Sustainable Green Energy Research Articles

Regional variation in the drivers of China's residential electricity consumption (REC) and policy orientation

Electricity consumption is crucial for decarbonizing transformation from coal, biomass to clean energy in the residential sector. REC has become the largest contributor to China's electricity consumption growth. However, we have encountered challenges in assessing the effectiveness of relevant energy policies and know little about the variability of macro factors. Consequently, by employing a decomposition analytical framework at different levels (nation, region, and province), we decompose and quantify the driving forces of REC and demonstrate the dominant determinants of REC, the differentiation of these factors across areas, and between urban and rural. The key findings show that urbanization positively affects the urban REC but negatively impacts the rural REC. Increasing per capita consumption expenditure in rural areas has a sustained promoting effect on REC, and the influence degree is higher than that in urban areas. The impact of the coal substitution factor on electricity in inland developing regions is much higher than that in developed coastal areas. However，the effect of the coal substitution factor on electricity in less developed rural areas is much higher than that in urban. These findings provide unique insights into the mechanism and dominant driving forces of REC growth and underscore the equality between urban and rural for a sustainable green energy consumption system.

A nexus between renewable energy, FDI, oil prices, oil rent and CO2 emission: panel data evidence from G7 economies

AbstractVolatile energy prices, supply chain vulnerability and ecological implications of depleting fossil fuels have obligated the nations to swap carbon energy resources with sustainable and consumption energy choices. The study aims to investigate the sustaining connection of renewable energy consumption with trade openness, oil rent (OR), oil prices (OP) and carbon dioxide emissions of the G7 nations over the period 1971–2019. Using FMOLS and DOLS approach to panel data for analysing the long‐run elasticity, and SUR for estimating the short‐run elasticities. The analysis outcomes suggested that in the long run, a rise in Foreign Direct Investment (FDI) incursion and OP and OR are found to be major drivers of renewable energy consumption. The long‐term elasticity was estimated from a panel co‐integrated FMOLS and DOLS model are mostly alike and present an encouraging association among different variables. Surprisingly, the average number of years to return to equilibria turned out to be 16 years, which is precisely in line with the voluntary commitment by G7 for overall net‐zero electricity emission by 2035. The findings reported in the study suggest the oil‐producing countries should be compensated to diversify energy production. Higher OR is desired to further strengthen the renewable energy generation and consumption as implied by the findings reported. FDI may also further be welcomed in greener know‐hows for sustainable green energy choices.

Asphalt Heat Recovery Application for Sustainable Green Energy

Increasing demand for energy due to comfort requirements in the built environment coupled with development of road networks and amplifying heat island effect call for a comprehensive approach that can answer both issues. The lifespan of an asphalt layer is affected by surface temperature. In this paper, we aim to study the feasibility of heat recovery and its effects in terms of energy harvesting efficiency and asphalt surface temperature by creating a numerical model and validating the model based on onsite measurements at laboratory scale. The experimental setup was developed at Technical University of Civil Engineering in Bucharest, and measurements were monitored during the summer. The heat recovery system used for this study was made of copper pipes, and material cost and layout optimization need to be addressed in future studies. The numerical model was validated using measured data. During this study, we obtained favorable results in terms of heat recovery, reducing surface temperature and selection of system materials. Further research is required for heat recovery system and pump automation (based on the surface temperature), in order to optimize energy consumption and improve overall efficiency.

Power Forecasting of Regional Wind Farms via Variational Auto-Encoder and Deep Hybrid Transfer Learning

Wind power is a sustainable green energy source. Power forecasting via deep learning is essential due to diverse wind behavior and uncertainty in geological and climatic conditions. However, the volatile, nonlinear and intermittent behavior of wind makes it difficult to design reliable forecasting models. This paper introduces a new approach using variational auto-encoding and hybrid transfer learning to forecast wind power for large-scale regional windfarms. Transfer learning is applied to windfarm data collections to boost model training. However, multiregional windfarms consist of different wind and weather conditions, which makes it difficult to apply transfer learning. Therefore, we propose a hybrid transfer learning method consisting of two feature spaces; the first was obtained from an already trained model, while the second, small feature set was obtained from a current windfarm for retraining. Finally, the hybrid transferred neural networks were fine-tuned for different windfarms to achieve precise power forecasting. A comparison with other state-of-the-art approaches revealed that the proposed method outperforms previous techniques, achieving a lower mean absolute error (MAE), i.e., between 0.010 to 0.044, and a lowest root mean square error (RMSE), i.e., between 0.085 to 0.159. The normalized MAE and RMSE was 0.020, and the accuracy losses were less than 5%. The overall performance showed that the proposed hybrid model offers maximum wind power forecasting accuracy with minimal error.

Copper mesh supported nickel nanowire array as a catalyst for the hydrogen evolution reaction in high current density water electrolysis.

Hydrogen generation by water splitting using various renewable energy sources will play an important role in the sustainable green energy supply of the future. Unfortunately, wide industrial adoption of this process is currently impeded by the necessity to use noble metal based electrolysis catalysts. In this study, a low-cost and highly efficient water electrolysis catalyst active in the hydrogen evolution reaction (HER) taking place in alkaline medium is developed. The catalyst preparation procedure consists of electrodeposition of a rough nickel layer onto a smooth copper mesh, followed by the growth of hierarchical Ni nanowires on its surface. The rough nickel layer provides plenty of active sites for nanowire formation, resulting in a synergetic effect between copper and nickel in the copper mesh supported nickel nanowire array, which effectively enhances the HER electrocatalytic performance of this novel material. The catalyst demonstrated an HER overpotential as low as 317 mV in 1 M KOH electrolyte at a current density of 1 A cm-2. The copper substrate's superior electrical conductivity to that of nickel is responsible for the excellent HER performance of the catalyst at high current density, making it a promising candidate to replace highly expensive noble metal based electrodes.

Integrating Nanomaterial and High‐Performance Fuzzy‐Based Machine Learning Approach for Green Energy Conversion

Biomass is a renewable and sustainable green energy material. It is made up of lignin, cellulose, and hemicellulose with considerable amount of water, extractives, and inorganic chemical compounds. The use of biomass materials and other biogenic wastes for energy recovery represents an eco‐friendly way. Biomass material selection is one of the most significant aspects for any energy conversion process, and it is a common outsourcing problem that includes material preparation, reactor performance, economic assessment, and calorific value of the products. Fuzzy systems can be quite useful in high‐performance computing during the selection of biomass materials. In each engineering process, material selection is a crucial step since each material is having its own set of characteristics. This study presents the application of type‐1 fuzzy set for the selection of suitable biomass material for yielding maximum bio‐oil. This study focuses on seven locally available materials such as rice straw (M‐1), sunflower shell (M‐2), hardwood (M‐3), wheat straw (M‐4), sugarcane bagasse (M‐5), corn cop (M‐6), and palm shell (M‐7). The study evaluated seven important properties of the materials such as lignin (P‐1), cellulose (P‐2), hemicellulose (P‐3), volatile matter (P‐4), fixed carbon (P‐5), moisture content (P‐6), and ash content (P‐7). The findings demonstrated that sugarcane bagasse (M‐5) is the best option for maximum bio‐oil yield. Furthermore, the potential of nanoscale catalysts in improving the yield of bio‐oil through real‐time experiments was studied. The findings of this work add to our understanding of the application of fuzzy‐based systems for energy applications.

Recent development and challenges in fuel cells and water electrolyzer reactions: an overview.

Water electrolysis is the most promising method for the production of large scalable hydrogen (H2), which can fulfill the global energy demand of modern society. H2-based fuel cell transportation has been operating with zero greenhouse emission to improve both indoor and outdoor air quality, in addition to the development of economically viable sustainable green energy for widespread electrochemical applications. Many countries have been eagerly focusing on the development of renewable as well as H2-based energy storage infrastructure to fulfill their growing energy demands and sustainable goals. This review article mainly discusses the development of different kinds of fuel cell electrocatalysts, and their application in H2 production through various processes (chemical, refining, and electrochemical). The fuel cell parameters such as redox properties, cost-effectiveness, ecofriendlyness, conductivity, and better electrode stability have also been highlighted. In particular, a detailed discussion has been carried out with sufficient insights into the sustainable development of future green energy economy.

Extensive solar light utilizing by ternary C-dots/Cu2O/SrTiO3: Highly enhanced photocatalytic degradation of antibiotics and inactivation of E. coli

Fabrication of a visible-light driven photocatalyst is of great vital for the elimination of antibiotics and microorganism in the wastewater and the construction of sustainable green energy systems. In this work, carbon quantum dots (C-dots) were integrated with Cu2O/SrTiO3 p-n heterojunction to optimize the photocatalytic activity. The excellent photocatalytic degradation efficiency of chlortetracycline hydrochloride (CTC·HCl) (92.6% within 90 min) and E. coli inactivation efficiency were observed over C-dots/Cu2O/SrTiO3 under visible light irradiation. It is the synergistic effect of p-n heterojunction and modification of C-dots that facilitates the separation and transfer of electron-holes. Meanwhile, the modification of C-dots improves the harvesting of long wavelength solar light of photocatalysts due to its unique up-conversion photoluminescence (UCPL) characteristics. Eventually, the possible photocatalytic degradation path of the catalyst was inferred by LC-MS spectra, and the degradation mechanism was analyzed. This study sheds light on new possibilities for the application of photocatalysts in various light sources and has broad application prospects in water treatment.

Investigation on Microbial Fuel Cells Fabricated from Recyclable Materials for Energy Generation and Wastewater Treatment

Microbial fuel cells (MFC) have gained focus due to their diversity in operating conditions & substrates for the generation of sustainable green energy. In the present study, novel MFC has been fabricated using the recyclable aluminum can as air-cathode and graphite rod as anode for the treatment of domestic wastewater and simultaneous power generation. Three different substrate (COD) concentrations, high (>800 mg.L-1), medium (250 mg.L-1 - 800 mg.L-1) and low (<250 mg.L-1) were used. The maximum COD removal efficiencies, voltage generation, power densities were found to be 80%, 0.71 V, and 304.46 mW.m-2 respectively in high strength wastewater setup. In both medium and low strength wastewater setups, after 288 hours, the COD was reduced below 50 mg.L-1 thus limiting the electricity generation substantially. Setup with low-strength wastewater produced a maximum CE (%) of 13.80. Overall results showed that although high-strength wastewater produced better and maximum power densities, medium and low-strength wastewater setups were more consistent in energy generation throughout the experiment.

Photocatalytic Hydrogen Energy Evolution from Sugar Beet Wastewater

AbstractHydrogen is a clean, environmentally friendly, storable, and sustainable green energy source as well as a potential fuel. It could be produced from various biomass, wastewater, or other sources by different processes. In this study, hydrogen was evolved from sucrose model solution and real sugar beet wastewater by photocatalytic oxidation using a perovskite catalyst under solar light irradiation. In this context, firstly, the graphene supported LaFeO3 (GLFO) was synthesized and then, a characterization study shows that GLFO is successfully synthesized. To optimize the reaction parameters (pH, catalyst loading, and initial hydrogen peroxide concentration), an experimental matrix was created using the Box Behnken model. Whereas the highest hydrogen evolution from sucrose model solution was observed as 3520 μmol/gcat, the highest hydrogen evolution from sugar beet wastewater was obtained as 7035 μmol/gcat. The highest TOC removal (99.73 %) from sugar beet wastewater was also achieved at the same reaction conditions.

Irregular Wind Energy Harvesting by a Turbine Vent Triboelectric Nanogenerator and Its Application in a Self-Powered On-Site Industrial Monitoring System.

Wind is a regenerative and sustainable green energy, but it is intermittent; especially, harvesting irregular wind energy is a great challenge for existing technologies. This study demonstrates a turbine vent triboelectric nanogenerator (TV-TENG), which can be utilized as both an irregular wind harvester and a self-powered environmental sensing system on the rooftops of buildings. At a wind speed of nearly 7 m/s, the TV-TENG delivers an open-circuit voltage of up to 178.2 V, a short-circuit current of 38.2 μA, and a corresponding peak power of 2.71 mW under an external load of 5 MΩ, which can be used to directly light up 120 green light-emitting diodes. Furthermore, a self-powered on-site industrial monitoring system has been developed, which can be improve the easiness and simpleness of the industry environment for temperature monitoring and safety warning. Increasing the fluidity of air inside and outside the device is a key factor in fabricating an efficient TV-TENG; it is a novel approach for harvesting irregular wind energy and is sensitive, reliable, waterproof, and easy to use. This work greatly expands the applicability of TENGs as energy harvesters for irregular wind and also as self-powered sensing systems for ambient detection.

Fabrication of novel hybrid Z-Scheme WO3@g-C3N4@MWCNT nanostructure for photocatalytic degradation of tetracycline and the evaluation of antimicrobial activity

Exploring highly efficient visible-light-driven photocatalyst for the elimination organic pollutants is a great concern for constructing sustainable green energy systems. In the current work, a novel hybrid ternary WO3@g-C3N4@MWCNT nanocomposites have been fabricated for visible-light-driven photocatalyst by self-assembly method. The as-prepared photocatalyst was examined by XRD, Raman, FESEM, HRTEM, XPS EDS, EIS, UV–visible DRS, and PL analysis. The experimental results revealed that the photocatalytic activity of WO3@g-C3N4@MWCNT nanocomposites on the degradation of Tetracycline (TC) is 79.54% at 120 min, which is higher than the binary WO3@g-C3N4 composite and pristine WO3. The improved degradation performance towards TC is recognized for its higher surface area, intense light absorption towards the visible region, and enhanced charge separation efficiency. Consequently, the fabricated catalyst endows a promising application for antibiotic degradation.

Interface-engineered Co3S4/CoMo2S4 nanosheets as efficient bifunctional electrocatalysts for alkaline overall water splitting

Exploring bifunctional electrocatalysts with high efficiency, inexpensive, and easy integration is still the daunt challenge for the production of hydrogen on a large scale by means of water electrolysis. In this work, a novel free-standing Co3S4/CoMo2S4 heterostructure on nickel foam by a facial hydrothermal method is demonstrated to be an effective bifunctional electrocatalyst for overall water splitting (OWS). The synthesized Co3S4/CoMo2S4 electrocatalyst achieves ultralow overpotentials of 143 mV@10 mA cm−2 for hydrogen evolution reaction (HER) and 221 mV@25 mA cm−2 for oxygen evolution reaction (OER), respectively, in 1 M KOH. Moreover, it presents a greatly improved durability and stability under operando electrochemical conditions. When used as catalysts for OWS, the Co3S4/CoMo2S4-3//Co3S4/CoMo2S4-3 electrodes just need 1.514 V to make it to the current density of 10 mA cm−2. It is supposed that the introduction of heterogeneous interface between Co3S4 and CoMo2S4 could give rise to plentiful active sites and enhanced conductivity, and thus boost excellent catalytic performances. Moreover, the porous feature of free-standing nanosheets on nickel foam could benefits catalytic performances by accelerating charge transport and releasing bubbles rapidly. This work proposes a bifunctional catalyst system with the heterogeneous interface, which could be used in a sustainable green energy system.

How do environmental regulations affect carbon emission and energy efficiency patterns? A provincial-level analysis of Chinese energy-intensive industries.

This study measures the environmental regulation effect and pattern of carbon emission and energy efficiency through data envelopment analysis and econometric estimation. One of the most important ways to achieve a green transition is promoting technical progress through environmental regulation. Though China has witnessed rapid economic growth over the last two decades, the country can improve it further through adopting sustainable green energy and establishing more energy-efficient industries to strike a good balance between economic and social developments. The oil and carbon dioxide emission performances form the most important metrics. This study uses panel data from 30 Chinese provinces from 2008 to 2017 to assess the effect of environmental regulation on energy production. The nonradial directional distance function (NDDF) is used to measure the total factor energy efficiency index (TFEEI). The panel system GMM model, which can effectively address endogenous problems and regional variability, is utilized to research the nonlinear relationship between environmental regulations and EEI under various environmental regulations to study it. The findings reveal a considerably modest total average EEI amount for energy-intensive industries, averaging between 0.55 and 0.58, which is way below the ideal value (i.e., 1). Furthermore, the results of the dynamic panel data model revealed a significant U-shaped relationship between China's EEI and environmental regulation. The results show that as the values of market-based environmental regulations (MERs) and command and control environmental regulations (CCERs) exceed the corresponding levels, the impact of environmental regulation on the TFEEI increases gradually. This study will aid policymakers in better understanding the efficacy of different levels of environmental regulations to make more educated decisions.

Metal-doped KNbO3 for visible light photocatalytic water splitting: A first principles investigation

Materials suitable for visible light photocatalytic water splitting provide sustainable green energy production and environmental pollution solution. Potassium niobate, KNbO3, is not widely used in photocatalytic applications because of its large bandgap, which is not appropriate for the visible range of the solar spectrum. However, doping of semiconductors may help reduce their bandgaps by pinning a dopant level near the top/bottom of the valence/conduction band. We employ first-principles calculations to gain insight into the electronic and optical properties of KNbO3 doped with a number of 3d and 4d transition metals to design and enhance its photocatalytic behavior. We demonstrate the substitutional doping with these elements at the Nb site decreases the bandgap and improves the optical and photocatalytic activities of KNbO3. Our calculations prove that the best candidates for water splitting and CO2 gas reduction are Ag- and Mn-doped KNbO3, respectively. Computational outcomes are compared and discussed with existing experimental ones for doped KNbO3 and KTaO3 structures. Meanwhile, we found out Tc-doped KNbO3 can be beneficial for spintronic applications. The results achieved in this study will initiate a number of experimental investigations for the full exploration of the cubic perovskites, especially in green energy production.

Hierarchical modulation of NiSe2 nanosheets/nanodendrites by phase engineering on N-doped 3D porous graphene as self-supporting anode for superior lithium ion batteries

In order to deal with the shortage of fossil fuel reserves and environmental pollution, lithium-ion batteries (LIBs) have been widely studied as a new type of sustainable green energy device. In the work, the NiSe2 nanosheets/nanodendrites hierarchical material with two phases is deposited on the framework of three-dimensional nitrogen-doped graphene (NPG) through the hydrothermal method to construct a self-supporting anode for LIBs. The NPG has ultralow self-weight as well as excellent electrical conductivity, which inhibits the agglomeration during the growth of active materials. Furthermore, the unique hierarchical NiSe2 active material also relieves its structural damage during the charge and discharge process. As a result, the NiSe2 combined with NPG (NiSe2@NPG) expresses specific capacities of 860 mAh/g and 568 mAh/g at current densities of 0.2 A/g and 3.2 A/g, respectively, making it a promising lithium-ion battery anode material.

Microwave-Assisted Rapid Synthesis of NH4V4O10 Layered Oxide: A High Energy Cathode for Aqueous Rechargeable Zinc Ion Batteries.

Aqueous rechargeable zinc ion batteries (ARZIBs) have gained wide interest in recent years as prospective high power and high energy devices to meet the ever-rising commercial needs for large-scale eco-friendly energy storage applications. The advancement in the development of electrodes, especially cathodes for ARZIB, is faced with hurdles related to the shortage of host materials that support divalent zinc storage. Even the existing materials, mostly based on transition metal compounds, have limitations of poor electrochemical stability, low specific capacity, and hence apparently low specific energies. Herein, NH4V4O10 (NHVO), a layered oxide electrode material with a uniquely mixed morphology of plate and belt-like particles is synthesized by a microwave method utilizing a short reaction time (~0.5 h) for use as a high energy cathode for ARZIB applications. The remarkable electrochemical reversibility of Zn2+/H+ intercalation in this layered electrode contributes to impressive specific capacity (417 mAh g−1 at 0.25 A g−1) and high rate performance (170 mAh g−1 at 6.4 A g−1) with almost 100% Coulombic efficiencies. Further, a very high specific energy of 306 Wh Kg−1 at a specific power of 72 W Kg−1 was achieved by the ARZIB using the present NHVO cathode. The present study thus facilitates the opportunity for developing high energy ARZIB electrodes even under short reaction time to explore potential materials for safe and sustainable green energy storage devices.

Intelligent Control System in Desert Areas Based on Photovoltaic Microgrid Power Supply

With the advent of the global energy crisis, the use of sustainable green energy has become more and more widespread and the utilization rate of photovoltaic industry in high-altitude desert areas is getting higher and higher. This article mainly studies the intelligent control system in desert area based on photovoltaic microgrid power supply. The system uses shielded twisted pair to transmit signals, and electrostatic interference and electromagnetic induction interference are effectively suppressed. After ensuring that the encoder feedback is correct and the motor movement direction is consistent with the command direction and the safety of the movement stroke, the PID parameters of the motor are tuned in PAMCturningpro. For the performance test of this system, the design and implementation of the test plan should be carried out according to the concurrent support target and response time target proposed in the performance analysis of the system. Concurrent users use the LoadRunner tool to create the customer test machine to simulate the concurrent access and operation of the system. In each iteration process, the test results are recorded in the same way as the benchmark test and finally the system can still achieve the expected performance when the actual concurrency reaches the limit of 5007. The results show that the intelligent control system in this paper has the advantages of low cost, convenient operation, perfect function, high precision, stable and reliable operation, etc. and has good practical value.

Converting of nuisance cyanobacterial biomass to feedstock for bioethanol production by regulation of intracellular carbon flow: Killing two birds with one stone

Outbreaks of cyanobacterial harmful algal blooms present a serious threat to ecosystem safety and human well-being. Removal of toxic cyanobacterial biomass from freshwaters and its safe utilization is a worldwide problem. We showed that regulation of intracellular carbon flow by applying nitrogen deprivation strategy with light and inorganic carbon manipulation during the cultivation of cyanobacterium Microcystis allows efficiently convert its biomass into the biofuel feedstock. The proteomic analysis demonstrated that highly efficient feedstock production can be achieved during Microcystis culturing by nitrogen deprivation, which slows down the catabolic consumption of carbohydrates in cells. Further, we developed a novel approach that allows converting the natural Microcystis biomass into carbohydrate-rich feedstock by maintaining a high rate of carbohydrate production at minimal biomass losses. The yield attains 0.22 g ethanol per gram dry biomass, which is in the range of the ethanol yield of sugarcane, but higher than the values of sugarcane waste. The suggested approach provides cost-effective technology for sustainable green energy production from nuisance cyanobacterial biomass. The massive removal of toxic cyanobacterial biomass for bioethanol productions can mitigate the harmful cyanobacterial blooms in eutrophic lakes, and positively affect their restoration.

Effect of n-butanol/diesel blends on performance and emissions of a heavy-duty diesel engine tested under the World Harmonised Steady-State cycle

In recent years, n-butanol produced from waste or lignocellulosic materials has become an attractive and sustainable green energy source for diesel engines because has a clear potential for the partial substitution of fossil-based diesel fuel. This study aims to analyse the effects of different n-butanol/diesel fuel blends on the performance and exhaust emissions of a Euro V heavy-duty diesel engine following the World Harmonised Steady-State Cycle (WHSC), as well as the effect of each mode on the test averaged results. The blends evaluated here were blends of conventional diesel fuel with 5%, 10% and 20% (by volume) n-butanol. Conventional diesel fuel was used as a reference fuel to compare the performance and emission characteristics of the different n-butanol blends. The main findings show that 10% butanol could be considered as a suitable proportion for blending n-butanol/conventional diesel owing to its favourable performance and reduction in particulate emissions, without significant changes in the gaseous emissions of NOX. Mode-by-mode comparative analysis results show improved engine performance with the use of n-butanol in most modes, regardless of speed or load conditions. CO emissions in general increase, despite the incorporation of n-butanol reduces CO emissions under high-load and low-speed conditions. THC emissions increase with n-butanol, being more critical under cold start conditions. The influence of n-butanol on NOX emissions does not have a clear trend, but it is observed that NO2 emissions decrease in all modes with the use of n-butanol blends, mainly in low load modes.