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China is the top emitter of greenhouse gases in the world. Pulp and paper industry is one of eight the strictly regulated industries about carbon emission in China. However, estimates of carbon emissions from China's pulp and paper industry have been suffered from remarkable uncertainty. It is summarized that is aroused by the inaccuracy of factors evaluating energy consumption, and the huge gap between the calculation standards for assessing energy consumption in the production process and the actual level recorded in the factories. This paper re-evaluated the carbon emission of China's pulp and paper industry from the perspective of life cycle assessment, and applied more accurate emission factors, taking account of the actual energy consumption of the production process in factories, to estimate the actual carbon emissions in maximum. It is found that the national standards about the energy consumption quota of China's pulp and paper industry is no longer applicable, and the actual energy consumption regulated in the production process is far better than the advanced level given by the national standards. The greenhouse gas emission have been seriously overestimated approximately 12.00%–56.17% by international organizations. • Life Cycle Analysis of the China's pulp and paper Industry is conducted thoroughly. • The pulp flow of China's pulp and paper Industry is analyzed. • Energy regulated in factory is far better than the national standards' required. • Carbon emissions of China's pulp and paper Industry are overestimated. [ABSTRACT FROM AUTHOR]

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

Abstract: This work analyzed the energy flow of the pulp and paper industry in Taiwan. The potential technology options that were examined focus on how to capture some of the energy currently lost in the processes and then identifying the areas with energy-saving potential that could also have large impacts across a variety of industries. In addition, the energy-saving potential of these options was evaluated. The energy-saving potential of the pulp and paper industry would be around 6939.9 KLOE/M. The greatest energy-saving potential lies with improving energy distribution and equipment efficiency, which would together potentially comprise 86.8% of total energy conservation. This analysis can serve as a benchmark for current pulp and paper making operations, and as a base case for stimulating changes toward more efficient energy utilization in the pulp and paper industry. [Copyright &y& Elsevier]

Abstract: In this study, a pulp and paper mill (PPM) in the SEKA Papermaking Plant in İzmit, Turkey, is analysed through energy and exergy balances. The plant utilises recycled waste paper for papermaking. This type of raw material input makes the process highly sophisticated. The pulping uses strictly mechanical processes, such as digestion, separation by screening and hydrocyclone, and refining. The milling, as an integrated process, provides the final operations necessary to prepare the conditions required for the end-product by stock-preparation, wiring, rolling, and drying by dewatering, pressing, and evaporation. The possibility of making the entire process more thermodynamically efficient is discussed by calculating the energy and exergy losses for all the mechanical and physical sub-processes. The study shows that the energy efficiencies for each of the mechanical and physical steps in the PPM vary between 34% and 97.4%, whereas the exergy efficiencies vary between 30.2% and 94.2%. In conclusion, based on the results from the energy and exergy flow analyses, the exergy output can be improved through more efforts directed primarily to further measurements toward more efficient energy utilisation in the PPM. [Copyright &y& Elsevier]

Abstract: The objective of this paper is to estimate the energy saving potential in China's paper industry by determining energy intensity under different scenarios. Cointegration model and stability test are applied to formulate the equilibrium equation. Results show that energy price, industry structure, profit margin and technology have significant impact as they are negatively correlated to changes in energy intensity. Three scenarios (BAU (business as usual), intermediate and active) are designed to estimate the future trend of energy intensity in paper industry. In 2010, the energy intensity (energy consumption per unit of value added) was 4 tce/10,000 RMB, and by the scenarios it is expected to considerably decline. According to the BAU (business as usual) scenario, the energy intensity will decrease to 2.56, 1.43 and 0.70 tce/10,000 RMB by 2015, 2020 and 2025, respectively. With respect to the intermediate scenario it is expected to drop further to 0.44 tce/10,000 RMB. Yet by the active scenario, the energy intensity is expected to considerably decline to 0.36 tce/10,000 RMB by 2025. Using the BAU forecast as a baseline, the quantity of energy savings is estimated to be 185.6 billion tce by 2025. Considering this important potential, we also provided some policy suggestions. [Copyright &y& Elsevier]

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

Abstract The paper drying process has the highest level of energy consumption in the pulp and paper production process. Analysis and optimization of the energy system during the paper drying process is critical for improving the energy efficiency of the entire paper mill. In the existing model for the paper drying process, the solution requires accurate boundary conditions such as the air temperature and humidity of the pocket area and the cylinder surface temperature, which are very difficult to obtain in the papermaking process. This can result in significant deviations between the model solution and the actual production process. This paper focuses on the single-tier dryer cylinder-based paper drying process that has been widely used with high-speed papermaking machines in recent years. A mathematical model is proposed based on real-time data. The verification via industrial production demonstrates that the proposed model is reliable for the paper drying process. Based on the simulation results, two optimization operations have been proposed. The energy consumption decreases from 1.51 t steam/t paper to 1.44 t steam/t paper, 4.6% of the steam and 1.26 × 106 RMB can be saved for a medium-scale paper mill with the annual production capacity of 105 t paper. Highlights • Mathematical model for the single-tier cylinder based paper drying process is proposed. • Both the steam-condensate system and the paper drying process are considered. • The simulation results are verified by the industrial data from a papermaking enterprise. [ABSTRACT FROM AUTHOR]

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

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

Abstract: Energy conservation is a central concern of the current industrial world, where increasing efficient energy usage is the only way of reducing a high energy demand. In the present study the optimization of a pulp and paper mill with a cogeneration plant has been carried out. The optimization was realized with a methodology which includes exergy, exergoeconomics, thermoeconomics and pinch analysis. The proposed methodology was useful in determining not only the best plant operating conditions but also establishing the components or subsystems with the highest irreversibilities. As a result of the study, operation changes in the recovery boiler, the turbogenerator, the thermal treatment and the deaerator were realized. Due to the higher irreversibility in the actual evaporator line, a new line of evaporators was proposed. Also, an innovative heat recycling technology as to the use of heat transformers was proposed in order to reduce waste heat discharged to the atmosphere. The results obtained with the proposed methodology, which integrates the different optimization methods, allowed reaching higher efficiencies and lower operational costs than those obtained with the optimization methods working separately. [Copyright &y& Elsevier]

Power generation by variable renewable energy (VRE) is expected to expand for decarbonization, although it is difficult to ensure stabilization of the energy demand due to fluctuations in the power output from VRE. To accelerate VRE implementation, wind-powered thermal energy systems (WT th -ES), which can provide dispatchable power using wind–thermal energy converter (WEC th) and thermal energy storage (TES), have been proposed. In this study, we are tackling the installation of VRE, i.e., wind energy, by TES with WEC th compared with wind turbine (WT p) in paper mills to demonstrate the applicability of TES in an industrial sector. Conventional and alternative energy systems in paper mills with TES were mathematically modeled and simulated to analyze the transmittable power output and the greenhouse gas (GHG) emissions by life cycle assessment. With the installation of TES and wind energy, transmittable power could be increased with GHG emissions reduction. However, their excess installation or unbalanced combination resulted in an increase in GHG emissions and a decrease in transmittable power. By comparing the cases, the combination of paper mill, TES, and WEC th could convert thermal energy efficiently and mitigate fossil fuel consumption for multiple production of paper and power from the paper mill. • A wind-thermal energy converter (WEC th) and a wind turbine (WT p) were compared. • Energy flows and GHG emissions of paper mills with TES and wind were simulated. • Paper mills reduced GHG of power generation less than fossil fuel-fired power plants. • Excess TES reduces dispatchable power generation and increases GHG emissions. • Paper mills with WEC th s and TES have the potential to be regional energy suppliers. [ABSTRACT FROM AUTHOR]

Daytime subambient radiative cooling is a most promising alternative to electricity-free building cooling. However, optical performance degradation arising from surface contamination and inevitable parasitic heat gain still pose unprecedented challenges to cool building at subambient temperatures. This paper proposed a superhydrophobic cellulose aerogel cooler (SHB-CAC) as building envelope by integrating self-cleaning capacity, passive daytime radiative cooling and thermal insulation to reduce environmental heat gain. The SHB-CAC demonstrates high solar reflectance (93%) and long-wave infrared emittance (91%), accomplishing a temperature drop of 8.5 °C lower than the ambient under sunlight of 800 W/m2 in the outdoor experiment. Notably, the SHB-CAC possesses low thermal conductivity (28 mW/(m∙K)) that suppresses parasitic heat gain from warmer surrounding and reduces cooling energy consumption. The self-cleaning property resulting from superhydrophobicity protects SHB-CAC from water wetting and dust contamination but also maintains its good surface radiation capacities under different humidity environment. A building energy simulation was conducted and results showed that 43.4% of cooling energy on average could be saved compared to the building baseline consumption, if SHB-CAC was widely used in China, indicating that the strategy of optical and thermal conductivity management of cooler has the potential to reduce the energy consumption of buildings. [Display omitted] • Waste paper is converted into aerogel cooler for building cooling. • High R solar and ε IR , thermal insulation, and self-clean are achieved in one design. • The aerogel achieves a high sub-ambient temperature drop of ∼8.5 °C on daytime. • The aerogel shows that 43.4% of cooling energy could be saved. [ABSTRACT FROM AUTHOR]

Abstract: A new tar destruction technology has been developed by integrating in-situ catalytic gasification and a hot-gas cleaning system in the catalytic gasification of paper-reject sludge. Experimental results indicated that the tar yield in syngas decreased significantly from 13.64 to 10.76 g/m3, when increasing the zeolite addition during in-situ catalytic gasification. When the syngas passed through the hot-gas cleaning system, the tar concentration measured from downstream was approximately 0.22 g/m3. The hot-gas cleaning system used in this research seems to have played a significant role in reducing tar concentration of the syngas. The major tar speciation identified after hot-gas cleaning system was 1-ring or 2-ring hydrocarbons, which included benzene, toluene, xylene, styrene and naphthalene. In summary, the energy yield efficiency of syngas will be enhanced due to the tar reduction and transformation. The syngas seems to be appropriate to use as a fuel in gas engines for electricity generation. [Copyright &y& Elsevier]

Abstract Long term electricity consumption forecasting has been extensively investigated in recent years in different countries due to its economic and social importance. In this context, the long term electricity consumption projections of a country or region are highly relevant for decision-making of companies and organizations operating in any energy system. In this paper, it is proposed a methodology that combines the bottom-up approach with hierarchical linear models for long term electricity consumption forecasting of a particular industrial sector considering energy efficiency scenarios. In addition, the Bayesian inference is used for model parameter estimation and, enabling the inclusion of uncertainty in the forecasts produced by the model. The model was applied to the Brazilian pulp and paper industry and it was able to capture the trajectory of the real consumption observed during the 2008–2014 period. The model was also used to generate long term point and probability distribution forecasts for the period ranging from 2015 until 2050. Highlights • Bottom-up Bayesian extension for the long-term electricity consumption forecasting. • The proposed model combines the bottom-up approach with hierarchical linear models. • The proposed model considers energy efficiency scenarios. [ABSTRACT FROM AUTHOR]

Conventional energy sources, including diesel and petrol, are decreasing day by day, while their reserves are limited. However, it is difficult to find alternative fuels to replace them. Hence, to fulfill the growing energy demand and reduce environmental emissions, it is vital to find a wide variety of oxygenated compounds to conventional diesel fuels that show low emission potential from transport engines. In this research, we investigated diesel engine performance, combustion, and exhaust emissions with the addition of 10 %, 15 %, 25 %, and 30 % of three different oxygenated blends, including Diethylene Glycol Dimethyl ether (DGM), Di- n -Butyl Ether (DBE), and Tripropylene-Glycol Monomethyl ether (TPGM). This paper presents the brake-specific fuel consumption (BSFC), Brake Thermal Efficiency (BTE), Brake Mean Effective Pressure (BMEP), and Brake Specific Energy Consumption (BSEC) for these fuel blends. The generated emissions of carbon dioxide (CO 2), Carbon Monoxide (CO), and nitrogen oxide (NO) were quantified for four different engine speeds of 1500, 1800, 2000, and 2400 rpm for neat (100 %) oxygenated blends. This research also shows the in-cylinder pressure and heat release rate (HRR) of these three oxygenated blends. The outcome of this paper will help future researchers to develop automobiles and vehicles for oxygenated blends with conventional fuels. • Up to 30% DGM, DBE, and TPGM biodiesel provides similar engine performance to diesel. • Variation of BSFC is low for DGM, DBE, and TPGM biodiesel for a wide range of engine speeds. • DGM, DBE, and TPGM oxygenated fuels show lower CO and CO 2 but high NO emissions. • DGM, DBE, and TPGM provide similar in-cylinder pressure and heat release rate (HRR) to diesel. [ABSTRACT FROM AUTHOR]

District heating and cooling systems have been present on the European market for more than a century, constantly evolving into more efficient energy infrastructures important for decarbonization of the heating and cooling sectors. With roughly 17,000 operational district heating systems, serving over 70 million citizens, currently, the final energy consumption attributed to district heating in Europe is estimated at over 450 TWh. As the European district heating and cooling sector varies extensively in every analyzed aspect, there are vast differences between European countries regarding technical preferences, technological development, market structure and regulatory frameworks of these systems. The main purpose of this paper is to provide a comprehensive overview of the present status of the district heating and cooling systems in Europe with insight(s) and suggestions into future trends. In addition, the provided data serves as a valuable baseline for future research. Finally, this paper draws attention to the new socio-demographic approach, observed when analyzing the district heating and cooling systems, that has been recently introduced into the scientific community. This new approach also reveals the opportunity for creating new indicators that will influence the modeling of existing and future district heating and cooling systems. • Provides a review on ongoing trends in European district heating and cooling sector. • Analyses European DHC systems from technological, market, and regulatory aspect. • Provides an overview on commonly used performance indicators and energy models. • Discusses the possibility of creating new DHC indicators. • Discusses the trends in future DHC development. [ABSTRACT FROM AUTHOR]

Due to the rapid development of the automotive industry and the increasing shift towards cleaner energy, electric and hydrogen vehicles have great potential for growth. Therefore, charging stations need to transition from single charging forms to electric-hydrogen coupling charging. This paper focuses on the problem of mismatch between source and load in time and space when operating a Charging-Hydrogenation Composite Station (CHCS) independently. This leads to resource waste in the full-cycle and long-time sequence, making it difficult to achieve economic benefits. The characteristics that differentiate CHCS, such as geographic location, operating equipment, charging demand, and temporal attributes, are taken into account. CHCS in suburban, residential, tourist attractions, and industrial areas are modeled differently. Through improved Nash negotiation game modeling, a peer-to-peer (P2P) energy trading mechanism is established. The CHCS leverages its resource endowment advantages to ensure that each subject and the alliance as a whole can benefit through the coupled electricity-hydrogen energy trading. To ensure privacy in CHCS transactions, a distributed alternating direction method of multipliers (ADMM) algorithm is used to iteratively solve the problem and achieve a fair distribution based on the energy contribution size of each CHCS. Finally, simulation examples are used to verify the validity of the proposed model. The study shows that the electricity-hydrogen energy coupling trading strategy proposed in this paper can realize the synergistic mutual benefit of each CHCS, and in terms of economy, the overall benefit of the alliance has been improved by 7.58 %, and in terms of new energy consumption, the regional PV has gone from a consumption rate of 49.98 % to realize 100 % full consumption, and the wind power consumption rate has increased from 52.38 % to 96.34 %. • The coupled trading and revenue sharing of electric and hydrogen energy among multiple CHCSs. • The differentiation of temporal attributes, spatial geography, equipment types, and load characteristics are considered. • Implications of Nash negotiations and energy sharing on the CHCSs costs and consumption of new energy are investigated. • Distributed ADMM algorithms are used to solve the Nash bargaining problem. [ABSTRACT FROM AUTHOR]

Exergoeconomics has played an important role in the study of new energy systems in the last decades. The use of exergy as a "carrier of value" has made it possible to define an unambiguous criterion for allocating costs among energy systems products. The paper shows how exergoecomic procedures of analytical optimization and design improvement on heuristic basis have been progressively replaced by more efficient procedures aimed at minimizing an objective cost function, leaving exergoecomic cost evaluation as the final step. It also highlights how growing concerns about climate change and the continued growth of inequalities in energy availability have broadened the set of objectives in the search for the optimal integration of the design and operation of energy conversion units and network with intelligent methodologies to reduce energy demands. The objective of the article is twofold: i) present the evolution of the main Exergoeconomic methods and show how they have paved the way for Thermo-X Optimization methods; and ii) outline the path for developing a general model of society's entire energy system that includes a broader set of objectives and constraints in addition to economic ones to help build the energy system of the future with a more sustainable perspective. • It is shown how Exergoeconomic procedures paved the way to Thermo-X Optimizations. • The paper outlines the path towards a general model of society's entire energy system. • The need to avoid wastes to reduce energy demand in wealthier nations is stressed. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) load integration and environmental concerns in recent years have motivated energy users to step forward in reducing carbon footprints by promoting energy trading. In the energy trading market, different energy stakeholders participate in trading energy with each other to maximize their utilities and improve trading efficiency. The social welfare (SW) of the energy market plays a significant role in keeping the market going and motivating new users to engage in energy trading. The SW of the energy market depends on its structure, where energy flows from producer to end user in a fair way. This paper introduces a hierarchical energy trading framework, formulated as a three-stage Stackelberg game, to enhance SW. In the first stage of this hierarchy, the retailer, acting as the sole leader, seeks to maximize profits through optimized energy sales to charging stations (CS). Subsequently, in the second stage, each CS operates as a leader for EVs, aiming to maximize profits by minimizing energy trading costs, while also acting as a follower in response to the retailer's strategies. The third and final stage involves EVs collaboratively acting as a unified strategic agent to minimize their trading costs through optimized charging and discharging schedules. This paper also introduces an average price penalty function to further enhance SW in the energy market. The proposed model, addressing the complexities of energy trading interactions, is formulated as a non-linear optimization problem with constraints. It has been implemented in a Python environment and solved using the Gurobi optimization solver, demonstrating its potential to improve the efficiency, fairness, and overall social welfare of the energy trading market. • Introduces a hierarchical energy trading framework using a three-stage Stackelberg game to enhance market social welfare. • Incorporates renewable energy at the retailer level, diversifying energy sources and boosting ecological relevance. • Establishes comprehensive pricing functions across all levels, including reciprocal EV discharge signals, for dynamic market interaction. • Implements an average price penalty at charging stations to preserve social welfare and promote efficient energy use. • Demonstrates model efficiency and practical applicability through tests on the IEEE 33 bus system, showing superior profitability and convergence. [ABSTRACT FROM AUTHOR]

There has been a growing demand for energy consumption statistics in the manufacturing industry to establish national energy and greenhouse gas policies. Despite its importance, the Korean government faces significant challenges in collecting energy data at a facility level in a precise and timely manner. To address the lack of timely data, this paper employs machine-learning models to predict the annual total energy consumptions of each manufacturing facility. We first designed four prediction models that take into account the characteristics and energy consumption behaviors of industry sub-sectors. As input variables, these prediction models mainly included electricity consumption, employee size, energy types, gas consumption and other accessible data. Finally, we conducted numerical experiments on approximately 100,000 facilities and evaluated the prediction performance of various machine-learning algorithms such as linear regression, decision tree regression, random forest regression, gradient boost regression, and extreme gradient boosting regression. The numerical experiments provided insights into which model and algorithm offer the best prediction performance for each industry sub-sector. In addition, we identified the important variables for predicting total energy consumption, revealing that not only electricity but also various other energy sources and variables representing industry-specific characteristics play a crucial role in improving prediction performance. • This paper predicts the annual total energy consumption of individual manufacturing facilities in Korea. • Several prediction models capable of considering industry-specific features are presented. • This paper employs machine-learning models. • The numerical experiments provided insights into which model and algorithm offer the best prediction performance. • Not only electricity but also variables representing industry-specific characteristics play a crucial role. [ABSTRACT FROM AUTHOR]

In this paper, a novel combined heating and power (CHP) system is proposed to realize full-scale utilization of geothermal energy and efficient multi-generation, which not only performs preferable overall performance than previous homogeneous system, but also offers an effective energy cascade utilization approach for self-condensing transcritical CO 2 (TCO 2) Rankine cycle. Based on the established mathematical models, the performance comparison is conducted for proving the superiority of the novel CHP system. Then, an overall performance analysis is implemented to reveal the combined effects for six key parameters on system thermodynamic, exergoeconomic and exergoenvironmental performances. Furthermore, multi-objective optimization considering system overall performance is conducted. The results show that for the novel CHP system, the largest relative improvement rate of system exergy efficiency (η exg) and declining rate of total unit product exergy cost (c P , total ) versus the previous CHP system are 15.03 % and 18.89 %, respectively. The final optimization results of η exg , c P , total and total unit product exergy environmental impact (b P , total ) are determined as 51.10 %, 14.12 $/GJ and 9.00 mPts/GJ, respectively. This paper fulfills an elaborate performance analysis and optimization for the novel CHP system, which fills the research gap of efficient and promising CHP system based on self-condensing TCO 2 Rankine cycle. • A novel self-condensing transcritical CO 2 cycle based cogeneration system is proposed. • Full-scale utilization of geothermal energy is realized by the novel system. • Overall performance superiority of the novel system is proved by comparison study. • Combined effects of six key parameters on system overall performance are revealed. • Multi-objective optimization of system overall performance is performed. [ABSTRACT FROM AUTHOR]

The energy sector prioritizes innovative clean energy production. This research explores a versatile hybrid solar-biomass trigeneration system, integrating organic Rankine cycle (ORC), a heat pump, and a proton exchange membrane electrolyser. Sustainable and adaptable, it produces electricity, heating, and hydrogen for Indian paper and pulp industries. The study assesses the system's thermodynamic, economic, and environmental feasibility. Initially, parametric analysis and first layer multi-objective optimization (MOO) are performed to select the efficient working fluid and operating temperatures. Benzene outperforms other fluids, achieving an energy utilization ratio (EUR) of 0.27 and exergy efficiency of 75 %. A typical heat requirement of 250 kW for processing 150 kg of paper necessitates collector areas of 3500 m2 and 4500 m2 in Aurangabad and Tiruchirappalli, respectively, and biomass waste of 0.05 kg/s. The combined system performance during day time and night time operation was studied by altering the critical solar radiation, ambient temperature and biomass waste flow rate. The second layer MOO revealed an optimum EUR of 0.25 and exergy efficiency of 9 % at the lowest cost of 33 $/h for biomass waste operation. In comparison to the conventional electricity-powered system, the proposed system would reduce CO 2 emissions by 90 kg/h to 270 kg/h. [Display omitted] • Solar and biomass waste powered systems proposed for paper and pulp applications. • ORC integrated heat pump system for heating, electricity, and green H 2 generation. • Collector area and biomass flow rate are designed for daily paper output of 300 kg. • Benzene is an efficient working fluid, produced 815 kW of heat and 3 kg/h of H 2. • The recommended system reduced CO 2 emissions by 90 kg/h to 270 kg/h. [ABSTRACT FROM AUTHOR]

This paper explores methods to improve the fuel economy of a two-stroke free piston engine generator (FPEG) with no additional cost. In order to solve the problem of low fuel capture and poor fuel economy of two-stroke FPEG using port fuel injection (PFI), the fuel injection method is optimised in this paper. This fuel injection system is semi-direct injection (SDI). In contrast to PFI, SDI also uses a low-pressure injector, which introduces fuel directly into the ports of the engine. This study explores the feasibility of SDI on a free-piston engine generator through numerical simulation. The results show that SDI can effectively avoid short-circuit losses and improve the fuel capture rate by more than 60 % relative to PFI. Meanwhile, the ISFC is reduced by 42.3 %, and the lowest fuel consumption rate is 259.7 g/(kW·h). The combustion efficiency of SDI is maintained at over 95 % under warm-up conditions. Compared with PFI, the stagnation period is shortened by up to 10.9 % and the centre of gravity of combustion is advanced by up to 16.7 %. SDI effectively reduces HC emissions and has a lower NOx emission level than PFI. At the same time, SDI produces more CO due to stratified combustion. • A free piston engine generator simulation model with port fuel and semi-direct injection was established and validated. • Semi direct injection can effectively reduce short-circuit losses and indicated specific fuel consumption. • Using semi direct injection can effectively improve early flame propagation. • Semi direct injection can reduce HC and NOx emissions. [ABSTRACT FROM AUTHOR]

Promoting energy transformation in rural areas, building a Rural Integrated Energy System, and developing multi-energy complementary and comprehensive utilization of rural energy in accordance with local conditions are important paths to support global low-carbon transformation and rural revitalization. However, how to consider the coupling synergy of multiple devices and plan the capacity of multiple devices in rural energy systems in an economical, low-carbon, and reliable manner is an urgent issue facing the development of Rural Integrated Energy System. This paper proposes a bi-level programming optimization method of Rural Integrated Energy System based on coupling coordination degree of energy equipment. Firstly, a multi-dimensional rural user energy utilization framework is designed that comprehensively considers agriculture, animal husbandry, industry, and lifestyle. Secondly, the bi-level optimization model of the Rural Integrated Energy System based on the coupling coordination degree of energy equipment is constructed. The upper-level planning model puts forward the equipment coupling coordination degree, and determines the equipment selection scheme for different types of Rural Integrated Energy System planning. The lower-level planning model considers economy, low carbon, and reliability, and uses the improved multi-objective Harris Hawk optimization algorithm to solve the capacity of different energy equipment in the Rural Integrated Energy System. Finally, the effectiveness and scientific nature of the model and method proposed in this paper are verified by the Integrated Energy Systems of a rural area in the north of China. In addition, by comparing the equipment selection scheme with the traditional method, it can be found that the optimization scheme in this paper that considers the coupling coordination of energy equipment reduces the annualized total cost by 29.34 %, reduces the annual carbon emissions by 60.43 %, and increases the reliability by 15.85 %. The collaborative planning of energy economy, low carbon energy and reliable energy has been realized. • A framework for the utilization of energy by multiple users in rural areas was designed. • A bi-level programming optimization method of RIES based on coupling coordination degree of energy equipment was proposed. • An improved multi-objective Harris Hawk optimization was constructed. [ABSTRACT FROM AUTHOR]

Industrial symbiosis has gained prominence in pursuing sustainable industrial practices, aiming to optimise resource utilisation and reduce environmental impacts. A critical aspect of this endeavour is the efficient management of energy resources within an industrial ecosystem. This paper presents a novel approach to enhance Total Site Heat Integration (TSHI) implementations by employing Blockchain as a facilitator for decentralised energy management. TSHI is an efficient and widely applied method for industrial symbiosis concerning energy flows, which employs the steam mains in site utility systems as the platform for exchanging heat between industrial processes at various temperature levels. It is shown that the synergy of Pinch Analysis and Smart Contract technologies is capable of facilitating energy integration of processes belonging to independent market actors, compared with the currently dominant integration inside a single company. The proposed framework leverages Blockchain as a distributed ledger to enable secure, transparent, and automated energy management across multiple industrial entities in an industrial symbiotic network. The integration of Pinch Analysis principles ensures that the Heat Integration process is optimised to improve the overall energy efficiency. Smart contracts enable automatic negotiation and execution of energy transactions based on predefined rules, minimising the time lag for concluding deals on energy resource exchange and conservation. This paper examines several scenarios to illustrate the implementation of the proposed Blockchain-based TSHI concept within an industrial symbiosis network. It is demonstrated that up to 16 % cost savings are possible by simply enabling transparency via Blockchain. The results could drive innovative development to revolutionise decentralised energy management in a complex industrial ecosystem, especially by synchronising energy exchanges in time. [Display omitted] • Blockchain concept for managing data of site-level industrial plants/prosumers. • Pinch-based smart contract on utility allocation with Total Site Heat Integration. • A cryptocurrency for the blockchain system can be used for utility transactions. • Bidding or selling of heat surpluses/deficits via Pinch-based smart contract. • Information transparency allows tangible cost savings up to 16 %. [ABSTRACT FROM AUTHOR]

Growing energy demand and increasing environmental challenges underscore the importance of precise forecasts for photovoltaic (PV) operations in renewable energy generation systems. At this stage, it is mainstream to combine both temporal and spatial factors to forecast PV power generation. However, there are fewer studies that consider factors at very large spatial scales. This paper proposes Hybrid Dual Stream ProbSparse Self-Attention Network (HDSPAN), a novel spatial–temporal photovoltaic power forecasting network architecture that can solve the above limitations. The model implements an encoder–decoder approach that extracts the required spatial–temporal information through a dual stream distilling mechanism. In addition, the ProbSparse self-attention mechanism is employed to improve model efficiency and reduce repetitive and redundant information processing. The hyperparameters are optimized using Tree-structured Patzen estimator to improve forecasting outcomes. This paper demonstrates the effectiveness of spatial–temporal PV forecasting by using ERA5 reanalyzed PV data as a case study. Our results show that the HDSPAN model achieves a 10% higher forecasting accuracy compared to the baseline models, significantly advancing PV power forecasting. • Introduces a novel method for spatial–temporal photovoltaic power forecasting. • The proposed method employs dual stream distilling mechanism for spatial–temporal data. • The proposed method employs ProbSparse self-attention for data processing. • Analyzes spatial factors with Euler distance for station grouping. • Employs Tree-structured Patzen estimator to enhance hyperparameter tuning. [Display omitted] [ABSTRACT FROM AUTHOR]

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

Carbon emission flow is an effective tool to obtain carbon emission distribution in power systems, which can guide the active carbon reductions of consumers through proper incentive schemes. With the increasing penetration of renewable energy, the existing single-valued carbon emission flow model based on deterministic forecasted power outputs cannot describe the impacts of renewable energy uncertainties on carbon flows. Therefore, this paper mainly focuses on the assessment of the probabilistic carbon emission flow through the power systems considering the impacts of load and renewable energy uncertainties. In this paper, the probabilistic carbon emission flow (PCEF) is innovatively proposed. With the PCEF, consumers can make better energy use plans considering their own risk appetites for carbon emission payment. Firstly, the impact factors of the PCEF are modeled, including load and renewable energy uncertainties and integrated carbon intensity of thermal plants. The uncertainties of load, wind power and photovoltaics are modeled based on a p-box method while the uncertainty of hydropower is modeled based on the multi-state model. Besides, the integrated carbon intensity model of thermal plants are proposed considering the carbon intensity variations during the operating, start-up, and shut-down stages. Furthermore, the probabilistic carbon emission evaluation framework is proposed to assess the probabilistic carbon distribution. In this framework, novel probabilistic carbon indices are defined to precisely characterize the carbon emission situation with probabilistic representations. A solution method based on an adaptive interval point estimation (AIPEM) algorithm is proposed to efficiently solve the developed evaluation problem. Tests on an IEEE-30 node system, and a real provincial power system in China validate the effectiveness and application of the proposed model. The results showed that the PCEF can be a useful tool for obtaining the probabilistic representation of carbon emission flow and guiding the consumers' active carbon reduction in power systems. • Probabilistic carbon emission flow is proposed considering multiple uncertainties. • Integrated carbon intensity model for thermal plants is developed. • A p-box model is used to describe the uncertainties of load and renewable energy. • PCEF can help consumers balance the cost and carbon risk when making decisions. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), have been widely used to predict the intermittency of wind power; however, the non-linear activation functions and backpropagation mechanisms in DL models increase computational complexity and energy consumption. This paper proposes a prediction model based on Green Learning (GL) to reduce energy consumption. The proposed GL model replaces the feature extraction of activation functions with a hybrid feature extraction approach combining categorical and numerical features. We also employ cluster centroids and quantile regression forest for classification/regression to eliminate the need for backpropagation in optimizing hyperparameters. Using Taiwan as a case study, this paper evaluates the risk of fluctuations in regional wind power generation in 2030. In simulations, the proposed GL model achieved excellent accuracy with energy consumption significantly lower than that of DL models. Our analysis also revealed that by 2030, fluctuations in wind power generation during the winter will exceed 40% of the peak supply capacity in the central region, indicating the need to enhance the resilience of regional power systems. • This is the first study to apply green learning to wind power prediction. • This study discriminated seasonal climate patterns at the regional level. • This study assessed the fluctuation risk under large-scale wind power integration. • The maximum hourly variability in wind power is projected to exceed 6,010 MWh by 2030. • We recommend the continued deployment of regional energy storage systems. [ABSTRACT FROM AUTHOR]

Digital infrastructure serves as a crucial foundation for development in the era of the digital economy. In light of this, this paper takes digital infrastructure construction as its focal point. Utilizing Python technology and the Meta-frontier non-radial directional distance function to calculate the level of digital infrastructure construction and industrial energy efficiency. It then employs a dynamic panel data model to analyze the impact effects and mechanisms of digital infrastructure construction on industrial energy efficiency. Additionally, threshold models and modified gravity models are employed to explore the nonlinear effects and spatial impacts of digital infrastructure construction on industrial energy efficiency. The research findings reveal that the enhancement of digital infrastructure significantly improves industrial energy efficiency. Moreover, indicate a shift in the impact of digital infrastructure construction on industrial energy efficiency from inhibitory to promotional as the level of digital infrastructure construction increases. The mechanism of this effect suggests that digital infrastructure construction can promote the improvement of industrial energy efficiency through green technological innovation and the advancement of industrial structure. Further analysis shows that the spatial network characteristics of digital infrastructure construction notably elevate the level of industrial energy utilization efficiency. This study provides practical policy implications for sustainable development. • Digital infrastructure construction markedly enhances industrial energy efficiency. • There is a non-linear picture of the impact of digital infrastructure construction on industrial energy efficiency. • The effects of green technology innovation and advanced industrial structure are crucial channels. [ABSTRACT FROM AUTHOR]

Floating offshore wind turbines, as large equipment for deep-sea wind energy utilization, must be carefully studied for their aerodynamic performances under platform motions. In experiments, the model wind turbine designed by the traditional thrust coefficient similarity is usually insufficient for unsteady aerodynamic reproductions of the prototype wind turbine. In this paper, a dynamic aerodynamic similarity criterion based on the mapping of the optimal tip speed ratio is introduced for wind tunnel model tests, and a model wind turbine is designed using the NREL 5-MW prototype wind turbine. In this case, to avoid the over-height model tower aroused in pitch motions, a method of equivalent surge motions instead of pitch motions is proposed and validated for feasibility. Then dynamic aerodynamic similarities under different pitch amplitudes and frequencies are validated by the Unsteady Blade Element Momentum theory. Results indicate that temporal and spatial characteristics and hysteresis effects of thrust and power coefficients all maintain better similarity with those of the prototype wind turbine than those of the model wind turbine designed by the traditional criterion. This paper provides a criterion with equivalent pitch motions for unsteady aerodynamic studies in wind tunnel model tests, especially the high-fidelity Hardware/Software-In-the-Loop test. • A new similarity criterion is proposed for FOWT wind tunnel model tests. • The feasibility of equivalent surge motions instead of pitch motions is verified. • The problem of over-height model tower is avoided by equivalent pitch motions. • Spatiotemporal and hysteresis characteristics of unsteady aerodynamics are analyzed. • Unsteady aerodynamic performances of the prototype can be reflected more accurately. [ABSTRACT FROM AUTHOR]

To achieve an efficient tidal current turbine (TCT), the impact of three biomimetic trailing edge designs (i.e., slabbing, slab-toothed, and serrated), a blade configuration design (i.e., sweep design) and their various combinations on TCT's power coefficient are numerically and experimentally studied in this paper. The novelty of this paper lies in its pioneering exploration of the synergistic effects of multiple biomimetic designs on the TCT's power generation and start-up performance, as well as the interplays between the sweep design and biomimetic trailing edge designs. Both numerical simulations and experimental studies provide compelling evidence that all three biomimetic trailing edge designs enhance the blade's energy capture efficiency. For example, at a 7° angle of attack, the lift-to-drag ratio for the slabbing trailing edge blade increases by 12.124% and the slab-toothed trailing edge blade increases by 11.770% compared to the standard blade. The incorporation of sweep design and biomimetic trailing edge design produces a further enhancement in both the TCT's power generation and startup performance. In particular, the simultaneous implementation of slabbed trailing edge design and sweep design makes the power generation efficiency of TCT increase by up to 54.72% when compared to TCTs using standard straight blades. • Paper explores synergic effects of multi-biomimetic designs on TCT's efficiency. • Slabbing and slab-tooted tailing edge designs surpass serrated trailing edge design. • Combined use of sweep and slabbing trailing edge design results in best performance. • All biomimetic designs considered in the study boost the TCT's startup performance. [ABSTRACT FROM AUTHOR]

This paper proposes a dual-objective eco-routing strategy aimed at optimizing driving routes by minimizing both energy consumption and travel time. First, the vehicle and powertrain dynamics are modelled. The energy consumption of three powertrain types, including hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV), are calculated based on a white-box model. Second, this paper models the traffic network by considering the intensity of each road. The energy consumption associated with different intensity levels is also modelled using a typical driving cycle. The dual-objective routing problem, considering both energy consumption and travel time, is solved by an Iterative Dijkstra's algorithm. Finally, several simulation results demonstrate that the proposed dual-objective eco-routing strategy achieves a favorable balance between energy consumption and travel time. Specifically, for HEV, the dual-objective eco-routing strategy resulted in a 5.3% reduction in travel time with a marginal increase of only 0.4% in fuel consumption compared to the single-objective eco-route. In addition, the eco-routing strategy exhibits distinct patterns across different vehicle powertrains. HEV and BEV tend to utilize heavy traffic conditions more frequently compared to the CV. • Build the energy consumption models of hybrid electric vehicle (HEV), battery electric vehicle (BEV) and conventional vehicle with internal combustion engine (CV). • Energy consumption associated with different intensity levels is modelled. • Formulate the dual-objective routing problem, considering both energy consumption and travel time. • Eco-routing strategy exhibits distinct patterns across different vehicle powertrains and traffic conditions. [ABSTRACT FROM AUTHOR]

Wind power is a critical source of renewable energy, but the variability of wind speed can impact the efficiency and reliability of wind power generation. To improve the accuracy of wind speed prediction, this paper proposes a novel system that combines deep learning and decomposition algorithms based on ensemble optimization methods. Firstly, the paper proposes an innovative hybrid modal decomposition (HMD) method that extracts accurate features from wind speed data using Singular Value Decomposition (SVD), modal number selection method, and Variational Mode Decomposition (VMD). Secondly, the variable screening method identifies relevant factors affecting wind speed as fixed inputs. Then, the stationary test method is used to identify the sequences of different features in the mode and establish different prediction models: a bidirectional long-term short-time neural network model (Bi-LSTM) model optimized by artificial hummingbird algorithm (AHA) is used for non-stationary sequences; an ARIMA model is used for stationary sequences. Data from Sotavento and Changma wind farms are used to test this forecasting system. Simulation experiments are carried out from multiple dimensions. The results show that all error indicators are significantly improved, and the proposed prediction system is better than other comparative forecasting schemes. • The research can be practically applied in the field of wind power generation. • The research can help improve the energy utilisation of wind power generation. • A new data decomposition algorithm called HMD is proposed. • A new prediction system based on ensemble optimization idea is proposed. • Combining AHA-optimized neural networks and ARIMA to predict wind speed. [ABSTRACT FROM AUTHOR]

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

Abstract: A bottom-up methodological framework was developed and applied for the period 1985–2002, to selected manufacturing sub-sectors in Greece namely, food, beverages and tobacco, iron and steel, non-ferrous metals, non-metallic minerals and paper. Disaggregate physical data were aggregated according to their specific energy consumption (SEC) values and physical energy efficiency indicators were estimated. The Logarithmic Mean Divisia index method was also used and the effects of the production, structure and energy efficiency to changes in sub-sectoral manufacturing energy use were further assessed. Primary physical energy efficiency improved by 28% for the iron and steel and by 9% for the non-metallic minerals industries, compared to the base year 1990. For the food, beverages and tobacco and the paper sub-sectors, primary efficiency deteriorated by 20% and by 15%, respectively; finally electricity efficiency deteriorated by 7% for the non-ferrous metals. Sub-sectoral energy use is mainly driven by production output and energy efficiency changes. Sensitivity analysis showed that alternative SEC values do not influence the results whereas the selected base year is more critical for this analysis. Significant efficiency improvements refer to “heavy” industry; “light” industry needs further attention by energy policy to modernize its production plants and improve its efficiency. [Copyright &y& Elsevier]

It is well known that energy forecasts play an important role in guiding energy policy, economic development and technological progress. Therefore, based on the purpose of energy consumption and production forecasting, this paper proposes a novel structure adaptive discrete grey Bernoulli model, which is innovative in terms of both accumulated generating operator and model structure. In terms of accumulated generating operator, a new fractional order accumulated generating operator is proposed in this paper. The new accumulated generating operator has a different information priority by adjusting the values of the parameters. In terms of model structure, a novel discrete grey Bernoulli model is proposed in this paper. The novel model is well adapted to time series data containing nonlinear information, and can well mine and utilize the information contained in the original data. In addition, the Particle Swarm Optimization (PSO) algorithm was chosen to optimize the model parameters based on algorithm comparison experiments. This enables the model to flexibly adapt to a variety of complex data and has the ability of structure adaptive. Moreover, this paper conducts comparative experiments between the novel model and eight other forecasting algorithms for time series data. The numerical results show that the novel model has better forecasting performance for the data of China's total energy consumption, China's total electricity generation and China's total domestic electricity consumption. In addition, for the model reliability problem caused by the optimization algorithm, the stability and accuracy of the model are verified by Monte Carlo simulation and probability density visualization analysis. Finally, the proposed model predicts the future development trend of energy consumption and production in China. • A novel structural adaptive discrete grey Bernoulli model is established. • The proposed model can grasp nonlinear and fluctuant in various datasets. • The robustness analysis of the novel model is established. • Comparative study shows that the novel model is superior to other models. • Relevant suggestions are put forward according to the prediction results. [ABSTRACT FROM AUTHOR]

Nowadays, climate change is a major global societal challenge that significantly increases environmental stress. Most international organizations and policies have promoted initiatives to minimize emissions, reduce fossil fuel dependence and increase renewable energy resource integration into different sectors. An energy transformation towards more renewable systems is thus a priority. Under this scenario, the present paper describes and evaluates an alternative energy conversion matrix-based model to combine sector electrification, power generation units from renewables, and new clean technologies. The proposed green matrix-based model allows analyzing future scenarios, including electricity participation in end-use consumption and electric power generated by renewables – potentially integrated into different sectors –. The proposed model is evaluated in the state of Maine (United States). This case study is focused on decarbonizing both residential heating and transport sector through the integration of large offshore wind power plant. Results and discussion is also included in the paper, providing expected energy demand reductions and decreasing emissions through the integration of renewables. This energy transition integration case study is proposed in three road-maps with different penetration rates and time scales. The proposed green matrix-based model can be also applied to other areas and energy resources, as an alternative way to analyze and estimate renewable integration into different sectors. • Green Electrical Matrix to link electricity supply and end-use for energy transition. • Assesses renewable potential and electrification tech for end uses, e.g., transport. • Applies model to Maine, USA to show reductions in fossil fuels and emissions. • GIS and MCDM for optimal offshore wind location, maximizing electricity generation. • Short, medium, and long-term projections of renewable integration into energy system. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide. • This paper attempts to provide a feasible framework for analyzing urban building energy use by utilizing real-time urban semantic perception information and building features. • The proposed framework have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings. • The proposed methodology can obtain regional energy consumption stocks and produce spatial distribution results, thus improving the interpretability of the data. • The results show that energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphoning effect is obvious in regions with high energy consumption. [ABSTRACT FROM AUTHOR]

In the background of exhaustion of the traditional fossil energy sources and environmental deterioration, developing renewable energy has become a strategic choice for countries to achieve energy sustainable utilization and carbon neutrality target. Different renewable energy technical plans have different characteristics under multiple criteria. Therefore, before the further exploitation of renewable energy sources, it is of great significance to evaluate the comprehensive performance of different plans and then determine the best renewable energy sources. However, the commonly used weight calculation methods in the existing renewable energy evaluation have obvious shortcomings, such as the randomness of the subjective method is strong and the gap in the weight allocation of the objective method is too large, which affect the reliability and accuracy of the sorting result. In this paper, a novel weight calculation method based on non-dominated sorting genetic algorithm (NSGA-II) is proposed, and is applied to renewable energy evaluation. The study in this paper focuses on methodology. Firstly, for the linear normalized and Zeros normalized evaluation data, the differences between renewable energy plans in the two normalized evaluation data are measured, and the ranking deviations of the two normalized data are calculated. Then, the multi-objective weight solving model is constructed in terms of plans differences minimization and ranking vectors deviations minimization. The NSGA-II algorithm is applied to the optimization of multi-objective functions to find the Pareto non-dominated solution, so as to determine the weight values of each evaluation index. Finally, the TOPSIS method is used to determine the relative closeness value of each plan, and the rankings of renewable energy technical plans are achieved. Based on the actual renewable energy development data of a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some popular renewable energy evaluation methods. [Display omitted] • Propose a novel evaluation method for renewable energy technical plans. • Apply non-dominated sorting genetic algorithm II to determine the index weights. • Compared with the existing evaluation method, the proposed method performs better. [ABSTRACT FROM AUTHOR]

To address the low density of high-pressure gaseous hydrogen and evaporation issues associated with liquid hydrogen storage, cryogenic supercritical hydrogen storage method is presented in this paper. Based on a dual parallel mixed refrigerant cycle (DPMR) and a dual cascade mixed refrigerant cycle (DCMR), the density of supercritical hydrogen product is 70.74 kg/m3, closely approaching liquid hydrogen. The processes are simulated in Aspen HYSYS, optimized by genetic algorithm for lower specific energy consumption (SEC). Thermodynamic analysis is conducted to compare with hydrogen liquefaction processes, demonstrating the feasibility and superiority of proposed process. The results show that the DPMR process has an SEC of 6.422 kWh/kg H2 and exergy efficiency of 52.66%, while DCMR is 6.872 kWh/kg H2 and 49.24%. Additionally, the inclusion of ortho-para convertors in DPMR process is investigated to further analyze the impact of heat release for ortho-para conversion. Furthermore, a techno-economic analysis is conducted on three processes, validating the economic feasibility. The study indicates that cryogenic supercritical hydrogen storage system is of great significance to the development of high-density hydrogen storage technology. The DPMR and DCMR presented in this paper can significantly improve the energy efficiency and provide valuable information for the design of high-density hydrogen storage system. • Two high-density cryogenic supercritical hydrogen storage systems are proposed. • Mix refrigerant cycles are applied to reduce energy consumption. • The processes have lower SEC and higher FOM than conventional LH 2 processes. • The volume storage density of hydrogen can reach 70.74 kg/m3 in the systems. • OPC is considered to analyze the thermodynamic performance. [ABSTRACT FROM AUTHOR]

The specific CO 2 production of fuels has commonly been expressed empirically in terms of kg CO2 (kWh)−1. No specifications regarding combustion conditions, transformation technology used, and efficiency are usually presented in detail. In this paper, however, we propose replacing this approach by the use of rigorous chemical engineering calculations based on the composition and the combustion conditions of the fuels. The MS EXCEL program, which is provided as supplementary material to this paper, calculates the specific CO 2 production considering energy losses, adiabatic flame temperature, calorific value and heat of fuel combustion, flue gas temperature, energy losses through flue gas, slag, and the overall energy efficiency of the whole energy conversion process. Results are presented for three fuel groups: coal, hydrocarbons, and renewable fuels. The calculated results are compared with the literature data. A benefit of our approach is that the procedure can be generalized for any fuel of known composition and for different types of combustion-based energy transformation. • Techno-economic energy strategies require accurate knowledge of CO 2 emissions. • The attached chemical engineering program in EXCEL provides the necessary data. • Fuel type and combustion conditions affect CO 2 emission per kWh. • The overall efficiency of various energy production processes was calculated. • The estimated and observed overall efficiencies are in good agreement. [ABSTRACT FROM AUTHOR]

Users' energy use behavior has great impact on the economic and efficient operation of integrated energy systems (IES). This paper proposes energy usage strategies based on integrated demand response (IDR) by developing a Stackelberg game based simulation model, in which the user-side flexible resources (FRs) and comfort cost are considered. In the paper, a framework of community integrated energy system (CIES) and an IDR model considering the consumer psychology were established first, in which the community users were viewed as a user aggregator (UA). A Stackelberg game simulation model was then developed for studying the energy interactions between community integrated energy service provider (CIESP) as the leader and UA as the follower based on the proposed IDR model. The benefit gain and energy use behavior of the UA were analyzed, by using the model developed, in six scenarios. The study found the strategy developed with the consideration of FRs and consumer psychology could make the UA's cost of purchasing energy reduced by 17.14 %. Meanwhile, the consumer surplus could increase by 11.58 %. The results also showed that by introducing user-side gas boilers and distributed energy storage more benefit gain for users and improved load regulation capabilities for CIES could be achieved. • A Stackelberg game based simulation model is developed for a CIES. • An IDR model considering the consumer psychology is established. • The optimal energy use strategy is obtained considering the user-side flexible resources and comfort cost. • This strategy can reduce the cost of energy use and increase the consumer surplus. [ABSTRACT FROM AUTHOR]

With recent energy and environmental crises, the energy consumption prediction of air conditioning (AC) is essential. Notably, existing general prediction systems use the air temperature, real-time AC usage, time, occupancy, and AC historical energy consumption as inputs and employ feedforward neural networks. However, such systems face certain challenges. First, the input system may be difficult to reflect the real-time interaction among the building environment, occupants, and energy system; Second, the algorithms need to be upgraded to further improve accuracy, speed up convergence, and avoid overfitting. his paper introduces a novel parameter (group occupant behavior probability, GOBP) in the input system; and uses swarm intelligence algorithms with chaotic mapping and adaptive weight adjustment as optimization with LSTM and BP as the basic algorithms. The verification results obtained from five different buildings indicate the following: (1) The novel prediction system improved prediction performance with 20.08%–78.37 % of MAPE and 3.99%–53.28 % of CV-RMSE, however, it will increase 148.70s–1065.11s of calculation time. (2) For accuracy, using GOBP will improve the 0.30%–51.87 % and 0.18%–64.77 % by using swarm intelligence optimization algorithms. (3) This paper discusses the priority of selecting suitable inputs, algorithms, and optimization to combine a whole prediction system. • The definition of a simplified model using the group occupant behavior probability is proposed. • The chaotic mapping and adaptive weight adjustment are used for optimizing the swarm intelligent algorithms. • A reconstituted AC energy consumption prediction system is proposed with novel inputs and improved algorithms. • The reconstructed prediction system demonstrates improved prediction accuracy and data acquisition difficulty\. • The best combination of inputs and algorithms is discussed based on 60 groups of comparative experiments. [ABSTRACT FROM AUTHOR]

Accurate prediction of building energy consumption is crucial to the rational scheme of building energy. Combining the entropy-weighted K-means (EWKM) with the random forest (RF) method, a feature selection (EWKM-RF) method is proposed in this paper. Based on the proposed EWKM-RF method, the classification and feature selection of the energy consumption influencing factors can be achieved exclusively. Meanwhile, based on the EWKM-RF method and the bi-directional long-short-term memory neural network (BiLSTM) optimized by the sparrow search algorithm (SSA), an RF-SSA-BiLSTM prediction model for building energy consumption is established in this paper. As the weight, learning rate, and hidden layer node parameters of the BiLSTM neural network are optimized with the SSA, the constraints for manually adjusting parameters are avoided in the proposed prediction model. To examine the accuracy of the proposed model, energy consumption data of a civil public building in Dalian city are collected and tested. Results show the prediction error of RF-SSA-BiLSTM after feature selection is reduced by 24.55 % in high and low energy consumption months. Compared with RF-BiLSTM, RF-PSO-BiLSTM, and RF-CNN-BiLSTM, the RF-SSA-BiLSTM has strong robustness. The average MAE, RMSE and MAPE values of energy consumption prediction in the four seasons are 1.30, 1.63 and 0.02. [Display omitted] • A new prediction method is developed for building energy consumption. • The integration of SSA and BiLSTM neural network is analyzed. • Combinations of EWKM with RF algorithm to reduce influencing factors. • Deep learning is used to extract features of building energy consumption data. • Comparison of the proposed prediction models with different optimization models. [ABSTRACT FROM AUTHOR]

Solar radiation is an essential meteorological parameter for building energy efficiency analysis, and the quality of the data directly affects the analysis results. This paper investigates the estimation of hourly solar radiation based on the generation of the typical meteorological year(TMY) using various real meteorological parameters and limited solar radiation data. The focus of this paper is to use two types of neural network algorithms to improve the estimation accuracy and applicability, and to solve the problem of hourly solar radiation data acquisition in non-radiation areas. First, select two city station data and use three methods to generate TMY. Then, two neural network models, BP Neural Network (BP)，Convolutional Neural Network (CNN) are used to estimate the hourly solar radiation data and verify the results. Finally, by constructing a photovoltaic-integrated office building model, the accuracy of the hourly solar radiation estimation model is verified using energy consumption simulation and photovoltaic (PV) power generation simulation. The results show that this paper can well solve the problem of limited radiation data, which provides a new idea for the study of building energy efficiency in areas where radiation data is missing. • Multiple neural network is used to estimate the generation of solar radiation data using other meteorological parameters. • An advanced neural network is used to provide an effective TMY generation method in missing hourly solar radiation data. • The accuracy of the experimental results is verified by energy consumption simulation and photovoltaic power generation. [ABSTRACT FROM AUTHOR]

The drive for improved energy efficiency and flexibility has birthed an innovative solution: an energy hub. This hub operates in sync with natural gas and electricity grids, integrating batteries and thermal storage to optimize renewable energy use, reduce costs, and bolster network stability. In addressing these objectives, a mathematical optimization model for energy hub operation is developed in this paper. This model captures the intricate interplays between electricity and natural gas networks, accounting for the intricacies posed by uncertainties and variations in renewable energy sources and demand profiles. Also, this paper uniquely delves into the integration of load response programs, an approach geared towards amplifying the energy hub system's flexibility and reliability. Through these programs, subscribers gain the agency to calibrate their energy consumption based on real-time pricing signals and incentives dispensed by the energy hub. The simulation findings serve as a compelling testament to the efficacy of the proposed system. Operating costs witness a substantial reduction, the adoption of renewable energy sources is markedly heightened, and the stability and reliability of the upstream energy networks are tangibly ameliorated. The integration of load response programs emerges as a particularly effective strategy in mitigating peak demand and augmenting the system's overall energy efficiency. Underpinning the entirety of this innovative solution is the transformation of the challenges into an optimization problem. This catalyzes the employment of a newly developed particle community algorithm, custom-tailored to surmount the challenges of local optima. This algorithm inherently bolsters the system's search capabilities, minimizing the risk of entrapment within localized solutions. Ultimately, the proposed energy hub system encapsulates a promising trajectory toward the realization of a sustainable and adaptable energy landscape. By seamlessly integrating diverse energy sources and storage systems, augmenting energy flexibility and efficiency, and aligning with broader energy transition and decarbonization goals, this solution presents a formidable contender. The simulation outcomes underscore its potential by showcasing a substantial 4.57 % reduction in the cost of procuring energy from the upstream electrical network. • Holistic energy hub engages in upstream electricity and heat markets, optimizing profit. • Empowered energy hub actively influences markets with optimized strategies. • Robust optimization ensures stable operations in uncertain renewable sources. • Advanced load management optimizes profiles, aligning consumption with pricing. • Particle swarm optimization algorithm enhances search for optimal solutions. [ABSTRACT FROM AUTHOR]

Accurate fuel consumption prediction is essential for optimizing the operation of wing-diesel hybrid vessels and improving energy efficiency. This paper proposes a grey box model (GBM) for wing-diesel hybrid vessel fuel consumption prediction based on feature construction. Both parallel and series grey box modelling methods, as well as six machine learning (ML) algorithms are adopted to establish twelve combinations of prediction models. Then, a feature construction method based on the aerodynamic performance of the wing and the energy relationship of the hybrid system is proposed. Three types of wing features, namely wing thrust, wing thrust power, and wing fuel consumption savings are constructed and introduced into each combination respectively. Finally, based on noon report data of a wing-diesel hybrid vessel, the combinations are trained and validated. The best combination is obtained by considering the root mean square error (RMSE), which is parallel modeling method, random forest (RF) algorithm, and wing fuel consumption savings feature. Its RMSE decreased by 41.7 % compared to the white box model (WBM). Therefore, the GBM proposed in this paper can predict the daily fuel consumption of wing-diesel hybrid vessels with high accuracy, facilitating operational optimization and contributing to the greenization and decarbonization of the shipping industry. • A GBM for predicting fuel consumption of wing-diesel hybrid ships is proposed. • Method proposed for constructing wing features to improve GBM prediction performance. • The data integration method is adopted to improve the quality of meteorological data. • The GBM prediction error (RMSE) is reduced by 41.7 % compared to the original WBM. • The GBM can predict the fuel consumption of hybrid system under various conditions. [ABSTRACT FROM AUTHOR]

The transportation sector is deemed one of the primary sources of energy consumption and greenhouse gases throughout the world. To realise and design sustainable transport, it is imperative to comprehend relationships and evaluate interactions among a set of variables, which may influence transport energy consumption and CO 2 emissions. Unlike recent published papers, this study strives to achieve a balance between machine learning (ML) model accuracy and model interpretability using the Shapley additive explanation (SHAP) method for forecasting the energy consumption and CO 2 emissions in the UK's transportation sector. To this end, this paper proposes an interpretable multi-stage forecasting framework to simultaneously maximise the ML model accuracy and determine the relationship between the predictions and the influential variables by revealing the contribution of each variable to the predictions. For the UK's transportation sector, the experimental results indicate that road carbon intensity is found to be the most contributing variable to both energy consumption and CO 2 emissions predictions. Unlike other studies, population and GDP per capita are found to be uninfluential variables. The proposed multi-stage forecasting framework may assist policymakers in making more informed energy decisions and establishing more accurate investment. • An interpretable multi-stage forecasting framework is proposed. • The drivers of energy consumption and CO2 emissions in UK's transport sector are identified. • A comprehensive comparison between different machine learning models is carried out. • The impacts of feature selection methods on machine learning models are investigated. [ABSTRACT FROM AUTHOR]

Black soldier fly larvae containing high lipids had been considered an novel sustainable feedstock for lipid extraction to biofuels. The insect lipid was extracted with microwave assisted solvent extraction method. The energy consumption of this way was analyzed to determine the feasibility of the lipid extraction process. The comparison of the effects of different extraction conditions (dehydration methods, water content, extraction temperature, extraction time, equipment power, and solute-solvent ratio) on the energy consumption of lipid extraction from the energy insect was investigated systematically. The dewatering of the insect biomass was the most energy consuming step via a dry extraction method in this paper, constituting about 40–80% of the total energy consumption demand. Within the ranges of extraction factors values compared in this analysis, the microwave radiation powder and lipid extraction temperature had a pronounced effect on the total energy consumption in the ranges of investigated parameter. At last, the lipid extraction yield-dependent evolution of energy consumption for different extraction conditions was also investigated. Anyway, the economic and environmental aspects of lipid extraction from the energy insect needed to be further improved for its large scale implementation in the future. • A new and promising feedstock for biodiesel production has been proposed. • The energy consumption for lipid extraction from the insect biomass was investigated. • The dewatering of the insect biomass was the most energy consuming step in this paper. • The results may provide valuable data for industrial scale-up applications. [ABSTRACT FROM AUTHOR]