Energy-related Industries Research Articles

Rational Strategies for Preparing Highly Efficient Tin-, Bismuth- or Indium Based Electrocatalysts for Electrochemical CO2 Reduction to Formic acid.

Electrochemical carbon dioxide reduction reaction (CO2RR) is an environmentally friendly and economically viable approach to convert greenhouse gas CO2 into valuable chemical fuels and feedstocks. Among various products of CO2RR, formic acid/formate (HCOOH/HCOO-) is considered the most attractive one with its high energy density and ease of storage, thereby enabling widespread commercial applications in chemical, medicine, and energy-related industries. Nowadays, the development of efficient and financially feasible electrocatalysts with excellent selectivity and activity towards HCOOH/HCOO- is paramount for the industrial application of CO2RR technology, in which Tin (Sn), Bismuth (Bi), and Indium (In)-based electrocatalysts have drawn significant attention due to their high efficiency and various regulation strategies have been explored to design diverse advanced electrocatalysts. Herein, we comprehensively review the rational strategies to enhance electrocatalytic performances of these electrocatalysts for CO2RR to HCOOH/HCOO-. Specifically, the internal mechanism between the physicochemical properties of engineering materials and electrocatalytic performance is analyzed and discussed in details. Besides, the current challenges and future opportunities are proposed to provide inspiration for the development of more efficient electrocatalysts in this field.

A novel approach for the direct production of lithium phosphate from brine and the synthesis of lithium iron phosphate

The secure supply of lithium is vital for the sustainable development of energy-related industries such as electric vehicles, and grid-level energy storage systems. Salt lake brines as important lithium resources, have a profound impact on the supply of lithium. Among them, carbonate-type brine has gained attention due to the advantage of not requiring lithium‑magnesium separation. However, carbonate-type brines have not been efficiently utilized due to their complex composition and high alkalinity. This paper introduces a method for directly obtained Li3PO4 from carbonate-type brine by seed-induced precipitation, avoiding the time-consuming evaporation and concentration processes in existing methods. The effect of each reaction condition on lithium recovery was investigated in detail and the optimal reaction conditions were determined. The recycling experiment of seeds confirms that the seed was reusable, reducing costs and environmental impact. Additionally, the LiFePO4/C with good electrochemical performance was synthesized using the Li3PO4 obtained from brine. This novel approach achieves a closed-loop production from mineral resources to battery materials, demonstrating strong competitiveness and industrial prospects.

3D Printed Carbon Framework with the Graphene Aerogel for Microbial Fuel Cell Application

There is a high interest in living organism-compatible materials associated with electrically active interfaces. Bacteria/electrode interfaces implement smart, functional systems with responses, based on which it is possible to elaborate self-regulating energy generation systems. Carbon materials have a number of advantages, such as biocompatibility, low electrical resistance, and the possibility of increasing the electrode surface on an industrial scale. The most promising approach for the industrial production of electrodes is 3D printing. We propose a 3D-printed carbon electrode – a novel lightweight material for electrodes in bioelectrochemical systems for efficient bioelectricity utilization.The pyrolytic process for manufacturing carbon electrodes is promising for upscaling and industrial applications. However, there is a problem of volume loss when 3D-printed polymers are pyrolyzed in an inert environment. We propose a new strategy for the thermal treatment of 3D-printed polymers that allow for reduced volume loss under pyrolytic carbonization. In addition, to achieve a higher electrode surface area, the graphene aerogel could be impregnated into the 3D-printed scaffolds. Chemical modification of graphene surface can enhance biocompatibility. Specifically, the oxidation of graphene leads to forming a hydrophilic and biocompatible material. We tune graphene hydrophilic properties and electrical conductivity via control over the thermal reduction of the oxidized form of graphene–graphene oxide1.Such sponge morphology affords 3D-printed carbon scaffolds an excellent lightweight host scaffold for microorganisms, in which the graphene nanowalls are homogeneously occupied by S. oneidensis MR-1. We demonstrate a novel sustainable method to produce graphene-based lightweight 3D printed electrode materials for green energy production from biomass. The proposed technology creates the opportunity for novel, innovative, disruptive graphene applications that can lead to the establishment of new energy-related industries and facilitate many startups in the ecosystem. Acknowledgments This work was supported by the Ministry of Education (Singapore) through the Research Centre of Excellence program (grant EDUN C-33-18-279-V12, I-FIM). References Xuanye Leng, Ricardo J. Vazquez, Samantha R. McCuskey, Glenn Quek, Yude Su, Konstantin G. Nikolaev, Mariana C.F. Costa, Siyu Chen, Musen Chen, Kou Yang, Jinpei Zhao, Mo Lin, Zhaolong Chen, Guillermo C. Bazan, Kostya S. Novoselov, Daria V. Andreeva, Carbon, 205, 2023, 33-39.

Optimization of turbine blade trailing edge cooling using self-organized geometries and multi-objective approaches

Gas turbines are widely utilized as essential power machinery across diverse energy-related industries. Efficient and durable cooling of gas turbine blades is crucial for the performance and life of gas turbine. However, achieving effective cooling for turbine blade trailing edges remained challenging due to the narrow, thin and structurally limited spaces. To address the aforementioned challenge, this study proposed a generative design method by employing self-organization equations to parameterize the topology of wedge-shaped channels within the trailing edge region as a substitute for the conventional pin-fin structures. Additionally, a multi-objective Bayesian approach was utilized to optimize the topological parameters, with comprehensive emphases on improving five design objectives. A comparative analysis of the performance was conducted between self-organized structures and pin-fin structures. Notably, the selected self-organized structure achieved an impressive 43 % increase in the total Nusselt number and a substantial 37 % reduction in local Nusselt number variance under the same pressure loss. The results demonstrated that self-organized structures exhibited superior heat transfer performance with lower pressure drop and improved uniformity. The performance improvement could be attributed to the strategic generation of solids by the proposed generative design algorithm, distributing the flow more reasonable.

How Does the Time-Varying Network Structure Evolve between the EU Carbon Futures Prices and Industrial and Energy-Related Indices? A Study Based on a Time-Varying T-Copula

Industrial and energy-related industries are major sources of carbon dioxide emissions, and their interdependence, as reflected in the financial field, has attracted the attention of scholars. For the purpose of exploring the evolutionary characteristics of the short-term dynamic correlation coefficient between the EU carbon futures price and the industrial and energy-related indices, this paper selected the settlement price of EU carbon emission quota futures, the MSCI energy I index on three dimensions, and the Dow Jones industrial index and West Texas crude oil futures price, as sample data. Using the time-varying t-copula model to measure the dynamic correlation coefficient between variables, the time-sliding window idea and coarse-grained method were combined to establish the correlation fluctuation mode, and a complex network theory and analysis methods were used to study the evolutionary traits of the time-varying network structure between the EU carbon price and the industrial and energy-related index. The results show that the transmission objects of the key correlation fluctuation modes in the network are stable and maintain their own state with a high probability. Second, the clustering effect exists in the transmission process. Some nodes with high mediating abilities are also the key correlation wave modes in the dynamic correlation evolution network. This study provides ideas for the study of the correlations between multiple variables and is also a useful reference for international investors.

Transparency and Involvement of the Energy-Related Industry in a Data Sharing Platform

The integration of renewable energy sources, the decentralization of the energy system, and the increasing digitization of energy-related processes require the integration of a wide range of energy-related data. In this context, a data sharing platform can serve as a hub for exchanging energy-related data and developing innovative solutions to improve the efficiency and sustainability of the energy system. However, especially because of the involvement of the energy-related industry in such a platform poses several challenges related to data protection, intellectual property, and business interests. This paper presents a framework for ensuring transparency and involvement of the energy-related industry in a data sharing platform, based on the FAIR data principles and a co-creation approach involving industry partners.

Design of p-CuS/n-CdFe2O4 heterojunction passivated with carbon nanofiber (CNF) for impressive performance in supercapacitor and photoelectrochemical water splitting

One of the most crucial strategies toward creating a greener future by minimizing fossil fuel use is the development of novel nanostructures with enhanced energy storage and energy conversion capacities. In this study, p-type copper sulfide (CuS)–n-type cadmium ferrite (CdFe2O4) heterojunction nanostructures were synthesized and passivated with a carbon nanofiber (CNF) to form a surface charge mediator, resulting in a novel material denoted as CuS–CdFe2O4/CNF. The developed hybrid heterojunction composite demonstrated a specific capacitance of 640 F g−1 at a current density of 0.5 A g−1 and photocurrent density of − 602 μA cm−2 (vs. Ag/AgCl) in the supercapacitor and photoelectrochemical (PEC) water splitting applications, respectively. In supercapacitors, these composites exhibited specific capacitances that were 2.0, 3.2, 1.6, 2.7, and 1.2 times higher than those of the pristine (CuS, CdFe2O4) and binary (CuS/CNF, CdFe2O4/CNF, and CuS–CdFe2O4) components, respectively. The CuS-CdFe2O4/CNF composite exhibited significant cycle stability (84%) even after 5000 cycles. It also demonstrated a photocurrent response of over 50% in PEC water splitting under light illumination compared with that under dark conditions. The creation of a heterojunction interface, which results from the semiconductor properties of p- and n-type materials connected to the CNF surface, is responsible for the outstanding performance of the composite. To develop a specialized charge-transport pathway that produces highly reactive sites, allows quick electron accessibility, reduces RCT, and encourages strong ion diffusion, we added material components to our design. Thus, we expanded the possible applications of current materials in energy-related industries, and dependence on the inherent qualities of materials was decreased.

A Breakthrough in Photocatalytic Wastewater Treatment: The Incredible Potential of g-C3N4/Titanate Perovskite-Based Nanocomposites.

Water pollution has emerged as a major global environmental crisis due to the massive contamination of water resources by the textile dyeing industry, organic waste, and agricultural residue. Since water is fundamental to life, this grave disregard puts lives at risk, making the protection of water resources a serious issue today. Recent research has shown great interest in improving the photocatalytic performance of graphitic carbon nitride (g-C3N4) for wastewater treatment. However, the photocatalytic removal activity of pure g-C3N4 is poor, owing to its minimal surface area, fast recombination of photo-generated electron-hole pairs, and poor light absorption. Recently, titanate perovskites (TNPs) have attracted significant attention in both environmental remediation and energy conversion due to their exceptional structural, optical, physiochemical, electrical, and thermal properties. Accordingly, TNPs can initiate a variety of surface catalytic reactions and are regarded as an emerging category of photocatalysts for sustainability and energy-related industries when exposed to illumination. Therefore, in this review article, we critically discuss the recent developments of extensively developed g-C3N4/TNPs that demonstrate photocatalytic applications for wastewater treatment. The different synthetic approaches and the chemical composition of g-C3N4/TNP composites are presented. Additionally, this review highlights the global research trends related to these materials. Furthermore, this review provides insight into the various photocatalytic mechanisms, including their potential impact and significance. Also, the challenges faced by such materials and their future scope are discussed.

Droplet Based Estimation of Viscosity of Water–PVP Solutions Using Convolutional Neural Networks

The viscosity of a liquid is the property that measures the liquid’s internal resistance to flow. Monitoring viscosity is a vital component of quality control in several industrial fields, including chemical, pharmaceutical, food, and energy-related industries. In many industries, the most commonly used instrument for measuring viscosity is capillary viscometers, but their cost and complexity pose challenges for these industries where accurate and real-time viscosity information is vital. In this work, we prepared fourteen solutions with different water and PVP (Polyvinylpyrrolidone) ratios, measured their different viscosity values, and produced videos of their droplets. We extracted the images of the fully developed droplets from the videos and we used the images to train a convolutional neural network model to estimate the viscosity values of the water–PVP solutions. The proposed model was able to accurately estimate the viscosity values of samples of unseen chemical formulations with the same composition with a low MSE score of 0.0243 and R2 score of 0.9576. The proposed method has potential applications in scenarios where real-time monitoring of liquid viscosity is required.

Study on the Impact of Carbon Tax Levying on Shanxi's Industrial Structure Transformation under the Dual Carbon Goals

Under the background of dual carbon goals, the high dependence of China's economy on energy puts forward higher requirements for carbon emission reduction work. Shanxi, as a major energy province, faces typical issues such as high energy consumption and high pollution. Therefore, the transformation and adjustment of Shanxi's industrial structure can provide profound reference significance for the carbon emission reduction actions of China's energy-related industries. Carbon tax, widely applied by many countries worldwide as an effective tool for carbon emission reduction, will inevitably have a tax burden impact on the development of energy-related industries. Therefore, continuously improving supporting measures and reasonably setting tax rates are not only related to the timely realization of dual carbon goals but also promote the high-quality development of China's economy.

Organic pollutants degradation using plasma with simultaneous ammonification assisted by electrolytic two-cell system

Atmospheric non-thermal dielectric barrier discharge (DBD) plasma has gained considerable attention due to its cost-efficiency, environmental friendliness, and simplicity. However, certain deficiencies restrict its broad application. Herein, the DBD plasma was used to disrupt three model pharmaceutically active compounds (PhACs), sulfamethoxazole (SMX), ibuprofen (IBP), and norfloxacin (NFX), by varying parameters, such as gas type (Ar, N2, O2, and air) and flow rate (1–4 L min−1). The air plasma discharge had the highest degradation efficiency, and the air flow rate was optimized at 2 L min−1. However, only 10% of IBP was removed by the sole plasma, whereas NFX and SMX were entirely removed after 30 min. Since the air plasma discharge generates reactive oxygen and nitrogen species in a chained reaction, the remaining NO2− and NO3− in the aqueous phase were problematic. Therefore, by coupling plasma with electrolysis using Cu/reduced Cu nanowire (R-CuNw) as the anode/cathode, all three PhACs were removed within 30 min, and NO2− and NO3− were completely reduced to NH3 with cathodic reduction. Moreover, the electrical energy per order (EEO, 0.04 kWh L−1) and treatment cost (0.003 USD L−1) were much lower than those of the single system. This system demonstrates great potential for water remediation, and the production of NH3 as a value-added by-product remarkably improves its practicality and is of great importance in agriculture and energy-related industries.

Classification of Droplets of Water-PVP Solutions with Different Viscosity Values Using Artificial Neural Networks

When a liquid flows, it has an internal resistance to flow. Viscosity is the property that measures this resistance, which is a fundamental characteristic parameter of liquids. The monitoring of viscosity is essential for quality control in many industrial areas, such as the pharmaceutical, chemical, and energy-related industries. Several instruments measure the viscosity of a liquid, the most used being the capillary viscometers. These instruments are complex, associated with high cost and expensive prices. This represents a challenge in several industries, where accurate viscosity knowledge is essential in designing various industrial equipment and processes. Using image processing and machine learning algorithms is a promising alternative to the current measurement methods. This work aims to extract characteristic information from videos of droplets of different samples using image processing algorithms. An Artificial Neural Network model utilizes the extracted characteristics to classify the droplets in the correct category, which is correlated with the viscosity of the sample. Different solutions samples were created using different ratios of Water and PVP (Polyvinylpyrrolidone) and videos of their droplets were taken and processed. It was found that for water-PVP solutions, the proposed ANN model was able to successfully classify the droplets using the data extracted from the videos with high accuracy. The results imply that the ANN model can recognize the features that affect the viscosity values.

Estimating the Carbon Emissions of Remotely Sensed Energy-Intensive Industries Using VIIRS Thermal Anomaly-Derived Industrial Heat Sources and Auxiliary Data

The energy-intensive industrial sector (EIIS) occupies a majority of global CO2 emissions, but spatially monitoring the spatiotemporal dynamics of these emissions remains challenging. In this study, we used the Chinese province with the largest carbon emissions, Shandong Province, as an example to investigate the capacity of remotely sensed thermal anomaly products to identify annual industrial heat source (IHS) patterns at a 1 km resolution and estimated the carbon emissions of these sources using auxiliary datasets and the boosting regression tree (BRT) model. The IHS identification accuracy was evaluated based on two IHS references and further attributed according to corporate inventory data. We followed a bottom-up approach to estimate carbon emissions for each IHS object and conducted model fitting using the explanatory strength of the annual population density, nighttime light (NTL), and relevant thermal characteristic information derived from the Visible Infrared Imaging Radiometer Suite (VIIRS). We generated a time series of IHS distributions from 2012 to 2020 containing a total of over 3700 IHS pixels exhibiting better alignment with the reference data than that obtained in previous work. The results indicated that the identified IHSs mostly belonged to the EIIS, such as energy-related industries (e.g., thermal power plants) and heavy manufacturing industries (e.g., chemistry and cement plants), that primarily use coal and coke as fuel sources. The BRT model exhibited a good performance, explaining 61.9% of the variance in the inventory-based carbon emissions and possessing an index of agreement (IOA) of 0.83, suggesting a feasible goodness of fit of the model when simulating carbon emissions. Explanatory variables such as the population density, thermal power radiation, NTL, and remotely sensed thermal anomaly durations were found to be important factors for improving carbon emissions modeling. The method proposed in this study is useful to aid management agencies and policymakers in tracking the carbon footprint of the EIIS and regulating high-emission corporations to achieve carbon neutrality.

Computational discovery of energy materials in the era of big data and machine learning: A critical review

The discovery of novel materials with desired properties is essential to the advancements of energy-related technologies. Despite the rapid development of computational infrastructures and theoretical approaches, progress so far has been limited by the empirical and serial nature of experimental work. Fortunately, the situation is changing thanks to the maturation of theoretical tools such as density functional theory, high-throughput screening, crystal structure prediction, and emerging approaches based on machine learning. Together these recent innovations in computational chemistry, data informatics, and machine learning have acted as catalysts for revolutionizing material design and hopefully will lead to faster kinetics in the development of energy-related industries. In this report, recent advances in material discovery methods are reviewed for energy devices. Three paradigms based on empiricism-driven experiments, database-driven high-throughput screening, and data informatics-driven machine learning are discussed critically. Key methodological advancements involved are reviewed including high-throughput screening, crystal structure prediction, and generative models for target material design. Their applications in energy-related devices such as batteries, catalysts, and photovoltaics are selectively showcased.

Experimental study on piloted ignition temperature and auto ignition temperature of heavy oils at high pressure

Explosion related safety problems of heavy oil and liquid oil are of great concern in energy related industries. For the petroleum industry, air injection assisted steam recycling (huff and puff) and in-situ combustion are important techniques for the exploitation and development of heavy oils, but a number of explosion accidents that occurred related to heavy oil have greatly restricted the application of the techniques. Aiming at the explosion problems in the air injection process for heavy oil production, the explosion conditions and characteristics of heavy oil-air mixtures at high pressure and high temperature were investigated via laboratory experiments using an improved explosion testing facility to simulate the explosion in a narrow pipe-like space. The effect of pressure (up to 15 MPa) on piloted ignition and auto ignition temperatures of different heavy oil types has been studied. The influences of pressure and properties of heavy oil on the evaporation and explosion detonation of heavy oil components were revealed. Based on the experimental and theoretical analysis results, the necessary conditions for the explosion and its influencing factors of heavy oil-air mixtures were determined, and the effect of average relative molecular weight of HC on auto ignition point was investigated.

Energy Recovery Potential in Industrial and Municipal Wastewater Networks Using Micro-Hydropower in Spain

The use of micro-hydropower (MHP) for energy recovery in water distribution networks is becoming increasingly widespread. The incorporation of this technology, which offers low-cost solutions, allows for the reduction of greenhouse gas emissions linked to energy consumption. In this work, the MHP energy recovery potential in Spain from all available wastewater discharges, both municipal and private industrial, was assessed, based on discharge licenses. From a total of 16,778 licenses, less than 1% of the sites presented an MHP potential higher than 2 kW, with a total power potential between 3.31 and 3.54 MW. This total was distributed between industry, fish farms and municipal wastewater treatment plants following the proportion 51–54%, 14–13% and 35–33%, respectively. The total energy production estimated reached 29 GWh∙year−1, from which 80% corresponded to sites with power potential over 15 kW. Energy-related industries, not included in previous investigations, amounted to 45% of the total energy potential for Spain, a finding which could greatly influence MHP potential estimates across the world. The estimated energy production represented a potential CO2 emission savings of around 11 thousand tonnes, with a corresponding reduction between M€ 2.11 and M€ 4.24 in the total energy consumption in the country.

Study on air pollution control under the influence of energy policy in Shanxi Province

At present, China’s resource constraints are becoming tighter and environmental problems are serious. Shanxi province’s energy policies implementation has a huge impact on the ecological environment. This paper focuses on the emission of air pollutants from coal-fired power generation, coal chemical industry, biomass power generation, coal bed methane power generation, as well as the impact of emission reduction measures such as "coal to electricity" and "coal to gas" on the atmospheric environment in Shanxi Province. In 2015, coal power, coal chemical and other energy-related industries in Shanxi province emitted 429,800 tons of sulfur dioxide, 348,000 tons of nitrogen oxides and 364,400 tons of dust, accounting for 38%, 37% and 25% of the province’s industrial emissions respectively. After the strict implementation of energy policies, the emissions of air pollutants SO2, NOx and dust from energy-related industries should be 121,500 tons, 236,100 tons and 19,300 tons respectively in 2020. Emission of air pollutants SO2, NOx and dust from energy-related industries should be reduced by 72%, 32% and 95% respectively in 2020 compared with that of 2015. However, we need to improve environmental access, tighten standards, strengthen environmental protection requirements in the energy industry, and continue to optimize the energy consumption structure.

Urban integrated energy demand forecasting method considering multi-station fusion

Under the background of energy reform, China’s energy-related industries actively innovate from the top to the bottom, vigorously promote the “Internet + Energy” strategy, and promote multi-station integration to deeply integrate energy, information communication and other pillar industries of the national economy to help China’s industrial upgrading and economic development. This paper proposes a method for forecasting urban comprehensive energy supply and demand that considers multi-station integration. Through the prediction and analysis of energy supply and demand under various scenarios of multi-station fusion, the comprehensive energy design scheme under different types of multi-station fusion scenarios is clarified to assist the early development of market developers and provide decision support for the promotion and application of comprehensive energy technology facing multi-station fusion.

A chance-constrained urban agglomeration energy model for cooperative carbon emission management

The impacts of climate change need to be mitigated by controlling the emissions of carbon dioxide. The restriction of carbon dioxide emissions will limit fossil-fuel-fired electricity and affect water consumption in energy-related industries. In this study, a chance-constrained urban agglomeration energy model is developed to plan a cooperative energy-water nexus system with carbon dioxide emission constraints at the regional scale. The model can not only reflect uncertainties expressed as random variables and interval values but also manage energy-water nexus systems under cooperation mechanism effectively. The model is applied to the Guangzhou-Shenzhen-Dongguan region, wherein two scenarios with/without cooperative carbon dioxide emission strategies under different water resource risks are analyzed. Results reveal that the system cost of the study region would decrease under the cooperative carbon dioxide emission strategy. Compared with the case without mitigation cooperation, the cost of those with cooperation would reduce by [4.3, 4.8] % (with 6.2% more carbon dioxide emission) in Shenzhen. The results also demonstrate that water consumption would increase as constraint-violation level decreases from 0.2 to 0. The developed model is efficient for facilitating regional dynamic analyses of carbon dioxide emission mitigation and the associated economic cost under different policy scenarios and system risks.

The effects of environmental policies in China on GDP, output, and profits

Abstract Critics of environmental policies often claim that such policies decrease productivity and profits. The effects of environmental policies on productivity, GDP, output, and profits is in part an empirical question, however, and may vary by firm, industry, sector, and type of policy. This paper examines the effects of environmental policies in China on GDP, industrial output, and new energy sector profits using province-level panel data over the period 2002 to 2013. Our econometric method employs instruments to address the potential endogeneity of the policies. We find that policies involving financial incentives or monetary awards have the potential of increasing the output and/or profits in some energy-related industries or sectors, but potentially at the cost of GDP in non-energy industries or sectors. In contrast, command and control policies and non-monetary awards appear to decrease GDP, output, and/or profits.