy modelling with analysis of a comprehensive set of technology-explicit energy efficiency measures. This approach overcomes the deficiencies of top-down analyses and better represents the complexities of multi-measure efficiency investments compared to piecewise studies of individual measures. In this study, we demonstrated application of our method to the case of the Canadian pulp and paper sector. Our analysis shows that the sector can reduce its natural gas and electricity use by 95% and 41%, respectively, by adopting economical efficiency measures. This equates to 71 PJ of natural gas and 44 PJ of electricity saved annually and would result in cost savings of $81 per tonne of output sector-wide. Notably, we find that efficiency and fuel switching have the technical potential to reduce natural gas consumption by 98% across the sector. Taken together, the results provide quantitative evidence that energy efficiency is an underestimated resource for cost-effective energy savings in the Canadian pulp and paper sector. Our analysis framework can be applied to any industrial sector in any region to provide insights regarding energy conservation strategies and policy design. [ABSTRACT FROM AUTHOR]

This study presents a comparative cradle-to-gate life cycle assessment (LCA) of distillers grain (DG) gum, a bio-based paper coating binder, and polyvinyl alcohol (PVA). Non-renewable energy use, greenhouse gas (GHG) emissions, and eutrophication potential were assessed for each binder. Economic, mass, and energy allocation were used to allocate the impacts of DG gum production with co-products (ethanol and livestock feed). DG production non-renewable energy use (269 to 183 MJ) surpassed that associated with PVA production (168 MJ). GHG emissions from DG gum production under mass and energy allocations were 28% and 37% lower than PVA production emissions, respectively. Corn cultivation is responsible for 55% to 78% of the eutrophication impacts of DG gum production under energy and economic allocation, respectively. Changes to natural gas consumption and fertilizer runoff had the largest influence on total energy use, GHG emissions, and eutrophication potential of DG gum production. [ABSTRACT FROM AUTHOR]

• Caustic recovery from green liquor using electrolysis. • Feed concentration, current density, flow rate, cell geometry etc. were optimized. • Current efficiency/energy consumption were evaluated with optimized process conditions. • Results were compared between the synthetic and industrial green liquor. In the chemical recovery section of pulp and paper mills, the cooking liquor (white liquor, mainly caustic, NaOH) is regenerated by addition of fresh lime (CaO) in green liquor solution (rich in sodium carbonate, Na 2 CO 3), where calcium carbonate (CaCO 3) get precipitated leaving NaOH in solution (causticizing process). Subsequently, CaO is regenerated from precipitated CaCO 3 following washing/drying/calcination steps which are highly energy intensive and environmentally unsafe. The current investigation reports conversion of green liquor to caustic solution using electrolysis through a cation exchange membrane (CEM) placed between two electrodes. Process parameters e.g. current density, feed concentration, flow velocity, flow promoter, electrode-membrane spacing, role of gravity flow were systematically investigated to find optimum values. Process optimization was performed with Na 2 CO 3 (highest constituent of green liquor) solution to avoid complexity, which were then extended to complex salt mixtures of green liquors (synthetic/ industrial sample). Performance analysis of any batch was carried out based on moles of NaOH produced and associated current efficiency and energy consumption observed. With 600 A·m−2 current density, 1.93 mol·L−1 of NaOH could be produced in 6 h of electrolysis through a cell of 66 × 10−4 m2 of CEM, where flow rates of both feed and catholyte streams were counter current and fixed at 3.472 × 10−3 m·s−1. [ABSTRACT FROM AUTHOR]

A general mathematical model is developed for electrochemical energy conversion in paper-based, capillary-driven, microfluidic flow cells. A two-dimensional version of the model is implemented to simulate a novel flow cell of this kind called PowerPAD. The methodology developed is validated against experimental data for this particular cell under both steady and unsteady modes of operation. It is shown that a 2D model can represent a 3D cell in unsteady flows provided that the Nernst-Planck equation is properly refined. The results are interpreted in terms of transient back-diffusion and crossover of the reactant species which are found to be the key factors controlling the electrochemical performance of this cell. The validated model is used to enhance the power output, runtime, and energy efficiency of this particular cell through altering its flow architecture and microstructure of electrodes and absorbent pad. The refined cell design is shown to roughly triple the power output and runtime of the device. The optimized design also outperforms the original design in terms of energy efficiency. The developed mathematical model, the fundamental understanding, and the proposed cell design may be utilized to develop new devices and concepts for capillary-based electrochemical systems, including biodegradable batteries, fuel cells, and electrolyzers. [Display omitted] • A dynamic mathematical model is developed for paper-based microfluidic flow cells. • A 2D model can represent a 3D cell subject to Nernst-Planck equation refinement. • The model reveals the unique working mechanism of imbibition and back-diffusion. • Crossover is identified as the key limiting factor for cell performance and runtime. • An optimized cell design is proposed with 3x performance improvement. [ABSTRACT FROM AUTHOR]