Applied insight: studying reducing the carbon footprint of the drying process and its environmental impact and financial return.

Harnessing solar energy is one of the most important practical insights highlighted to mitigate the severe climate change (CC) phenomenon. Therefore, this study aims to focus on the use of hybrid solar dryers (HSDs) within an environmentally friendly framework, which is one of the promising applications of solar thermal technology to replace traditional thermal technology that contributes to increasing the severity of the CC phenomenon. The HSD, based on a traditional electrical energy source (HSTEE) and electrical energy from photovoltaic panels (HSPVSE), was evaluated compared to a traditional electrical (TE) dryer for drying some medicinal and aromatic plants (MAPs). This is done by evaluating some of the drying outputs, energy consumed, carbon footprint, and financial return at 30, 40, and 50°C. The best quality of dried MAP samples in terms of essential oil (EO, %) and microbial load was achieved at 40°C. The HSTEE dryer has reduced energy consumption compared to the TE dryer by a percentage ranging from 37% to 54%. The highest CO2 mitigated ratio using the HSTEE dryer was recorded in thyme, marjoram, and lemongrass samples, with values ranging from 45% to 54% at 30, 40, and 50°C. The highest financial return obtained from energy consumption reduction and carbon credit footprint was achieved at 50°C, with values ranging from 5,313.69 to 6,763.03 EGP/year (EGP ≈ 0.0352 USD) when coal was used as a fuel source for the generation of electricity. Moreover, the HSPVSE dryer achieved a 100% reduction in traditional energy consumption and then reduced CO2 emissions by 100%, which led to a 100% financial return from both energy reduction and carbon credit. The highest financial returns were observed at 50°C, with values ranging from 13,872.56 to 15,007.02, 12,927.28 to 13,984.43, and 11,981.99 to 12,961.85 EGP/year (EGP ≈ 0.0352 USD) for coal, oil, and natural gas, respectively. The HS dryers show potential for environmental conservation contribution; furthermore, earning money from energy savings and carbon credit could help improve the living standards and maximize benefits for stakeholders.

The use of energy management ISO 50001 to increase the effectiveness of water treatment plants: An application study on the Zai water treatment plant.

This study sought to determine the impact of implementing the energy management system ISO 50001 on the Zai Water Treatment Plant's energy efficiency performance and demonstrate how this implementation affected the cost and rate of energy consumption. The proposed study model contained three dependent variables-energy consumption, energy efficiency, and the cost of energy consumption. It also contained an independent variable-the energy management system ISO 50001. All these variables were used to develop various questions to help accomplish the study's goals. Planning was done by selecting pumping stations, selecting the most energy-consuming type of pump, and finally, choosing a pump maintenance project to improve energy performance. The researcher used the case of the Zai water pumping station as an example where the ISO 50001 energy management system was applied along with the stages of the Deming Cycle of management. Four pumping units from the Zai water pumping station served as the research sample for the study. •Find the impact of implementing the ISO 50001 energy management system on the energy efficiency performance of the Zai water treatment plant.•The effects of implementing ISO 50001 energy management system on cost and energy consumption at the Zai water treatment plant.•What effect does the ISO 50001 energy management system affect the Zai Water Treatment Plant's energy efficiency? After applying the ISO 50001 energy management system, several conclusions were drawn. Energy costs and consumption rates in the pumping units dropped while the energy efficiency in the chosen pumping units increased.

Data pipeline for real-time energy consumption data management and prediction.

With the increasing utilization of data in various industries and applications, constructing an efficient data pipeline has become crucial. In this study, we propose a machine learning operations-centric data pipeline specifically designed for an energy consumption management system. This pipeline seamlessly integrates the machine learning model with real-time data management and prediction capabilities. The overall architecture of our proposed pipeline comprises several key components, including Kafka, InfluxDB, Telegraf, Zookeeper, and Grafana. To enable accurate energy consumption predictions, we adopt two time-series prediction models, long short-term memory (LSTM), and seasonal autoregressive integrated moving average (SARIMA). Our analysis reveals a clear trade-off between speed and accuracy, where SARIMA exhibits faster model learning time while LSTM outperforms SARIMA in prediction accuracy. To validate the effectiveness of our pipeline, we measure the overall processing time by optimizing the configuration of Telegraf, which directly impacts the load in the pipeline. The results are promising, as our pipeline achieves an average end-to-end processing time of only 0.39 s for handling 10,000 data records and an impressive 1.26 s when scaling up to 100,000 records. This indicates 30.69-90.88 times faster processing compared to the existing Python-based approach. Additionally, when the number of records increases by ten times, the increased overhead is reduced by 3.07 times. This verifies that the proposed pipeline exhibits an efficient and scalable structure suitable for real-time environments.

Coupling of Electric and Flow Fields to Enhance Ion Transport for Energy-Efficient Electrochemical Tap-Water Softening.

Electrochemical-induced precipitation is a sustainable approach for tap-water softening, but the hardness removal performance and energy efficiency are vastly limited by the ultraslow ion transport and the superlow local HCO3-/Ca2+ ratio compared to the industrial scenarios. To tackle the challenges, we herein report an energy-efficient electrochemical tap-water softening strategy by utilizing an integrated cathode-anode-cathode (CAC) reactor in which the direction of the electric field is reversed to that of the flow field in the upstream cell, while the same in the downstream cell. As a result, the transport of ions, especially HCO3-, is significantly accelerated in the downstream cell under a flow field. The local HCO3-/Ca2+ ratio is increased by 1.5 times, as revealed by the finite element numerical simulation and in situ imaging. In addition, a continuous flow electrochemical system with an integrated CAC reactor is operated for 240 h to soften tap water. Experiments show that a much lower cell voltage (9.24 V decreased) and energy consumption (28% decreased) are obtained. The proposed ion-transport enhancement strategy by coupled electric and flow fields provides a new perspective on developing electrochemical technologies to meet the flexible and economic demand for tap-water softening.

Examining the relationship between international digital trade, green technology innovation and environmental sustainability in top emerging economics.

Ensuring preserving a sustainable environment is a crucial concern for individuals worldwide. In previous research, CO2 emissions have been used to measure environmental deterioration. However, in this study, we have expanded the scope to include carbon emissions and several other gases. This comprehensive measure is referred to as the ecological footprint (EFP). More significant international digital trade (IDT) has the potential to achieve several positive results, including reducing EFP (economic frictions and barriers), stimulating economic growth, and minimizing trade risk and volatility. These benefits can be realized by implementing structural reforms in significant production and development sectors. Green technology innovation (GTI) has the potential to make substantial progress in ecological quality and energy efficiency. Nevertheless, previous studies still need to adequately prioritize examining rising economies in terms of international trade diversification and GTI. This study examined the effects of IDT, GTI, and renewable energy consumption (REC) on EFP in BRICST countries. The study utilized data from the period between 1995 and 2022. The cross-sectionally augmented autoregressive distributed lag (CS-ARDL) model demonstrates that EFP negatively correlates with trade diversification, REC, and GTI in the long and short term. These countries have demonstrated a significant presence of eco-friendly products in their trade portfolios, and their manufacturing processes are shifting towards GTI. The objective is to enhance the REC sources and minimize EFP from consumption. Conversely, the increasing economic growth within this economic group has a compounding impact on the environment's decline since it amplifies the carbon emissions from increased consumption. To reduce the EFP level, the paper suggests increasing investment in GTI, promoting worldwide digital trade, and embracing renewable energy sources.

Scaling Supercapacitive Swing Adsorption of CO2 Using Bipolar Electrode Stacks.

Supercapacitive swing adsorption (SSA) modules with bipolar stacks having 2, 4, 8, and 12 electrode pairs made from BPL 4 × 6 activated carbon are constructed and tested for carbon dioxide capture applications. Tests are performed with simulated flue gas (15%CO2 /85%N2) at 2, 4, 8, and 12 V, respectively. Reversible adsorption with sorption capacities (≈58 mmol kg-1) and adsorption rates (≈38 µmol kg-1 s-1) are measured for all stacks. The productivity scales with the number of cells in the module, and increases from 70 to 390 mmol h-1 m-2. The energy efficiency and energy consumption improve with increasing number of bipolar electrodes from 67% to 84%, and 142 to 60 kJ mol-1, respectively. Overall, the results show that SSA modules with bipolar electrodes can be scaled without reducing the adsorptive performance, and with improvement of energetic performance.

Increased arthroplasty surgeon energy consumption when performing primary total hip arthroplasty compared to total knee arthroplasty.

Introduction: Previously published studies have hypothesized that total hip arthroplasty (THA) requires the surgeon to expend more energy that total knee arthroplasty (TKA). However, techniques for performing these procedures have evolved. Therefore, we sought to compare if primary THA had increased energy expenditure compared to primary TKA. Methods: We prospectively recorded the heart rate, respiratory rate, minute ventilation, cadence, and energy expenditure of a single fellowship-trained arthroplasty surgeon while performing primary THA and TKA on 372 patients. Patient demographics and operative records were reviewed to evaluate differences in the physical demands of each surgical case. Age (64.3 versus 65.9 years, p = 0.1) and gender (54.8% versus 51.0% female, p = 0.5) were similar between THA and TKA, but TKAs had a higher body mass index (31.1 versus 28.7 kg/m2, p < 0.001). Chi-square and independent-samples t-tests were used to compare cohorts. Significance was set at p < 0.05. Results: THA tended to have 1.1 times longer operative time than TKA (102.2 versus 88.9 min, p < 0.001). THA had a statistically higher heart rate compared to TKA, although this is unlikely to be clinically significant (82.5 versus 80.7 beats/minute, p < 0.001). Respiratory Rate was 1.1 times higher (15.9 versus 14.9 respirations/minute, p < 0.001) and minute ventilation was 1.2 times higher (19.6 versus 16.9 L/min, p < 0.001) when performing THA. Cadence was 1.5 times higher when performing TKA (4.2 versus 2.8 steps/minute, p < 0.001). THA had a 1.2 times higher energy expenditure/patient (378.8 versus 312.0 Calories/patient, p < 0.001) and a 1.1 times higher energy expenditure/minute (3.7 versus 3.5 Calories/minute, p = 0.01) compared to TKA. Discussion: THA is associated with longer operative time and increased energy expenditure per compared to TKA. Despite THA and TKA procedures becoming more efficient, arthroplasty surgery continues to have heavy physical burden on the surgeon. Further research is needed to understand ways to decrease surgeon energy expenditure and promote career longevity.

Optimal configuration method of demand-side flexible resources for enhancing renewable energy integration.

Demand-side flexible load resources, such as Electric Vehicles (EVs) and Air Conditioners (ACs), offer significant potential for enhancing flexibility in the power system, thereby promoting the full integration of renewable energy. To this end, this paper proposes an optimal allocation method for demand-side flexible resources to enhance renewable energy consumption. Firstly, the adjustable flexibility of these resources is modeled based on the generalized energy storage model. Secondly, we generate random scenarios for wind, solar, and load, considering variable correlations based on non-parametric probability predictions of random variables combined with Copula function sampling. Next, we establish the optimal allocation model for demand-side flexible resources, considering the simulated operation of these random scenarios. Finally, we optimize the demand-side resource transformation plan year by year based on the growth trend forecast results of renewable energy installed capacity in Jiangsu Province from 2025 to 2031.

Combining the role of urbanization and renewable energy in sustainable development: testing the EKC hypothesis for Africa.

The pressing necessity to curb greenhouse gas emissions due to climate change has sparked significant scientific interest in comprehending the factors behind CO2 emissions, particularly concerning environmental sustainability challenges. Nonetheless, there exists a notable gap in our understanding of how the process of urbanization interacts with the utilization of renewable energy to impact CO2 emissions. This research endeavor seeks to evaluate the complex interplay among urbanization, renewable energy, and CO2 emissions across 46 African nations spanning from 1990 to 2019. To accomplish this objective, a variety of econometric methodologies are employed, including Driscoll-Kraay standard errors, IV-GMM, and method of moments quantile regression (MMQR) panel estimations to address issues like cross-sectional dependencies, endogeneity, heterogeneity, and panel Granger causality examination. The empirical results suggest that urbanization leads to an increase in CO2 emissions, whereas the consumption of renewable energy plays a role in enhancing environmental quality by reducing CO2 emissions. A significant outcome of the study is the revelation that a combination of urbanization and renewable energy leads to a decrease in carbon emissions. Moreover, the Environmental Kuznets Curve (EKC) hypothesis is validated. Lastly, through the Dumitrescu-Hurlin panel causality test, it is uncovered that urbanization and renewable energy consumption exhibit a bidirectional relationship with CO2 emissions. To reduce dependence on fossil fuels and curb CO2 emissions, policymakers should promote renewable energy usage in urban areas.

Unveiling energy security in agriculture through vital indicators extraction and insights.

Despite advancements in meeting various human needs, energy supply remains a top priority for all countries worldwide. The escalating energy consumption in the agricultural sector underscores the necessity to scrutinize its energy usage. Presently, there exists an absence of a precise tool for accurately measuring this consumption. Hence, this study aims to identify indicators for measuring energy security in agriculture, conducted in three phases: content analysis, indicator validation, and field investigation. In the content analysis phase, energy security indicators were extracted and grouped into four categories: accessibility, availability, utilization, and sustainability. Following this, a two-stage validation process led to the identification of 18 indicators for assessing energy security in agriculture. In the field phase, a tailored questionnaire was distributed to 160 randomly selected farmers. The findings revealed that the availability component held the highest rank in establishing energy security, with an average score of 3.31. However, the current status of the access component indicates a more unfavorable situation compared to other dimensions. Consequently, to achieve energy security in agriculture, particular emphasis should be placed on enhancing energy access. Key areas to address include reducing transportation costs and minimizing the use of chemical pesticides. This indicates a necessity for focused interventions aimed at improving both energy access and sustainability within the agricultural sector. These efforts would contribute to enhancing economic efficiency and promoting environmental conservation.

An innovative MGM-BPNN-ARIMA model for China's energy consumption structure forecasting from the perspective of compositional data.

Effective forecasting of energy consumption structure is vital for China to reach its "dual carbon" objective. However, little attention has been paid to existing studies on the holistic nature and internal properties of energy consumption structure. Therefore, this paper incorporates the theory of compositional data into the study of energy consumption structure, which not only takes into account the specificity of the internal features of the structure, but also digs deeper into the relative information. Meanwhile, based on the minimization theory of squares of the Aitchison distance in the compositional data, a combined model based on the three single models, namely the metabolism grey model (MGM), back-propagation neural network (BPNN) model, and autoregressive integrated moving average (ARIMA) model, is structured in this paper. The forecast results of the energy consumption structure in 2023-2040 indicate that the future energy consumption structure of China will evolve towards a more diversified pattern, but the proportion of natural gas and non-fossil energy has yet to meet the policy goals set by the government. This paper not only suggests that compositional data from joint prediction models have a high applicability value in the energy sector, but also has some theoretical significance for adapting and improving the energy consumption structure in China.

A review of emerging membrane-based microalgal-bacterial processes for wastewater treatment: Process configurations, biological and membrane performance, and perspectives.

Microalgal-bacterial (MB) consortia create an excellent eco-system for simultaneous COD/BOD and nutrients (N and P) removals in a single step with significant reduction in or complete elimination of aeration and carbonation in the biological wastewater treatment processes. The integration of membrane separation technology with the MB processes has created a new paradigm for research and development. This paper focuses on a comprehensive and critical literature review of recent advances in these emerging processes. Novel membrane process configurations and process conditions affecting the biological performance of these novel systems have been systematically reviewed and discussed. Membrane fouling issues and control of MB biofilm formation and thickness associated with these emerging suspended growth or immobilized biofilm processes are addressed and discussed. The research gaps, challenges, outlooks of these emerging processes are identified and discussed in-depth. The findings from the literature suggest that the membrane-based MB processes are advanced biotechnologies with a significant reduction in energy consumption and process simplification and high process efficiency that are not achievable with current technologies in wastewater treatment. There are endless opportunities for research and development of these novel and emerging membrane-based MB processes.

Data-driven evaluation of electric vehicle energy consumption for generalizing standard testing to real-world driving.

Standard energy-consumption testing, providing the only publicly available quantifiable measure of battery electric vehicle (BEV) energy consumption, is crucial for promoting transparency and accountability in the electrified automotive industry; however, significant discrepancies between standard testing and real-world driving have hindered energy and environmental assessments of BEVs and their broader adoption. In this study, we propose a data-driven evaluation method for standard testing to characterize BEV energy consumption. By decoupling the impact of the driving profile, our evaluation approach is generalizable to various driving conditions. In experiments with our approach for estimating energy consumption, we achieve a 3.84% estimation error for 13 different multiregional standardized test cycles and a 7.12% estimation error for 106 diverse real-world trips. Our results highlight the great potential of the proposed approach for promoting public awareness of BEV energy consumption through standard testing while also providing a reliable fundamental model of BEVs.

Markov chain-based impact analysis of the pandemic Covid-19 outbreak on global primary energy consumption mix.

The historic evolution of global primary energy consumption (GPEC) mix, comprising of fossil (liquid petroleum, gaseous and coal fuels) and non-fossil (nuclear, hydro and other renewables) energy sources while highlighting the impact of the novel corona virus 2019 pandemic outbreak, has been examined through this study. GPEC data of 2005-2021 has been taken from the annually published reports by British Petroleum. The equilibrium state, a property of the classical predictive modeling based on Markov chain, is employed as an investigative tool. The pandemic outbreak has proved to be a blessing in disguise for global energy sector through, at least temporarily, reducing the burden on environment in terms of reducing demand for fossil energy sources. Some significant long term impacts of the pandemic occurred in second and third years (2021 and 2022) after its outbreak in 2019 rather than in first year (2020) like the penetration of other energy sources along with hydro and renewable ones in GPEC. Novelty of this research lies within the application of the equilibrium state feature of compositional Markov chain based prediction upon GPEC mix. The analysis into the past trends suggests the advancement towards a better global energy future comprising of cleaner fossil resources (mainly natural gas), along with nuclear, hydro and renewable ones in the long run.

Data aggregation algorithm for wireless sensor networks with different initial energy of nodes.

Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5-20% lower communication overhead and energy consumption, 10-20% higher security, and 10-30% lower node mortality than existing algorithms.

Impact of temperature and forward osmosis membrane properties on the concentration polarization and specific energy consumption of hybrid desalination system.

This study investigates how temperature and forward osmosis (FO) membrane properties, such as water permeability (A), solute permeability (B), and structural parameter (S), affect the specific energy consumption (SEC) of forward osmosis-reverse osmosis system. The results show that further SEC reduction beyond the water permeability of 3 LMH bar-1 is limited owing to high concentration polarization (CP). Increasing S by 10-fold increases FO recovery by 177.6%, causing SEC decreases by 33.6%. However, membrane with smaller S also increases external CP. To reduce SEC, future work should emphasize mixing strategies to reduce external CP. Furthermore, increasing the temperature from 10 to 40 °C can reduce SEC by 14.3%, highlighting the energy-saving potential of temperature-elevated systems. The factorial design indicates that at a lower temperature, increasing A and decreasing S have a more significant impact on reducing SEC. This underlines the importance of developing advanced FO membranes, particularly for lower-temperature processes.

Green Hospital as A New Standard in Japan: How far can Neurosurgery go in Japan?

BACKGROUND: Climate change is a significant challenge that the medical community must address. Hospitals are large facilities with high water and energy consumption, as well as high levels of waste generation, which makes it important to pursue green hospital initiatives. Neurosurgery requires substantial energy for surgeries and tests. METHODS: Based on the keywords "Climate change," "green hospital," "neurosurgery," "energy consumption," "environmental impact" listed in this paper, we extracted representative manuscripts, and the practices employed in the authors' hospital were assessed. RESULTS: The "Guidelines for Environmental Consideration in Hospitals" and "Guidelines for the Sustainability of Hospital Environments" have been developed; however, they are not implemented in most hospitals in Japan. Inhalational anaesthetics were found to contribute significantly to greenhouse gas emissions. Educating patients and staff and employing the "8 Rs" (rethink, refuse, reduce, reuse, recycle, research, renovation and revolution) showed promise in achieving green hospital standards. CONCLUSION: The advent of 'green hospitals' in Japan is imminent. The active participation of neurosurgeons can play a crucial role in diminishing the environmental footprint of health care while simultaneously enhancing medical standards. Given the pressing challenges posed by climate change, there is a critical need for an overhaul of medical practices. It is imperative for neurosurgeons to pioneer the adoption of new, sustainable medical methodologies.

Spatial effect of biomass energy consumption on carbon emissions reduction: the role of globalization.

As globalization proceeds, increasing biomass energy consumption is an important pathway to replace fossil fuels for tackling climate change by reducing emissions. This study explores the spatial spillover effect in biomass energy carbon reduction, which is frequently ignored when investigating environmental factors. It uncovers whether globalization and its dimensions can strengthen the spatial effect of biomass energy carbon reduction. Besides, we reveal whether biomass energy consumption can promote CO2 emissions reduction while ensuring economic progress. Results show that (1) owing to the spillover effect, biomass energy consumption plays a significant role in direct and indirect enhancing carbon emissions reduction, with their feedback effects of - 0.003 or 3.3% of the direct effect. (2) Increasing overall, social and political globalization enhances biomass energy consumption's carbon reduction effect. (3) In countries with higher economic development, overall, economic and political globalization has a better promotion in the spatial effect of biomass energy carbon reduction. (4) Developing biomass energy can support the environment quality while enhancing economic growth.

Energy demand forecasting using convolutional neural network and modified war strategy optimization algorithm.

Predicting the electricity demand is a key responsibility for the energy industry and governments in order to provide an effective and dependable energy supply. Traditional projection techniques frequently rely on mathematical models, which are limited in their ability to recognize complex patterns and correlations in data. Machine learning has emerged as a viable tool for estimating electricity in the last decade. In this study, the Modified War Strategy Optimization-Based Convolutional Neural Network (MWSO-CNN) has been provided for electricity demand prediction. To increase the precision of electricity demand prediction, the MWSO-CNN approach integrates the benefits of the modified war strategy optimization technique and the convolutional neural network. To improve efficiency, the modified war strategy optimization technique is employed to adjust the hyperparameters of the CNN algorithm. The suggested MWSO-CNN approach is tested on a real-world electricity demand dataset, and the findings show that it outperforms many state-of-the-art machine learning techniques for predicting electricity demand. In general, the suggested MWSO-CNN approach could offer a successful and cost-effective strategy for predicting energy consumption, which will benefit both the energy business and society as a whole.