Clean Alternatives Research Articles

Chemical-Saving Potential for Membrane Bioreactor (MBR) Processes Based on Long-Term Pilot Trials.

Membrane bioreactors (MBRs) have gained attraction in municipal wastewater treatment because of their capacity to meet strict water quality standards and support water reuse. Despite this, their operational sustainability is often compromised by high resource consumption, especially regarding the use of chemicals for membrane cleaning. This study explores innovative membrane-cleaning strategies to enhance the sustainability of MBR processes. Through long-term pilot trials at Stockholm's largest wastewater treatment plant, this study showed that alternative cleaning strategies can reduce chemical use by up to 75% without sacrificing treatment performance. The results further suggest that these alternative strategies could result in cost reductions of up to 70% and a reduction in environmental impacts by as much as 95% for certain indicators. Given that MBRs play a crucial role in addressing increasing treatment demands and advancing circular water management, the outcomes of this study are beneficial for the broader adoption of MBR processes. These results also have implications for existing installations, offering a pathway to more sustainable wastewater treatment. Moreover, the presented cleaning strategies provide significant opportunities for lowering operational costs and reducing the environmental footprint of new and existing MBR installations.

Chemical material as a hydrogen energy carrier: A review

In light of climate change imperatives, there arises a critical need for technological advancements and research endeavors towards clean energy alternatives to replace conventional fossil fuels. Additionally, the development of high-capacity energy storage solutions for global transportability becomes paramount. Hydrogen emerges as a promising environmentally sustainable energy carrier, devoid of carbon dioxide emissions and possessing a high energy density per unit mass. Its versatile applicability spans various sectors including industry, power generation, and transportation. However, the commercialization of hydrogen necessitates further technological innovations. Notably, high-pressure compression for hydrogen storage presents safety challenges and inherent limitations in storage capacity about 30%–50% lost production of hydrogen. Consequently, substantial research endeavors are underway in the domain of material-based chemical hydrogen storage that reactions to occur at temperatures below 200 ℃. This approach enables the utilization of existing infrastructure, such as fossil fuels and natural gas, while offering comparatively elevated hydrogen storage capacities. This study aims to introduce recent investigations concerning the synthesis and decomposition mechanisms of chemical hydrogen storage materials, including methanol, ammonia, and Liquid Organic Hydrogen Carrier (LOHC).

Impact of haze on potential pathogens in surface bioaerosol in urban environments

Air quality considerably affects bioaerosol dynamics within the atmosphere. Frequent haze events, with their associated alterations in bioaerosol composition, may pose potential health risks. This study investigated the microbial diversity, community structure, and factors of PM2.5 within an urban environment. We further examined the impact of haze on potentially pathogenic bacteria in bioaerosols, and analyzed the sources of haze pollution. Key findings revealed that the highest levels of microbial richness and diversity were associated with lightly polluted air conditions. While the overall bacterial community structure remained relatively consistent across different air quality levels, the relative abundance of specific bacterial taxa exhibited variations. Meteorological and environmental conditions, particularly sulfur dioxide, nitrogen dioxide, and carbon monoxide, exerted a greater influence on bacterial diversity and community structure compared to the physicochemical properties of the PM2.5 particles themselves. Notably, haze events were observed to strengthen interactions among airborne pathogens. Stable carbon isotope analysis suggested that coal combustion and automobile exhaust were likely to represent the primary source of haze during winter months. These findings indicate that adoption of clean energy alternatives such as natural gas and electricity, and the use of public transportation, is crucial to mitigate particle and harmful pollutant emissions, thereby protecting public health.

Small modular nuclear reactors: A pathway to cost savings and environmental progress in SAGD operations

Small Modular Nuclear Reactors (SMRs) offer a promising option for environmentally friendly bitumen recovery operations. The extraction of oil sands in Western Canada is vital for the economy, but traditional methods like Steam-Assisted Gravity Drainage (SAGD) contribute significantly to greenhouse gas (GHG) emissions. In SAGD, steam generation, primarily fueled by natural gas combustion, is the main source of emissions. Given the imperative to reduce carbon intensity, less emissive recovery methods are needed to sustain production and economic viability in Canadian oil sands. Currently, there are limited non-carbon alternatives for steam generation in oil sands applications. The utilization of SMRs for steam generation presents a clean alternative. In this study, we examine the feasibility of employing SMRs in in-situ oil sands recovery operations. Through standardized economic metrics and sensitivity analysis, we demonstrate that integrating SMRs into SAGD operations eliminates GHG emissions significantly and can potentially outperform conventional natural gas-based steam generation in terms of net present value, under certain operational scenarios. Hence, our findings indicate that SMRs hold promise for decarbonizing oil sands recovery processes.

Advancing hydrogen storage predictions in metal-organic frameworks: A comparative study of LightGBM and random forest models with data enhancement

The escalating consumption of fossil fuels has given rise to a substantial upsurge in greenhouse gas concentrations and global temperatures, which, in turn, has triggered severe climate-related consequences. The critical imperative to reduce CO2 emissions and combat global warming has spurred extensive investigations into clean energy alternatives, with hydrogen emerging as a compelling zero-emission energy source. As a pivotal component of clean energy strategies, hydrogen requires designing compact, lightweight, and efficient storage systems. This study focuses on the development and evaluation of machine learning models for predicting the efficiency of Metal-Organic Frameworks (MOFs) in hydrogen storage, a key aspect of advancing clean energy technologies. MOFs, a class of nanoporous materials, show remarkable potential for hydrogen storage due to their high surface area and porosity. However, selecting the most suitable MOF for this application from a vast array of possible structures is a daunting task. In this context, machine learning algorithms offer an efficient alternative for predicting MOF suitability by considering their structural and chemical properties. We used ensemble learning methods, specifically Light Gradient Boosting Machine (LightGBM) and Random Forest (RF), to predict hydrogen uptake of MOFs based on a dataset of 219 experimentally tested samples. Two modeling scenarios were considered: one using the entire dataset, and the other involving strategic data pre-processing, including outlier removal and feature engineering. The results demonstrate that the measures taken to refine the dataset significantly enhance the predictive performance of the developed models, reducing prediction errors and improving overall goodness of fit. Specifically, the Mean Absolute Error (MAE) values for both the LightGBM and random forest models were reduced from 0.48 and 0.94, respectively, to 0.16 for both models, and the coefficients of determination (R2) increased substantially from 0.84 and 0.72 to 0.95, in both cases. Moreover, feature importance analysis unveiled that pressure-related features make the most significant contributions to the formation of tree ensembles during the model training process. A parametric sensitivity analysis was conducted revealing that H2 uptake in MOFs is most sensitive to changes in adsorption enthalpy, followed by surface area and temperature, while showing lower sensitivity to variations in pressure, consistent with established literature. These results underscore the pivotal role of data enhancement methods in refining machine learning models and can be instrumental in accelerating the development and optimization of MOF materials for clean energy applications.

Assessing Worker and Pedestrian Exposure to Pollutant Emissions from Sidewalk Cleaning: A Comparative Analysis of Blowing and Jet Washing Techniques

Sidewalk cleaning operations are essential to maintaining a clean and safe urban environment. Despite their vital role, these activities, particularly the blowing of road dust, can lead to the resuspension of road dust and associated pollutants, which poses risks to human health and the environment. While the role of blowers on particulate matter resuspension has been investigated, there is limited information on emitted bioaerosols. This study aimed to compare the occupational exposure of operators and passersby during sidewalk cleaning using two manual methods—blowing and jet washing—in two distinct urban environments. The study focused on metal road traffic tracers (copper (Cu), zinc (Zn), manganese (Mn), cadmium (Cd), and lead (Pb)) and cultivable/non-cultivable microorganisms. We showed that blowing resuspends inhalable particles containing metals (Cu, Zn, and Mn, but not Cd or Pb) and bioaerosols (fungi and Gram-negative bacteria) throughout the year. This represents an important source of exposure for the blower operators and poses a potential long-term respiratory health risk for them. Operators working in cabs are shielded from such exposure, but passersby, especially vulnerable populations, may be at risk. While jet washing reduces operator exposure to Gram-negative bacteria in comparison to blowing, it does not mitigate fungal exposure, particularly in vegetated sites. These findings underscore the necessity for the implementation of effective protective measures and the development of alternative cleaning methods to mitigate exposure risks.

Feasibility of sludge deep dewaterability improvement for incineration disposal by combined conditioning of freeze-thaw and sawdust

Sludge incineration is the main strategy for sludge reduction in China. The combined conditioning of lime and chemical agents has been proven to achieve sludge dewatering by disrupting the extracellular polymeric substances (EPS) of sludge and reducing its compressibility. However, when incineration is the intended disposal purpose, this method poses challenges such as incomplete combustion, equipment corrosion, secondary pollution, and decreased calorific value of sludge cake. In contrast, freeze-thaw conditioning, coupled with sawdust as a high-calorific-value bio-waste, emerges as an efficient and clean alternative. The research investigates the synergistic effects of freeze-thaw and sawdust co-conditioning on various sludge properties, including dewaterability, compressibility, consolidation, permeability, microscopicity, and calorific value. The study reveals that the combined conditioning significantly reduces water content and compressibility while increasing void ratio, consolidation, permeability, and enhancing the calorific value of the sludge cake. Specifically, sludge cake conditioned with 60% dried solids (DS) sawdust and freeze-thaw achieved a water content (Wc) of 49.07% and a calorific value of 1422.3 kcal/kg, meeting standards for self-sustained incineration. With heat recovery, the combined conditioning generates an economic revenue of 25.1 $/t DS after deducting costs, thereby reducing the overall cost of sludge reduction treatment. This research offers a clean and practical solution for sludge incineration and reduction, demonstrating great economic value and application potential.

Decarbonizing energy: Evaluating fossil fuel displacement by renewables in OECD countries.

Energy transition to greener systems has been a focal point in climate policy agendas across countries as the negative environmental impacts of fossil fuel technologies have become more evident Displacing fossil fuels with clean energy alternatives in this regard is essential for meeting global climate objectives. In this context, the study analyzes the role of disaggregated renewable energy sources on fossil fuel displacement in 36 Organisation for Economic Cooperation and Development (OECD) countries in the period 2000-2020. The findings demonstrate a discernible trend in the displacement of fossil fuels by various forms of renewable energy sources. It is found that to effectively displace 1% of fossil fuels, it is necessary to achieve an average increase of 1.15% in renewable generation capacity. In addition, a one-to-one displacement of fossil fuels occurs with hydropower, demonstrating its higher level of competitiveness and effectiveness in displacing fossil fuels. Moreover, there is a partial displacement of fossil fuels by solar and wind power. These findings suggest that renewable energy sources are progressively advancing towards effectively displacing fossil fuels.

Ammonia-dependent reducing power redistribution for purple phototrophic bacteria culture-based biohydrogen production

The global energy crisis has intensified the search for sustainable and clean alternatives, with biohydrogen emerging as a promising solution to address environmental challenges. Leveraging photo fermentation (PF) process, purple phototrophic bacteria (PPB) can harness reducing power derived from organic substrates to facilitate hydrogen production. However, existing studies report much lower H2 yields than theoretical value when using acetate as carbon source and ammonia as nitrogen source, primarily attributed to the widely employed pulse-feeding mode which suffers from ammonia inhibition effect on nitrogenase. To address this issue, a continuous feeding mode was applied to avoid ammonia accumulation in this study. On the other hand, other pathways like carbon fixation and polyhydroxyalkanoate (PHA) formation could compete reducing power with H2 production. However, the reducing power allocation under continuous feeding mode is not yet clear. In this study, the reducing power allocation and hydrogen production performance were evaluated under various ammonia loading, using acetate as carbon source and infrared LED at around 50 W·m−2 as light source. The results show that (a) The absence of ammonia resulted in the best performance for hydrogen production, with 44 % of the reducing power distributed to H2 and the highest H2 volumetric productivity, while the allocation of reducing power to hydrogen production stopped when ammonia loading was above 7.6 mg NH4-N·L−1·d−1; (b) when PPB required to eliminate reducing power under ammonia limited conditions, PHA production was the preferred pathway followed by the hydrogen production pathway, but once PHA accumulation reached saturation, hydrogen generation pathway dominated; (c) under ammonia limited conditions, the TCA cycle was more activated rendering higher NADH (i.e. reducing power) production compared with that under ammonia sufficient conditions which was verified by metagenomics analysis, and all the hydrogen production, PHA accumulation and carbon fixation pathways were highly active to dissipate reducing power. This work provides the insight of reducing power distribution and PPB biohydrogen production variated by ammonia loading under continuous feeding mode.

Sustainable Solutions: Integrating Renewable Energy and Electric Vehicles for Cleaner Operations

In the face of escalating environmental concerns and the urgent need to mitigate carbon emissions, integrating renewable energy and electric vehicles (EVs) has emerged as a promising avenue for achieving cleaner and more sustainable operations across various sectors. This paper explores the synergistic potential of combining renewable energy sources with EV technology to revolutionize energy consumption and transportation systems. The first part of the paper discusses the pressing environmental challenges posed by traditional energy sources and internal combustion engine vehicles, emphasizing the need for a transition towards cleaner alternatives. It highlights the detrimental effects of fossil fuel combustion on air quality, public health, and climate change, underscoring the imperative for sustainable energy solutions. Next, the paper delves into the advantages of renewable energy sources such as solar, wind, and hydropower in providing clean and abundant energy. It examines the advancements in renewable energy technologies, including decreasing costs and increasing efficiency, making them increasingly viable alternatives to fossil fuels. Subsequently, the paper explores the pivotal role of electric vehicles in reducing greenhouse gas emissions and dependency on fossil fuels in the transportation sector. It discusses the environmental benefits of EVs, including zero tailpipe emissions and lower lifecycle emissions compared to conventional vehicles.

Status and Prospects of China's Petroleum and Petrochemical Industry under the Dual Carbon Targets

The article delves into the current landscape and prospects of China's petroleum and petrochemical sector amidst the dual-carbon mandate. This sector has garnered considerable attention ever since the Chinese government set forth the ambitious objectives of attaining peak carbon emissions by 2030 and achieving carbon neutrality by 2060. Despite its pivotal role in the nation's economy, the petroleum and petrochemical industry continues to grapple with elevated carbon emissions. Consequently, imperative actions are warranted to curtail these emissions, leveraging advancements in technology and the adoption of clean energy alternatives. Presented within this article is a transformative trajectory for the petrochemical industry in alignment with the dual-carbon agenda. This trajectory encompasses the phasing out of obsolete production capacities, recalibrating the energy framework, and fortifying avenues for technological innovation. Furthermore, the article elucidates on the prospective evolution of China's petrochemical domain under the ambit of the dual-carbon directive. Emphasizing its instrumental role in facilitating the energy transition, achieving heightened equilibrium between supply and demand dynamics, and propelling forward technological innovation and industrial refinement.

A holistic approach to refinery decarbonization based on atomic, energy and exergy flow analysis

As one of the largest industrial emitters, today’s refineries must take action to reduce emissions in response to global climate change and net-zero targets. However, the decarbonization of refineries is complex due to the diverse range of processes involved, high energy requirements, and the limited availability of cost-effective clean alternatives. In this study, a holistic approach is proposed for refinery decarbonization based on atomic, energy, and exergy flow analysis by characterizing feedstocks and products using their stoichiometric formulas (CHjOkNmSn) and thermodynamic properties. Applied to the Blueprint model, representing a typical European refinery, it is revealed that 2.4% and 1.3% of carbon emissions stem from the exit of oxygen atoms during combustion and hydrogen production, accounting for 4.3% of total energy input. Decarbonization options for future refineries such as product portfolio changes and alternative feedstocks were further explored. While maximizing naphtha for chemicals leads to increased CO2 emissions due to higher hydrogen-to-carbon ratios in products, biomass with electrolysis emerges as a preferable option for its low carbon losses and high exergy efficiency. The thermodynamic analysis of this study can provide valuable insights and theoretical instructions to industrial operators into the transition to future refineries.

Adsorption mechanism study of sour natural gas and sulfur in high-sulfur carbonate rock gas reservoirs

As the world pivots towards green and renewable energy sources, natural gas stands out as a clean and efficient alternative. In particular, there has been an upsurge in the exploration and development of gas fields rich in hydrogen sulfide (H2S). However, an escalating issue of sulfur deposition is notably pronounced in carbonate rock reservoirs predominantly comprised of dolomite, as exemplified in China's Yuan Ba gas field. This study examines sulfur adsorption in high-sulfur gas reservoirs and its effects on carbonate rock formations. Molecular simulation analysis reveals that the sour natural gas density in 20 Å dolomite nanopore structure experiences an average increase of 30% at the nanoscale. Notably, CO2 and H2S manifested strong adsorption tendencies in smaller pore sizes, with CO2's strong adsorption state layer thickness measuring approximately 2.93 Å, and H2S's at about 1.871 Å. Additionally, it was found that the adsorption energy of sour natural gas decreases with rising temperature and pore size, and falling pressure, yet the temperature's effect on excess adsorption density is minor above 40 MPa. As the pore size reaches 160 Å, the strong adsorption states of CO2 and H2S dissipate. Beyond 160 Å, the influence of nanoscale dimensions is negligible, with only a 2.23% increase in adsorbed phase density compared to the bulk phase. The introduction of S8 molecules amplified the adsorption proclivities of CO2 and H2S, urging them closer to the pore wall. The study reveals that CO2 or H2S reinjection into formations could effectively mitigate sulfur deposition in pores smaller than 160 Å, offering valuable insights for the development of high-sulfur gas fields.

School Bus Rebate Program and Student Educational Performance Test Scores

Students who ride older school buses are often exposed to high levels of exhaust during their commutes, which may adversely affect health and school attendance. As a result, the US Environmental Protection Agency (EPA) has awarded millions of dollars to school districts to replace older, highly polluting school buses with newer, cleaner buses. To leverage the EPA's randomized allocation of funding under the 2012-2016 School Bus Rebate Programs to estimate the association between replacing old, highly polluting buses and changes in district-average standardized test scores. This study examined changes in reading and language arts (RLA) and math test scores among US school district applicants to the EPA's 2012-2016 national School Bus Rebate Programs 1 year before and after each lottery by selection status. Data analysis was conducted from January 15 to July 30, 2023. Selection to receive EPA funding to replace older school buses with newer, cleaner alternatives. School district changes in RLA and math test scores among students in grades 3 through 8 before and after the EPA funding lotteries by selection status were measured using an intention-to-treat approach. This study included 1941 school district applicants to the 2012-2106 EPA School Bus Rebate Programs. These districts had a mean (SD) of 14.6 (33.7) schools per district, 8755 (23 776) students per district, and 41.3% (20.2%) of students with free lunch eligibility. Among the applicants, 209 districts (11%) were selected for the clean bus funding. District-average student test scores did not improve among selected districts overall. In secondary analyses, however, districts replacing the oldest, highest polluting buses (ie, pre-1990) experienced significantly greater improvements in district-average test scores in the year after the lottery for RLA and math (SD improvement in test scores, 0.062 [95% CI, 0.050-0.074] and 0.025 [95% CI, 0.011-0.039], respectively) compared with districts without replacements. In this study, the EPA funding was not associated with student test scores overall, but in secondary analyses, the replacement of the oldest school buses was associated with improved educational performance. These findings support prioritizing clean bus replacement of the oldest buses as an actionable way for improving students' educational performance.

Corruption experience does not make voters harsher towards the corrupt incumbents – evidence from a survey experiment in Poland

ABSTRACT This study investigates the linkage between the experience of petty corruption and a more harsh stance on corruption scandals. A randomised vignette experiment in Poland tests how people tend to condone corruption. There are substantial mitigating effects of the arguments based on the implicit exchange and lack of credible information and no effect for the argument based on the lack of clean alternatives. Yet, people directly exposed to petty corruption were not less sensitive to any of the justifications. Thus, the linkage between the experience of corruption and more punitive attitudes towards corrupt officials may not work as expected.

Thermo‐responsive polymer to clean fouled forward osmosis membrane after wastewater treatment: Excellent flux recovery

AbstractThermally responsive polymers (TRPs) have gained significant attention in the past decade due to their unique phase transition behavior, those exhibiting changes in properties with temperature variations. In water, hydrophilic block copolymers form helical structures that can transform into hydrophobic globules above the lower critical solution temperature (LCST). Fouling is one of the limiting issues for the forward osmosis (FO) process in wastewater treatment. In this work, after the treatment of wastewater from the paper and pulp industries, foulants were removed from the surface of the commercial polyamide thin‐film composite membrane of the FO process using polyvinyl methyl ether (PVME), a TRP with an LCST of 35–37°C. PVME was mixed with different aqueous solutions to form cleaning solutions. Foulant cleaning experiments utilized a 1% PVME (w/v) solution in 300 mL of de‐ionized (DI) water, while 1 M NaCl solution served as the draw solution (DS) in the FO process. Comparing the water flux (J) and change in membrane hydraulic resistance (Rf) among pristine, fouled, and cleaned membranes revealed a 56.67% increase in water flux and a 51.68% decrease in membrane resistance after PVME mixed with DI water cleaning solution showcasing its effectiveness in improving membrane performance and reducing fouling. The cleaning efficiency of two solutions, that is, 1% PVME mixed in DI water and 1% PVME mixed in 1 M aqueous urea solution, is compared. Results showed that the former cleaning solution helped to obtain 32.62% higher water flux and 56.05% less membrane resistance. Various membrane characterizations are performed, such as field emission scanning electron microscopy (FE‐SEM), which characterizes the membrane's pore structure and surface morphology, while a contact angle analyzer assesses hydrophilicity. This research highlights the use of TRPs as effective cleaning agents, enhancing membrane performance by effectively removing foulants on the membrane surface. Investigating alternative cleaning solutions also provides insights for optimizing water treatment processes.Highlights Cleaning of fouled thin‐film composite membrane using thermal‐responsive polymers (TRPs). Polyvinyl methyl ether, a TRP with an lower critical solution temperature of 35–37°C used as a cleaning agent. 60% increase in water flux was observed after the single‐step cleaning process. Multiple uses of TRP for cleaning performed with no membrane damage.

Multivariate Evaluation of Photovoltaic Utilization Potential of Primary and Secondary School Buildings: A Case Study in Hainan Province, China

Modernization and industrialization have significantly increased energy consumption, causing environmental problems. Given that China is the largest energy user, the rise in building energy consumption necessitates clean energy alternatives. The purpose of this study is to summarize typical building models for primary and secondary schools in Hainan Province, and to use software to simulate and calculate the photovoltaic utilization potential of primary and secondary school buildings. In China, the government is usually the manager of primary and secondary schools, and due to their architectural characteristics, these buildings can be used to assess photovoltaic applications. The aim is to drive the application of photovoltaic systems in all types of buildings and promote urban energy reform. This study summarizes the types of primary and secondary school buildings in Hainan Province and analyzes them. It evaluates rooftop photovoltaic projects at the Second Middle School and the Siyuan School in Wanning City, Hainan Province, and uses PVsyst 7.2 software to assess the photovoltaic utilization potential. The results show that the optimal orientation in Hainan Province is south-facing, and the optimal inclination angle is 10° to 20°. The most favorable orientations of facade photovoltaic systems are 20° southeast or southwest. The longest dynamic investment payback period is approximately 15 years, and the environmental benefits are $0.012/kWh. The findings indicate significant potential for photovoltaic applications in primary and secondary school buildings. A combination of facade and rooftop photovoltaics can result in the zero-energy consumption of these buildings, reducing the pressure on urban power grids and achieving sustainable utilization.

Neural Network-Based Analysis and Its Application to Spectroscopy for Mango

Sugar derived from crops is a crucial organic energy source studied in the Earth sciences, serving as a renewable and clean energy alternative. Biofuels produced from crop sugars are more environmentally friendly than traditional fossil fuel sources and contribute to solar energy storage and conversion within the Earth’s cycle. Using mangoes as a case study, this research employs near-infrared spectral analysis technology to develop an algorithm for a mango brix detection device. The study investigates the relationship between brix and absorbance, as well as changes in brix levels, and their application for on-site mango brix detection. Near-infrared spectral data in the range of 1300 nm to 2300 nm were collected during the mango ripening season in summer and preprocessed using various techniques. A neural network-based least squares modeling approach was utilized to develop a mango sugar content detection model, resulting in a correlation coefficient of 0.9055 and a root-mean-square error of 0.2192. To enhance model accuracy and avoid local optimization issues, this study incorporated the simulated annealing algorithm for model optimization, leading to a correlation coefficient of 0.9854 and a root-mean-square error of 0.0431. The findings demonstrate that the non-destructive testing model of mangoes based on near-infrared spectroscopy effectively detects brix changes and storage potential post-harvest, offering valuable insights for mango quality assessment, optimal picking and selling times, and market selection.

Daily natural gas load prediction method based on APSO optimization and Attention-BiLSTM.

As the economy continues to develop and technology advances, there is an increasing societal need for an environmentally friendly ecosystem. Consequently, natural gas, known for its minimal greenhouse gas emissions, has been widely adopted as a clean energy alternative. The accurate prediction of short-term natural gas demand poses a significant challenge within this context, as precise forecasts have important implications for gas dispatch and pipeline safety. The incorporation of intelligent algorithms into prediction methodologies has resulted in notable progress in recent times. Nevertheless, certain limitations persist. However, there exist certain limitations, including the tendency to easily fall into local optimization and inadequate search capability. To address the challenge of accurately predicting daily natural gas loads, we propose a novel methodology that integrates the adaptive particle swarm optimization algorithm, attention mechanism, and bidirectional long short-term memory (BiLSTM) neural networks. The initial step involves utilizing the BiLSTM network to conduct bidirectional data learning. Following this, the attention mechanism is employed to calculate the weights of the hidden layer in the BiLSTM, with a specific focus on weight distribution. Lastly, the adaptive particle swarm optimization algorithm is utilized to comprehensively optimize and design the network structure, initial learning rate, and learning rounds of the BiLSTM network model, thereby enhancing the accuracy of the model. The findings revealed that the combined model achieved a mean absolute percentage error (MAPE) of 0.90% and a coefficient of determination (R2) of 0.99. These results surpassed those of the other comparative models, demonstrating superior prediction accuracy, as well as exhibiting favorable generalization and prediction stability.

HARNESSING DATA ANALYTICS FOR SUSTAINABLE BUSINESS GROWTH IN THE U.S. RENEWABLE ENERGY SECTOR

The U.S. renewable energy sector is at the forefront of addressing global environmental challenges, with a growing emphasis on sustainability and clean energy alternatives. This review explores the pivotal role of data analytics in fostering sustainable business growth within this dynamic industry. As renewable energy sources continue to gain prominence, leveraging data analytics becomes imperative for companies aiming to optimize operations, enhance efficiency, and contribute to long-term ecological objectives. Data analytics serves as a powerful tool for harnessing insights from vast and complex datasets generated by renewable energy systems. This review delves into the ways in which organizations can extract actionable intelligence from data, enabling informed decision-making and strategic planning. From predictive maintenance of renewable infrastructure to real-time monitoring of energy production, data analytics empowers businesses to streamline processes and mitigate operational risks. Furthermore, the review sheds light on the role of data analytics in promoting innovation and research within the U.S. renewable energy sector. By analyzing patterns, trends, and historical data, organizations can identify areas for technological advancements, thereby accelerating the development of cutting-edge solutions. This proactive approach not only contributes to sustainable business growth but also positions the sector as a driver of innovation in the broader landscape of clean energy technologies. The review also highlights the challenges and opportunities associated with the integration of data analytics in the renewable energy domain, considering factors such as data security, regulatory compliance, and the need for skilled professionals. The synthesis of data-driven insights with sustainability objectives not only enhances the competitiveness of businesses in the U.S. renewable energy sector but also aligns them with global efforts to address climate change and achieve a more sustainable energy future. 
 Keywords: Data, Business, USA, Renewable Energy, Sustainability, Review.