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A novel approach for paper drying using airborne ultrasound technology is presented. A unique experimental setup is developed, and a systematic study is conducted using 23 factorial design of experiments and Analysis of Variance. Three controlling factors are considered in the experiments including the initial moisture content, basis weight and refining condition. The outcome of the experiments is compared to a previous work on direct-contact ultrasonic drying of paper. The results confirm that similar to direct-contact, for airborne ultrasonic drying, the basis weight/thickness of the sample is the most important factor in ultrasonic drying and it is followed by the effect of initial moisture content. Using linear regression model, a correlation for predicting the total time of ultrasonic drying is provided. Quality of the dried samples is evaluated, and the permeability measurements confirmed the effect of pore characteristics on ultrasonic drying. The analysis for energy consumption reveals that airborne ultrasonic drying is more efficient at higher moisture contents. [ABSTRACT FROM AUTHOR]

The air-drying of resin impregnated paper sheets in industrial lines, formed by a serial array of furnaces, presents a high number of different controllable operational parameters whose adjustment, usually done by the maintenance staff, leads to non-efficient configurations. A model-based numerical tool, which predicts accurately in a few seconds the evolution of the paper temperature and paper grammage along the line for a given combination of the input operational parameters (direct design), was used coupled to an optimization tool to select appropriate operational parameters (inverse design) that ensure a drying process quality (i.e., fulfills an objective grammage profile) with a minimum of energy consumption. The numerical tool was capable of selecting suitable configurations with an energy reduction of up to 50% for several tested industrial cases, making the model an essential tool in the framework of the increasingly relevant role of digital twins in industry. [ABSTRACT FROM AUTHOR]

Furthermore, very complicated multiphase and thermal processes are the corner stone in modern industry, high technology development, energy conversion and utilization, renewable and sustainable energy technologies, just to name a few here. Therefore, it is essential to develop new technology to improve energy utilization efficient, sustainable energy and low carbon/zero carbon technology to reduce carbon footprint. [Extracted from the article]

Life cycle assessment (LCA) is an attractive approach to implement the waste management system in terms of environmental sustainability. The variety of environmental options has been made an emergence requirement a methodology to choose the best options for decision-makers. This paper aims to generate a comprehensive model to facilitate the life cycle products to develop a cleaner production strategy in paper manufacture. In the first step, the requirement and adopting models are applied to determine the main criteria of LCA. Then, an analytical hierarchy process (AHP) model is designed to assess the LCA qualification of the paper manufacturing process according to the selected criteria. The environmental impact, production process, technical capability, raw material, energy consumption, human resource, manufactured products, and economic are selected as a higher priority after considering the requirement and adopting the decision model. Pair-wise comparative of 52 sub-criteria showed that circulating of water and solid waste reusing from wastewater treatment plant through dissolved air flotation (DAF) and disk filter enables us to have many advantages not only the pollutants removal but also the enhancement of the LCA framework. [ABSTRACT FROM AUTHOR]

Paper drying is a highly energy-intensive and complicated multivariate process. The dryer section plays an important role in the energy consumption of a paper machine, especially of thermal energy. A comprehensive method for assessing the energy performance of the dryer section was investigated in this study to improve energy efficiency. This method was divided into three component processes: energy and evaporation load audit, field test and observation, and energy flow analysis and energy efficiency estimation. In a case study, we found that the method could, in addition to analyzing the key factors that restrict drying efficiency, also depict the details of energy consumption clearly. At the same time, several significant energy-saving measures were suggested to improve the energy efficiency of the paper-drying process. [ABSTRACT FROM PUBLISHER]

Based on a study of policy-induced changes in the Swedish pulp and paper industry, this paper follows a process of socio-technical regime destabilisation. Results from the study show that in industries where established firms have significant power, processes of endogenous renewal are more likely to destabilise established regimes than processes based on niche solutions. Further, the study shows how policy measures aimed to destabilise the current regime may result in different responses, owing to the different capabilities of individual firms. The analysis suggests that heterogeneous capabilities within established industries provide possibilities for policy makers to initiate change. [ABSTRACT FROM AUTHOR]

This article examines the economic possibilities for the Swedish pulp and paper industry in general and the kraft pulp industry in particular of increasing the share of internally generated energy by using readily available production residues such as black liquor. It is suggested that the industry's reliance on fossil fuels and purchased electricity has decreased over time, thereby reducing the impact of external energy prices on energy investments. The results suggest that the use of biomass will only increase to the point where the existing capacity to do so is met unless heavy investment subsidies are made available. [ABSTRACT FROM AUTHOR]

Production of biomass briquettes of good strength and combustion characteristics usually requires the addition of a binder material to the feedstock. This study was aimed at developing a methodology for producing briquettes from water hyacinth at low compression pressures without using a binder material. Fresh water hyacinth was chopped to a maximum length of 3 cm, sun-dried to a moisture content of about 10%, mixed with shredded waste office paper in four water hyacinth to paper proportions on weight basis (100:0, 90:10, 80:20 and 70:30). The mixtures were soaked in water for three days and briquetted using a 6-cylinder screw briquetting machine. The resulting briquettes were subjected to standard strength and combustion tests. A Completely Randomized Design with four treatments replicated five times was used as the experimental design. Results from the study indicate no significant differences in compressed density, relaxed density, relaxation ratio and calorific value between the briquettes. The results further indicate that briquettes made without a binder material have significantly low but adequate shatter resistance, low ignition time, high burning rate and take a longer time to bring water to boil. It can be concluded that good quality water hyacinth briquettes can be produced at low compression pressures without using a binder material. This methodology can potentially increase adoption of water hyacinth briquettes in areas with poor access to binder materials. The originality of the research is the development of a water hyacinth briquetting methodology that does not use binder materials. [ABSTRACT FROM AUTHOR]

Due to high energy consumption in the Chinese paper industry, this study considers higher-energy efficiency for the multicylinder dryer section of paper machines. A common situation in the Chinese paper industry is that energy is consumed in extensive mode. In order to improve the energy efficiency of the paper machine dryer section, deeper analysis and optimization of process parameters are necessary. A NLP optimization method is developed for integration of steam system and air system to reduce the steam consumption and decrease the loads of centrifugal blowers in the multicylinder dryer section of a paper machine. Equality constraints of the optimization model are extracted from different process modules based on material and energy balance. Inequality constraints are from the production capacity, operating condition, etc. Two illustrative examples are presented in this paper. The results show that the optimization model is adaptive and convenient for application. For a newsprint machine, less dry air and steam are used and the energy consumption can be reduced by about 8% in the dryer section. Applied on a linerboard machine which has surface sizing, the method can reduce the energy consumption by 5.6%. [ABSTRACT FROM AUTHOR]

A static energy model was developed to describe the conventional paper drying process for a multicylinder paper machine. The model is based on the mass and energy balance relationships written for fiber, steam, air, and water. With the purpose of developing a clear understanding of the paper drying process and evaluating its drying performance, the model developed was made up of seven basic blocks according to the technical art of paper drying. In this work, a multicylinder newsprint machine was chosen to illustrate the energy model. The module schematic and instructions are presented first, followed by mathematical descriptions for each block. In addition, paper drying models are reviewed. The specific drying rate, specific heat consumption, and energy efficiency are introduced to evaluate the drying performance. Experiments were conducted to measure the operating parameters and the newsprint drying process was simulated based on the model. A Sankey diagram is also presented. The results are compared with a typical newsprint machine to identify more energy-saving opportunities. The calculations indicate that the consistency of the energy model with experimental results and experience is reasonable. A series of simulations was performed to examine the effects of supply air temperature and exhaust air humidity on specific heat consumption. The results show that the specific heat consumption was decreased by 3.3% when the supply air temperature decreased from 112 to 100°C by adjusting the steam flow into the air heaters. Correspondingly, increasing the exhaust air humidity to 0.14 kg water/kg dry air from 0.10 kg water/kg dry air by adjusting the fan speed led to a 4.6% reduction in specific heat consumption and a 32% power savings for the fans. [ABSTRACT FROM AUTHOR]

The paper sheet drying process consumes about 70% of the total energy required in coated papermaking, and almost all the thermal energy used in the process can be found in the exhaust air; thus, it has significant potential to recover the heat. With the aim of saving energy, the recovered energy is usually used to heat different process streams instead of steam. This article examines the drying process of an operating coating paper machine to demonstrate an optimization method. To study the possibility of improving energy efficiency, thermodynamic analysis was conducted. The reasons why there is so much heat lost during drying were investigated. Based on the results of the energy and exergy analysis, a new waste heat integration scheme is presented. Furthermore, the performance of the proposed scheme has been evaluated. The results of the case study show an energy efficiency improvement of 7.3% and a specific energy consumption reduction of 4.6% with profitable investments. [ABSTRACT FROM AUTHOR]

Several studies have investigated the relationship between the energy performance of buildings and housing prices. First, this paper identifies a price premium for energy efficiency within the German rental market. Then, the indexed price differences and associated marginal benefits are compared with the marginal costs of energy retrofits. An extensive database of Germany's largest online platform for housing over a time span from 2016 to 2020 is used in a hedonic regression approach. In addition, to extract the marginal costs of energy consumption abatement, a dataset of 1048 rental units regarding green-retrofit measures is utilized. Although a significant green premium is identified in the rental market, the findings suggest that it is not high enough to compensate landlords for the money they have to spend to retrofit. The marginal costs exceed the marginal benefits by far. Furthermore, it is found that the German government's recent plans to split the carbon tax between landlords and tenants do not change this because the price per metric ton of carbon is insufficiently high. Limitations with respect to the data basis and consequently to the interpretation of the results exist. Nevertheless, the findings can help both tenants and landlords in their decision-making, as well as policy makers in the implementation of decarbonization efforts. [ABSTRACT FROM AUTHOR]

In this work, the influences of the air temperature and velocity on the drying kinetics of recycled paper pulp were analyzed. The increase of both variables positively influences the process, concerning the final moisture content and the drying time. However, optical microscopy and visual observation showed worse quality of the paper for drying conditions of high air temperature and velocity, presenting less uniformity and overdried surfaces. The dryer energy efficiency was evaluated by performance parameters and the results were compared with the ones obtained for other types of industrial paper dryers, presenting good agreement. [ABSTRACT FROM AUTHOR]

Hyperlocal delivery service is a digital platform business that receives ondemand orders from customers to deliver products from a local offline shop in logistics. Deploying drones/Unmanned Aerial Vehicles (UAVs) on the hyperlocal market is still a relatively unexplored phenomenon and that demands attention on sustainable drone delivery strategies. This paper introduces both non-flexible and flexible drone delivery strategies for the hyperlocal market. Further, drone operational complexities like deciding a precise location for landing and take-off in congested cities, limited flight endurance, incorporating docking stations in drone routes and drone safe-return strategy are investigated. This problem is formulated as the Mixed-Integer Linear Programming (MILP) model, which minimises the drone’s total distance by the optimal energy consumption to complete the scheduled services. For practical feasibility, this paper focuses on heuristic solutions that effectively solve the problem efficiently and near optimally. The approach facilitates the operations manager to decide on an appropriate drone travel capacity based on service distance and the size of on-demand customers in an urban hyper-local logistics environment. [ABSTRACT FROM AUTHOR]

Comments on the paper "Embodied and Operational Energy Consumption in the City," by P. Troy, D. Holloway, S. Pullen and R. Bunker, in the March 2003 issue of "Urban Policy and Research,' which explored the use of existing data to estimate at the small-area scale of the aggregate transport, building and infrastructure energy consumption attributable to urban development. Assertion that the methodology can be described as life cycle energy/emissions analysis; Limitations of area-based data; Underestimation of transport energy variability.

This paper addresses the question whether energy consumption variables and the disaggregation of output matter in the context of efficiency analysis of container terminals. While it is obvious that the energy consumption of refrigerated cargo is higher than the energy consumption of non-refrigerated cargo, this work investigates whether those differences show in an overall efficiency analysis of terminals. This would point to a potentially important input for efficiency measures, to be considered in future productivity and efficiency analysis of terminals. Starting with a discussion on theoretical concepts and variable selection for measuring the energy dimension of terminal efficiency, this is the first paper that applies data envelopment analysis (DEA) comparing results with and without energy consumption, as well as differentiating productive outputs (dry and reefer container handling). The results reveal how the output disaggregation leads to substantially different efficiency scores and are a first step to show the relevance of output disaggregation and the inclusion of the energy variables as inputs in container terminal efficiency studies. [ABSTRACT FROM AUTHOR]

This paper investigates the fixed-time consensus control for a class of multi-agent systems subject to unknown bounded disturbances under undirected topology. Firstly, the nonlinear controllers and the measurement errors are designed based on the hyperbolic tangent function to avoid the non-differential problem. Secondly, a distributed fixed-time control protocol is designed, which can realise the fixed-time convergence and improve the system convergence rate independent of the system's initial state. Thirdly, the dynamic event-triggered mechanism is proposed, such that the triggering frequency of events is reduced and communication energy consumption can be saved. The stability analysis is presented by employing the Lyapunov theory and the Zeno phenomenon is excluded. Finally, the simulation examples are presented to show the effectiveness and advantages of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

This research investigates how supply chain finance (SCF) affects the investment efficiency of new energy firms by adopting a sample of Chinese listed new energy enterprises from 2011 to 2019. With the help of data envelopment analysis (DEA) combined with the Banker-Charnes-Cooper (BCC) model, we calculate efficiency values and analyze the regression results of the Tobit model, and find that SCF significantly boosts the investment efficiency of new energy corporations by improving their cash-holding position. The results of mechanism analysis also suggest that both supply chain concentration and digital finance positively magnify the relation between SCF and corporate efficiency. After comparing the impact in the new energy firms with different characteristics, we conclude that the SCF effect in promoting corporate investment efficiency is more prominent for non-stateowned new energy enterprises located in the western region that have low equity concentration. The conclusions of this paper shed new light on the fusion of the SCF business into building a new energy industry cluster. [ABSTRACT FROM AUTHOR]

Scheduling problems should not only focus on minimizing completion time, but other performances, such as minimizing energy consumption, are urgent to investigate. Many companies and researchers are competing to develop various methods to solve this problem. One problem that has received much attention lately is the energy-efficient Hybrid Flow Shop Scheduling Problem (HFSP). The HFSP problem is also popularly called the flexible flow shop problem. This research provides a comprehensive review of articles that discuss energy-efficient HFSP scheduling to determine the trend of energy-efficient HFSP research and research gaps for future research interests. This review uses a systematic literature review method. The total articles reviewed in this study are 90 articles from January 2008 - December 2022. The articles are classified by year, country, journal/conferences, publisher, objective function, and method. The analysis results show that the trend of energy-efficient HFSP research continues to increase. In addition, the multiobjective problem is the dominating problem in energy-efficient HFSP scheduling problems. The study also presents a gap research analysis and a review of future research directions. [ABSTRACT FROM AUTHOR]

In a complex and dynamic job shop containing logistics factor, schedule needs to be generated rapidly, so the real-time scheduling method is more suitable for such scenario. Such method takes advantage of local information within a short time due to the rapid changes of information under uncertain environment. Therefore, how to make use of the future information by prediction while ensuring the robustness of schedule is a valuable problem. To solve it, firstly, a new real-time scheduling model and algorithm is proposed. There is a new kind of release moment of task information which can give AGVs the longest time to prepare for the task than existing research. Secondly, a real-time information update mechanism is designed to increase schedule's robustness. Finally, a large-scale and dynamic job shop simulation experimental platform is developed. Dynamic factors include the random insertion of orders and failures of equipment. Results show that the method proposed outperforms existing research in terms of customer satisfaction, equipment utilisation and energy consumption. The robustness of schedule can also be acceptable. This paper also finds a rule that in job shop with the large proportion of logistics transportation time, the above method can achieve more competitive results. [ABSTRACT FROM AUTHOR]

The electrification of commercial vehicles has led to new wheel torque allocation options for propulsion and braking of articulated heavy vehicles. Each unit or axle can be individually braked or propelled whilst the other units or axles are excluded from such action. This may achieve the best energy efficiency. However, this can also lead to potential yaw stability problems such as jackknifing and trailer swing, especially under bad loading and weather conditions. This paper describes the above instabilities and introduces a nonlinear single-track model to study the vehicle dynamics of the tractor-semitrailer combination. The effects of different vehicle and environment parameters are analysed with this vehicle model. A safe operating envelope for limiting the wheel forces is obtained using this vehicle model. Since the vehicle model introduced in this paper is computationally effective, it can be run online in real vehicles, with an instantaneous safe operating envelope obtained for the momentary conditions. Thus, yaw instabilities such as jackknifing and trailer swing may be avoided. [ABSTRACT FROM AUTHOR]

This study presents an integrated approach to assess energy performance during hood retrofitting from a semi-closed hood to a closed hood. According to the principle of heat and mass transfer, a simulation program was established by a mathematical model and some necessary model parameters. It could be used to simulate the paper-drying process and calculate steam and power consumption during paper drying. The method was applied to a corrugated paper machine. After a fundamental field test and observation, the simulation program determined appropriate model parameters. Finally, we found a 13.2% steam consumption reduction and a 13.5% total energy consumption reduction. [ABSTRACT FROM PUBLISHER]

Future adaptive applications require lightweight and stiff materials with high active strain but low energy consumption. A suitable combination of these properties is offered by carbon nanotube-based actuators. Papers made of carbon nanotubes (CNTs) are charged within an electrolyte, which results in an electrical field forming a double-layer of ions at their surfaces and a deflection of the papers can be detected. Until now, there is no generally accepted theory for the actuation mechanism. This study focuses on the actuation mechanism of CNT papers, which represent architectures of randomly oriented CNTs. The samples are tested electrochemically in an in-plane set-up to detect the free strain. The elastic modulus of the CNT papers is analyzed in a tensile test facility. The influence of various ion sizes of water-based electrolytes is investigated. During the tests, four parameters that have a significant influence on the mechanical performance of CNT papers were identified: the test conditions, the electrical charging, the microstructure and the ion size. All of these influencing factors point to the mechanically weak inter-tube linking at which the actuation seems to take place. Quadratic voltage-strain correlation suggests a combination of electrostatic and volumetric effects as the possible reason for CNT paper actuation. Abbreviations: CNT: carbon nanotubes; CV: cyclic voltammetry; CVD: chemical vapor deposition; HiPCO: high pressure carbon monoxide; IL: ionic liquid; MWCNT: multi-walled carbon nanotube; MW: multi-walled; NHE: normal hydrogen electrode; PDMS: polydimethylsiloxane; PMMA: polymethylmethacrylate; PPy: polypyrrole; PVDF: polyvinylidenefluoride; SCE: saturated calomel electrode; SWCNT: single-walled carbon nanotube; SW: single-walled; 1M: one molar concentration [ABSTRACT FROM AUTHOR]

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

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

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

Given the high demand for energy in the manufacturing industry and the increasing use of renewable but volatile energy sources, it becomes increasingly important to coordinate production and energy availability. With the help of incentive-based programmes, grid operators can incentivise consumers to adjust power demand in critical situations such that grid stability is not threatened. On the consumer side, energy-efficient scheduling models can be used to make energy consumption more flexible. This paper proposes a bi-objective job-shop scheduling problem with variable machine speeds that aims on minimising the total energy consumption and total weighted tardiness simultaneously. We use a genetic algorithm to solve the model and derive Pareto frontiers to analyse the trade-off between both conflicting objectives. We gain insights into how incentive-based programmes can be integrated into machine scheduling models and analyse the potential interdependencies and benefits that result from this integration. [ABSTRACT FROM AUTHOR]

The energy problem in green manufacturing has attracted enormous attention from researchers and practitioners in the manufacturing domain with the global energy crisis and the aggravation of environmental pollution. The distributed blocking flow shop scheduling problem (DBFSP) has considerable application scenarios in connection with its widespread application in the industry under the background of intelligent manufacturing. A multi-objective discrete differential evolution (MODE) algorithm is proposed to solve the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP) with the objectives of the makespan and total energy consumption (TEC) in this paper. The cooperative initialisation strategy is proposed to generate the initial population of the EEDBFSP. The mutation, crossover, and selection operators are redesigned to enable the MODE algorithm as applied to discrete space. A local search strategy based on the knowledge of five operators is introduced to enhance the exploitation capability of the MODE algorithm in the EEDBFSP. The non-critical path energy-efficient strategy is proposed to reduce energy consumption according to the specific constraints in the EEDBFSP. The effectiveness of each strategy in the MODE algorithm is verified and compared with the state-of-the-art algorithms. The numerical results demonstrate that the MODE algorithm is the efficient optimiser for solving the EEDBFSP. [ABSTRACT FROM AUTHOR]

Membranes can significantly reduce energy consumption during concentration of black liquor (BL) in the Kraft papermaking process, but the harsh conditions (pH ~12, 80°C–95°C, ~15 wt% solids) make this challenging. We elucidate challenges and opportunities for membranes in BL applications. We critically review membrane materials, processes, and operational modes investigated in the literature. Future advances will involve fabrication of higher-rejecting (≥95% lignin and inorganics), BL-resistant, NF, and RO membranes. Opportunities exist for molecular sieving and electrically driven membranes to recover other valuable chemicals such as carboxylic acids. We also discuss the economics of BL concentration with a single-stage membrane process. [ABSTRACT FROM AUTHOR]

Papermaking process is highly energy-intensive, complicated, and influenced by multiple unit processes, which plays an important role in the energy consumption, especially of thermal energy and electricity energy. An energy management system (EMS) was built to acquire the systematic, consistent, and accurate online information of the production process in a typical coated paperboard mill. The EMS first fulfilled the online energy information calculation from the process data acquisition and integration, second provided the massive historical data to find out the internal variation of production process for inferring decisions. In a case study, the online energy flow analysis and the estimation of specific energy consumption (SEC) were carried out. Based on data-driven techniques, three different operation conditions were recognized by adopting the kernel principal component analysis and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) when the basis weight was 220 g/m2. The energy consumption was described in each cluster. Particular attention needed to be paid to LP steam in the high-speed production process with the change in environmental temperature and humidity in cluster 3. A novel indicator was proposed to appraise the level of energy consumption in different paper machine speeds. The benchmarking SEC was extracted from the massive historical data for the estimation of energy-saving potential. With the designed capacity of 300,000 ton/year, the potential economic benefits will be 20127 thousand RMB/year. [ABSTRACT FROM AUTHOR]

Energy cost optimisation in manufacturing systems has gained more and more attention. Although there are many papers about energy consumption optimisation in serial production lines, energy cost optimisation in serial production lines has rarely been focused. In this paper, we formulate an energy cost optimisation problem in two-machine Bernoulli serial line under time-of-use pricing. We analyse the structural characteristics of the problem and transform the problem into optimally allocating the production rate among the time periods of different electricity rates. A definition of the extreme allocation is proposed and completed, and the optimal allocation is proved to be one of the extreme allocations. Using the property, an efficient method to solve the optimal allocation is proposed. With the help of the method, the multi-electricity-rate problem is transformed into several single-electricity-rate problems, which has been solved in the literature. [ABSTRACT FROM AUTHOR]

This paper discusses margins in the context of building services, where the capacity in many buildings, such as hospitals, exceeds the requirements by huge amounts. This oversizing has a direct impact on building efficiency, capital, maintenance and operational costs, as well as environmental and societal impacts throughout its lifecycle. This paper analyses two in-depth case studies of oversized boiler and chiller systems within NHS hospitals, whereby a number of key factors leading to the oversized systems are identified. The excessive and uncoordinated use of margins that are applied during the various stages of building service projects for a variety of reasons, is one significant factor. The lack of requirements information such as energy demand profiles and the use of vague and unreliable data for initial project requirements specifications, is also a major contributing factor. The paper points to the need to develop robust processes that capture the scope and rationale for the margins applied, to communicate project assumptions and stakeholder requirements in a clear and unambiguous format and to develop systems for improved data capture and analysis. [ABSTRACT FROM AUTHOR]

Due to the non-deterministic and probabilistic qualities of renewable energy resources (RER), integrating these resources has posed a challenge for energy companies and government policy planners. Hybrid power systems require frequent voltage regulation to address the changes in renewable generation and facilitate voltage management. This leads to an increment in the operating time of the tap changer in the power transformer, thus resulting in accelerated degradation of the on-load tap changer (OLTC) device. The primary focus of this paper is to optimally manage the penetration level of renewable generation while maintaining the minimum time for proper operation of voltage regulation devices. Accordingly, the major contribution of this paper is to address the switching cycle issues of the on-load tap-changing transformer. Initially, a framework is proposed to optimize the location and size of renewable power generation using a multi-objective cuckoo search optimization algorithm. The main goal here is to reduce power losses and tap changer operations while improving the system voltage profile under different levels of renewable energy penetration. The optimization problem takes into consideration the fluctuating nature of wind speed and solar irradiance for sunny and cloudy days over 24 h. The framework has been applied to the IEEE 57-bus and 118-bus systems with different load levels. The performance of the proposed approach has been benchmarked by comparing it with the genetic optimization algorithm to identify the higher potential buses for renewable generation allocation. The cuckoo search algorithm (CSA) shows outstanding results in reducing the number of switching cycles to about (37 – 43) % whereas the genetic algorithm (GA) only (17 – 18). In this sense, the number of changes in tap position when using the CSA is 1956 for sunny days and 1763 for cloudy days compared to GA 2558 for sunny days and 2547 for cloudy days. The voltage profiles for both algorithms are maintained in the range of (1.06 and 0.94) per unit. The results affirm the effectiveness of the proposed approach in determining the optimal placement and size of RER with voltage profile improvement and reduction in the switching cycles of OLTC. [ABSTRACT FROM AUTHOR]

Self-harvesting and consumption of electrical energy from a small-scale photovoltaic (PV) system became quite a beneficial option for households who seek for an economical, independent and environment-friendly power alternative. However, in practice, prosumers without battery storage systems face some energy flow management issues, when only part of the generated energy is consumed instantly in place, while the rest is exported to the distribution grid or simply lost. The paper examines profiles of some preselected prosumers with small-scale PV installations with the purpose to evaluate losses caused by the inability to consume instantly all generated energy and thus resulting in redundant energy exchange between prosumer's microgrid and distribution grid. The analysis reveals certain anomalies, when local PV system produces enough power to cover all the electricity demand of the household, but energy is still imported from the DSO grid at the same time. These simultaneous flows of import and export active energy were calculated and showed quite significant numbers. The paper digs into this problem through a quantitative evaluation of energy flows within one preselected microgrid using one-second resolution experimental data. The results presented here help us distinguish the main factors leading to inefficient distribution of generated power in households. [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]

Marine fishery is a highly mechanized sector, Industrialization and industrialization "Big agriculture. Among them, marine fishing is the main part of the fuel consumption of fishing vessels, which will produce a large amount of carbon emissions; the electricity consumption of advanced pond aquaculture and factory aquaculture in seawater will also bring high indirect emissions. It is of great practical significance to study the optimization of marine fishery industrial structure for promoting the high-quality development of marine fishery. Based on the data of marine fishery in Zhejiang Province from 2008 to 2017, this paper calculates the carbon emission efficiency of marine fishery and discusses the impact of industrial structure optimization of marine fishery on carbon emission efficiency. Based on the analysis of the research results, this paper puts forward some suggestions on optimizing the structure of marine fishery industry: increasing the technical input and personnel introduction of energy intensive industries, and developing five high-tech pillars strategically. From the perspective of regional industrial structure optimization, high-efficiency areas should enhance the ability of modern manufacturing industry to undertake international industrial transfer and form innovation clusters; the medium-efficiency areas should use industrial bases to develop characteristic leading industries and modern service industries; low-efficiency areas should develop high-tech industries in line with local resource endowments and advantageous industries, and improve the allocation efficiency of environmental protection investment expenditure. By optimizing the industrial structure of Zhejiang Province, carbon emission efficiency can be improved. Improve fishermen 's environmental awareness and management ability, vigorously develop and promote green aquaculture technology, and provide technical support for industrial transformation and upgrading. [ABSTRACT FROM AUTHOR]

This paper presents a 7-day Travel Demand Model (TDM) for UK rural areas to aid the Electric Vehicle (EV) transition in these regions. Utilising data from both the UK Census Survey and UK National Travel Survey (NTS), private passenger vehicle travel patterns for a rural village in the Peak District National Park (UK), were modelled. This model is adaptable to any rural community within the UK, requiring only publicly available information on households and vehicles for that community. Using a novel approach through the development of lifestyle scenarios to understand the required household activities, the TDM incorporates five different trip purposes as the building blocks for a vehicle's activity. Over a period of one week, 13,520 miles were driven by 84 vehicles across 49 households, that shows an EV fleet serving this community would consume 3562 kWh energy per week. [ABSTRACT FROM AUTHOR]

Among the most significant solutions suggested for estimating energy consumption and cooling load, one can refer to enhancing energy efficiency in non-residential and residential buildings. A structure's characteristics must be considered when estimating how much heating and cooling is required. To design and develop energy-efficient buildings, it can be helpful to research the characteristics of connected structures, such as the kinds of cooling and heating systems needed to ensure sui interior air quality. As an important part of energy consumption and demand of buildings, the assessment of cooling load conditions from the envelope of large buildings has not been comprehensively understood yet. In the present paper, a new conceptual system has been developed to anticipate cooling load in the sector of residential buildings. Also, the paper briefly describes the major models of the developed system to maintain continuity and concentrate on the prediction model of the cooling load. To predict cooling load, authors have modelled two methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in conjunction with teaching-learning-based optimization (TLBO). This article aims to illustrate how artificial intelligence (AI) approaches play an essential role in addressing the mentioned necessity and help estimate the optimal design parameters for various stations. The value of the multiple determination coefficient is also determined. The values of the training R² (coefficient of multiple determination) are 0.96446 and 0.97585 for TLBO-MLP and TLBO-ANFIS in the training stage and 0.95855 and 0.9721 in the testing stage, respectively, with an unknown dataset which is acceptable. The training RMSE values for TLBO-MLP and TLBO-ANFIS are 0.0685 and 0.11176 for training and 0.07074 and 0.12035 for testing, respectively, for the unknown dataset, which is acceptable. The lowest RMSE value and the higher R² value indicate the favourable accuracy of the TLBO-MLP technique. According to the high value of R² (97%) and the low value of RMSE, TLBO-MLP can predict residential buildings' cooling load. [ABSTRACT FROM AUTHOR]

Electrohydrodynamic (EHD) drying is considered a highly efficient technology due to its ability to create powerful convective airflow with low energy consumption. EHD efficiency depends on multiple factors, such as voltage, current, environmental conditions, drying material, and electrodes geometry. This research is exploring the effect of collecting electrode geometry on the drying rate and energy efficiency of EHD drying. Although numerical studies predict a better performance of mesh collectors compared to solid plate collectors, experimental verification is still missing. This research addresses this gap by comparing the performance of plate versus mesh collecting electrodes in drying experiments with three discharge electrodes (nails, needles, pins) and two materials (wet paper and apple slices). Our experiments confirmed the benefits of mesh collector compared to the plate, namely up to 25% higher drying flux and a 20–35% smaller specific energy consumption. For example, EHD drying of apple slices with the mesh resulted in specific energy consumption of 719–731 kJ/kg compared to 935–968 kJ/kg with the plate collector. The better energy efficiency of the mesh collector provides a unique opportunity for the industrial upscaling of EHD drying technology. [ABSTRACT FROM AUTHOR]

"Residents Against Dirty Energy" (RADE) are a community-led energy activist group in Bristol city, UK. Since their inception, they have effectively lobbied the city council for the deployment of battery storage units in place of diesel generators in Lockleaze, north Bristol. As a result, Lockleaze now hosts battery storage facilities with 15MW of storage capacity able to store excess energy from the grid. Drawing on a focus group (n = 8) with local energy actors in Bristol and in-depth interviews (n = 2) with RADE, alongside document analysis of key secondary data sources, the paper uses three tenets of energy justice (distributional, recognition and procedural justice) to shed light on the role of local energy activism in shaping low-carbon energy infrastructures. The qualitative data reveals RADE's efforts to ensure that local communities are closely involved in energy decision-making, to ensure that the siting of new energy technologies does not burden marginalised communities. However, questions around distributionaleconomic benefits and ownership are secondary to RADE; their primary concern is with the improvement of local air quality and reducing harm to the local population and environment. These issues connect strongly to Bristol's divided landscape, underpinned by an unequal geography of socio-economic division and racial inequality. As such, the paper sees the further integration of "spatial justice" as a core tenet of energy justice as necessary to enhancing its explanatory power, concluding with a call for scholars and policymakers to further consider the visions and efforts of local and civil society energy activists in shaping just transitions. [ABSTRACT FROM AUTHOR]

In view of water eutrophication, global energy crisis and the carbon neutrality policies, the ammonia recovery from wastewater with high efficiency and low energy consumption is crucial for preserving the ecological environment and achieving sustainable development. Transmembrane electro-chemisorption (TMECS), which integrates transmembrane chemisorption with electrochemical systems to reduce the addition of chemicals and improve recovery efficiency, is a promising technology for ammonia recovery from wastewater. Accordingly, this paper first reviews the recent advances in TMECS for ammonia recovery from wastewater. In particular, the technology principles, including ammonia stripping by cathodic base, ammonia recovery by TMECS with authigenic acid and base, and ammonia recovery by membrane cathode with in situ cathodic base are elucidated. Couplings of TMECS with other electrochemical systems, including electrodialysis, flow-electrode capacitive deionization, and electrochemical precipitation, are further summarized and compared. Finally, the challenges and prospects of the TMECS technology are addressed. [ABSTRACT FROM AUTHOR]

This paper provides new and useful insights into the performance and efficiency of geotechnical seismic isolation (GSI) systems with gravel-rubber mixtures containing 10%, 25% and 40% volumetric rubber content (VRC). Finite element numerical models were developed in OpenSees and subjected to sinusoidal ground motions with varying input base acceleration and frequency. The best seismic performance was attained for VRC = 40% at a frequency of 8 Hz. Using a newly developed GSI efficiency index, the energy dissipation efficiency of VRC = 40% was found to vary between good to excellent, while that of VRC = 10% and 25% is poor. [ABSTRACT FROM AUTHOR]

This paper presents a novel probabilistic data-driven approach to trip-level energy consumption estimation of battery electric vehicles (BEVs). As there are very few electric vehicle (EV) charging stations, EV trip energy consumption estimation can make EV routing and charging planning easier for drivers. In this research article, we propose a new driver behavior-centric EV energy consumption estimation model using probabilistic neural networks with model uncertainty. By incorporating model uncertainty into neural networks, we have created an ensemble of neural networks using Monte Carlo approximation. Our method comprehensively considers various vehicle dynamics, driver behavior, and environmental factors to estimate EV energy consumption for a given trip. We propose relative positive acceleration (RPA), average acceleration, and average deceleration as driver behavior factors in EV energy consumption estimation, and this paper shows that the use of these driver behavior features improves the accuracy of the EV energy consumption model significantly. Instead of predicting a single-point estimate for EV trip energy consumption, this proposed method predicts a probability distribution for the EV trip energy consumption. The experimental results of our approach show that our proposed probabilistic neural network with weight uncertainty achieves a mean absolute percentage error of 9.3% and outperforms other existing EV energy consumption models in terms of accuracy. [ABSTRACT FROM AUTHOR]

AbstractGrain is the root of the people, and grain storage is a top priority. It is necessary to improve the efficiency of grain storage operations and reduce work intensity, so it is important to develop an automated or even intelligent grain leveling robot for the precise operation of grain silos. In this paper, we propose an area classification method based on target leveling height for the special working mode of truss-type grain leveling robot, simplify the 3D map to a 2D map, reduce the difficulty of path planning, and improve the working efficiency. Based on multi-level path planning and genetic algorithm to achieve the planning of the working path of the grain-leveling robot, it solves the problem of a large number of useless trips under the adoption of full-coverage operation. It saves a lot of time, improves grain-leveling efficiency, reduces energy consumption, optimizes the effect of grain-leveling, and helps realize the precise storage operation of grain silos. The experiment of rough leveling operation was carried out through the grain leveling robot prototype and the simulated experimental warehouse, and the results show that the height difference between the peak of the grain surface and the target leveling height is within 5 cm, which verifies that the path planning method in this paper is feasible, and shows that the grain leveling robot can complete the task of grain leveling and the effect is good. [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 paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from real-time videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, and fuel consumption. The results of the various MOEs were found to be promising. We also demonstrated that estimating MOEs in real-time is achievable using drone data. Such models can track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions. [ABSTRACT FROM AUTHOR]

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

Integrated energy system (IES) has become significant in the global energy revolution because of its unique advantage of promoting clean energy use and improving energy efficiency. However, its low-carbon operational optimization and capacity allocation challenges have sharply increased because, as its scale continuously grows, its inherent strongly coupled multi energy flow and high nonlinearity characteristics have become more prominent. This study proposes a distributed low-carbon operational optimization mechanism for an IES using integrated demand response (IDR) and ladder carbon trading (LCT). First, a low-carbon IES was modeled as a coupled model between energy operators (EO) that contain combined cooling, heating, and power systems (CCHP) and flexible users (FU) based on the Stackelberg game. Second, the operational optimization model was formulated as a source-load synergistic operation scheduling mechanism based on IDR and the LCT. Finally, the strategy of each stakeholder was obtained using a modified distributed algorithm of genetic algorithm nested quadratic programming (GA-QP). The effectiveness of the proposed model was verified through a case study. The economic benefits for each stakeholder improved, as confirmed through the experimental results. The energy cost of the FU decreased by 4.42%, and the revenue from energy sales of the EO increased by 11.9%; renewable energy was prioritized for consumption, with a consumption rate of 100%; the total output of the core devices for energy cascade utilization increased by 15.6%, significantly improving energy supply efficiency; the total carbon emissions decreased by 8.64%; and power and heat loss rates were 0% and 0.00059%, respectively. Highlights With the rapid development of renewable energy, The integrated energy system relies on its inherent operational advantages of high flexibility, high toughness, and multi energy coupling cascade utilization, demonstrates unique advantages in serving as a clean and renewable energy carrier and adapting to the development of distributed energy The energy cost of flexible users has decreased by 4.42%, and the revenue from energy sales of energy operators has increased by 11.9%; Renewable energy is prioritized for consumption, with a consumption rate of 100%; The total output of the core devices for energy cascade utilization has increased by 15.6%, significantly improving energy supply efficiency; The total carbon emissions decreased by 8.64%, both power and heat loss rates are 0%, the error rate of energy supply is 0.00059% Based on the above research, this paper is established a distributed low-carbon operation optimization model for Integrated energy system coupled the EO that contain CCHP and the FU based on Stackelberg game In view of the gradual deepening of the coupling degree of the IES, the increasingly obvious characteristics of multi-agent, the difficulty of centralized scheduling to protect the interests of each independent entity, and the inadequacy of the system's energy cascade utilization advantages, this paper establishes a bilateral distributed operation optimization model based on Game theory In response to the insufficient utilization of the advantages of multi energy coupling in existing research on IES, insufficient attention paid to the interaction between supply and demand on both sides of the system, and insufficient enthusiasm of users as independent entities to participate in system operation scheduling, this article adopts a integrated demand response as the load side operation scheduling strategy [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) based surrogate models have been widely used in place of high-fidelity simulation tools. Error metrics such as mean absolute error, and root mean squared error (RMSE) have been widely used as de facto criteria. However, whether the ANN-based surrogate model can adequately reproduce the interwoven relationships and nonlinear causalities between design variables and simulated outputs are often overlooked. In this regard, the authors designed a case study regarding four ANN-based surrogate models. It was found that despite all of the models having low RMSEs, the models failed to adequately predict the causal relationships between input variables and energy use. In other words, the surrogate models were not always capable of providing accurate assessments of expected energy use reduction as a result of design changes. In this paper, we present a workflow for validating whether a surrogate model can reproduce the causal relationships between inputs and outputs. [ABSTRACT FROM AUTHOR]

In the paper production, doctor (scraping) blades are placed in contact with press rolls during wet pressing so as to purge the surface of the rolls from processing water, contamination and stickies. The contact is achieved by mounting the blade on a holder, which is tilted around a rotation axis until the blade tip contacts with the roll. The contact force is determined by the supply pressure of the air forced through the tube that is placed at the bottom of the holder. Owing to contact, the blade wears off and needs to be replaced periodically. Our aim is to optimise the cleaning performance of the system by modelling the tribological contact between the doctor blade and press roll in order to achieve an optimum cleaning performance, thus increasing the blade lifetime and reducing energy consumption. The model is susceptible to an inextensive numerical evaluation as compared to that of a more advanced modelling approach, e.g. in terms of a full finite element analysis of the beam deflection. A first comparison with experimental findings is encouraging. [ABSTRACT FROM AUTHOR]