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Increasing energy efficiency in manufacturing has significant environmental and cost benefits. Turning on or off a machine dynamically while considering the production rate requirements can offer substantial energy savings. In this work, we examine the optimal policies to control production and turn on and off a machine that operates in working, idle, off, and warmup modes for the case where demand inter-arrival, production, and warmup times have phase-type distributions. The optimal control problem that minimises the expected costs associated with the energy usage in different energy modes and the inventory and backlog costs is solved using a linear program associated with the underlying Markov Decision Process. We also present a matrix-geometric method to evaluate the steady-state performance of the system under a given threshold control policy. We show that when the inter-arrival time distribution is not exponential, the optimal control policy depends on both the current phase of the inter-arrival time and inventory position. The phase-dependent policy implemented by estimating the current phase based on the time elapsed since the last arrival yields a buffer- and time-based policy to control the energy mode and production. We show that policies that only use the inventory position information can be effective if the control parameters are chosen appropriately. However, the control policies that use both the inventory and time information further improve the performance. [ABSTRACT FROM AUTHOR]

Energy efficiency is crucial in contemporary industry and controlling the resource power state by switching off/on commands is a promising measure. The control problem of deciding when to switch off/on the machines depending on the state of the system at a given time is not trivial due to the effect the control might have on the system production rate. Threshold-based policies using buffer occupancy information to control the machines can be effectively used to reduce energy consumption. Nevertheless, highly complex control policies are difficult to be applied and costly to be managed in practice. Buffer-based threshold policies to control multiple machines simultaneously in a serial production line for energy efficiency purposes are analysed in this work. The optimal control minimises the energy consumption while assuring a certain target production rate for the system. The effects of controlling different combinations of machines simultaneously with different number of thresholds have been investigated through numerical experiments with discrete event simulation. Insights regarding the trade-off between the complexity of the control and the performance gains are provided. The proposed policy works effectively and the effect of a proper selection of the controlled machines or thresholds is significant. [ABSTRACT FROM AUTHOR]

The residential sector is a critical contributor to global energy consumption. In Ghana, it accounts for 47% of the total electricity consumed. However, inadequate knowledge of categories of residential consumers, their profiles, and the implications on their consumption patterns could impede the development of targeted and sustainable interventions. This study examined the behavioral patterns among low-income urban households in Kumasi, using their socio-demographic and housing characteristics, and self-reported values. This is coherent to the current discourse on Ghana’s transition to zero carbon by 2070 given that Kumasi is the fastest-growing city in the country. Using quantitative methods within a transdisciplinary research design, a survey of 285 households was conducted. The findings yielded varied outcomes. Households reported good electricity use behavior (e.g. covering liquids in the refrigerator) and poor behaviors (e.g. not adjusting the power setting of refrigerators to weather conditions). Logistic regression operations showed associations between energy use behavior, the type of housing units and the type of electricity meters. A significant proportion of respondents exhibited pro-environmental behaviors across all nine value domains. The study concludes that low-income households engage in energy-saving practices. We suggest that policy mediation could enhance the effectiveness of behavioral interventions toward electricity use efficiency. [ABSTRACT FROM AUTHOR]

This study aims to provide energy-efficient strategies for collections care in the face of financial and environmental challenges. Focusing on paper-based enclosures in storage rooms, the research proposes strategies to enhance energy efficiency by relaxing tight environmental controls. The five-stage process began with a standard test of hygrothermal properties and buffering capacity of the enclosures. In the second stage, a computer model was developed using the first stage results. This model was used in a series of specially designed tests simulating the transient heat, air, and moisture transfer between the room and enclosure to determine an acceptable room environment in which the enclosure conditions could meet collections care standards. The modelling results were used, in the third stage, to develop an AI model that predicted the enclosure conditions using room temperature and relative humidity (RH) inputs. In the final stage, a model for energy consumption of air-conditioning system operations was developed to predict across various control accuracies. Results indicate that the tight maintenance of room RH levels can be relaxed from 47–53% to 33–65%, with a corresponding reduction in energy consumption of approximately 16–18%. This relaxation, while maintaining enclosure conditions in line with conservation standards, was demonstrated through a simulation case at the National Library of Scotland. This study underscores the broader applicability of the developed procedure, asserting its relevance for cultural institutions seeking energy-saving potential through customized enclosure models. [ABSTRACT FROM AUTHOR]

Résumé: De manière agrégée, les PME sont de grandes consommatrices d'énergie et ont des impacts environnementaux considérables au niveau mondial. Malgré cela, elles attirent encore peu l'attention des chercheurs et des responsables des politiques publiques. Pourtant, sans leur contribution, l'atteinte des cibles nationales en matière énergétique et environnementale est improbable. En ce sens, il devient essentiel de mieux comprendre ce qui les incite à devenir plus mature au niveau énergétique. La présente étude adopte une analyse multiniveau en considérant les aspects individuel, organisationnel, contextuel pour identifier les déterminants de la maturité énergétique des PME. Une étude menée auprès de 409 PME québécoises permet d'établir que ce sont les facteurs organisationnels intangibles qui sont les principaux déterminants de la maturité énergétique dans les PME québécoises. Les facteurs contextuels n'ont pas d'influence et un seul facteur individuel, l'âge du dirigeant, exerce une influence limitée. Cette reconnaissance de la prépondérance des facteurs organisationnels intangibles est un apport aux écrits sur le sujet qui ne font pas cette distinction lorsqu'ils considèrent les freins et accélérateurs des pratiques d'efficacité énergétique. Cela procure aussi un enseignement pratique important: l'acquisition de compétences et d'attitudes proactives dans les PME envers l'efficacité énergétique est un levier d'action à prioriser. In an aggregate manner, SMEs are very large consumers of energy and have significant environmental impacts worldwide. Nevertheless, they still attract little attention from researchers and public policymakers. Yet, without their contribution, the achievement of national energy and environmental targets is unlikely. In this sense, it becomes essential to better understand what motivates them to become more energetically mature. This study adopts a multi-level analysis that considers individual, organizational, contextual aspects to identify the determinants of SME energy maturity. A study of 409 Quebec SMEs shows that intangible organizational factors are determinants of energy maturity in Quebec SMEs. Contextual factors have no influence and only one individual factor, the age of the leader, has a limited influence. This recognition of the preponderance of intangible organizational factors is a contribution to the literature on the subject, which does not make this distinction when considering the obstacles and the accelerators of energy efficiency practices. It also provides an important practical lesson: the development of proactive skills and attitudes in SMEs towards energy efficiency is a lever of action to prioritize. [ABSTRACT FROM AUTHOR]

The rental market, notably within the UK, is facing increased scrutiny with the tightening of regulation in relation to energy performance and the increases in energy costs and rental unaffordability. Whilst a sizeable volume of research has examined the pricing effects of EPCs and house prices, scrutiny of this relationship within the private rental sector remains more embryonic. This study, using 2,914 transactions for Northern Ireland, extends the traditional analysis beyond the conditional mean estimate by examining the quantiles of the relationship between EPCs and rental prices. The findings provide evidence of a rental premium of 0.2% for a one-point improvement in energy efficiency. In terms of EPC ratings, we find premiums for B- and C-rated dwellings of 8.2% and 2.4% and also discount effects for E, F and G-rated properties ranging between 3.9%-5.5%. The quantile findings exhibited a parabolic effect across the price distribution demonstrating the lowest and highest priced properties to comprise higher price premiums of 13% and 16% for B-rated dwellings, however more pronounced discount effects for F- and G-rated properties at the lowest and highest deciles. The findings provide evidence to help alleviate the split-incentive problem between landlords and tenants within the UK. [ABSTRACT FROM AUTHOR]

Energy efficiency is considered critical towards sustainability for all businesses, given that traditional energy intensification is not only an expenditure but also a proportional source of carbon emissions. However, it is unclear how investors consider a firm's energy efficiency while deciding on its cost of debt. This study aims to analyse the relationship between firms' energy efficiency and the cost of debt using panel data regression with a fixed effect on 17,175 firm-year observations of 2284 companies. The endogeneity effects of variables have been controlled using instrumental variables. The study found evidence that energy efficiency leads to a reduction in the cost of debt. This study also analyses the impact of energy efficiency on investments made in research and development. The findings suggest that efficient energy firms are technologically advanced through higher investments in research and development, effectively improving the firm's values and helping to reduce their cost of debt. The results support that energy-efficient firms are technologically advanced and better positioned regarding firm value. Thus, they are regarded as secure funding with reduced costs of debts. This research demonstrates that lenders must be responsive to modern-day cultures' social concerns through better social engagements in the context of global warming and climate change. [ABSTRACT FROM AUTHOR]

AbstractTo elucidate the effects of energy input modes on energy efficiency and berry quality in continuous microwave drying, four energy input modes with variational working ways of magnetrons were investigated by using numerical simulation and bench experiments in a continuous microwave dryer. Energy efficiency was divided into absorption and conversion efficiency to study the change rule of drying process, and the retention rate of anthocyanins of berry’s representative nutrition ingredient was studied considering both of the temperature and its uniformity. Results indicate that the energy input modes affect the absorption efficiency by changing the distribution uniformity of electric field of material layer, and the conversion efficiency by altering the matching degree between the energy allocation and drying characteristics of berry, which are both determined by the ways of working magnetrons of different energy input modes. The conversion efficiency has a greater influence on the final energy efficiency, so the energy input mode should be paid attention to the provision of microwave energy by selecting proper way of working magnetrons according to the drying characteristics. The effect of temperature uniformity on anthocyanin degradation is more obvious with average temperature increasing gradually, where better uniformity of electric field strength should be provided to guarantee the quality of dried berry by adjusting the energy input mode. The research results provide significant insights to optimize the energy input mode of taking into account both the energy efficiency and dried quality of material. [ABSTRACT FROM AUTHOR]

Through energy and exergy analysis, the present work revisits the combustion processes generated using different fuels (CH4, C3H8, H2, LPG, Natural gas and Biogas) under the same operating conditions. First, numerical computations, in which the turbulent dynamic k-ϵ model coupled with a probability density function (PDF) approach together with different sub-models that ensure the reliability of the results, are used. Then, the First and Second Laws of thermodynamics are applied to all fuel combustion cases considered previously to predict the energy loss, exergy destruction and their corresponding efficiencies. Next, variations in the air factor, the air inlet temperature and the fuel inlet temperatures are considered to analyse the response of combustion system performance as well as energy and exergy losses for the different fuels under investigation. The results show an amelioration of combustion parameters for the mixture of fuel and air using values of λ greater than 1. Also, the preheating of the inlet air temperature has a positive effect on energetic parameters. However, preheating the inlet fuel does not take a considerable role in the efficiency amelioration and loss reduction of the combustion system. It further turns out that biogas features higher energy efficiency compared to other fuels, at approximately 89.64%, while hydrogen fuel exhibits higher exergy efficiency at around 78.70% followed by biogas with 74.96%. [ABSTRACT FROM AUTHOR]

Germany has not yet made significant progress toward achieving SDGs 7 and 13. This challenge can be attributed to the underlying financialization problem in Germany, as well as implementation issues related to energy efficiency, particularly in coal and gas, and renewable energy consumption. Given these circumstances, Germany is facing challenges in reducing greenhouse gas emissions. Addressing this issue may necessitate a policy realignment, which is the primary focus of this study. In this context, our study employs various wavelet-based tools, including wavelet coherence, multivariate wavelet coherence, and wavelet-based causality, to delve into the co-movements, causality, and direction of causality between the load capacity factor (LF) and key factors such as energy efficiency (gas and coal), technological innovation, renewable electricity, and financial development, focusing on Germany during the period from 1990Q1 to 2020Q4. The results from wavelet coherence, corroborated by wavelet cohesion, indicate that in the short and medium term, improvements in coal efficiency, gas efficiency, financial development, and renewable energy are associated with an increase in LF, thereby contributing to ecological quality. Furthermore, the multivariate wavelet coherence results underscore the significant impact of considering or not considering the effect of a third variable in the interrelationship between LF and its determinants, with the effect being more pronounced when the third variable is taken into account. Although this policy framework primarily targets the objectives of SDG 13 and 7, its applicability extends to other EU nations. The primary significance of this study is its proposal of an SDG-focused policy framework. [ABSTRACT FROM AUTHOR]

The marine vessel performance is quite critical in the maritime applications. In addition to internal variables like fuel, propulsion type, and loading condition, external factors such as fouling, sea state, currents, and more affect ship performance. The goal of this study is to create a model to predict ship slip rate based on specified noon report data which contain sea, wind, WED, and, DAS. 30 different noon report values of a crude oil tanker have been conducted on the same route with similar loading condition and they have been analysed by the MLR and the ANN respectively. Although a meaningful relationship between the variables couldn't create among the variables by the MLR, predicted ship slip values are very close to the verified ones in the ANN. Minor differences could be caused by changes in the amount of hull fouling, vessel loading conditions, reverse or different currents, quite unsettled weather, and sea conditions. [ABSTRACT FROM AUTHOR]

Residential energy policies for individual dwellings predominantly provide minimum design standards for thermal performance and energy efficiency on a square metre basis. This paper quantifies predicted dwelling energy use on a national scale to enable evaluation of the effectiveness of this policy model. Utilizing Australia as a case study, analysis of 580,956 thermal energy design certificates for new detached dwellings verifies that, although minimum energy efficiency design standards were achieved, predicted energy use for thermal comfort increased by 10.4% per dwelling and 5.6% nationally in 2022 compared to 2018. The study examines the impact of both the number and floor area of new dwellings on dwelling energy use and highlights the disparate jurisdictional outcomes resulting from inconsistent implementation of national policy across multi-state governance. To deliver reductions in household energy use it is imperative that residential energy policies target household energy use, rather than only energy efficiency, of new dwellings, of which floor area is a key determinant. Furthermore, to achieve global climate change objectives, there is a need for complementary policy mechanisms that consider the collective energy use of the residential dwelling sector. [ABSTRACT FROM AUTHOR]

Energy saving and the development of renewable energies already form part of the daily discourse in our society. Climate change and the environmental problems arising from it affect all of us on the planet. In view of the recent energy supply crisis in Europe and in line with the EU's Sustainable Development Goals, it is necessary to accelerate the ecological transition. This means, among many other things, encouraging energy efficiency and energy savings. This research is based on the EURONET 50/50 max project, which applied a methodology with no upfront capital outlay, but based on changing user habits to save energy in educational buildings. It was carried out in twelve educational centres of the Spínola Foundation throughout Spain. Significant reductions in energy and water consumption were achieved. The maximum reduction obtained in water consumption was 29%, 24% in electricity consumption and 33% in heating consumption. The average reduction in consumption obtained was 13% compared to 11% achieved in the 50/50 max project. It was concluded that, on the journey to optimising our energy consumption, working on patterns of usage is fundamental, raising awareness and involving users in order to give rise to permanent processes over time, gradually reversing climate change. [ABSTRACT FROM AUTHOR]

This study aimed to develop a novel, sustainable, highly efficient, and affordable salt processing technology tailored to local salt producers. Novel technology converts raw solar sea salt into the finest dried salt, which is widely accepted by various industries. Experiments were conducted at a drying temperature of 80°C, with an air velocity of 30 m/s, and a feed rate of 125 kg per hour to evaluate the proposed novel salt processing technology. Performance evaluation was based on product quality parameters (e.g. wet moisture content, sodium chloride (NaCl) percentage, and whiteness) and specific energy consumption. The economic assessment relied on financial analysis and payback period parameters. The results indicated a reduction in salt sample moisture content from 10.37% ± 0.4% to 0.47% ± 0.02%, with NaCl percentage and whiteness of the salt produced at 98.5% ± 0.3% and 81 ± 0.7, respectively. The specific energy consumption for the novel salt processing technology was 0.119 ± 0.07 kWh/kg of the final salt product. Initial investment requirements totalled IDR 275,435,000, with a payback period of 1.66 years. This proposed novel salt processing technology has the potential to generate high-quality dried salt with a production efficiency of 89.5%. Consequently, it could enhance local producer’s businesses, seize opportunities in the national salt market, support rural industrialisation, and be best configured for Indonesia’s local salt producers. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid fuzzy-Particle Swarm Optimization (PSO) approach for power allocation in bidirectional Light Fidelity (LiFi)-enabled Internet of Things (IoT) communication systems. The approach integrates fuzzy logic and PSO to achieve efficient and robust power allocation, considering energy efficiency and Quality of Service (QoS) requirements. The hybrid approach leverages flexibility of fuzzy logic to handle uncertainties and variations in system parameters, such as channel conditions and QoS requirements. Fuzzy sets and membership functions are defined to represent linguistic variables, and fuzzy rules are formulated based on expert knowledge and system-specific considerations. This allows the approach to adaptively adjust power allocation based on varying channel conditions and QoS requirements, enhancing the robustness of the power allocation strategy. The PSO algorithm is employed for iterative optimization to explore for ideal power allocation solution. The particles in swarm signify possible power allocation configuration, and its position in the search space corresponds to a specific power allocation. The particles update their positions based on local best-known positions and globally best-known positions, allowing the swarm to converge toward the optimal solution. Through extensive simulations and analysis, the proposed hybrid fuzzy-PSO approach is evaluated with regard to energy efficiency and QoS satisfaction. The results demonstrate its effectiveness in achieving energy-efficient power allocation while meeting diverse QoS requirements. The proposed approach outperforms existing methods such as channel- and Orthogonal Multiple Access (OMA)-based power allocation schemes. [ABSTRACT FROM AUTHOR]

One of the top priorities of most countries around the world is sustainable development, to achieve which financial inclusion is identified as one of the key elements. Thus, unlike previous studies, the current study aims to channel the impact of financial inclusion on energy efficiency by constructing a parametric-based index that covers availability, accessibility, and usage of the former. The index provides an overall picture of financial services in all the three covered aspects. The analysis based on novel econometric time-series approaches, the method of moment quantile regression and the robust least square approach, shows that financial inclusion increases energy efficiency. The impact is overall positive for each quantile, i.e. 25th–90th. The impact of green innovation, human capital, and political risk is also supportive for energy efficiency. However, the impact of political risk improvement exhibits negative in the initial stages and turns positive later. In terms of policy implications, broadening financial inclusion base with the promotion of green innovation and human capital is important to achieve higher energy efficiency. In the meantime, improvement in the political risk profile should also be considered for a stable financial system to pave the way for increasing energy efficiency. [ABSTRACT FROM AUTHOR]

The gap in demand and supply of energy across Sub-Saharan African (SSA) countries has increased energy insecurity in the region. Therefore, power outages have become pervasive, causing unemployment and a decline in production output. Among the several energy-saving factors identified in the literature across developing economies, technological spillover driven by trade openness appears to be a prominent factor in improving energy efficiency. Thus, this study evaluates the impact of machinery imported from OECD and non-OECD countries and its corresponding research and development (R&D) spillover on energy efficiency performance in 18 SSA countries from 1995 to 2017. Using a stochastic energy distance function, we discover that aggregated data from OECD and non-OECD countries have no significant effect on energy efficiency performance across SSA countries. However, results from disaggregated data for OECD and non-OECD machinery imports show that OECD machinery imports improve energy efficiency contrary to non-OECD imports. Thus, technology spillover from OECD countries is advantageous for SSA countries to reduce long-term energy-based emissions. Furthermore, our results show that human capital has no significant effect on SSA energy efficiency. Consequently, the results possess some policy implications; for instance, policymakers responsible for promoting science and technology could increase investment in human capital development by developing technology and engineering expertise and increasing GDP allocation to R&D activities. For energy efficiency scores, we observe substantial differences in efficiency across SSA countries – implying potential improvements in energy efficiency across SSA countries. [ABSTRACT FROM AUTHOR]

Buildings are responsible for around 45% of total energy consumption and one-third of CO2 emissions annually in Switzerland. Policymakers have argued that an increased rate of energy-saving retrofits in existing buildings will play a critical role in meeting the energy and climate policy goals. This article examines the determinants for the households' investment decisions to undertake energy-efficient retrofits and investigates the role of policy measures. We estimate random effects probit models using a rich data for 1663 owned single-family Swiss households for retrofits undertaken during 2010 to 2014. Results suggest that while the building vintage appears to be a relevant determinant; energy-related attitudes of decision makers and policy instruments are also likely to play an important role. In particular, we find a suggestive evidence of tax deduction policy in supporting households' decisions to undertake energy-saving retrofits as well as the intensity of renovation. Direct monetary policies such as tax deductions should be focused particularly upon poorly insulated old buildings and those that rely on non-renewable energy sources for heating purposes. [ABSTRACT FROM AUTHOR]

Reducing the power consumption of oil production wells is one of the important directions of oil industry development in the context of transition of many fields to the late stages of operation. At the same time, there is often a need to limit hydrocarbon production, which is often accomplished by shutting down wells completely. In this paper, the authors propose an approach to improve the energy efficiency of the electric centrifugal pump unit through the use of periodic operation mode, which allows to reduce the specific power consumption of the electric centrifugal pump unit. Also, this mode can be considered as a way to regulate the well flow rate. According to the results of calculations it was obtained that the reduction of specific power consumption of the unit when operating at nominal frequency can reach 5% relative to the continuous mode of operation. An assessment was made of the effect of reducing the frequency of the supply voltage in a periodic mode on the parameters of the operation of the installation and it was found that the well flow rate increased by 1.43% with a reduction in specific power consumption by 1.64%. [ABSTRACT FROM AUTHOR]

This study validates the mechanism of ultrasonic dehydration without transition of water into vapor. The process can be realized by cavitation dispersion of liquid initiated by collapse of cavitation bubbles of cylindrical shape formed in capillaries. The developed mathematical model considers the complete life cycle of cavitation bubbles, encompassing three stages: slow growth, rapid expansion with deformation, and collapse. The optimum range of sound pressure level from 150 dB (critical level) to 170 (limit level) at which cavitation dispersion of liquid from capillaries takes place has been determined. The optimum sample size corresponding to the ultrasonic wavelength in gas, at which the dehydrated productivity is maximized, has been determined. The mechanism of ultrasonic dispersion of liquid during drying was confirmed by experiments. Practical implementation of ultrasonic drying of food products (carrots) confirmed effectiveness of implementation of dehydration mechanism and provided a reduction of drying time by 1.9 times with a reduction of power consumption by 1.7 times. [ABSTRACT FROM AUTHOR]

Due to the temperature control function, the unit energy consumption of cold chain transportation is more than that of ordinary transportation. Although rail transportation is known as an energy-efficient means, little attention has been given to the energy efficiency of railway cold chain transportation (RCCT). To address this gap, in this research, detailed waybill data for China's railway cold-chain is applied. First, we investigate the RCCT market in China and provide an energy consumption calculation method for RCCT. Then, the provincial energy consumption and the efficiency of RCCT are compared based on a DEA-SBM model. Finally, the Tobit model is utilized to assess how various factors influence energy efficiency. The findings could provide support for energy-saving policy-making in the cold chain transportation sector at the national and regional level, which has been discussed in scenario analysis. [ABSTRACT FROM AUTHOR]

AbstractIn this paper I discuss a case of ‘green’ redevelopment of a social housing complex in former East Germany, and unpack the key elements that have led to its comprehensive regeneration without widespread negative social externalities. In Drewitz, a former GDR housing estate in the Berlin/Brandenburg metropolitan region, it has been possible to preserve existing social housing units, achieve high standards of energy efficiency, expand public green space and provide improved opportunities for education and health. I identify the key factors that have enabled this ‘renovation without renoviction’: 1) redevelopment has been undertaken by a municipal housing company under public control; 2) the scheme has benefited from EU, national and local funding; 3) phased retrofit has made the scheme financially viable, while reducing the incidence of forced relocations; 4) community participation has led to greater acceptance and buy-in of the plan. Based on in-depth interviews with longstanding residents and local stakeholders, I assess the community’s response to the regeneration plan. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANETs) are the future communication infrastructure for all devices that operate without the elaborate design found in wired infrastructure networks. Constructing a routing protocol that helps MANETs to achieve (QoS). It is quite challenging to achieve energy-efficient routing, due to its limited resources and node mobility concerns, which would impact node resource stability and lead to congestion and lower user QoS. A novel Trustworthy Energy Efficient AODV protocol has been proposed for improving path selection and locating trusted stations to maximize QoS. This approach employs location data, transmission speed, and directions to estimate the energy usage and trustworthiness of distinct nodes. The proposed TEE-AODV protocol includes two new control information namely Trust Request packet and Trust Reply. (T-RREP) AODV's packet includes a neighbor list and channel trustworthiness. The number of working neighbors in every path is counted, and this information is used to efficiently choose paths. The TEE-AODV calculates a QoS supporting metric for each detected route to identify a traffic-free path at any moment and determines whether it is a trusted route. The NS-2 simulator has been used to assess the suggested approach in terms of specific characteristics, including energy consumption (EC), (PDR), end-to-end delay (), and number of dead nodes. According to the experimental results, the suggested framework enhances reliable route selection while lowering e nd-to-end latency and improving energy efficiency. The experimental findings indicate that the suggested TEE-AODV protocol achieves high energy efficiency rate in the selection of trustworthy route and minimizing the $ {E_D} $ ED. [ABSTRACT FROM AUTHOR]

Limiting CO2 emissions is crucial for sustainable development, environmental protection, and climate change mitigation, ensuring a more resilient and cleaner future. Prioritizing sustainable urbanization renewable energy, and energy efficiency is vital for achieving this prosperous and sustainable future. As a result, we adopt the Fourier testing approach for stationary, cointegration, estimator, and causality, to scrutinize the role of renewable energy, energy efficiency, and urbanization on CO2 emissions in Finland over the period between 1990 and 2020, controlling economic growth and trade openness. The empirical analysis of the Fourier ARDL estimator establishes that energy efficiency measures such as gas and oil efficiency exhibit an adverse effect on CO2 emissions, so energy efficiency mitigates CO2 emissions. Similarly, the study also found that urbanization and renewable energy reduce CO2 emissions. Conversely, economic growth and trade openness induce CO2 emissions. Additionally, the Fourier-Toda-Yamamoto method provides robust evidence regarding the long-term implications of the examined factors. Based on this result, the study emphasizes that implementing a regulatory framework to enhance energy efficiency, promote renewable energy adoption, and support sustainable urban development will be an essential strategy in the global effort to attain the net-zero emissions target, which offers valuable insights to other countries seeking to transition towards cleaner and more sustainable energy sources. [ABSTRACT FROM AUTHOR]

In one of the largest demand-side energy efficiency programs at the time, the Mexican government supported more than 11 million, mostly low-income households in replacing their old light bulbs and appliances with more efficient models. Previous evaluations of this program focused exclusively on appliances, which made up almost 90 per cent of the total program cost, and found modest benefits in terms of energy savings. This study compares the respective effects of replacing light bulbs and appliances simultaneously in a single econometric framework, using data from nationally representative household surveys and a difference-in-differences approach which exploits geographical variation in treatment intensities. Despite using different sources and types of data, our results for the appliances replacement intervention are largely in line with the estimates of previous studies. In addition, we find that the impact on energy consumption (proxied by the amount paid for electricity) of replacing light bulbs was of comparable magnitude as that of replacing appliances, although the average cost per participating household was much smaller. Overall, our results suggest that low-cost investments that help poor households reduce their energy consumption for lighting can have high returns on energy efficiency in the residential sector. [ABSTRACT FROM AUTHOR]

Utilization of organic wastewater for hydrogen production has dual advantages of clean energy generation and bioremediation, which is sustainable for a longer period. To maximize the energy recovery from starch rich wastewater, a two stage system comprising of thermophilic dark fermentation coupled with microbial fuel cell was employed. A single parameter optimization strategy was implemented for the operation of the batch system. The maximum cumulative hydrogen production obtained was 2.56 L L−1 with a 48% reduction in COD under the optimal conditions of 35 g L−1 initial substrate concentration (COD), temperature 60 °C, and pH 6.5. The H2 yield and H2 production rate were 6.8 mol H2/kg CODreduced and 731.3 mL L−1 h−1, respectively. The effect of the organic loading rate (OLR) on H2 production rate was studied in a continuous stirred tank reactor (CSTR). A maximum hydrogen production rate of 913 mL L−1 h−1 was observed at an OLR of 5.6 g L−1 h−1. Effluent recycle played an important role in the improvement of H2 production. A maximum H2 production rate of 1224 mL L−1 h−1 was observed at a recycle ratio of 0.6. Power density of 4.2 W m−3 was observed with MFC using the dark fermentative spent media neutralized with carbonate buffer at an optimal pH of 7. A total COD reduction of 86% was observed. [ABSTRACT FROM AUTHOR]

In this study, we employ the Shephard energy distance function to evaluate the impacts of three broad aspects (i) government involvement, (ii) firm's managerial behaviour and (iii) technology adoption on energy (in)efficiency. Using a true fixed-effect model with a firm-level dataset of eight major industries in Vietnam from 2011 to 2016, the results suggest that these three aspects influence energy efficiency at varying degrees. The novel finding is that government involvement in business improves energy efficiency in less efficient industries. Furthermore, import activities negatively impact energy efficiency while the quality of labour increases efficiency. However, production expansion investment worsens the energy inefficiency situation. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks have been used for sensing and gathering data about an environment from a remote location for many years in a variety of engineering applications. In WSN, nodes must overcome energy consumption to function efficiently. To resolve these issues and extend the usefulness of the network, clustering and routing algorithms are promising. One of the main issues with WSNs is that they lack restricted energy sources. To overcome this issue, a Hybrid Optimized Energy-Efficient Adaptive Clustered Routing approach (HOEEACR) has been proposed for WSNs. This paper presents the Genetic Bee Colony (GBC) Algorithm for Cluster Head Selection by considering distances to neighbors, residual energy, node degrees, and node centralities. Furthermore, an optimal routing path for the cluster heads is found by utilizing the Aquila with African Vulture Optimization (AAVO) algorithm. The AAVO optimizes network performance using residual energy, node degree, and distance. The proposed HOEEACR method has been extensively tested to ensure network lifetime and energy efficiency. According to experimental results, the proposed HOEEACR method consistently outperforms existing techniques on a variety of performance metrics. [ABSTRACT FROM AUTHOR]

GreenTotal Factor Productivity (GTFP) is a critical indicator for measuring the green development and transformation of the economy, with profound implications for achieving energy conservation and developing the economy. In the context of the digital economy, digital entrepreneurship, as one of the important channels for digital industrialization, will have an effect on the promotion of GTFP. This study combines panel data from 219 Chinese cities at the prefectural level and above from 2007 to 2020 to examine the effect of digital entrepreneurship activities on GTFP. The empirical results show that digital entrepreneurial activity significantly reduces both GTFP and energy efficiency. Meanwhile, the effect of digital entrepreneurship activity on GTFP through industrial structure upgrading is not significant. Furthermore, a substantial negative correlation is identified between GTFP and digital entrepreneurship activity in the digital manufacturing industry, and in the eastern and central region. [ABSTRACT FROM AUTHOR]

Efficient energy management in WSNs is pivotal for prolonged network lifetime and sustained performance. This research introduces a novel approach to energy optimization through the integration of an ARIMA-driven feature selection process and an Actor-Critic Reinforcement Learning model. The Intel Lab dataset, encompassing data from 54 nodes distributed over a month, serves as the basis for experimentation. In the proposed methodology, the experimental setup ensures the validity of the study, leveraging the realism of the Intel Lab dataset. The data collection process captures the intricacies of WSN dynamics, with temperature records collected from strategically positioned nodes. The ARIMA-driven feature selection method refines the dataset, capturing temporal dependencies critical for energy prediction. The Actor-Critic model, a hybrid of policy and value-based reinforcement learning, dynamically adapts energy allocation strategies based on learned policies and offers a dynamic solution to the challenges posed by WSNs' ever-changing environments. Comparative analysis with methods reveals lower energy consumption of 0.32 mJ, an extended network lifetime of 1501 rounds, and higher prediction accuracy of 98%. The study's implications extend beyond the specific algorithms, suggesting a shift toward adaptive learning models in WSNs. The findings open avenues for future research in the integration of machine learning models for sustainable and efficient WSN deployments, emphasizing the growing importance of dynamic adaptation in sensor networks. Moreover, limitations, such as simulation realism and computational complexity, are acknowledged, prompting avenues for future research. [ABSTRACT FROM AUTHOR]

In situations where a regular network may not be appropriate or is unable to function correctly, data collection and real-time transmission are commonly done using Wireless Sensor Networks (WSNs). In order to accomplish this type of operation, the network's durability and performance are extremely vital. One of the key elements that directly affect a network's lifespan and performance is the routing protocol. This study proposes a Geographic Forwarding Energy Efficient Routing Protocol (GF-EERP), an enhanced version of the Geographic Energy Aware Routing (GEAR) protocol that helps to extend the network's lifespan and performance. The introduction of node categorization, a unique technique for choosing the region head, use of multi-hop communication method and removal of dead nodes serve as the foundation of the GF-EERP. In this study, the proposed protocol's performance is also compared to other protocols already in use, like Geographic Adaptive Fidelity (GAF), Geographic Energy Aware Routing (GEAR), Greedy Perimeter Stateless Routing (GPSR) and Multihop-Geographic Energy Aware Routing (M-GEAR), using a variety of performance metrics including Network Delay, Data Delivery Ratio and Network Throughput. The simulation outcomes prove that the performance of GF-EERP, with its distinct node categorization and region head selection mechanism is superior to the existing protocols. [ABSTRACT FROM AUTHOR]

This research suggests a new flexible method for the optimal operation of multi-carrier energy systems based on optimization techniques for different weather conditions and seasons. The results apply to different modes (a hot summer day, a cold winter day and a normal day). A new cost function from the operator's point of view is suggested considering the constraints on consumable gas supply and electrical energy in the cold and hot seasons respectively. Furthermore, a novel algorithm is suggested that gives the economical values of energy carriers, final cost, and the percentage of the direct electrical energy supply and outside the hub. In this method, load data, related prices, constraints, and penalties are applied to the suggested model, and afterward hub optimization assists in finding the optimal operation and costs. MATLAB software is used to check the results and the simulation outputs confirming the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The yarn suction gun (YSG) is used to draw the running yarn in the initial phase and when changing the bobbin in a continuous yarn production and is usually operated with compressed air. With a typical overall efficiency of compressed air generation, treatment and delivery, the electrical power requirement increases to over 75 kW just to operate a single YSG. Such high electrical power consumption must be addressed. One of the most effective ways to reduce the cost of compressed air generation and treatment is to replace the existing technology with one that is less energy intensive. Based on centrifugal blowers used in vacuum cleaners, a prototype was built and connected to various mixing chambers. Simulations and measurements were performed to evaluate the tension force acting on the yarn. Research on the use of vacuum for yarn suction is presented. It is estimated that after optimizing the suction by using vacuum, only 20 kW of electrical power will be required for one YSG, halving the cost of the current approach using compressed air generation. HYPOTHESIS 1: To significantly improve the efficiency of yarn suction gun (YSG), the existing technology must be significantly modified or reinvented. HYPOTHESIS 2: The application of convergent-divergent nozzles enables supersonic airflow velocities in rough vacuum. HYPOTHESIS 3: A modified YSG can be operated with a rough vacuum instead of compressed air to achieve significantly higher overall efficiency and reduce power input and carbon footprint. The use of vacuum for yarn suction is presented. The application of convergent-divergent nozzles enables supersonic airflow velocities in rough vacuum. The results of this preliminary study show tremendous potential for energy savings and carbon dioxide emission reductions from an alternative approach to yarn suction. [ABSTRACT FROM AUTHOR]

Puppeteer crawler technology is used to obtain regional industrial policy documents, and we make classification and assignment from the perspective of the legal effect of industrial policy documents to quantify industrial policy variable. At the same time, Super-SBM model is used to measure energy efficiency in different areas, and spatial Durbin model is constructed according to the panel data of 30 provinces in China from 2006 to 2018. The main conclusions are as follows : (1) The spatial distribution of China's energy efficiency has a remarkable positive correlation from the global perspective. (2) The local industrial policy has a significant positive effect on its energy efficiency, while a negative spatial effect on energy efficiency in neighbouring regions. [ABSTRACT FROM AUTHOR]

The greenhouse gas emissions and global energy consumption associated with construction materials can be greatly reduced by incorporating phase change materials into buildings. These materials can absorb and release energy during a phase transition, thus reducing the energy consumption while achieving human thermal comfort. Molecular dynamics simulations were used to study the miscibility of both linear and branched polyethylene with two types of paraffin (octadecane and hexatriacontane) at two different temperatures. The results showed that blending was better at high temperatures for both polymers, and branched polyethylene was more miscible with both paraffins than linear polyethylene was. Hexatriacontane exhibited better blending characteristics. From the molecular dynamics results, we concluded that linear polyethylene/paraffin blends were more suitable as phase change materials in buildings than branched polyethylene/paraffin blends, as they were miscible at high temperatures but separated into two phases at ambient temperature. The optimal blends were subsequently incorporated into a cement model to study their influence on the mechanical properties of this construction material. The introduction of the blends into the cement model deteriorated its mechanical properties. [ABSTRACT FROM AUTHOR]

Green manufacturing and remanufacturing revolve around recycling end-of-life (EOL) products. One crucial aspect of this process is the disassembly line balancing problem (DLBP), which involves assigning disassembly tasks to workstations in a logical order. However, current DLBP models often overlook operator-related factors and energy efficiency. This paper introduces a multi-objective DLBP model that considers both operator and energy factors. The model aims to minimise disassembly line idle rate, improve line smoothness, reduce energy consumption, and balance operator workload under multiple constraints. Since this problem is NP-hard, we also develop a hybrid intelligent algorithm that combines simulated annealing and the water cycle algorithm (SWCA) to solve DLBP effectively using real-world case disassembly. For multi-objective optimisation problems, choosing multiple non-inferior solutions can be challenging. In this work, we embrace an ‘optimisation before decision-making' approach, integrating multi-criteria decision-making techniques with neural networks optimised by a particle swarm optimisation (PSO) algorithm to identify the most satisfactory solution. [ABSTRACT FROM AUTHOR]

This study develops a stylized theoretical model to analyze how digital economy agglomeration affects energy efficiency. Then, we employ a two-way fixed effect model to identify the impact of digital economy agglomeration on energy efficiency. We find that a higher degree of digital economy agglomeration significantly increases a city’s energy efficiency. This result is robust when we address endogeneity concerns by exploiting a policy shock, geographic distance, and historical variation as instruments. We explore several explanations, including advancements in green technology, upgrading of industrial structures, and enhancements in public and governmental environmental concerns. Our findings provide evidence that substantiates each of these mechanisms. Our heterogeneous analysis indicates that the impact of digital economy agglomeration on energy efficiency is only significant in cities with higher attention to digitization, stricter environmental regulations, greater energy consumption, and better industrial development. This study provides the first city-level evidence of the relationship between digital economy agglomeration and energy efficiency, which has important implications for the energy transition in developing countries. [ABSTRACT FROM AUTHOR]

Hybridisation of networks is a trend that will continue to evolve for more efficient communication solutions. Hybrid power line communication (PLC) and wireless networks deployed for the Internet of Things (IoT) applications have improved the efficiency of low-power and lossy networks (LLNs). State-of-the-art solutions on hybrid networks are mainly application specific and does not provide a mathematical model to evaluate the network performance. Furthermore, the parallel PLC and wireless networks result in more power consumption due to the additional circuitry required to support and switch between two networks. There is a need for a generalized study on hybrid network architectures. A mathematical model must be developed to evaluate the performance of hybrid networks. Unlike parallel PLC and wireless networks, some intermediate solutions must be proposed that can offer the benefits of wired and wireless networks without increasing power consumption and circuit complexity. The paper presents a generalized study on hybrid network architectures. A hybrid network has been proposed that can offer the benefits of wired and wireless networks in the best possible way. A novel Markov-based stochastic model has been developed to evaluate the reliability of hybrid network designs. The analytical results obtained from Continuous Time Markov Chains (CTMC) have been validated by simulation using MATLAB. Spectral and energy efficiency have been evaluated for the hybrid designs along with a trade-off analysis. The proposed hybrid network (with 50% jump nodes being hybridized) is 33.53% more reliable for data transmission than parallel wired and wireless networks after 100 hours of operation. [ABSTRACT FROM AUTHOR]

VANETs (Vehicular Ad-hoc Networks) are a kind of Mobile Ad-hoc Networks (MANETs) with increased mobility as well as occasional geographic changes. Clustering is used in VANETs to divide networks into groups of adaptable vehicles to optimize routing, information gathering, as well as routing. As large data block transmission contains several stages and inside those, one of the important stages such as optimal model selection was these selections of quintessential features helps in a further stage called data transmission much more effective. This stage will also bring energy optimization and also improves the effectiveness of the optimal path for data transmission. This paper brings a comparative analysis of this optimal relay node selection. Here, we use Linear Scaling based Red Colobuses Monkey technique and are contrasted with other state-of-the-art models over various measures. Analysis proves that the proposed optimal relay node selection technique boosts the data transmission model. [ABSTRACT FROM AUTHOR]

Position-controlled systems driving repetitive tasks are of significant importance in industrial machinery. The electric actuators used in these systems are responsible for a large part of the global energy consumption, indicating that major savings can be made in this field. In this context, motion profile optimization is a very cost-effective solution as it allows for more energy-efficient machines without additional hardware investments or adaptions. In particular, mono-actuated mechanisms with position-dependent system properties have received considerable attention in literature. However, the current state-of-the-art methods often use unbounded design parameters to describe the motion profile. This both increases the computational complexity and hampers the search for a global optimum. In this paper, Chebyshev polynomials are used to describe the motion profile. Moreover, the exact bounds on the Chebyshev design parameters are derived. This both seriously reduces the computational complexity and limits the design space, allowing the application of a global optimizer such as the genetic algorithm. Experiments validate the added value of the chosen approach. In this study, it is found that the energy consumption can be reduced by 62.9% compared to a standard reference motion profile. [ABSTRACT FROM AUTHOR]

Conditions that maximize the performance of an accelerator-driven system related to particle beam and energy and accelerator type are analyzed. The toolkit Geant4 simulated the interaction of protons and ions with masses up to 20Ne and energies from 0.2 to 2 GeV/n. The beam intensity considered is 1.5 × 1016 p/s. The core of the reactor is modeled as an assembly of fuel rods surrounding a cylindrical beryllium converter, with a criticality coefficient of 0.985 and lead-bismuth eutectic coolant. Lower enrichment generates better utilization of fuel (20% to 25% from the initial actinide mass can fission in a cycle keeping neutron damage in clad below 200 displacements per atom). Data on particle fluence and energy released obtained from the simulation are used to calculate total electric power produced and isotope evolution. Power spent to accelerate the beam depends on accelerator type and is calculated by scaling from data on accelerator efficiency for a reference particle. Optimal proton energy is ~1.5 GeV when the beam is accelerated in a linac with energy gain G ~ 14 and is 0.75 to 1 GeV in the case of a cyclotron (G ~ 12). Ion beams starting with 4He realize higher G values than protons: 20 to 50 in a linac and 15 to 35 in a cyclotron. [ABSTRACT FROM AUTHOR]

This research introduces an innovative analytical framework for studying the behavior of electric vehicles (EVs) in power grids. The approach primarily examines the dynamic State of Charge (SOC) and energy reserves of EV batteries. By thoroughly examining patterns of EV charging demand and operational states (charging, discharging, idling, and waiting) across various time intervals, including day and night periods, this study aims to provide a comprehensive understanding of EV dynamic behavior in power networks. The proposed model goes beyond traditional assessments by incorporating human behavioral patterns and responsiveness to economic signals, specifically investigating the impact of electricity pricing on user behavior. The core analysis centers on exploring the Vehicle-to-Grid (V2G) concept, viewing EVs not only as energy consumers but also as potential contributors to grid stability and efficiency. The study delves into how the interplay of dynamic EV behavioral characteristics and owner responses to pricing influences the overall energy consumption curve of the grid, thereby shaping the capability of EVs in providing grid support services. To quantitatively validate the effectiveness of the model, a detailed case study is conducted, offering empirical evidence of its practical utility. The research findings are quantified, highlighting the significant implications for decision-makers, grid managers, and stakeholders in the energy sector. The paper emphasizes the main outcomes, demonstrating how the proposed analytical framework can empower stakeholders in making informed decisions about policy and infrastructure development. The robust methodological approach presented in this paper serves as a valuable tool for evaluating the impacts of adopting V2G, providing strategic insights for a more sustainable and economically viable paradigm [ABSTRACT FROM AUTHOR]

This research study presents a novel approach to enhance the efficiency and performance of Battery Energy Storage Systems (BESSs) within microgrids, focusing particularly on the integration of wind energy. The inherent inconsistency and unpredictability of Renewable Energy Resources (RERs) necessitate the development of effective integration solutions to accommodate them. Our article provides a customized iteration of the metaheuristic algorithm referred to as the Contracted fitness-dependent optimizer. The proposed methodology involves the adaptive adjustment of migration rates based on habitat suitability indices, while also considering variations in perturbations. The incorporation of Lévy flight and an elimination phase significantly enhances the algorithm’s efficacy in problem-solving. In order to establish the superiority of our strategy over alternative optimization approaches, we conduct simulations across diverse conditions and subsequently compare the outcomes. It was calculated that the daily scheduling cost was 235.2$ and the daily operational cost was USD 268$. In Scenario 3, the total operating expenses and arranging costs for 1 day, respectively, were determined to be 165$ and 115$. Further, the highest depth of discharge level was around 77%. Moreover, a −9.18 kW and 23.02 kW maximum charging and discharging power adjustment was made. The findings of our study emphasize the economic and operational benefits associated with appropriately sized BESSs within microgrid contexts. These advantages have the potential to enhance battery longevity and promote the development of more sustainable energy systems. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate agricultural sustainability as a collective issue involving multiple rather than individual farms. Through the utilization of energy consumption as a proxy, we propose a novel methodology that evaluates the impact of farm consolidations on agricultural sustainability while accounting for resource preferences. Our approach incorporates the ordered weighted averaging (OWA) operator within an inverse data envelopment analysis (IDEA) model to identify post-merger farms that meet preset efficiency targets. We employ a DEA cross-efficiency (DEA-CE) procedure to select merger plans that maximize agricultural sustainability for each preference scenario. By analysing a case study of 43 tomato greenhouse farms in Biskra, northern Algeria, our findings demonstrate that mergers can significantly enhance agricultural sustainability, surpassing the potential of individual farms by a factor of over 15. Additionally, the adoption of the most sustainable merger plan can lead to energy savings of more than 69%. Irrespective of the preference scenario, substantial energy savings in machinery, fertilizers, diesel, and electricity ranging from 22.92% to 73.73% were observed. These results emphasize the strategic role of merger processes in promoting agricultural sustainability and optimizing resource utilization. [ABSTRACT FROM AUTHOR]

The architectural engineering and construction sector accounts for about 30-40% of global energy consumption. The European goal of reducing this consumption and the linked greenhouse gas emissions calls for an increased capacity to implement building renovations. Building Information Modelling (BIM) and Building Performance Simulations (BPS) are among the most promising tools for fostering interdisciplinary, efficient processes and feasible analysis and design solutions to support this goal. Of the whole building stock, heritage buildings represent the most challenging part, although their potential as a driver for mitigating climate change and supporting sustainable development is being increasingly recognized. This paper presents a Heritage BIM and BPS-based workflow to support the energy and environmental improvement of publicly-owned historical buildings, that was applied to 9 case studies of 7 different Mediterranean countries. The overall aim of this research is to enhance the capacity of public local administrations and professionals to upgrade the historical building stock and demonstrate the scalability of the proposed workflow to the entire building stock of the Mediterranean area. [ABSTRACT FROM AUTHOR]

Industry 4.0, smart manufacturing and its related technologies are now becoming the leading trend in the development of the manufacturing industry. One of the key drivers of Industry 4.0 is big data analytics, which can transform large amounts of data into useful information, enabling astute and rapid decision-making strategies when combined with expert domain knowledge. The semiconductor industry is the most important high-tech industry in Taiwan, but it is also one of the most energy-consuming industries in the country. Therefore, it is critical to improve the efficiency of the manufacturing process and reduce the overall energy consumption of facility systems. This research demonstrates how to apply big data analytics in the semiconductor industry to explore the relationships of various machine parameters, develop predictive models for machine energy efficiency and apply optimisation tools to minimise energy consumption, while meeting the production demands. An empirical study is conducted in conjunction with a semiconductor corporation in Taiwan, targeting the air compressor system in its factory. The research framework is shown to be capable of assisting semiconductor fabrication plant decision-makers in optimising machine configurations, resulting in more than 10% savings on energy consumption and significantly decreased manufacturing costs. [ABSTRACT FROM AUTHOR]

There is a lack of studies on joint optimisation of flexible job-shop scheduling problem (FJSP) considering energy consumption and production efficiency in the machining-assembly system. Thus, in this paper, we propose a methodology for multi-objective optimisation of energy-aware flexible job-shop scheduling during machining and assembly operations. First, a mixed integrated mathematical model is developed to improve production efficiency and minimise energy consumption. Then, a novel heuristic algorithm integrated particle swarm optimisation (PSO) and genetic algorithm (GA) is developed to address the established multi-objective problem. Moreover, numerical examples are carried out to verify the validity and performance of the solving methods in achieving energy awareness in the manufacturing system. Computational results are presented to demonstrate the advantage of solving the problem compared with the exact method and common heuristic algorithms, and the trade-off between production efficiency and energy efficiency is analysed to make the final decision for managers. [ABSTRACT FROM AUTHOR]

AbstractThe issue of designing servo-actuated systems for enhancing their energy efficiency is dealt with in this paper. Starting from a detailed representation of the energy dissipation in a motor-reducer set, the overall energy cost associated with the execution of a motion task is expressed, in a closed form, as a function of the main design parameters. The analysis is based on the use of scaling rules, which allow to condense the main physical parameters of the motors and of the gearboxes by means of a reduced set of parameters. The resulting framework is implemented into an automatic procedure which selects all feasible designs and evaluates their associated energy costs. Two design test cases are taken into consideration, and the results are used to discuss the traditional sizing method based on the inertia matching rule, showing that it is often unsuited to capturing an energy-optimal design. [ABSTRACT FROM AUTHOR]

Although severe air pollution has been recognised to be associated with coal combustion, coal is still the main living energy for domestic purposes because of the living habits and low fuel price. Pollutant emission control incoal combustion is urgently needed in the residential sector. In this study, the combustion performanceand gaseous pollutant emissions of a natural draft stove with cross-draft combustor were determined using Shenmu raw coal under different sieved sizes (8–16 mm, 16–20 mm, 20–25 mm, and 25–50 mm). The results showed that the particle size of coal had different effects on firepower and energy efficiency. The coal fuel with a large particle size (25–50 mm) achieved the highest energy efficiency of 82.0%, while the firepower was limited to 13.4 kW. Reducing particle size was helpful to increase the firepower. Low emission factors of CO, NO and SO2 were found in this combustion system with large size coal fuel and the changes were nonlinear. Moreover, manual operation played an important role in pollutant emissions. The heating phase with steady combustion released fewer gaseous pollutants than the ignition and complex cooking/heating phase. These results can provide support for the research on the matching of stoves and coal fuels to deliver clean combustion performance. [ABSTRACT FROM AUTHOR]

Power generation comprises high environmental and ecological impacts. The global power industry is under pressure to develop more efficient ways to operate and reduce the impacts of inherent process variability. With the rapid development of technologies within the energy sector, large volumes of data are available due to in-time operational measurements. With increased computer processing capabilities, machine learning is applied to explore these in-time operational measurements for improved process understanding. This research paper investigates machine learning algorithms for energy efficiency improvement at coal-fired thermal power plants by conducting a systematic literature review. This research is essential since it provides guidelines for applying machine learning towards sustainable energy supply and improved decision-making. Subsequently, efficient processes result in the reduction of fuel usage, which results in lower emission levels for equivalent power generation capacity. Furthermore, this study contributes towards future research by providing valuable insights from academic and industry-related studies. [ABSTRACT FROM AUTHOR]