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

With the surging energy price and environmental concerns, measures to improve energy efficiency have attracted increasing concerns of the manufacture sector, especially energy-intensive manufacturing industries such as tissue paper mills. Energy-efficiency scheduling, as a novel energy-efficient method, has attracted the attention of an increasing number of researchers in recent years. Drying process is the most energy-intensive production process in tissue paper mills, which has a great energy-saving potential. This paper aims to reduce the energy costs for the tissue paper mill, consisting of processing energy cost and set-up energy cost, through integrating drying process optimization with energy-efficient scheduling. First, the energy cost model and the scheduling model were built. Then, the energy cost of the drying process of every job in a given scheduling problem was optimized using particle swarm optimization (PSO). Afterwards, the energy cost was further optimized using energy-efficiency scheduling. In addition, a hybrid non-dominated sorting genetic algorithm II (NSGA-II) was utilized to solve the energy-efficiency scheduling problem. Finally, several real scheduling problems from a real tissue paper mill were addressed using the proposed approach to demonstrate its effectiveness in energy saving. The experiment result showed that there is a great energy-saving potential in the drying process, accounting for up to 12.53% of the total energy consumption. Moreover, the maximum energy saving ratio of the proposed approach could reach 9.03%. On the whole, the proposed approach can provide a new energy-saving method for tissue paper mills or other manufacturing industries. [ABSTRACT FROM AUTHOR]

Abstract Papermaking enterprises are currently under both environmental pressure and economic pressure for sustainable development in China. Thus, the efficiency, effectiveness, and benefits of resource utilization need to be improved. High-consumption and high-pollution companies should manufacture paper using sustainable methods. This study highlights a resource value flow analysis from the circular economy perspective, developing an extension of material flow cost accounting and modifying it by accounting for environmental damage as well as economic benefits. With reference to the Plan-Do-Check-Act cycle, this specific case study was conducted to verify the comprehensive utility of resource value flow analysis by establishing decision-making prioritization according to the dualistic diagnosis of "internal resource loss–external environmental damage costs." In general, applying a resource value flow analysis can both reduce resource consumption and minimize environmental damage, enhancing the sustainable development of a process industry with limited resources. Highlights • Material flows and value flows form an interactive unit in the resource value flow analysis model. • The resource value flow analysis model extends the environmental dimension of material flow cost accounting. • A dualistic diagnosis of "internal resource loss and external environmental damage costs" can achieve both economic profits and environmental benefits. • The Plan-Do-Check-Act cycle was introduced to strengthen the practical application of the resource value flow analysis. [ABSTRACT FROM AUTHOR]

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

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

In recent years, the pulp and paper industry has changed a lot and products have been constructed in quite different methods using new technologies. It can be due to the emergence of challenges and constraints such as shortage of raw materials, energy consumption (fossil fuels, electric, etc.), management of the resulting pollutants, environmental issues, ongoing legal requirements and cleaner production strategies. In Iran, the pulp and paper industry's technological development is not keeping pace with global growth. Iran's pulp and paper mills supply a small fraction of domestic market. The reasons can be due to old technologies being used in factories, aging machinery, low productivity of production systems and lack of coordination with modern technologies. It leads to low-quality of paper products and willingness to buy local products. The aim of this study is to select the most appropriate technologies in the pulp section, due to high impact of pulp quality on the quality of final paper. Using a 2-filterd fuzzy decision-making, hierarchical structure of research is designed to assess technological alternatives according to the selected criteria. Findings show that after passing through two filters, Kraft method was chosen as the most appropriate method. The Kraft process has still many advantages like the high quality of the produced pulp and self-sufficiency in energy supply. This framework offers an appropriate procedure for identifying and selecting technologies that has not been used in past researches. Using this framework facilitates using a clear procedure for evaluating and selecting technology and prevents future losses for organizations. [ABSTRACT FROM AUTHOR]

China's paper industry accounts for a larger proportion of the global paper industry and consumes large amounts of energy. Energy conservation and pollution reduction of the industry have become the focus of China's national development strategy. Energy efficiency is an important index which directly determines the amounts of energy consumption and pollution emissions. To understand the energy efficiency evolution of China's paper industry, this paper establishes an input-output data of the paper industry for 29 provinces in China and applies DEA method and econometric models to estimate and comprehensively discuss the energy efficiency issues based on the framework of total factor efficiency. Our results indicate that China's paper industry has a large energy saving potential even though its energy efficiency has improved during the period 1990–2013. The saving potential is about 65% under the meta-frontier and 44% under the group-frontier. Moreover, there are significant differences between different regions in production technology and energy efficiency. The performance of eastern China is better than other regions. But the technology gaps between the different regions are shrinking and converging over time. Finally, some policy recommendations are proposed to improve the energy efficiency of the industry. [ABSTRACT FROM AUTHOR]

China has become the world largest paper and paperboard producer. However, pulp and paper industry has involved high resource consumption and severe pollution especially for water. This research has assessed the eco-efficiency of China's pulp and paper industry at the national level and provincial level. An overview regarding the cleaner production of China's pulp and paper industry was presented to reveal the measures for reducing environmental impact in the last two decades. Slacks-based measure was used to analyze the efficiency levels of 16 provinces' pulp and paper industries. To uncover the underlying causes of eco-efficiency performance, Malmquist–Luenberger index was calculated to discover the drivers of productivity growth of pulp and paper industries. Our results showed that the pollution treatment of China's pulp and paper industry has made progress in terms of water consumption and water pollution, although the absolute amount of pollution discharge is still large. Chemical oxygen demand emissions are still the first critical influencing factor of pulp and paper industry's inefficiency. Furthermore, efficiency progress was the dominating contribution of the industry's productivity growth between 2010 and 2013. The policies for adjusting the industrial structure of pulp and paper industry have resulted in the scale effects through eliminating backward production capacity and accelerating merger and acquisitions. Moreover, the productivity of pulp and paper industry was underestimated when the undesirable outputs were ignored. It indicates that the stricter environmental regulations have positive effects on paper companies to internalize environmental pressures in the production activities through environmental management. In the future, pulp and paper companies should further internalize the cost of pollution treatment through scale effects and technology improvement. [ABSTRACT FROM AUTHOR]

The pulp and paper industry accounts for approximately 5% of total industrial energy consumption and contributes 2% of direct carbon dioxide (CO 2 ) emissions from industries. World paper and paperboard demand and production are increasing significantly, leading to an increase in this sector's energy use and CO 2 emissions. Although current studies identify a wide range of energy-efficiency technologies that have already been commercialized for the pulp and paper industry, information is limited or scattered regarding new energy-efficiency technologies that are not yet fully commercialized. Development of emerging or advanced energy-efficiency technologies and their deployment in the market will be crucial for this industry's mid- and long-term energy saving and climate change mitigation strategies. This paper compiles available information on energy savings, environmental and other benefits, costs, and commercialization status for 25 emerging technologies to reduce the energy use and CO 2 emissions. The purpose is to provide a well-structured comprehensive review on these emerging energy-efficiency technologies for engineers, researchers, investors, policy makers, pulp and/or paper companies, and other interested parties. [ABSTRACT FROM AUTHOR]

The pulp and paper industry is a high energy consuming and polluting sector, and carbon emissions emitted from this sector are worthy of attention. This article, based upon an analysis of China’s Pulp and Paper Industry (CPPI), provides estimates of each of the following: carbon emissions from energy consumption, pre-treatment sector, combustion of condensed black liquor, and methane emitted from incomplete aerobic digestion during sewage treatment of CPPI. During the study period (2005–2012), total CO 2 emissions ranged from 126.0 Mt to 155.4 Mt. Energy consumption was estimated to be the largest source of carbon emissions, however, due to the application of the local emission inventory rather than the IPCC inventory, energy consumption decreased by 4.7%, a lower percentage than was calculated in a previous study. According to this study’s estimation, the emissions caused by the recovery of biomass energy contributed 26–29% of the total CO 2 emissions. CH 4 generated from sewage treatment accounted for 9–11% of the total carbon emissions. The CO 2 intensity dropped during the study period, which reflected the improvement of energy efficiency in the pulp and paper industry. The outcome of this study provides not only detailed information about CPPI’s carbon emissions, but also a calculation framework for studying carbon emissions from pulp and paper sector in the other regions. It suggests that the local carbon emissions inventory should be used for estimating carbon emissions and to reduce the fossil fuel energy, increase energy recovery from biomass, and that promoting cleaner production is essential to achieve a low carbon development of the pulp and paper industry. [ABSTRACT FROM AUTHOR]

The paper presents an original method for synthesizing high-density composite ceramics with a Mo 2 C/Mo 3 Co 3 C composition using the technology of high-speed Spark Plasma Sintering (SPS) of a mechanically activated powder mixture of Mo 2 C-10 wt%Co. The activated homogeneous starting mixture was obtained by wet milling/activation of the initial powders of Mo 2 C and Co in an anhydrous isopropanol medium. The ceramic samples were consolidated by SPS under vacuum (10−5 atm.) at 1000, 1100, 1150, and 1200 °C with a heating rate of 87.3 °C·min−1 under constant external uniaxial pressing pressure of 50 MPa. The sintering kinetics were studied, the twofold nature of sintering was established, and the temperature range of active densification was determined. The ceramic synthesis is accompanied by a reactive interaction of the components, resulting in the formation of the Mo 3 Co 3 C phase and a change in the crystal lattice of the non-stoichiometric Mo 2 C x compound. The ceramic obtained in the temperature interval of the isothermal sintering stage of 1150–1200 °C is homogeneous in the distribution of Mo and Co, consists of a Mo 2 C/Mo 3 Co 3 C phase mixture, has a density value of 98.13 ± 0.5 % theoretical density, and has an average Vickers hardness value of ∼1526 HV. Increasing the sintering temperature to 1200 °C results in the formation of almost monolithic structures in the samples, but also induces the formation of large defects due to the collective consolidation of pores within the bulk material and increases the susceptibility of the ceramic to cracking. The paper presents preliminary results from electrochemical testing. The proposed method for synthesizing Mo 2 C/Mo 3 Co 3 C ceramics has potential for use in optimizing the composition and production methods of electrode materials for the realization of active components in heterostructured electrodes used for hydrogen evolution. [ABSTRACT FROM AUTHOR]

Much research has been focused on maintaining stable humidity conditions in buildings housing heritage collections while reducing energy use. Moisture buffering by collections themselves can have a marked effect on the stabilisation of relative humidity (RH), the key parameter for preservation. Modelling of moisture transport using COMSOL Multiphysics was applied to transform three-dimensional paper and wooden objects into their one-dimensional representations, without changing the moisture uptake and release characteristics. The results were coupled to the modelling of indoor microclimate and energy consumption in collection storage spaces with the use of WUFI ® Plus software. The study revealed the crucial impact of air exchange rate of the building on the stability of indoor RH and the humidification and dehumidification loads required to maintain it. In the adequately air-tight library store, a sizeable paper collection was found to reduce the RH fluctuations from ±9% to ±6% around the yearly average and the energy consumption due to the humidification and dehumidification load by 38% when compared with the empty space, for a high-quality climate control scenario. In turn, a wooden collection, occupying a realistic fraction of a museum store was not large enough to significantly narrow down the RH variations and reduce the energy consumption. [ABSTRACT FROM AUTHOR]

Papermaking is a water intensive industry. The growth of paper demand will further intensify the need of fresh water resources. Papermaking involves complex processing routes. Previous investigations of water consumption in papermaking industry have focused primarily on key processes and pathways, ignoring the impacts of many intermediate and inter-related processes in paper production cycles and there underestimating the sustainability impacts. Herein, the results of a life cycle analysis of water consumption for papermaking industry in China using an extensive system boundary that includes the water embedded in intermediate processing are presented. The results show the life cycle water consumption and water saving potentials for different papermaking pathways. The advocacy of "forest pulp and paper integration" may have few practical contribution to reducing water consumption due to the low indirect water consumption of raw materials and energy for transportation. [ABSTRACT FROM AUTHOR]

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

Deploying renewable energy to unit design energy-efficient technologies may fulfill the additional demand of various energy-intensive industries for their sustainability and resiliency. Multiple Stage Evaporator (MSE) is one of the most energy-intensive units used to extract the water content of the weak liquor in various industries, including the paper industry. This energy intensiveness may be reduced by integrating various Energy Reduction Schemes (ERSs). Hence, this work proposes a ERSs integrated MSE model to analyze its energy efficiency. These ERSs includes Thermo-Vapor Compressor, Steam-, Feed- Split, Feed Preheater, and Flash Tanks. Further, the performance of the proposed model is investigated under two important real-time plant complexities: Boiling Point Elevation and Fouling to achieve more realistic results. The performance analysis is initiated with the formulation of a nonlinear constrained optimization problem to increase the steam economy (SE). Also, the simulation is extended to validate at two different product concentrations (52% and 65%) by employing state-of-art optimization algorithms: CONOPT, and SCA in GAMS and MATLAB respectively. The simulated results shows an increment 1.64% and 1.37% of SE for both 52% and 65% concentration of weak liquor respectively in case of CONOPT than SCA. Also, the waste heat of the condensate, feed, and product may be further utilized for the heat recovery by incorporating the flash tanks which leads to a countable energy saving. Eventually, integrating the solar fields: PTC and LFR ensures a notable reduction in conventional heat utilization by 85.96% and 92.85%, respectively and hence, enhance the energy efficiency. • Expounded energy modeling of ERSs based MSE with BPE and Fouling. • Implementation of NLP and metaheuristic approaches for performance optimization. • Reutilization of waste heat to maximize the energy efficiency. • Integration of solar fields (PTC and LFR) to reduce conventional fuel utilization. • Prospecting the challenges and direction of energy management optimization. [ABSTRACT FROM AUTHOR]

Current management of solid waste from pulp and paper activities represents an environmental and economic burden worldwide due to pollution emissions. This study investigates the potential of hydrothermal carbonisation (HTC) treatment as a sustainable alternative for producing cleaner and energy-dense solid fuel from paper mill sludge. The effect of process parameters (temperature, reaction time and solid load) on hydrochar fuel formation from paper sludge was evaluated and, for the first time, the paper sludge-derived hydrochar was optimised to maximise the mass yield and calorific value using response surface methodology (RSM). The physicochemical characteristics, thermal fuel behaviour, energy recycling efficiency and electricity generation potential were assessed by proximate and ultimate analysis, thermogravimetry, bomb calorimeter, scanning electron microscopy and process energy assessment. Results showed that hydrochar fuel formation and properties were mainly influenced by the process temperature and residence time, and governed by dehydration and decarboxylation reactions which reduced the atomic H/C and O/C ratios by 35.5% and 64%, respectively. The produced hydrochars presented low sulphur, nitrogen and ash content with a maximum calorific value (HHV) of 22.9 MJ/kg, equivalent to the HHV of coal for commercial utility in South Africa. The HHV of the hydrochar corresponded to a 49.80% increase over the HHV of the initial feedstock. The optimum operating conditions were 231 ± 1 °C and 1.99 h for a hydrochar yield of 74.4% and calorific value of 18.5 MJ/kg. The energy assessment showed that up to 58.34% of the energy produced by hydrochar fuel combustion may be recycled as heat or power, while the remaining 41.66% of the combustion energy could be utilised to sustain the HTC treatment of paper sludge. The substantial water demand was concluded to be a drawback. Thus, water recirculation and the potential to catalyse the HTC reactions to increase overall process efficiency will constitute a future study to make the process more environmentally friendly for industrial-scale application. [Display omitted] • Dehydration and decarboxylation were predominant. • Optimum energy yield of 18.5 MJ/kg and 74.4% hydrochar was achieved under 231 ± 1 °C and 1.99 h. • Higher HTC reaction severity enhanced the physicochemical properties of the hydrochar. • Hydrochars produced have low nitrogen and sulphur (<1%) content. [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]

Space-based solar power systems (SSPS) envision the usage of focused microwave beams to transfer power from space to ground. To supply a large amount of power and to be able to focus a microwave beam efficiently, a large infrastructure must be assembled in orbit, thus, making the implementation of SSPS an arduous task. In the history of SSPS designs, aside from economical obstacles, technical obstacles such as large ohmic losses due to long DC lines in the structure, large rotary joints to decouple beam pointing and solar power collection, heating problems from power lines converging into a single point, and complex control mechanisms, manufacture, and assembly are present. A simpler implementation using multiple foldable thin film membrane satellite modules is proposed as a feasible design, born from the advancement of thin film electronics. Its economical feasibility is achieved through the reduced weight, large surface area, and membrane's ability to be stowed. The thin film SSPS combines advantageous properties of past designs such as a simple structure, simple control mechanism, and a distributed power collection and transfer architecture avoiding the ohmic loss and heating problems. However, due to the thin film membrane hosting both the antenna and the solar cells, competition occurs between maximum solar power collection and high efficiency power transfer capabilities. In this paper, we propose a framework to analyze the performance of the thin film SSPS solar cell-antenna integration design in terms of the wireless power transfer efficiency and fuel consumption, from which we identified the trade-offs on designing and operating the station. We used this framework to analyze three different antenna element designs in combination with three different attitude control goals, and found out that the key aspects of the antenna design are beam width, radiation efficiency, and efficient solar cell integration. [ABSTRACT FROM AUTHOR]

In this paper, a novel Model Predictive Control (MPC) technique for multi-satellite formation flying geometry acquisition and maintenance in high-drag environment is presented. The proposed MPC relies on a linearized and convexified quasi-nonsingular Relative Orbital Elements (ROE) model based on state transition matrices propagation, allowing to include the effect of perturbations in the prediction to optimize fuel efficiency and tracking accuracy. The formation is controlled with respect to a non-decaying orbiting point to perform absolute and relative station keeping simultaneously. For this purpose, a new dedicated plant matrix to include drag effects on ROE in the propagation is derived and validated with respect to numerical results. In all simulations, the satellites are assumed to be equipped with a single low-thrust propulsion unit, therefore, specific constraints are included in the controller to obtain a feasible solution in a real operational scenario. Moreover, a collision avoidance constraint is added in case of close proximity operations exploiting a linear mapping between the set of ROE and cartesian coordinates expressed in the Local-Vertical-Local-Horizontal (LVLH) reference frame. The controller response is simulated in several realistic mission contexts with a high-fidelity orbital propagator and the results are validated for fuel efficiency by comparing them to similar approaches available in literature and to optimal solutions obtained respectively with a direct single shooting algorithm and with a closed-form impulsive formulation. [ABSTRACT FROM AUTHOR]

This paper explores the feasibility of utilizing lead-free multilayer piezo stacks, particularly focusing on Zr-doped 0.95(Bi 0. 5 Na 0.5)TiO 3 -0.05(BaTiO 3) piezoceramics, for energy harvesting. Various lead-free BNT-BT disk samples with different Zr concentrations undergo structural, microstructural, and electrical characterizations. The results illustrate that Zr-doping improves piezoelectric constitutive parameters, notably at a 2.0 mol% concentration. Electromechanical tests were conducted on multilayer piezo stacks, involving variations in electric load, mechanical load, frequency, and number of stacked disks. These tests demonstrate the ability of the multilayer transducer to produce up to 1.5 V (peak) under particular conditions: a 250 Hz frequency, a 10 N mechanical load, and an open circuit electric load, utilizing a stack comprising 5 disks. Employing an analytical mathematical model, an effective d 33 e f f of the system is determined, yielding values ranging from 588 to 625 pC/N for a stack composed of 5 disks. These findings highlight the considerable potential of lead-free multilayer piezo stacks in energy harvesting applications, offering crucial insights for the development of sustainable and eco-friendly energy solutions. [ABSTRACT FROM AUTHOR]

Closed-form models are important because they offer more design insights, require less computing time and prevent convergence problems in modeling compliant mechanisms. However, most of the available closed-form models, e.g., beam constraint models, are only appropriate for beam flexures within the intermediate deflection range. In this paper, a method for developing closed-form models based on the chained beam constraint model through the combined use of strain energy and Castigliano's first theorem is proposed. A closed-form model for beam flexures with a large prediction range is derived using this method. By modeling simple beams and compliant mechanisms using the proposed closed-form model, it is shown that the model has a significantly broader prediction range than the beam constraint model and is more efficient than the chained beam constraint model. The large prediction range and high efficiency of the proposed closed-form model make it potentially useful for optimizing compliant mechanisms. • A method to develop closed-form models for beam flexures is proposed. • A closed-form model for CBCM with 3 discretized elements is obtained. • Closed-form load-displacement relations for beam flexures are obtained. • The proposed molded is demonstrated to reduce the computational time. [ABSTRACT FROM AUTHOR]

• Novel damped conformable fractional accumulation method. • Prediction of energy related series with the uncertainties. • Design of damped conformable fractional grey bernoulli model. • Improved prediction accuracy, robustness and applicability. • Prediction of world coal consumption and production in 2025–2026. Energy resources, such as oil, coal are important to people's life and the development of the economy. Predicting the energy consumption and production with uncertainties can help the government and policymakers make the reasonable energy strategy and give constructive guidance. In this paper, a novel damped conformable fractional accumulation operator with two parameters are proposed to have control of both accumulation generation and new information involvement. The new accumulation can be adjusted from linear approximation to high order accumulation. A structure adaptive grey Bernoulli model based on proposed accumulation method is established, with the parameters optimized by particle swarm optimization algorithm. For testifying the effectiveness and the robustness of the model, two validation cases and two application cases are used to build the prediction model of diverse series with unexpected trends or fluctuations in energy fields. Compared with other existing fractional grey models and non-grey models, the proposed model has best prediction accuracy, better model stability and greatly improved applicability. The global coal consumption by industry is predicted as 30.85×106 TJ in 2025 and 30.81×106 TJ in 2026. The world coal production could be 182.93–187.73 exajoules in 2025 and 180.85–182.16 exajoules in 2026. [ABSTRACT FROM AUTHOR]

Increasing energy prices and market volatilities force energy intensive industries to implement energy management systems in order to ensure competitiveness. In this paper, we focus on the strategic level of energy management, specifically, on the analysis of the internal and external conditions that form the basis for strategic development. For this, we selected the case of the Austrian paper and pulp industry. We applied a hybrid method by combining an analysis of strengths, weaknesses, opportunities and threats with an analytic hierarchy process which was based on a survey of expert opinion. The results show that cost-related factors predominate. According to our analysis, the four most important factors are all directly linked to energy costs, energy efficiency, and the energy market. Experts pay little attention to environmental issues or to energy market volatilities. Apart from that, the combination of an analysis of strengths, weaknesses, opportunities and threats with an analytic hierarchy process promises to be a valuable tool for strategic energy management. This is especially true for energy intensive companies and/or sectors, since it enables decision makers to take strategic decisions based on a systematic understanding of the main issues. [ABSTRACT FROM AUTHOR]

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

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

In an era of increasing energy demands and environmental concerns, optimizing energy consumption within buildings is crucial. Despite the vast improvements in HVAC and lighting systems, plug loads remain an under-studied area for enhancing building energy efficiency. This paper studies smart plug active operating mode detection, plug-level load forecasting, and plug scheduling methodologies. This research leverages a unique dataset from the University of California, San Diego, consisting of readings from over 150 smart plugs in several office buildings for more than a year, notably during the post-Covid era. This dataset is made publicly available. A comprehensive literature review on plug, i.e. , appliances operating mode detection is presented. Novel unsupervised learning approaches are applied to identify plug operating modes. A pipeline integrating the detected modes with forecasting and scheduling is developed, aiming at building energy consumption reduction. Our findings offer valuable insights and promising results into smart plug management for energy-efficient buildings. • A novel year-long dataset of power readings from 169 office smart plugs is introduced. • The first review of plug load mode detection methods and terminology is presented. • The paper proposes an unsupervised method to identify plugs load operating modes. • The proposed smart plug scheduling pipeline reduces energy consumption by 20%. [ABSTRACT FROM AUTHOR]

This paper investigates the position and attitude (pose) tracking control of a rigid body with uncertain mass and inertia. By virtue of the dual quaternion description, a six-degree-of-freedom (6-DOF) relative motion model is established in an integrated and compact way. Then an adaptive pose tracking controller is proposed based on the immersion and invariance (I&I) theory, where the key integrability obstacle is overcome by the dynamic scaling technique. Compared with the existing filter system-based method that constructs a filter system to circumvent the integrability obstacle, the dynamic scaling-based method is directly designed based on the original system, which is capable of retaining all benefits of the I&I design with much lower dimension dynamic extensions. Comparisons with the filter system-based I&I method and the classical certainty equivalent method are first carried out from the theoretical analyses, which elucidate benefits of the proposed method in lower control demands. Then the comparative simulations are conducted to verify the features and effectiveness of the proposed dynamic scaling-based I&I adaptive control scheme. • In the framework of dual quaternions, a six-degree-of-freedom adaptive pose tracking controller is proposed based on the immersion and invariance. • Substantial dimension reduction and structure simplification are achieved by using the dynamic scaling technique. • Ideal closed-loop performances are guaranteed to be recovered by the proposed adaptive controller. • Control demands and energy consumptions are barely affected by the adaptive gain and can always be kept close to those in the ideal case. • Thorough theoretical analyses are presented to elucidate the benefits of the proposed adaptive controller. [ABSTRACT FROM AUTHOR]

Sustainable power generation for portable electronic devices is of high interest due to increasing energy demands. Miniaturized biofuel cells are well-reported for this purpose where ubiquitous fluids, like glucose or lactate, can be leveraged. However, such biofuel cells need enzymes to complete the catalysis, leading to many prevalent issues like the longevity of enzymes, complicated immobilization, additional mediators etc. Therefore, non-enzymatic glucose biofuel cells (NEGBFCs) are gaining interest due to their cost-effectiveness, high repeatability and excellent stability. However, based on our best knowledge such glucose biofuel cells are not reported in microfluidic environment, and therefore their potential is not exploited. Realization of the functionality of such NEGBFCs in a microscale is crucial to replace them with their enzymatic counterparts. In this work, for the first time, a microfluidic non-enzymatic glucose biofuel cell (μNEGBFC) is developed on a paper-based platform using metallic catalysts. The catalysts for electrocatalytic energy generation is fabricated using electrodeposition method and their detailed physicochemical characterizations are done. The μNEGBFC is capable of producing a power density of 12.5 μW/cm2 with a stable potential of 410 mV. This green power output can be used to operate low-power micro-devices and lab-on-chip sensors in a more continual and viable manner. [Display omitted] • A simple, cost-effective electrodeposition technique was used for electrode fabrication. • Gold deposited anode and copper deposited cathode were used as electrodes. • A paper-based Microfluidic Non-Enzymatic Biofuel Cell (μNGBFC) was constructed. • The developed μNGBFC generated a power density of 12.5 μW/cm2. • The μNGBFC was capable of retaining 56% of the initial power after 72 h. [ABSTRACT FROM AUTHOR]

A fully stretchable photosynaptic device with low energy consumption is achieved by the intrinsic stretchability of conjugated block copolymers and the accommodation of perovskite quantum dots. The strategy emulates photosynapse functionalities and provides an artificial muscle's neural network with high recognition accuracy, thereby representing potentially major advances in soft optoelectronics. [Display omitted] Emulation of photonic synapses offers a promising avenue for developing low-energy consumption of soft electronics, neurologically inspired robotics, and neuromorphic network computation. In this paper, a fully stretchable photosynaptic device with ultralow energy consumption using intrinsically stretchable poly(δ -decanolactone) (PDL)-based conjugated block copolymers (BCPs) with perovskite quantum dots (PeQDs) is first reported. The findings reveal that selectively choosing solvents for the PDL-based BCPs effectively regulates the assembly of P3HT and the accommodation of PeQDs, leading to improved self-aggregation of PeQDs, increased grain size, and optimized interfaces between P3HT and PeQDs The BCPs/PeQDs composite effectively emulates significant features of photonic synapses, such as paired-pulse facilitation (PPF), spike-dependent and short/long-term neuroplasticity, demonstrating excellent performance, including the fastest response time (1 ms), the highest current contrast (4.9 × 105), PPF (1.93) and ultra-low energy consumption (0.3 aJ) at an operating voltage of –0.1 mV. Furthermore, the BCP/PeQDs exhibit remarkable neuromuscular synapse characteristics, including high strain and bending tolerance and spike-dependent plasticity, enabling the devices to achieve high classification accuracy in artificial neural network simulations during tensile strain. The accommodation solvent selectivity of BCP/PeQDs suggests a promising strategy for advancing neurologically soft electronics, human-like pattern recognition, and neuromorphic computation. [ABSTRACT FROM AUTHOR]

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

• A simple, large-scale fabrication of cooling paper via pulping and papermaking technology. • The cooling paper is facilely deconstructed and regenerated to architect the secondary cooler. • The cooling paper features a high solar reflectance of 94% and emissivity of 0.95. • The cooling paper shows the sub-ambient temperature drop of 6 ∼ 8.8 °C under directly solar radiation at daytime. Passive radiative cooling materials provide coldness without energy consumption for all-day thermal comfort by reflecting solar irradiation and emitting heat into the 3 K universe. However, the conventional radiative cooling materials involve complex multilayer structures and unsustainable polymers, showing the non-recyclable disadvantage, which induces resource waste and environmental issues. Herein, we reported a low-cost, scalable, and eco-friendly radiative cooling material (namely cooling paper), consisting of the delignified, fibrillated cellulose fibers and nano-sized hydroxyapatite (HA) via the mature pulping and papermaking process. The resulting cooled paper can be recycled, and it can be easily deconstructed to harvest cellulose fibers and HA, individually. The recovered source materials can be reused to prepare secondary products that show comparable performance to the original cooling paper, including a high solar reflectance of ∼94% and an infrared emissivity of ∼0.95 in the atmospheric transparency window. Such cooling paper enables superior cooling performance with the temperature drop of 6 ∼ 8.8 °C under directly solar radiation. Based on the EnergyPlus simulation, our cooling paper shows a average cooling energy saving of ∼29 % in buildings across China. The recyclable cooling paper with high cooling capability and scalability, as well as environmental friendliness, shows promising potential for sustainable energy-efficient buildings. [ABSTRACT FROM AUTHOR]

Low–thrust propulsion systems of high specific impulse are advantageous in the fuel efficiency. The fuel expenditure is further reduced together with optimization techniques though fuel–optimal low–thrust trajectories result in a discontinuous bang–bang control structure that can cause numerical difficulties. The present paper introduces regularized formulations into the direct multiple shooting method of minimizing fuel expenditure for low–thrust spacecraft trajectories. Instantaneous velocity changes approximating low–thrust maneuvers are expressed by regularized variables based on the Levi–Civita or Kustaanheimo–Stiefel transformation. The new formulation removes singularities due to null thrust impulses from derivatives of an objective function and constraints. The method automatically and robustly finds fuel–efficient bang–bang control structures for multi–revolutional transfers between periodic orbits in the Earth–Moon circular restricted three–body problem. [ABSTRACT FROM AUTHOR]

This paper addresses the trajectory planning and control problem of multi-spacecraft formation reconstruction in the presence of obstacles. By expressing spacecraft dynamics relative to a leader spacecraft in the formation, an integrated planning and control approach named predictive null-space-based behavior control (PNSBC) is proposed. First, a planner using null-space-based behavior control (NSBC) is designed at an upper layer to resolve multi-task conflicts. Here, both global tasks, e.g. formation keeping, and local tasks, e.g. obstacle avoidance, are considered. Second, a tracking controller is designed at the bottom layer using decentralized model predictive control. Unlike traditional two-layer approaches that treat planning and control separately, the proposed PNSBC integrates the planning and control in two ways: 1) the planner provides reference trajectories for the controller to track; 2) the model predictive control (MPC) controller provides predicted trajectories that can be employed by the planner for future task priority predictions, which extends the capability of NSBC from one-step planning to multi-step predicting. In addition, the computational burden of the MPC controller is greatly reduced by putting the nonlinear obstacle avoidance constraints into the planner as a local task. Simulation results show that such integrated approach has better performance in terms of safety constraint guarantee, fuel consumption and travel distance when compared against traditional non-integrated approaches, or all-in-one MPC methods, while constraints and task objectives are fully satisfied. [ABSTRACT FROM AUTHOR]

The primary requirement for multimedia applications is energy efficient design along with computational efficiency of portable devices. This work proposes an energy efficient MAC with error compensating novel approximate adder and approximate multiplier. The proposed approximate hierarchical adder (AXHA – N) has the advantage of zero carry propagation and consists of only two gate levels which deliver very high speed and very low energy consumption along with the near to zero mean error distances. The proposed approximate 4 - bit multiplier also has no carry propagation from Least Significant Bit (LSB) to Most Significant Bit (MSB), which leads to high speed with very low energy consumption and acceptable error profiles. Higher bit size multipliers can be built using the proposed modified recursive multiplication scheme. This work proposes three variants A X M A C 1 , A X M A C 2 , A X M A C 3 basing on one or both of adder and multiplier being approximate. The proposed A X M A C 3 with any k , has energy savings ranging from 40% to 60% for different A x L when compared to accurate with Mean Error Distance between − 358 and 4305. The MED for A X M A C 3 with k = 8 , A X L = 4 is only 0.7% of the maximum possible value with 60% energy savings. The MED for A X M A C 3 with k = 12 s , A X L = 4 is only 0.8% of the maximum possible value with 57% energy savings. Mean Relative Error Distance (MRED) and Normalised Mean Error Distance (NMED) are also evaluated and presented in the paper for wholesome evaluation of AXMAC. The (Energy savings, MRED, MED) tuple 8 − b i t MAC for design of Adams et al., (2019) with k = 8 is (26.9%, 0.1, − 15048), for proposed A X M A C 3 with similar configuration has (57%, 0.01, 3663). • This work proposes an energy-efficient unsigned MAC with self-error compensating novel approximate adder and approximate multiplier. • The proposed higher size multipliers (AXM - N) recursively using the proposed approximate 4 - bit multipliers by which a balance between error tuning and parallelism can be obtained. • The proposed approximate hierarchical adder has the advantage of zero carry propagation and consists of only two gate levels which deliver very high speed and very low energy consumption along with the near to zero mean error distances. • This work proposes three variants AXMAC1, AXMAC2, AXMAC3 basing on one or both of adder and multiplier being approximate. • The proposed AXM AC3 with any k, has energy savings ranging from 40% to 60% for different AxL when compared to accurate with MED between − 358 and 4305. The deviation i.e., MED for AXMAC3 with k = 8, AXL = 4 is only 0.7% of the maximum possible value for 60% energy savings. The deviation i.e., MED for AXMAC3 with k = 12, AXL = 4 is only 0.8% of the maximum possible value for 57% energy savings. [ABSTRACT FROM AUTHOR]

• A double flow controlled discrete-time two-lane traffic system with vehicle-to-infrastructure communication is proposed. • A stable condition of the traffic system is derived with lyapunov stability theorem and given by linear matrix inequality. • The double flow control scheme can suppress traffic congestion efficiently. • The relative flow is more effective than the delayed flow in stabilizing traffic flow. • The control scheme can lower energy consumption of the traffic system. Through vehicle-to-infrastructure communication, the road-side units could collect traffic information of a selected road section and send the refined traffic information to the vehicles on the road after information processing with edge computing servers. To study the impact of traffic information in different road sections on traffic flow, a double flow controlled two-lane traffic system considering vehicle-to-infrastructure communication is introduced in this paper. The new traffic system is analyzed with Lyapunov stability theorem and a sufficient stable condition is provided in linear matrix inequality. Through simulation, the traffic flow evolution properties and energy consumption rules under different control gains and lane changing parameters are exhibited. The results show that traffic congestion and energy consumption of the two-lane traffic system can be suppressed by taking the delayed flow control information, the relative flow control information, and lane changing effect into account. Also, the role of the control strategy on traffic flow is revealed microscopically through a two-lane car-following model by transforming the macroscopic traffic flow to vehicle's velocity. It further proved the stabilization effect of the control information on traffic flow. This research can provide some theoretical guidance for traffic control through vehicle-to-infrastructure information interaction. [ABSTRACT FROM AUTHOR]

A significant focus of research and new technologies for reducing energy use in buildings is on operating the systems more efficiently during the times when the systems are active. However, there is a large potential for energy savings in determining the periods when the systems should or should not be active. This scheduling aspect of operation is often overlooked even though relatively simple solutions can unlock substantial energy savings. This paper describes a smart building start (SBS) algorithm that considers multiple zones in a building to determine individual schedules for room controllers as well as the central systems based on solving a simple optimization problem. Application of the SBS algorithm to multiple interconnected systems enables a staggered start-up that minimizes peak loads and also ensures comfort is within a target range with minimal system run time. The SBS algorithm extends the capability of traditional optimal start and is designed to be simple to deploy and robust. Simulation results as well as results from tests in a real building with a VAV system are presented. The presented algorithm is applicable to any type of building with a zonal or multi-zone HVAC system. To function, it needs to be able to change setpoints in rooms and monitor room temperatures, as well as, if desired, turn the central system on or off. [ABSTRACT FROM AUTHOR]

Water pollution and freshwater scarcity are growing problems not only in Europe but all over the world. Water reuse contributes significantly to water sustainability and brings many environmental, economic, and social benefits. The European Union (EU) has recently been addressing water-related problems with great commitment. Unfortunately, the total production of wastewater in the European Union is unknown. The situation in the EU is not satisfactory, as only 2.5% of treated wastewater is reused. The first contribution of this study is the estimation of the total amount of wastewater produced annually in the EU. It is estimated that half of this amount comes from industry, namely over 25,500 million m3. Therefore, this study aims to review and analyze the current state of the industrial wastewater (IWW) issue in the EU and to put it in the context of current EU legislation, targets, and water reuse policy. Moreover, since water reuse technologies are energy intensive, the focus is given to the techno-economic impact of IWW reuse. The energy consumption and investment costs in relation to the amount of water treated have been determined for filtration only, minimum liquid discharge (MLD), and zero liquid discharge (ZLD) systems. Reclaiming 80% of IWW would increase industrial energy consumption by only 4.1%, and investment costs would amount to only 1.5% of the EU GDP. Further, the assessment was focused on the paper & pulp industry, which is among the largest wastewater producers. Implementing efficient water reuse technology in this industry could mean up to 3,449 Mm3/year of reclaimed water. [Display omitted] • Overview of EU legislation with recommendations for future EU policy is presented. • Total amount of wastewater from industry was determined as over 25,500 million m3. • Significant water savings in the industry are possible (80%) using MLD. • Water savings in the paper and pulp industry can be up to 3449 Mm3/year. • 80% of IWW reuse would cost only 1.5% of EU GDP and increase ECI only by 4.1%. [ABSTRACT FROM AUTHOR]

Hydrothermal carbonization (HTC) is a promising process for the upgrading of wet biomass residues. Models of HTC processes, in particular at continuous pilot-scale, are needed to move HTC from lab-scale to industrial scale. This study presents a process model for mild HTC, dewatering and conversion to intermediate energy carriers (bio-pellets and biogas for power and/or heat production) of three wet biomass residue streams: paper sludge, olive pomace and orange peels, based on lab- and pilot-scale experiments. In addition, the process energy efficiency and feedstock utilization of the HTC process is calculated and compared with conventional treatment options for the chosen residues, i.e., direct anaerobic digestion (olive pomace, orange peels) or combustion after conventional dewatering (paper sludge). The process model indicates that the HTC pilot-scale process is much more efficient in terms of feedstock utilization to produce heat and/or power than the reference scenarios. The process energy efficiency of the HTC process (pilot-scale) was calculated to be 26 %, 63 % and 40 % for paper sludge, olive pomace and orange peel feedstocks, respectively. For all feedstocks, both the solid and liquid-generated products are equally important for improving the overall process energy efficiency. This study demonstrates the potential benefits of HTC processes for upgrading wet biomass waste streams based on continuous pilot-scale data. • Hydrothermal carbonization successfully upgraded wet biomass residues for energy. • Hydrothermal carbonization was more efficient for heat/power than reference cases. • Solid and liquid products are both important to improve process energy efficiency. [ABSTRACT FROM AUTHOR]

This paper presents an integrated mathematical decision-making model to reduce environmental footprints using process integration. By doing so, we address the research question: How can supplier selection, ergonomic factors, and green practices be integrated into mathematical decision-making models based on the vendor-managed inventory policy ? We use the Branch and Bound method to provide optimal solutions and a metaheuristic optimization approach in computational intelligence for large-scale problems. This paper employs a multi-criteria decision-making method to compare proposed algorithms results based on six criteria. Our findings reveal that integrating the sourcing process with inventory decisions significantly improves cost performance. Furthermore, our results demonstrate that the proposed model effectively balances environmental and economic objectives using a carbon tax. Moreover, sensitivity analysis results indicate that optimizing water and energy consumption can significantly improve carbon footprints. In addition, the study shows that ergonomic factors, including lighting and temperature, have a substantial impact on performance. The primary contributions of this paper include the incorporation of a multi-sourcing strategy, green practices, ergonomic factors, and supplier selection into vendor-managed inventory decision models. [Display omitted] • This paper presents a process integration for sustainable supply chains. • An analytical model is proposed to reduce environmental footprints using a carbon tax. • Water and energy consumption are included in the decision-making processes. • The impacts of ergonomic factors, such as light and temperature, are investigated. • Exact solvers and computational intelligence techniques optimize the proposed model. [ABSTRACT FROM AUTHOR]

The paper addresses challenges arising from the unpredictable and probabilistic nature of wind speed and solar irradiance, leading to variable power production in renewable sources. Power imbalance issues in grids result from the difficulty in accurately estimating these variables. The study introduces an improved data-driven uncertainty set, utilizing a neural network trained with extensive historical data from a multi-microgrid system. This set efficiently identifies strong coupling and extracts characteristic scenarios, ensuring high reliability with a minimal number of scenes. The proposed probabilistic model, implemented in the coordination of pumped storage, wind, and solar power plants, incorporates energy limits and storage characteristics. A four-mode IEEE model for storage units allows quick response to system needs. Simulating storage unit states every hour, considering system load, output power, and energy limitations, optimizes power dispatch based on battery capacity. Additionally, the paper addresses the uncertainty in electric power demand in microgrids, proposing an improved data-driven uncertainty set obtained from a neural network. By eliminating unrealistic worst-case scenarios, the approach accurately captures system characteristics, maintaining high reliability while significantly reducing convergence time. The proposed uncertainty set is applied in a novel two-stage robust optimization dispatch model, transforming the optimization problem into a mixed-integer linear programming problem, and solving it using a column and constraint generation algorithm. Numerical case studies verify the feasibility and superiority of the proposed approach in terms of economy and convergence performance compared to existing robust optimization methods. [ABSTRACT FROM AUTHOR]

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

This research paper presents a comprehensive study focused on optimizing innovative tailplane configurations for transport jet aircraft. This study aims to enhance the aerodynamics of the aircraft's rear end by introducing a novel tail arrangement. The ultimate goal is to reduce the size of the horizontal empennage, which will have a positive impact on aircraft fuel efficiency. To fulfill its objectives, this work develops a systematic approach, integrating advanced methodologies such as the Design of Experiments and Response Surface Models for both aerodynamics and structural disciplines. By levering response surfaces methodology, the aerostructural optimization has been performed towards a size reduction of the horizontal tail. Results indicate a potential 9% reduction in the tailplane reference area, which could result in enhanced aerodynamic performance and weight saving. This target would only be achievable if the innovative design includes a Leading Edge Extension device to maintain the stability and handling characteristics of a conventional reference aircraft. The impact of the innovative optimized tail arrangement developed in this study at the aircraft level resulted in a 1% reduction in block fuel for a mission range of approximately 3,400 nautical miles for a 180-seat capacity jet aircraft (similar to A320neo). Furthermore, from an industrial perspective, this paper also demonstrates how an automated workflow assists with CAD modeling, mesh creation, simulation execution, and surrogate models to accelerate complex multidisciplinary optimization processes, thereby reducing the time and costs associated with the development of a new aircraft configuration. [ABSTRACT FROM AUTHOR]

The partial denitrification (PD) coupled with anaerobic ammonium oxidation (Anammox) (PD/A) process is a unique biological denitrification method for sewage that concurrently removes nitrate (NO 3 −-N) and ammonium (NH 4 +-N) in sewage. Comparing PD/A to conventional nitrification and denitrification technologies, noticeable improvements are shown in energy consumption, carbon source demand, sludge generation and emissions of greenhouse gasses. The PD is vital to obtaining nitrites (NO 2 −-N) in the Anammox process. This paper provided valuable insight by introduced the basic principles and characteristics of the process and then summarized the strengthening strategies. The functional microorganisms and microbial competition have been discussed in details, the S-dependent denitrification-anammox has been analyzed in this review paper. Important factors affecting the PD/A process were examined from different aspects, and finally, the paper pointed out the shortcomings of the coupling process in experimental research and engineering applications. Thus, this research provided insightful information for the PD/A process's optimization technique in later treating many types of real and nitrate-based wastewater. The review paper also provided the prospective economic and environmental position for the actual design implementation of the PD/A process in the years to come. [Display omitted] • PD/A was considered the most economically and environmentally beneficial new nitrogen removal process. • Principles and recent advances of the PD/A process are discussed. • The functional microorganisms and their competition in PD/A process are analyzed. • Influencing factors control and reinforcement strategies of PD/A was classified and conceptualized. • Knowledge gaps and prospects are presented to improve the future of PD/A process. [ABSTRACT FROM AUTHOR]

• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

CaO-CaCO 3 thermochemical energy storage is a promising technology for solar energy utilization and storage. Alkaline papermaking waste (APW) from paper mills, which is mainly composed of CaCO 3. Herein, TiO 2 /MnFe 2 O 4 co-modified APW was synthesized. The energy storage capacity of TiO 2 /MnFe 2 O 4 co-modified APW was studied during CaO-CaCO 3 thermochemical energy storage cycles in a duel fixed bed reactor. TiO 2 /MnFe 2 O 4 co-modified APW mainly consists of CaO, MgO, CaTiO 3 , CaMnO 3 , and Ca 2 Fe 2 O 5. The introductions of TiO 2 and MnFe 2 O 4 significantly improve the cyclic stability and optical absorption property of APW in 30 cycles. The co-modified APW (the mass ratio for CaO:TiO 2 :MnFe 2 O 4 = 100:5:5) possesses high sintering resistance, energy storage, and optical absorption capacities in 30 cycles. The energy storage conversion and capacity of the co-modified APW are 0.72 and 2288.57 kJ/kg after 30 cycles, respectively. The optical absorptance of the co-modified APW reaches 49%, which is 9.25 times as high as that of APW. CaTiO 3 and CaMnO 3 effectively mitigate the sintering of CaO grains. CaMnO 3 and Ca 2 Fe 2 O 5 significantly increase the optical absorptance of APW. The co-modified APW possesses stronger basicity and more oxygen vacancies, which is favourable for the carbonation reaction. Thus, TiO 2 /MnFe 2 O 4 co-modified APW is a good energy storage material. • TiO 2 /MnFe 2 O 4 co-modified papermaking waste is used for CaO-CaCO 3 energy storage. • The composite mainly consists of CaO, MgO, CaTiO 3 , CaMnO 3 , and Ca 2 Fe 2 O 5. • The composite has high and stable energy storage and optical absorption capacities. • The energy storage density of the composite is 2288.57 kJ/kg after 30 cycles. • The composite has high sintering resistance, stronger basicity, and oxygen vacancy. [ABSTRACT FROM AUTHOR]

In recent decades, the use of fossil fuels has had a harsh impact on the climate, and the frequency and intensity of various extreme weather events have increased, which has triggered an increased the use of renewable energy sources and reflection on technological advances. Many countries are trying to promote renewable energy to reduce their impact on climate change, but the impact of national energy policies in the climate risk framework remains poorly understood. Based on the panel data of 84 developed/developing countries around the world from 2006 to 2019, this paper studies and analyzes the impact of renewable energy use on climate risk. The study found that the increase in the proportion of renewable energy use can reduce the climate risk, and this result is still robust after undergoing robustness tests such as changing variables, samples, and research methods. The findings offer an important solution for European countries, where climate risks are most acute. The heterogeneity analysis shows that the higher the level of economic development, population density and the lower the level of incorruptibility, the weaker the inhibitory effect of renewable energy use on climate risk. The mechanism analysis also shows that the increase in the proportion of renewable energy use can reduce climate risk by reducing carbon dioxide (CO 2), methane (CH 4), nitrogen monoxide (NO), PM2.5 emissions, and the proportion of fossil fuel use, but it does not reduce climate risk by increasing biodiversity. In particular, we find that technological progress has not played a role in the use of renewable energy to reduce climate risk, breaking the stereotype. The policy recommendation of this paper is that under the increasing pressure of climate change, the transition to clean energy is an indispensable option, and the need to accelerate the energy transition becomes more urgent. There is a greater need to further protect biodiversity and enhance technological progress. • The increase in the proportion of renewable energy use can reduce the climate risk. • The mechanism is to reduce CO 2 , CH 4 , NO, PM2.5 emissions and fossil fuels use. • The use of renewable energy cannot reduce climate risks by increasing biodiversity. • The impact of renewable energy use on climate risk is heterogeneous. • Technological progress has not played a role in the use of renewable energy to reduce climate risk. [ABSTRACT FROM AUTHOR]

With the increasing complexity of the power grid, the precision of the thermal power units' execution of automatic generation control (AGC) commands is gradually increasing the impact on the online regulation of the power grid. The deviation between the actual output of thermal power units and the AGC command of the grid will not only affect the consumption of new energy output, but also endanger the safe operation of the grid. This paper introduces "deep learning" technology to solve the problem. Firstly, an AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. The algorithm builds a neural network model to accurately predict the unit output and the confidence evaluation of the prediction results. Next, a high-dimensional input preprocessing strategy based on variational autoencoder (VAE) is proposed to reduce the dimensionality of the model input attributes, improving the convergence and accuracy of the model. Finally, an AGC optimal command fast inversion solution method (AOCFISM) is designed. This method transforms the unit output deviation problem into an objective optimisation problem. And improve the efficiency of solving the optimal AGC command value by constraining the unit command value. The calculation results show that the error of the prediction results of the model proposed in this paper is 5% lower than that of the traditional neural network. The difference between the output value of the optimal AGC command and the expected output value obtained is less than 0.5 MV, which can support AGC online decision-making.© 2017 Elsevier Inc. All rights reserved. • An AGC command execution effect identification and confidence evaluation algorithm (ACEEI-CEA) is proposed. • Results show the ACEEI-CEA performs well in command prediction compared to the state-of-the-art methods. • ACEEI-CEA achieves confidence assessment of predicted values. • The pre-processing strategy based on variational autoencoder shortens the training time of the model. • The AGC optimal command fast inversion solution method (AOCFISM) can obtain the AGC optimal command value. [ABSTRACT FROM AUTHOR]

The roundtrip electrochemical energy efficiency is improved from 63% to 76% at a current density of 200 mA cm −2 in an all-vanadium redox flow battery (VRFB) by utilizing modified carbon paper electrodes in the high-performance no-gap design. Heat treatment of the carbon paper electrodes in a 42% oxygen/58% nitrogen atmosphere increases the electrochemically wetted surface area from 0.24 to 51.22 m 2 g −1 , resulting in a 100–140 mV decrease in activation overpotential at operationally relevant current densities. An enriched oxygen environment decreases the amount of treatment time required to achieve high surface area. The increased efficiency and greater depth of discharge doubles the total usable energy stored in a fixed amount of electrolyte during operation at 200 mA cm −2 . [ABSTRACT FROM AUTHOR]

• A multi-constraint model for impulsive orbital pursuit-evasion game (OPEG) is constructed. • A learning-based algorithm (i.e. PRD-MADDPG) for impulsive OPEG is designed. • The Predict-Reward-Detect (PRD) training framework for improving MADDPG is established. • The PRD-MADDPG performs better than conventional MADDPG in impulsive OPEGs. • The winning mechanism of the game for both players under multiple factors is analyzed. This paper comprehensively investigates the problem of impulsive orbital pursuit-evasion games (OPEGs) by using an artificial intelligence-based approach. First, the mathematical model for the impulsive OPEGs in which the pursuer and evader both perform their orbital maneuvers by imposing the impulsive velocity increments is constructed. Second, the problem of impulsive OPEGs is transformed into a bilateral optimization problem with a minimum–maximum optimization index in terms of terminal time and multiple constraints such as maneuverability, total fuel consumption, and mission time, etc. To determine the optimal impulsive maneuvers for both sides, a PRD-MADDPG (Predict-Reward-Detect Multi-Agent Deep Deterministic Policy Gradient) algorithm in the frame of multi-agent reinforcement learning is designed. This novel algorithm uses the basic MADDPG to achieve the strategies training and learning, and applies the supplemental PRD to predict the change of game state during the interval between two adjacent impulsive maneuvers and incorporate these information into the algorithm training in the form of predicted reward. Finally, some pursuit-evasion missions near the Geosynchronous Earth Orbit are numerically analyzed to verify the validness and effectiveness of the algorithm. The results prove that the PRD-MADDPG algorithm is very efficient to find applicable strategies even considering rather complex constraints. It is also shown that the learning-based strategies can be effectively applied in the extended scenarios which are not seen in the training process. [ABSTRACT FROM AUTHOR]