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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]

Paper mill sludge (PMS) can be efficiently utilized to produce fuels and chemicals. However, wastewater is usually generated during the de-ashing step of the PMS for fibrous materials recovery. Per process requirements, the wastewater stream must be treated which results in an increase in the overall process production cost. Therefore, this research aims at reusing the wastewater produced during the de-ashing step as a substitute for freshwater addition during the conversion of PMS into ethanol. The advantages of this approach include reducing the amount of wastewater produced and enhancing the overall efficiency of the process. It will contribute to the circular economy of zero waste discharges. The results showed that 30% of the process wastewater can be recycled without affecting the enzymatic hydrolysis and ethanol fermentation. Hence, the amount of wastewater that needs to be treated is reduced by 30% resulting in a cost reduction of 22.5%. The results also showed that wastewater recycling minimized the energy demands in the distillation and evaporation units by 1206 kJ/kg. The energy reduction is due to the increase of metals and total soluble solids in the broth stream after fermentation. This process configuration enhanced the process economy, saved energy and managed waste streams. • Reuse of wastewater in the paper mill sludge conversion. • A certain degree of recycling has no impact on the glucose yield. • Wastewater recycling reduced the energy demand of the process by 1251 kJ/kg metric tons wastewater. • Cost-saving of wastewater treatment by 22.5% when a 30% recycling degree achieved. [ABSTRACT FROM AUTHOR]

Abstract: Supercritical water gasification (SCWG) is a method by which biomass can be converted into a hydrogen-rich gas product. Wet industrial waste streams, which contain both organic and inorganic material, are well suited for treatment by SCWG. In this study, the gasification of two streams of biomass resulting from the pulp and paper industry, black liquor and paper sludge, has been investigated. The purpose is to convert these to useful products, both gaseous and solids, which can be used either in the papermaking process or in external applications. Simple compounds, such as glucose, have been fully gasified in SCWG, but gasification of more complex compounds, such as biomass and waste, have not reached as high conversions. The investigated paper sludge was not easily gasified. Improving gasification results with catalysts is an option and the use of alkali salts for this purpose was studied. The relationship between alkali concentration, temperature, and gasification yields was studied with the addition of KOH, K2CO3, NaOH and black liquor to the paper sludge. Addition of black liquor to the paper sludge resulted in similarly enhancing effects as when the alkali salts were added, which made it possible to raise the dry matter content and gasification yield without expensive additives. [Copyright &y& Elsevier]

Accurate understanding of the marginal energy-saving costs of energy-using entities is critical for regulatory authorities to formulate and implement energy-saving policies. This article brings out an easy-to-operate analytical framework to measure the marginal energy-saving cost and construct energy conservation supply curve with the help of data envelopment analysis and quantitative model. Furthermore, taking China's pulp and paper industry as an example, the authors discuss the difference of economic costs between industry energy-saving task assignment with and without considering the difference of marginal energy-saving costs of different energy using entities with the help of linear programming model. The main empirical results are as follow. First, there is a big difference in the industrial marginal energy-saving costs in different provinces. The difference between the highest and lowest energy-saving costs is twice as large. Second, the optimal energy-saving task assignment based on an accurate energy conservation supply curve can help to save nearly 35% of total economic cost. • An alternative method of measuring energy conservation cost is provided. • An optimal energy saving task assignment model is constructed. • The problem of trade-off between efficiency and equity in policy making is discussed. [ABSTRACT FROM AUTHOR]

A novel MEA (membrane electrode assembly) structure of passive μ-DMFC (micro-direct methanol fuel cell) controls water management and decreases methanol crossover. The CNT (carbon nanotube) paper replacing CP (carbon paper) as GDL (gas diffusion paper) enhances water back diffusion which passively prevents flooding in the cathode and promotes low methanol crossover. Moreover, the unique structure of CNT paper can also enhance efficiency of oxygen mass transport and catalyst utilization. The passive μ-DMFC with CNT-MEA exhibits significantly higher performance than passive μ-DMFC with CP-MEA and can operate in high methanol concentration, showing the peak power density of 23.2 mW cm −2 . The energy efficiency and fuel utilization efficiency are obviously improved from 11.54% to 22.7% and 36.61%–49.34%, respectively, and the water transport coefficient is 0.47 which is lower than previously reported passive μ-DMFC with CP. [ABSTRACT FROM AUTHOR]

In this study, through orthogonal experiments design, the influences of ultrasonic pretreatment (frequency, power and treatment time) on co-pyrolysis and catalytic pyrolysis of municipal solid waste and paper mill sludge were explored. The differential thermogravimetry curves became steeper and the peak values were larger after pretreatment, which made the pyrolysis reaction rapidly and the volatile separated out timely. However, the change of pyrolysis characteristics was not obvious after adding MgO. The catalytic effect of MgO increased with the increase of sludge ratio. After pretreatment, the average activation energy of the samples had an overall increasing trend, but the change was small. MgO had a decreasing effect on activation energy, and with the increasing proportion of sludge the degree of reduction became significant. In addition, high ultrasonic frequency had a negative effect on reducing activation energy. • Study on the co-pyrolysis of municipal solid waste and paper mill sludge. • The differential thermogravimetry curves became steeper after pretreatment. • Distributed activation energy model was used to calculate the kinetic parameters. • The average activation energy had a minor increasing trend after pretreatment. • High ultrasonic frequency had a negative effect on reducing activation energy. [ABSTRACT FROM AUTHOR]

Catalytic co-pyrolysis of biomass and plastics (HDPE (high density polyethylene), PP (polypropylene) and PET (polyethylene terephthalate)) has been performed in a fixed-bed reactor in presence of cobalt based alumina, ceria and ceria-alumina catalysts to analyze the product distribution and selectivity. Catalysts are synthesized using co-precipitation method and characterized by BET (Brunauer–Emmett–Teller) surface area and XRD analysis. The effect of catalytic co-pyrolysis at different temperature with product distribution has been evaluated. The results have clearly shown the synergistic effect between biomass and plastics, the liquid products gradually increases forming with rise in the plastic content in the blend. Gaseous products have yielded most during pyrolysis of blend having biomass/plastics ratio of 5:1 with the presence of 40% Co/30% CeO 2 /30% Al 2 O 3 catalyst with hydrogen gas production touched its peak of 47 vol%. Catalytic performance enhanced with increase with the cobalt loading, with best performance attributing to 40% Co/30% CeO 2 /30% Al 2 O 3 catalyst. [ABSTRACT FROM AUTHOR]

Abstract: The paper presents the improvement of energy management in a steam system used for corrugated board production. The system consists of a steam boiler (fired with natural gas), steam and condensate pipelines and a corrugated board production installation (steam consumer). In the existing system, the boiler works in unison with an open condensate tank. This tank is responsible for energy losses due to secondary steaming. Therefore, a system of a closed tank has been proposed to eliminate these energy losses. The coefficients of thermal performance and the energy losses in both cases (open and closed tank) are compared. The study shows that the losses of the closed steam system are always lower than those of the open system. The coefficient of performance could be increased by about 8% by the proposed modernization. The recovery of expenses for modernization should be very quick, taking place within a year. [Copyright &y& Elsevier]

To implement solar photocatalytic water detoxification in industrial processes, problems have to be identified. The effluents from paper mills contain non-biodegradable substances like polyphenolic polymer lignin. Photocatalysis is a suitable method to degrade this class of substances. Especially in good solar regions, like Brazil, solar radiation should be ideally used for that process. The German Aerospace Center and the Federal University of Uberlaˆndia-MG, Brazil are cooperating in a project funded by the German International Bureau of the Federal Ministry of Education and Science and CNPq, Brazil to implement solar photocatalysis in the treatment of paper mill effluents. Therefore, the following tasks are worked on: model compounds for the contaminants have been identified and compared to the real effluents. Different photocatalysts and oxidizing agents were tested to shape the degradation process for use in an industrial application. Tests were carried out in lamp reactors as well as in solar reactors to determine the influence of the reactor on the degradation. The kinetic of the degradation was also determined. The test results have shown that the non-biodegradable substances can be very effectively degraded by photocatalytic treatment. Especially in solar reactors like the CPC type reactor, degradation takes place very fast. Total mineralization of the contaminants can be reached. The paper describes the project as well as the test results and will provide an outlook to the implementation of solar photocatalytic detoxification technology in Brazil. [Copyright &y& Elsevier]

With the continuous increase in underground engineering depth, the risk of coal–rock dynamic disasters, such as rockburst, is becoming increasingly serious and complex threatening the safety of coal resources. To study the effect of horizontal stress on fractal characteristics of rockburst fragments in coal mining, this paper uses a digital drilling instrument to perform rockburst proneness tests on andesite and granite specimens with different horizontal stresses. Fractal theory is used to calculate the fractal dimension of the rockburst fragments for several combinations of parameters. The residual elastic energy index is combined with the fractal dimension of rockburst fragments to assess the propensity of rockbursts from macroscopic and microscopic perspectives. The experimental results show that the positive correlation between the fractal dimension and the A EF obtained under the two methods. The fractal dimensions of andesite and granite are 1.507–1.80 and 1.734–1.979, respectively. According to the rockburst proneness criterion, it is predicted that andesite and granite are all strong rockbursts under confining pressures of 20 MPa, 30 MPa and 50 MPa. Therefore, it is of some theoretical relevance to investigate the rockburst mechanism in terms of both the A EF and fractal dimensions of fragments under different unloading conditions. • Rockburst proneness is studied for rocks using digital drilling instrument with different horizontal stresses. • The residual elastic energy index is obtained from the drilling parameters to estimate rockburst proneness. • The residual elastic energy index is combined with the fractal dimension to assess the rockburst proneness. • The fractal dimensions of andesite and granite are 1.507–1.80 and 1.734–1.979, respectively. • The andesite and granite are all strong rockbursts under confining pressures of 20 MPa, 30 MPa and 50 MPa. [ABSTRACT FROM AUTHOR]

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]

Abstract: This paper evaluates system aspects of biorefineries based on biomass gasification integrated with pulp and paper production. As a case the Billerud Karlsborg mill is used. Two biomass gasification concepts are considered: BIGDME (biomass integrated gasification dimethyl ether production) and BIGCC (biomass integrated gasification combined cycle). The systems analysis is made with respect to economic performance, global CO2 emissions and primary energy use. As reference cases, BIGDME and BIGCC integrated with district heating are considered. Biomass gasification is shown to be potentially profitable for the mill. The results are highly dependent on assumed energy market parameters, particularly policy support. With strong policies promoting biofuels or renewable electricity, the calculated opportunity to invest in a gasification-based biorefinery exceeds investment cost estimates from the literature. When integrated with district heating the BIGDME case performs better than the BIGCC case, which shows high sensitivity to heat price and annual operating time. The BIGCC cases show potential to contribute to decreased global CO2 emissions and energy use, which the BIGDME cases do not, mainly due to high biomass demand. As biomass is a limited resource, increased biomass use due to investments in gasification plants will lead to increased use of fossil fuels elsewhere in the system. [Copyright &y& Elsevier]

Abstract: Implementation of absorption heat pumps (AHPs) in a Kraft pulping process was studied using a new methodology for the optimal integration of those devices in a process. Two generic opportunities were identified for an energy and water optimized mill: (i) integration of a double lift chiller in the bleaching chemical making plant to produce chilled and hot water simultaneously, using MP steam as the driving energy and, (ii) installation of a single stage heat pump to concentrate the black liquor and produce useful hot water by upgrading heat from the bleaching effluent and using MP steam as driving energy. The principles of AHPs operation and their efficient integration into a process are described. The simple payback time (SPB) and net present value (NPV) were used to evaluate the interest of such implementations. Considering 63 $/MWh for the steam price, SPB of 2.7 and 1.7 years have been estimated for the two cases. [ABSTRACT FROM AUTHOR]

Microfluidic electrolyzers have been fabricated using graphite coated paper electrodes to electrolyze sea water into oxygen (O 2) and hydrogen (H 2) when integrated with a photovoltaic (PV) cell. The 'open' electrolyzer (OME) consists of a microchannel confined by a pair of graphite electrodes, which is drawn with the help of pencil tips. A sea water microdroplet has been dispensed at the junction of the electrodes while the PV cell is integrated to the electrodes to supply current. The microscale width of channel generates a high intensity electric field even at a lower potential, which facilitates the microdroplets to electrolyze into H 2 and O 2 near the cathode and anode. The rate of production of the gases increased with field intensity. In the 'close' electrolyzer (CME), the paper decorated with graphite electrodes is covered with a substrate embedded with polymeric microchannels. The microcapillaries are placed closer to the anode and cathode for in situ separation of H 2 and O 2. The economic, flexible, and metal-free micro-electrolyzer facilitates the water-splitting at a much lower applied voltage at an efficiency of 1–2%. Use of multiple separation channels in CME for production and separation of H 2 and O 2 shows the potential for micro-very-large-scale-integration (μ-VLSI). Paper based microfluidic reactor for production and separation of H 2 an O 2 gases. [Display omitted] • Development of a paper-based electrodes coated with graphite or RGO. • Electrolysis of sea water on the paper based microelectrolyzer. • Development of paper microelectrolyzer integrated with graphite/RGO electrode for electrolysis. • Integration of the solar panel to generate high intensity electric field for water splitting. • Synthesis and separation of hydrogen and oxygen in the OME and CME. [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: Hydrogen produced from renewable biofuel is both clean and CO2 neutral. This paper evaluates energy and net CO2 emissions consequences of integration of hydrogen production from gasified black liquor in a chemical pulp mill. A model of hydrogen production from gasified black liquor was developed and integration possibilities with the pulp mill''s energy system were evaluated in order to maximize energy recovery. The potential hydrogen production is 59000 tonnes per year if integrated with the KAM reference market pulp mill producing 630000 Air dried tonnes (ADt) pulp/year. Changes of net CO2 emissions associated with modified mill electric power balance, biofuel import and end usage of the produced hydrogen are presented and compared with other uses of gasified black liquor such as electricity production and methanol production. Hydrogen production will result in the greatest reduction of net CO2 emissions and could reduce the Swedish CO2 emissions by 8% if implemented in all chemical market pulp mills. The associated increases of biofuel and electric power consumption are 5% and 1.7%, respectively. [Copyright &y& Elsevier]

In hot and dry climatic zones, direct evaporative cooling (DEC) system is used as an economical and efficient alternative to traditional air conditioning systems. The present paper concerns comprehensive experimental investigations and analysis of heat and mass transfer characteristics and thermal performance parameters of a bee-hive construction of corrugated-cellulose papers as a new cooling pad material. The performance parameters used to evaluate the effectiveness of the cooling pad materials are the outlet air temperature and relative humidity, pressure drop, humidifier effectiveness, rate of the evaporated water, sensible cooling capacity, specific cooling capacity (SCC), coefficient of performance (COP), specific water consumption (SWC), and Nusselt and Sherwood numbers. The cooling pad performance was investigated for a wide range of air and water temperatures and flow rates and pad thicknesses. The results show the enhancements of heat and mass transfer coefficients and the performance parameters with rising air temperature and water flow rate to humidifier. High values of the performance parameters (h c = 45 W/m2oC and h m = 0.23 m/s, ε h u m = 0.85 and COP = 170) are obtained compared to the traditional pad materials. Experimental correlations for the evaporative cooler performance parameters in terms of air and water temperatures and flow rates pad thicknesses are presented. • Cellulose paper, bee-hive structure has efficient thermal to be used as evaporator cooling pads. • Good thermal-hydraulic characteristics at a wide range of operating and design conditions are obtained. • Presented empirical correlations predict the performance parameters very well. • High values of the performance parameters of the proposed cooling pad were obtained. • The highest saturation efficiency is 84% with associated temperature and pressure drops 18 °C and 8 Pa. [ABSTRACT FROM AUTHOR]

This study focuses on waste paper gasification in carbon dioxide atmosphere with molten alkali carbonates including potassium, sodium, lithium carbonate or their intermixtures as catalyst. The molten catalysts is capable of facilitating a desired reaction (C+CO2→2CO), which was hardly feasible even at a high temperature of 973K if catalyst absences. It is that the catalysts which provides a gas–liquid interface between carbon and carbon dioxide, replacing original gas–solid, then allows the two reactants contact each other effectively. Further experimental results demonstrate that the intermixture carbonates exhibit strongly enhancement on catalytic activity than any carbonate salts in the form of single. The reaction rate depends on temperature evaluating manners, a rapid heating processes is favorable to the aimed reaction. With respect to recycling of carbon dioxide, the process provides a conversion in 30%. [Copyright &y& Elsevier]

A traditional system-level thermoelectric model requires enormous computing power and time for simulation analysis, especially when multiple optimization algorithms are combined. This paper initially proposed a comprehensive surrogate mode for the accurate modeling, field analysis, and fast optimization of thermoelectric generators. The surrogate model is made up of an artificial neural network (ANN) and a conditional generative adversarial network (cGAN), which can achieve a prediction accuracy of 97 % and a structural similarity (SSIM) of 0.954. Using a twisted tape annular thermoelectric generator (TT-ATEG) as an example, the generalization capability of the comprehensive surrogate model is demonstrated by studying the change of the twisted tape geometry parameters on the output performance and field distribution of the annular thermoelectric generator. The combination of the surrogate model and NSGA-II achieves fast optimization of key parameters under different working conditions, and the computational efficiency is improved by 99.97 %. Meanwhile, the cGAN part can predict the pressure and heat flow fields within 5s to provide intuitive visual feedback. The trained surrogate model requires less computer arithmetic power compared to the traditional multi-physics field simulation software. The successful application of the comprehensive surrogate model in this work provides a new solution idea and an optimization method for system-level TEG. • A comprehensive surrogate mode for performance prediction and optimization. • Surrogate Model Integrates ANN for Data-Based and cGAN for Image-Based Modeling. • Data-based performance computational efficiency improved by 99.97%. • Rapid physical field visualization provided within 5 seconds. [ABSTRACT FROM AUTHOR]

Abstract We herein report a simple and effective strategy to fabricate the bending-durable membrane-electrode assembly for bendable polymer electrolyte membrane fuel cells (PEMFCs) by simply coating conventional carbon papers with manually fabricated silver nanowires (Ag NWs). Due to the high stretchability and flexibility of Ag NWs, we expect that the introduction of Ag NWs to carbon fibers would relieve the increase of ohmic and charge transfer resistances derived from the disconnections between each carbon fiber and the resulting decrease of electrical conductivity by repeatedly bending the bendable PEMFCs. We find that by using Ag NWs as an added component to carbon paper, the performance of Ag NWs-applied PEMFC can be maintained after repetitive bending. Carbon papers with the Ag NWs also show higher performance than the PEMFC without Ag NWs in the previous research. From the electrochemical impedance spectra of Ag NWs-coated PEMFC, it is clearly demonstrated that the coated Ag NWs effectively connect between carbon fibers and thereby play a role as a good buffer medium when carbon fibers are disconnected by mechanical bending. Highlights • Bending-durable polymer electrolyte membrane fuel cell was realized. • Gas-diffusion layer was simply coated with percolated silver nanowires. • Fuel cell with silver nanowires showed high durability after repetitive bending. • Silver nanowires maintained electric connection between disconnected carbon fibers. [ABSTRACT FROM AUTHOR]

The current study analyzes the potential of an environmentally benign integration of a parabolic trough solar collector (PTSC) with a heat-intensive multi-effect evaporator (MEE) used in pulp and paper industry. Actual plant data was used to simulate the MEE and estimate the steam economy (SE) and steam consumption (SC), which are used to judge the performance of PTSC-MEE system. Four distinct alternatives for the integration are investigated with Self-adaptive Differential Evolution (SaDE) utilized for optimization. Initially, a normal PTSC with four different model configurations of MEE, basic or with energy reduction schemes (ERSs) are tested and found that SE is slightly increased for each. Next, a geometrically optimized/combinatorial optimized PTSC with basic MEE is examined, a rise of ∼3.4% in SE is noted for both, with current integration outperforming one out of three ERSs. Subsequently, geometrical plus combinatorial optimized PTSC with basic MEE is inspected, increase in SE reached 7.59% and two of the three ERSs outperformed. Economic analysis is conducted to estimate the steam saving with different PTSC-MEE systems and corresponding payback periods. The possibility of decarbonization with the various integration possibilities is also explored by evaluating the fuel savings and related CO 2 savings. • Integration of PTSC with an MEE used in pulp and paper industry is investigated. • The fusion of PTSC with MEE is studied with various possibilities. • Self-adaptive Differential Evolution (SaDE) is used for various optimizations. • The steam savings and payback times are estimated for various PTSC-MEE systems. • Decarbonization is studied by analyzing fuel savings and CO 2 reductions. [ABSTRACT FROM AUTHOR]

By purchasing emission reduction equipment or reducing production scale, it is possible to effectively decrease CO 2. Therefore, understanding the marginal abatement cost (MAC) associated with these methods is crucial for making decisions. However, previous studies using the directional distance function (DDF) approach often mis-specified production functions and neglected data noise. They also assumed decision-making units (DMUs) to be on the production frontier and used proportional changes in inputs and outputs as abatement paths. This paper addresses these limitations by developing the convex quantile non-radial directional distance function (CQR-NDDF) method, which estimates the MAC of CO 2 and determines optimal abatement paths for DMUs without assuming a specific production function, employing linear programming techniques. Applying this method to 30 provinces in mainland China from 2011 to 2019, the study finds that China's CO 2 MAC increased from 182 to 247 yuan/ton. The lowest-cost abatement path varies by province and time. The club convergence and ordered probit model are employed to conclude that the second industry and urbanization increase the MAC of CO 2 , while factors such as foreign direct investment, openness level, and human capital decrease the MAC. Moreover, the CQR-NDDF method yields significantly lower MAC estimates than the NDDF method. In conclusion, this paper provides new insights into China's CO 2 MAC, emphasizing the importance of considering inefficiency and data noise in MAC estimation. We anticipate that utilizing CO 2 MAC as a benchmark for carbon trading market prices could lead to an increase in prices within China's carbon trading market. • A novel method for estimating CO 2 MAC has been developed. • Ignoring inefficiency will result in an overestimation of CO 2 MAC. • Three different emission reduction pathways are considered in the estimation. • The heterogeneity of the lowest cost abatement pathways has been identified. [ABSTRACT FROM AUTHOR]

Achieving precision in the prediction of buildings' dynamic heat load is crucial for the advancement of smart heating systems. This research highlights the urgent need to enhance the accuracy of models predicting dynamic heat load. Through literature review, distinguished machine learning and regression algorithms were chosen to formulate prediction models. These models employ a data time-step adaptive strategy, a physics-guided loss function, and fundamental principles of heat transfer. Optimization algorithms of a mathematical nature were utilized to fine-tune the parameters and the framework of long short-term memory (LSTM) and multi-layer perceptron (MLP) models. An analytical comparison was undertaken between physics-guided models and those not guided by physics. Principal conclusions are: 1) Pelican optimization algorithm (POA)-LSTM model emerges as superior in heat load prediction accuracy of an office building, with percentage errors for actual and simulated datasets ranging from −6.7 % to 5.8 % and −5.2 %–4.5 %, respectively, and the mean absolute percentage error (MAPE) standing at 2.3 % and 1.3 %. 2) The linear regression model exhibits the lowest precision, with a MAPE of 17.5 % and 4.0 % for the 7-day prediction results in the actual and simulated datasets, respectively. These findings provide support for improving heat load prediction in heating systems. • The authors have implemented a total of 20 physically-guided models for predicting building thermal load, among which the POA-LSTM model demonstrates the highest accuracy. • The disparities in thermal load prediction accuracy between the physically-guided machine learning models and the physically-guided mathematical regression models have been identified. • The accuracy of thermal load prediction was compared between the five physical guidance models proposed in this paper and the five non-physical guidance models mentioned in other papers. [ABSTRACT FROM AUTHOR]

The utilization of supercritical carbon dioxide (sCO 2) in the Brayton cycle presents several advantages, such as compact equipment, high efficiency, and rapid response time. The sCO 2 power cycle can be applied in coal-fired power, solar thermal power, and nuclear power systems. Over the past 10 years, many scholars have researched sCO 2 power systems. The compression (pressurization) process is a core thermodynamic process in sCO 2 power cycles, achieved through the use of compressors. Therefore, compressors are important components in sCO 2 power systems. This paper provides a summary of recent studies on compression processes and compressors for sCO 2 power systems. The impact of near-critical-point characteristics of the compression process on equipment and sCO 2 power cycle systems is discussed. The investigations of the performance parameters, design considerations, design methods, and performance prediction methods of sCO 2 compressors are reviewed. The typical research results on CO 2 condensation at the compressor inlet, the effects of operating conditions on compressor performance, as well as optimization of design parameters, are also summarized. Additionally, it provides a summary of sCO 2 compressor prototypes developed by research institutes worldwide and experimental studies. Finally, the current issues with sCO 2 compressors are addressed, and the main future research directions are proposed. This paper will contribute to the development of compressors and promote the acceleration of the commercialization of sCO 2 power systems. • Summary of recent studies on compression processes for sCO 2 power systems. • Near-critical-point characteristics of compression process is discussed. • Summary of compressor prototypes by research institutes and experimental studies. • Current issues and future research directions on sCO 2 compressors are presented. [ABSTRACT FROM AUTHOR]

This paper analyses the effect of energy poverty on respiratory health for a sample of 34 sub-Saharan African countries over the period 2000–2020. Using ordinary least squares (OLS), fixed effect, Driscoll-Kraay, Lewbel 2SLS, Kiviet and S-GMM, the results provide strong evidence of a negative and significant effect of access to electricity and access to clean energy for cooking on respiratory disease in sub-Saharan Africa. This result remained robust to changes in the variables of interest and to alternative estimation techniques. Furthermore, the results showed the existence of a non-linear relationship between energy indicators and respiratory diseases, including a U-shape and an N-shape. Based on these results, we encourage policy makers to better orient their energy policies towards the least polluting sectors in sub-Saharan Africa. In this way, the respiratory health of the inhabitants of sub-Saharan Africa can be improved. • This paper differs from existing work on energy poverty worldwide in the following respects: • It proposes a relevant determinant of respiratory health that has not been sufficiently studied in Sub-Saharan Africa. • It uses robust analytical methods to produce these estimates (Lewbel 2SLS and Kiviet). • It proposes useful policy recommendations for reducing energy poverty and reducing respiratory disease. [ABSTRACT FROM AUTHOR]

Accurate photovoltaic (PV) power forecast is crucial to power systems. Solar irradiance forecast is the fundamentals of PV power forecast. Current researches merely focus on improving the forecast accuracy. There hasn't been comprehensive study on the spatial-temporal characteristics of solar irradiance forecast errors. Thus, this paper analyzed the error characteristics of solar irradiance forecast provided by European Centre for Medium-Range Weather Forecasts (ECMWF), one of the most accurate numerical weather prediction (NWP) products. ECMWF's forecasts were compared with ERA5 solar irradiance reanalysis data to reveal the error distribution characteristics. Four-year (2017–2020) data from the geographical region bounded by 63°N, −126°W, 21°S, and 36°E (covering most parts of Europe and North America) were studied. Experiments show that solar irradiance forecast errors peak at noon of the local time. Furthermore, correlations between solar irradiance forecast errors and other weather variables were also revealed. Experiments show that solar irradiance forecast errors have negative correlation with low cloud cover forecast errors and positive correlation with air temperature forecast errors. The possible reasons for the correlation relationship were also analyzed in detail. This paper systematically reveals the spatial-temporal characteristics of solar irradiance forecast errors and provides a useful guideline for solar PV system operation. • Error distribution of ECMWF's solar irradiance forecast in Europe and North America is revealed. • Solar irradiance forecast errors in Europe are smaller than those in North America. • Solar irradiance forecast errors peak at noon of local time. • Solar irradiance forecast errors have slight seasonal and monthly differences. • Correlation between solar irradiance forecast errors and other weather variables is revealed. [ABSTRACT FROM AUTHOR]

There is a long way for solid-state batteries from the laboratory to large-scale application and commercialization. To overcome a series of challenges, researchers and innovators seek to further understand the processing-structure-properties relationships of solid-state batteries. However, less literature explores the advances and opportunities in solid-state battery technology based on patent analysis. The paper adopts the technology of Natural Language Processing (NLP) to analyze patent documents and reveal the advances and opportunities for developing solid-state battery technology by constructing the patent Information Relation Matrix (IRM). This paper finds innovation activities in developing solid-state batteries have been increasingly active in recent decades, but are uneven across organizations. The electrolyte is a priority area of technology development, and the advances in developing solid-state batteries are perfecting conductivity, reducing interfacial resistance, and improving density and stability. By contrast, the opportunities are to reduce cost, prevent short circuits, and prolong the life cycle. The paper perfects the extant method of constructing IRM and gives insight into the advances and opportunities for developing solid-state batteries. Our findings can help innovators better understand advances in solid-state batteries or opportunities for developing solid-state batteries, from a global perspective. • Adopting the technology of Natural Language Processing to analyze patent documents. • Using the Information Relation Matrix to reveal technology advances and opportunities. • There are advances and opportunities for developing solid-state battery technology. • The advances are perfecting conductivity, reducing interfacial resistance, etc. • The opportunities are to reduce cost, prolong the life cycle, etc. [ABSTRACT FROM AUTHOR]

Wind speed prediction is a crucial aspect in the utilization of wind energy. In this paper, a wind speed prediction model based on an outlier-robust ensemble deep random vector functional link network (ORedRVFL) and arithmetic optimization algorithm-optimized variational mode decomposition (AOA-VMD) is designed. First, the penalty factor and the number of mode decompositions of VMD are optimized using the AOA algorithm and the original data are decomposed using the optimized VMD. Then the decomposed data is predicted using the ensemble deep random vector functional link network (edRVFL) model. The edRVFL uses rich intermediate features for the final decision, which can make the final result closer to the real data. In order to strengthen the anti-interference ability to the outliers, this paper robustly improves the edRVFL model, and the improved model is called ORedRVFL. ORedRVFL reduces the impact of outliers by introducing regularization and norm to balance the relationship between training error and weights. The experiments have proved that the model proposed in this paper outperforms other models in terms of anti-interference ability and prediction accuracy. • An outlier-robust edRVRFL prediction model is developed. • Use VMD to decompose the data and enhance the model prediction capability. • Fuzzy entropy is used as a criterion for data aggregation. • AOA optimizes the VMD hyperparameters to enhance the decomposition effect of VMD. [ABSTRACT FROM AUTHOR]

This work describes the one-dimensional, thermo-hydraulic model of the sCO 2 cycle Sofia developed to investigate optimal control methods and the behaviour of the cycle during operation. This dynamic model includes all devices such as turbomachinery, heat exchangers, valves, and piping including heat loss, in line with concept of the 1 MWe sCO 2 cycle, to be realised in the site of a fossil power plant in the Czech Republic. The model assembly and calculations were conducted using the commercial Modelica-based library ClaRa + using the simulation environment Dymola and in combination with another Modelica-based library, UserInteraction; the real-time simulations, with some parameter changes during the calculation, are made and described in this paper. Nominal parameters were achieved during the steady-state simulation, except for the lower mass flow of sCO 2. Transient simulation of power turbine start-up from standby state and results are also presented in this paper. The nominal state is achieved with the semi-automatic procedure in approx. 3 h. The simulation results allow more detailed analyses of control methods and a better understanding of real device control and behaviour during start-up, shutdown, or other transients. Careful manipulation of turbine valves in coordination with the pressuriser operation was identified as crucial for optimal control of the system. Also, the initial amount of CO 2 in the pressuriser affects its behaviour during transients. • Models of the PCHE and BPHE heat exchangers were prepared, and turbomachines models determined by CFD simulations were used. • Control system of the electric heater, which is the largest facility in the Sofia cycle, was designed. • Real-time simulations were performed using ClaRa+ and UserInteraction Dymola libraries. • Careful trubine valves manipulation and corresponding pressuriser control were identified as key control interventions. • Initial amount of CO 2 in the pressuriser affects its behaviour during transients. [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]

Climate change leads to the frequent occurrence of natural disasters. It has had a severe impact on agricultural economic growth in China. Based on data in China from 2004 to 2020, this paper uses a fixed-effect model to discuss the effects of natural disasters on agricultural economic growth. The empirical results are as follows. (1) Natural disasters negatively affect agricultural economic growth. The regression coefficient of natural disasters on agricultural economic growth is −0.1183. (2) The inhibition of agricultural economic growth by natural disasters is more prominent in areas with low government attention and average agricultural production conditions. (3) The mechanism analysis shows that natural disasters inhibit agricultural economic growth through channels that cause the spread of diseases and pests. (4) Expanding the scale of agricultural insurance can prevent the impact of natural disasters on agricultural economic growth. This research provides policy recommendations for preventing the negative impacts of natural disasters. Agricultural producers should raise awareness of disaster management and improve the accuracy of disaster warning mechanisms. • This paper measures the level of natural disasters in China. • This paper expands the application field of functional data analysis methods. • This paper examines the mechanism of natural disasters affecting agricultural economic growth. • This paper provides policy suggestions for ensuring agricultural economic growth. [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]

Preventing the impact of natural disasters on urban-rural income inequality helps to achieve shared prosperity. This paper uses the functional entropy weight method to calculate the level of natural disasters from 2004 to 2020. Then, this paper discusses the impact of natural disasters on the urban-rural income gap. The empirical results are as follows. (1) Natural disasters have expanded the urban-rural income gap. The regression coefficient is 0.2663, which is significant at the 1% level. (2) In areas with a high proportion of the rural population, the impact of natural disasters is more prominent. (3) Mechanism analysis indicates that natural disasters have widened the urban-rural income gap by restraining rural investment and agricultural production. Finally, this paper suggests strengthening investment in the agricultural sector to mitigate the negative impact of natural disasters. • This paper uses the functional entropy weight method to calculate the level of natural disasters. • This paper quantitatively measures the impact of natural disasters on the urban-rural income gap. • This paper examines the mechanism of the impact of natural disasters on the urban-rural income gap. • This paper provides policy suggestions for narrowing the urban-rural income gap. [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]

The paper explores Coastal Energy-Based Industrial Clusters (EBICs) and their role in advancing energy efficiency and sustainability through collaborative innovation. Economic growth theory and energy sustainability have been introduced into industrial clusters to illustrate indicators that have a greater impact on the development of EBICs. This paper proposes an EBICs development model based on the Cobb-Douglas function, in which accounts for various factors that drive the progress of such clusters. The outcomes of the economic model also provide insights into how the interaction of various factors affects the economic growth of EBICs and their eventual dominance in the energy market of coastal regions, dependent on gradually investing in areas such as research and development (R&D). Different development strategies demonstrate that the final development of a cluster has low dependence on the cluster's initial advantages. The study also illustrates how clusters can gradually monopolize the energy market, even with initial disadvantages. Through quantitative analysis, it showcases the transformation process of developing advantageous clusters into sub-clusters. Next, an energy symbiosis framework for coastal is proposed, which places greater emphasis on the multi-energy complementary system and reduces production costs. Finally, this paper also sheds light on shaping energy strategies for public authorities who shape economic policies at various levels of aggregation and in diverse dimensions. • Multi-energy complementarity provides a new path for sustainable development of coastal energy. • Hardware investment is critical to the development of energy-based industry clusters. • Clusters with better natural advantages may become sub-clusters due to incorrect development strategies. • The essence of energy cluster development is the result of the interaction of multiple influencing factors. • The economic growth of energy clusters depends on the proportion of investment in software and hardware. [ABSTRACT FROM AUTHOR]

The core of the energy transformation under the carbon-neutral vision is the gradual replacement of high-carbon energy by zero-carbon and low-carbon energy. However, the large-scale deployment of clean energy technology in turn relies on key minerals such as copper, aluminum, zinc, and nickel. Based on this, this paper conducts research on the demand for key minerals in clean energy transition and the role of key minerals in clean energy transition. In particular, this paper calculated the demand for mineral supply for China's carbon neutrality target, and the impact of some mineral resource constraints on the carbon neutrality path. Relevant results can provide a decision-making basis for the future development and utilization of key mineral resources, as well as the implementation of future carbon-neutral goals. • We evaluate the impact of crucial mineral resource on the carbon neutrality path. • Copper's availability greatly affects the adoption of new energy technologies. • Limited resources hinder early carbon capture and storage development. • Negative emission technologies are necessary for China's carbon neutrality. [ABSTRACT FROM AUTHOR]

To accelerate the realization of the "carbon neutrality and carbon peaking" (dual carbon) goal, the "Opinions on Financial Support for Carbon Neutrality and Carbon Peaking" (the "Opinions") advocates the comprehensive adoption of tax incentives, government procurement and market-diversified investment measures to support the green low-carbon innovation transformation of key industries. Taking China's strategic new energy vehicle (NEV) industry from 2009 to 2021 as a sample, this paper empirically investigates the innovation effect of the measures in the "Opinions" on listed NEV enterprises. The results show that: (1) Both "supply-side" tax incentives and "demand-side" government procurement encourage NEV enterprises to innovate independently, jointly and efficiently, and the "supply-side" tax incentives have a stronger innovation effect. At the same time, compared with joint innovation, government policies are more conducive to independent innovation of NEV enterprise. (2) Market venture capital also encourages NEV enterprises to innovate. Government policy signals to the market can attract venture capital, and the "demand side" government procurement signal effect is stronger. (3) Tax incentives and venture capital, government procurement and venture capital have played an innovative synergistic effect on NEV enterprises. (4) There is an innovation failure in the combination of tax incentives and government procurement, but the innovation effect appears after the introduction of market venture capital. This paper not only supports the effectiveness of the innovative measures in the "Opinions", but also recognizes the continuation of the vehicle purchase tax exemption policy for NEVs. • "Opinions" measures encourage NEV to innovate independently, jointly and efficiently. • "Supply-side" tax incentives have a stronger innovation effect. • "Demand side" government procurement has a stronger signal effect. • Government policy and venture capital play an innovative synergistic effect. • There is an innovation failure in policy mix before venture capital intervention. [ABSTRACT FROM AUTHOR]

The scale of pig breeding in China is the largest in the world, and the pollution problem of breeding wastewater is serious. Supercritical water gasification (SCWG) process is a clean and efficient treatment technology for breeding wastewater. This paper involved constructing the molecular model of the primary organic components in pig breeding wastewater. The effects of time, temperature and mass concentration, and the interactions of different components on the SCWG characteristics of pig breeding wastewater were studied by molecular dynamics (MD) simulation, and the decomposition process of lignin and lipid molecules in the SCWG process were analyzed, and the possible intermediates were explored. The results showed that the effect of parameters on carbon gasification efficiency (CE) and H 2 production follows the order of temperature > time > mass concentration. During the gasification of lignin and lipid molecules, the C–O–C bond was broken first, and the gasification rate of lignin molecules was slow due to the benzene ring. Interactions among different organic decompositions hindered the gasification process. This paper will provide theoretical guidance for the selection of operation parameters and the control of products in the actual pig breeding wastewater treatment process. • The MD model of wastewater containing complex organics was established. • The effects of key parameters on SCWG of pig breeding wastewater were studied. • The detailed SCWG mechanisms of pig breeding wastewater were analyzed. • The intermediate products from SCWG of pig breeding wastewater were explored. • The interaction mechanisms of different organics during SCWG were analyzed. [ABSTRACT FROM AUTHOR]

Currently, lignite-fired boilers often feature low thermal efficiency, difficult coal pulverizing and poor combustion stability owing to lignite having high water content. A novel lignite-fired power generation process is proposed in this paper with using a superheated-steam-drying open-pulverizing-system boiler (SSD-OPSB) and a deep utilization of waste heat from the steam for coal drying. Through using an open pulverizing system, the problems of low furnace temperature and low boiler efficiency are resolved. Meanwhile, the operation safety of the coal pulverizing system is ensured by using superheated steam as the drying agent. This paper described the working principle and engineering feasibility of the SSD-OPSB process, and established analysis model for its thermo-economic performance. Energy savings and environmental benefits of applying the process were calculated and discussed based on an in-service 600 MW supercritical power unit firing lignite with water content of 39.5% at its turbine heat acceptance condition. The result shows that for the power unit, applying the SSD-OPSB process can decrease coal consumption by 6.86% under the same power supply, meanwhile recovering water by 132.2 t/h and reducing CO 2 emission by 27.78 t/h. It indicates that the SSD-OPSB process would significantly benefit lignite-fired power plants regarding energy-saving and environmental protection. • Lignite-fired boilers can benefit greatly from applying open coal pulverizing systems. • The benefits include marked coal-savings, enhanced coal combustion and water recovery. • A novel superheated-steam-drying open-pulverizing-system was proposed for applications. • Waste heat and water recovery system was designed to the new power generation process. • The model of analyzing thermo-economic performance of this process has been developed. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of solar gas heaters that utilize an air gap between the glass cover and the absorber, along with radiating gas. By using a participating fluid as the working gas, thermal radiation can be absorbed, emitted, and scattered, enhancing the heat transfer between the absorber and the flowing gas. Free convection airflow inside the air gap adds to the forced convection airflow through the solar collector's duct. The study utilizes the κ-ε model and the discrete ordinate method for radiative intensity computation to analyze the flow and energy equations. Numerical results indicate a significant increase in thermal efficiency, particularly at lower gas mass flow rates, when employing radiating gases with high radiative absorption coefficients. The research shows about 40% improvement in thermal efficiency is obtained in test cases at a gas mass flow rate of 0.01 kg/s. A gas flow optical thickness of τ = 2 is found to be optimal among the investigated parameters. In addition, this paper demonstrates high efficiency of up to 70% for plane solar collectors without the need for configuration changes by using working gases with high radiative properties, such as pressurized CO2. • Effect of radiating gas on the performance of solar gas heaters (SGHs) is examined. • An air gap is embedded between the glass cover and the absorber. • The efficiency of SGH has increased 40% for low mass flow of 0.01 kg/s. • The optical thickness of τ = 2 for the gas flow gives the best performance. • A simple plane collector could have efficiency of 70% just by utilizing a working gas with high radiative properties. [ABSTRACT FROM AUTHOR]

This paper establishes a multi-objective optimization model for railway heavy-haul trains, focusing on reducing carbon emissions and improving transport efficiency. The model integrates optimization of the route and the vehicle load rate, significantly reducing carbon emissions and enhancing transport efficiency. It addresses the challenges and characteristics of heavy-haul trains, introducing multi-objective optimization problems related to transport carbon emissions and efficiency. Using a pigeon-inspired optimization algorithm, the model considers joint constraints between carbon emissions and transport efficiency objectives. To overcome challenges in multi-objective transportation problems, the paper proposes a forward-learning pigeon-inspired optimization algorithm based on a surrogate-assisted model. This approach calculates the quality of the candidate solution using a surrogate model, reducing time costs. The algorithm employs a forward-learning strategy to enhance learning from non-dominant solutions. Experimental validation with benchmark functions confirms the effectiveness of the model and offers optimized solutions. The proposed method reduces carbon emissions while maintaining transport efficiency, contributing innovative ideas for the development of sustainable heavy-duty trains. [Display omitted] • A model is build for carbon emissions reduction and transport efficiency enhancement. • The model combines route and load rate optimization. • The model achieves carbon emission reductions while improving transport efficiency. • A novel meta-heuristic algorithm for solving the railway heavy-haul trains model. • The proposed algorithm introduces fresh insights for railway heavy-haul trains. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind speed is significant importance in various applications such as renewable energy management, weather prediction, and aviation safety. However, more papers only focus on the time series of wind speed, ignoring the impact of other factors on wind speed, which will reduce the effectiveness of wind speed prediction. Therefore, an innovative methodology is suggested for both point and interval forecasts of multivariate wind speed time series using a Dual-layer Long Short-Term Memory (NVDL) in this paper. The proposed multivariate forecasting model takes into account the dependencies and correlations among different variables, which are essential for capturing the complex dynamics of wind speed variations. The first layer of the Dual-layer LSTM is responsible for capturing the temporal dependencies within each variable individually, while the second layer captures the interdependencies among the variables. By incorporating this dual-layer architecture, our model effectively captures the complex spatiotemporal patterns present for multivariate wind speed information. The results obtained from both interval and point prediction demonstrate that the proposed methodology outperforms all comparative models in the precision and stability of wind speed forecasting. Therefore, the proposed forecasting methodology, characterized by minimal prediction errors and exceptional generalization ability, can be a reliable tool for smart grid programming. • This article proposes an enhanced version of the dual-layer LSTM structure. • A self-adaption preprocessing to manage the impact of noise-induced interference. • Fuzzy interval forecasting can give the upper and lower of wind speed effectively. [ABSTRACT FROM AUTHOR]

Porous surfaces are a common modified surface used to enhance boiling heat transfer. In this paper, a method for preparing gradient porous surfaces is presented, that is, magnetic stirring is added to the electrolyte and the cathode surface to be deposited is placed vertically downward. Within the Cl ion concentration range of 30–140 mg/L, increasing the Cl ion concentration results in lager pore sizes. When the Cl concentration was greater than 120 mg/L, the average pore size of the surface gradually increased from the center to the edge, and the number of pores gradually decreased from the center to the edge. All surfaces showed superhydrophilicity. The critical heat flux of Sample#3 is 158.72 W/cm2, which is 67% higher than the smooth surface. The wall superheat of Sample#3 was only 15 °C in the critical state. This porous surface has plenty of nucleation sites. These nucleation sites are gradually activated when the heat flux increases. At low heat fluxes, heat transfer on these porous surfaces presents a disadvantage because the nucleation sites are not fully activated. At high heat fluxes, these porous surfaces show a decrease in wall temperature. Consequently, the porous surface prepared in this paper has good boiling heat transfer at high heat flux. [Display omitted] • An inverted deposition method was designed to prepare gradient porous surfaces. • The gradient porous surface is superhydrophilic and provides a large number of nucleation sites. • An appropriate increase in Cl-ion concentration increases the pore size and pore wall thickness. [ABSTRACT FROM AUTHOR]

Lithium-ion battery is a kind of energy storage devices with complex internal reaction and many factors affecting its performance. Accurate prediction of its SOH (State of Health) is of great significance to prolong its service life and improve safety performance. However, the current prediction for SOH has the difficulties of selecting health factors and using data-driven methods with opaque mechanisms. In this paper, a data-driven model will be established with the capacity change during aging of lithium-ion batteries as a health indicator to realize precise prediction of capacity degradation. Firstly, the feature parameters were extracted and analyzed from the battery cyclic aging test dataset. Two in-situ nondestructive characterization methods, incremental capacity analysis curve and differential thermal voltammetry curve, were utilized to resolve the evolution paths of the feature parameters. After that, a data-driven battery capacity degradation estimation is realized based on the GEP (Gene Expression Programming) algorithm, the performance is compared with the existing vehicle-end and cloud-end models, and a higher-accuracy SOH stacking model is developed with an increase of less than 1 ms in computational time. The results indicated that the GEP model proposed in this paper has obvious advantages in terms of physical explain-ability, computational efficiency and robustness. • Establishing a high-precision battery SOH estimation model based on the GEP algorithm. • The relationship between ICA and DTV curves with aging evolution laws is revealed. • Fusion of GEP model with existing vehicle-end and cloud-end models using stacking strategy. [ABSTRACT FROM AUTHOR]

Decarbonising the heating sector is one of the key elements to realizing the ambitious dual carbon goals of China, which is the largest carbon emitter and energy consumer globally. Currently, district heating (DH) systems have penetrated approximately 88% of the urban heating areas in Northern China. Nevertheless, around 90% of the heating demand in China still relies on fossil fuels. A larger scale integration of renewable energy and waste heat sources into the DH systems is critical for decarbonising the entire heating sector in China. However, a deeper level of comprehension is required to harness its full potential. This paper provides a thorough investigation of the status, potential, and national policy schemes of renewable energy and waste heat recovery in the DH systems of China. Combined with a critical review of recent literature on relevant areas published in both international and Chinese domestic sources, the trends, challenges, and future perspectives are discussed from scientific research and practical implementation aspects. This paper highlights the synergy of the integration of renewable energy and waste heat sources in DH, the energy efficiency improvements as well as the use of thermal storage technologies through the implementation of 4th generation district heating and smart energy systems that could offer a more economically viable pathway forward. • Focus on renewable energy and waste heat (REWH) in the district heating (DH) systems in China. • A systematic review of REWH in Chinese DH systems from status, policy, and literature aspects. • Identification of research gaps based on the review of both international and Chinese journals. • Policy recommendations proposed to promote REWH in Chinese DH systems. • The introduction of international experiences on REWH in DH for analysis in the Chinese context. [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 attain China's target of carbon neutrality by 2060, the implementation of the Energy Consumption Trading (ECT) system serves as an effective approach to improve energy efficiency and mitigate CO 2 emissions. Nevertheless, it is crucial to consider not only the importance of carbon reduction effect caused by ECT system but also the associated cost. Therefore, this paper applies the spatial difference-in-differences (SDID) model to assess the direct and spillover impacts of the ECT policy on the carbon abatement cost, which is measured by shadow price of CO 2 emissions and calculated using SBM-DEA model. Panel data encompassing 279 Chinese cities from 2010 to 2019 are employed for analysis, with additional insights provided through heterogeneity and mechanism analyses. The results show that ECT policy increases the local carbon abatement cost in pilot cities but decreases the cost of surrounding cities. Meanwhile, such a declination effect in neighboring cities with a spatial attenuation boundary of 1000 km. The heterogeneity analysis suggests that the implementation of ECT policy leads to an increase in the carbon abatement cost in cities located in non-Eastern Coastal regions, cities with abundant natural resources and cities with small populations. The mechanism analysis indicates that the ECT policy implementation facilitates the improvement of green technology in the neighboring cities of the pilot project, consequently lowering the carbon abatement cost. Besides, this policy also results in a decrease in the carbon intensity of the pilot cities. Owing to the U-shaped association between carbon intensity and the carbon abatement cost, the policy implementation stimulates an increase in the cost within the pilot cities. • This paper estimates the carbon abatement cost (MAC) using SBM-DEA method. • The SDID model is used to investigate the impacts of ECT policy on carbon MAC. • We test whether green technology and carbon intensity are mediating variables. • ECT policy increases the carbon MAC in pilot cities through carbon intensity. • ECT policy lowers the carbon MAC of adjacent cities through the green technology. [ABSTRACT FROM AUTHOR]

The risk associated with the dramatic volatility of oil prices has become an important factor affecting the stability of the financial system. Through a complex network viewpoint, this study uses firm-level data to investigate systemic financial risk contagion from the oil market to the financial markets. Our study considers the risk amplification effect in financial network and provides an in-depth empirical analysis of risk contagion among financial institutions under the oil market risk shock. According to the findings of this paper, systemic financial risk increases dramatically during oil crisis events. This paper also captures important characteristics of the network, including major risk receivers and risk transmitters, and finds that banks suffer more risk losses from the oil market. Furthermore, considering the important role of the financial system in the macro economy, the systemic financial risk caused by oil market risk has long-term negative effects on economic output. • This study examines the risk contagion from oil market to financial markets from complex network perspective. • This paper suggests that systemic financial risk rises significantly during oil crisis events. • This paper finds that banks suffer more risk losses from the oil market risk. • Systemic financial risk caused by oil market risk has long-term negative effects on economic output. [ABSTRACT FROM AUTHOR]

The carbon neutrality agenda requires that carbon lock-in enters an endgame. A well-developed energy system, which is secure, equitable, and sustainable is paramount to getting rid of carbon lock-in and realizing decarbonization. We carry out systematic research to empirically study the possible carbon lock-in reduction effect of energy trilemma based on the instrumental variable-generalized method of moment (IV-GMM) model. We also detect heterogeneous effects in terms of geographical locations and endowments of capital, as well as the impact mechanisms. Moreover, this paper highlights the mediating and threshold role of environmental regulation. We thus get the following conclusions. (1) The paper's primary findings underscore that the continuous increase of energy trilemma exacerbates carbon lock-in and hampers the process of decarbonization; (2) Energy trilemma has a higher stimulating effect on carbon lock-in in provinces that lie in the central region and that have a comparatively low level of physical, social, and human capital; (3) Energy trilemma can also indirectly aggravate carbon lock-in by inhibiting technology innovation and exacerbating carbon emissions intensity; (4) The interaction of environmental regulation and energy trilemma can alleviate the energy trilemma - carbon lock-in nexus, and environmental regulation also shows a significant threshold effect. • We investigate the impact of the energy trilemma on carbon lock-in. • Energy trilemma eradication is an effective way to realize the carbon lock-in endgame. • The nexus between energy trilemma and carbon lock-in is more prominent in provinces with high capital endowments. • Environmental regulation shows significant mediating and threshold effects. • The exacerbate carbon lock-in effect of energy trilemma depends on technology and intensity effect. [ABSTRACT FROM AUTHOR]