Related Energy Consumption Research Articles

The Cost Benefit of Refinery Effluent Pretreatment Upstream of Membrane Bioreactors.

The established classical method of treating oil refinery effluent is flotation followed by biological treatment. Membrane bioreactors (MBRs) offer more advanced treatment, producing a clarified and potentially reusable treated effluent, but demand robust pretreatment to remove oil and grease (O&G) down to consistent, reliably low levels. An analysis of a full-scale conventional oil refinery ETP (effluent treatment plant) based on flotation alone, coupled with projected performance, energy consumption and costs associated with a downstream MBR, have demonstrated satisfactory performance of flotation-based pretreatment. The flotation processes, comprising an API (American Petroleum Institute) separator followed by dissolved air flotation (DAF), provided ~90% removal of both total suspended solids (TSS) and O&G coupled with 75% COD (chemical oxygen demand) removal. The relative energy consumption and cost of the pretreatment, normalised against both the volume treated and COD removed, was considerably less for the API-DAF sequence compared to the MBR. The combined flotation specific energy consumption in kWh was found to be almost an order of magnitude lower than for the MBR (0.091 vs. 0.86 kWh per m3 effluent treated), and the total cost (in terms of the net present value) around one sixth that of the MBR. However, the nature of the respective waste streams generated and the end disposal of waste solids differ significantly between the pretreatment and MBR stages.

Investigations on factors influencing physical properties of recycled cement and the related carbon emissions and energy consumptions

Cement production emits large amounts of carbon dioxide and has long been considered a significant source of global atmospheric CO2 emissions. The production of recycled cement (RC) could be an effective and sustainable way for the cement industry. This work studied the effect of different factors, including burn temperature (450 °C, 650 °C, and 800 °C) for preparing RC, carbonation degree of precursor, particle size of RC and water-cement (w/c) ratio on the compressive strength, hydration products and microstructure of RC pastes through mechanical, XRD, TG-DTG and SEM tests. The results show that burn temperature is the most important parameter for RC, and 650 °C RC performed best and had the highest compressive strength of the paste. The higher carbonation degree of the precursor resulted in a slightly higher strength of the RC paste due to the finer particle size of the cement particles and more calcite in produced RC, which all contributed to accelerated cement hydration. Similar as OPC, smaller particle size and w/c produced a higher strength of RC paste. RC paste (w/c = 0.5) with a compressive strength of 35 MPa at the age of 28 days can be achieved by producing the RC under 650 °C and a particle size range of 0–300 μm. Noticeably, compared to the OPC production process, more than 70% CO2 emissions and 60% energy consumption could be reduced for RC production under 650 °C.

Charting China’s Export Trajectories: Energy Factors and the Evolution of Competitive Advantage in Relation to the U.S.

Accompanied with energy price fluctuating frequently, energy-saving technology updating rapidly, energy factor plays an important role in China’s export trade to US via energy relative price and energy consumption technology differentiation. According to industry energy consumption density, the paper classifies industries into high and low energy consumption industry, combining Costinot et al. productivity method with forward linkages increase export value added, calculates Chinese high and low energy consumption industries export comparative advantage index to US during 2000-2014, as well as using Kernel density method to evaluate the transition of them, finds that comparative advantage of China’s high/low energy consumption industry to U.S. has a trend of weaken/strength which shows extreme phenomena. furtherly, utilizing Probit method to estimate the factors which affects the comparative advantage, the results turn out that the gap of energy consumption technology between two countries is the main reason which contributes to China’s comparative advantage of both high and low energy consumption industry to U.S. The energy relative price just has significant impact on high energy consumption industry, which will affect the achievement of China’s carbon emission reduction targets in ‘Responding to Climate Change: China’s Policies and Actions’.

A concept to maximise energy self-sufficiency of the housing stock in central Europe based on renewable resources and efficiency improvement

In this article, an innovative method for designing energy-self-sufficient housing communities is presented, emphasising proactive user participation. The overall concept for achieving such communities is first described, and then the current research is presented – focusing on the electricity generation and storage within the community. Three key degrees of freedom are exploited: buildings with very low and coordinated energy demand; strong residents participation; a large share of local energy generation with its storage in the houses. The starting point is to maximise the energy efficiency of individual buildings. As the buildings in the analysed community were built according to the obtained optimisation results and are inhabited by users, the authors assessed the actual energy consumption in relation to the design value in Design Builder software. The detected discrepancy was only 3.8%. That modelling accuracy allows further steps to achieve a fully energy-self-sufficient community. This is illustrated using a real example of a 40-building nZEB community in Poland, independent of the energy supply from the grid. The presented approach demonstrates a Smart City solution for a community of single-family houses with confirmed very low final energy demand and potential Greenhouse Gas reduction of up to 96%.

A facile synthesized robust catalyst for efficient regeneration of biphasic solvent in CO2 capture: characterization, performance, and mechanism

Amine-based technology is promising for CO2 capture from the flue gas. However, it encounters high energy penalty and thus hampers its industrial application. Currently, the catalytic solvent regeneration is developing to lower down regeneration temperature, accelerate regeneration rate, and cut down energy consumption for monoethanolamine (MEA)-based process. However, the information on catalytic regeneration of biphasic solvent is seldom available. This work develops a facile synthesized robust catalyst, α-Fe2O3, to promote the biphasic solvent regeneration such as triethylenetetramine (TETA) and 2-(diethylamino) ethanol (DEEA) blend. Experimental results revealed that, with the aid of α-Fe2O3, the efficient regeneration of TETA-DEEA occurred at 94 °C, which is far below the conventional MEA-based process (110–120 °C). The corresponding CO2 stripping rate increased by 2.4 times compared with uncatalytic scenario. Furthermore, the relative energy consumption was furtherly decreased by 41.6%. Different from MEA regeneration, the biphasic solvent regeneration with α-Fe2O3 depended on the enhancement of water molecules proton donation rather than the abundant Brønsted acid and Lewis acid of the catalyst. Based on DFT calculation and 13C NMR spectra analysis, the water molecules on α-Fe2O3 surface and the protonated DEEA were parallel served as proton donors for carbamate decomposition. Consequently, a novel proton transfer mechanism was proposed for the biphasic solvent TETA-DEEA regeneration. The water molecules adsorbed on α-Fe2O3 surface generated oxyhydrogen groups and thus donate protons for the carbamate decomposition. This work proposes a novel proton transfer mechanism for solvent regeneration and advances its industrial application with a low energy penalty.

Brain-wide dendrites in a near-optimal performance of dynamic range and information transmission

Dendrites receive and process signals from other neurons. The range of signal intensities that can be robustly distinguished by dendrites is quantified by the dynamic range. We investigate the dynamic range and information transmission efficiency of dendrites in relation to dendritic morphology. We model dendrites in a neuron as multiple excitable binary trees connected to the soma where each node in a tree can be excited by external stimulus or by receiving signals transmitted from adjacent excited nodes. It has been known that larger dendritic trees have a higher dynamic range. We show that for dendritic tress of the same number of nodes, the dynamic range increases with the number of somatic branches and decreases with the asymmetry of dendrites, and the information transmission is more efficient for dendrites with more somatic branches. Moreover, our simulated data suggest that there is an exponential association (decay resp.) of overall relative energy consumption (dynamic range resp.) in relation to the number of somatic branches. This indicates that further increasing the number of somatic branches (e.g. beyond 10 somatic branches) has limited ability to improve the transmission efficiency. With brain-wide neuron digital reconstructions of the pyramidal cells, 90% of neurons have no more than 10 dendrites. These suggest that actual brain-wide dendritic morphology is near optimal in terms of both dynamic range and information transmission.

Multirobot Task Planning Method Based on the Energy Penalty Strategy

In multirobot task planning, the goal is to meet the multi-objective requirements of the optimal and balanced energy consumption of robots. Thus, this paper introduces the energy penalty strategy into the GA (genetic algorithm) to achieve the optimization of the task planning of multiple robots in different operation scenarios. First, the algorithm model is established, after which the objective function is constructed by taking the energy excess of the relative average energy consumption of each robot as the penalty energy, along with the total energy consumption of multiple robots. In the genetic operation, two-segment chromosome coding is used to realize the iterative optimization of the number and task sequences of robots through diversified cross and mutation operators. Then, in the task scenario with obstacles, the A* (A-Star) algorithm and GA are used to plan the optimal obstacle avoidance path and to realize the secondary optimization of the robot task sequence without changing the number of tasks. During optimization, the energy penalty strategy imposes punishment on the objective function through the size of the penalty energy, enabling the robot energy consumption to reach an equilibrium state by maintaining the total energy consumption at the minimum. Finally, the MATLAB platform is used to conduct the simulation experiments to compare with basic genetic algorithms and penalty function algorithms, after which the optimal allocation scheme and energy consumption iteration of the algorithm are analyzed under different robot numbers, task numbers, and task scenarios, and the simulation results include robot task sequences, total energy consumption, average energy consumption, and standard deviation of energy consumption.

Electroseparation of lysozyme from egg white by electrodialysis with ultrafiltration membrane

Lysozyme (LYZ) is a vital bioactive protein due to its chemical stability, non-toxicity, and biological functions including antibacterial, antiviral and anti-inflammatory activities. LYZ’s separation and purification processes are essential for its applications in the food, pharmaceutical and some other fields. Electrodialysis with ultrafiltration membrane (EDUF) is a recently developed technique that has been proven to have a high selectivity in fractionating natural biologically active compounds. This study was conducted to isolate and purify LYZ from a model solution containing ovalbumin (OVA) and LYZ using an EDUF process, and to evaluate the effects of the operational parameters on its separation performances. Results showed that an excessive or inadequate applied voltage or solution flow rate was not conducive to the migration of LYZ. Additionally, the volume ratio of feed to permeate solution was inversely related to the amount of LYZ recovered. Under the most favorable conditions, with a voltage of 5 V, solution flow rate of 50 mL/min and volume ratio of 1:1.5, LYZ was recovered with a total yield of 32.2% and a purity of 100% after 240 min of running, with a relative energy consumption of 1.52 kW•h/g. This study demonstrated that LYZ could be successfully separated from the mixtures of OVA and LYZ by an EDUF process. EDUF technology provides an effective and innovative method for the active protein separation in the egg product-related industry.

Event‐triggered distributed model predictive control scheme for temperature regulation in multi‐zone air conditioning systems with improved indoor thermal preference indicator

SummaryDriven by the increasing needs in multi‐zone buildings for reducing the air conditioning (A/C) system related energy consumption and increasing the occupants' thermal comfort, an event‐triggered distributed model predictive control (DMPC) scheme for building temperature regulation with improved indoor thermal preference indicator is proposed. To this end, a temperature‐energy model of the A/C system in a zone which considers multiple types of heat transfer, and the system model of the multi‐zone A/C system are derived first. On this basis, an improved indoor thermal preference indicator that trades off the A/C systems related energy consumption with the aggregate thermal comfort of the occupants is proposed for the multi‐zone A/C system, then the optimal indoor temperature set‐points for each zone are determined by using the distributed consensus algorithm. It is followed by an event‐triggered DMPC scheme by adjusting the A/C systems to arrive at the optimal indoor temperature set‐points for all the zones with lower communication and computation load. The performance and effectiveness of the proposed methods are demonstrated through a case study on an actual multi‐zone A/C system.

Dense and hard TiWC protective coatings grown with tungsten ion irradiation using WC-HiPIMS/TiC-DCMS co-sputtering technique without external heating

Titanium tungsten carbide (TiWC) coatings are deposited by a combined high-power impulse and dc magnetron co-sputtering (HiPIMS/DCMS) technique. No external heating is applied during deposition phase, instead, the thermally driven adatom mobility is substituted by heavy ion irradiation. DCMS sources equipped with titanium carbide targets provide constant neutral fluxes to establish the predominant coating structures, whereas tungsten carbide target in HiPIMS mode serves as the source of heavy metal-ions. Substrate bias of −60 V is synchronized to W+ ion-rich time domains of HiPIMS pulses to minimize the contribution from working gas ions. The influence of W+ ion flux intensity, controlled by varying peak target current density (JT), on film properties is investigated. X-ray photoelectron spectroscopy reveals the presence of over stoichiometric carbon forming an amorphous phase, the amount of which can be fine-tuned by varying JT. Changes in film composition as a function of JT are explained based on the in-situ ion mass spectroscopy analyses. Dense TiWC coatings by hybrid process exhibit hardness higher than 30 GPa, which are comparable to TiWC films deposited by DCMS with dc substrate bias and external heating. The relative energy consumption in the hybrid process is reduced by 77 % as compared to high-temperature DCMS processing.

Ultra-processed foods and the nutritional quality of the diet of Brazilian pregnant women

The aim of this study was to evaluate the consumption of ultra-processed foods by Brazilian pregnant women and its association with the nutritional quality of the diet. This is a prospective and cross-sectional study with food consumption data of Brazilian pregnant women from the 2017 to 2018 Family Budgets Survey (Pesquisa de Orçamentos Familiares). Food consumption was measured using two 24-h food recalls, and the foods were categorized according to the NOVA classification. The averages of absolute and relative energy consumption for each of the NOVA groups and subgroups were estimated. The sociodemographic characteristics described the diet's caloric contribution of ultra-processed and non-ultra-processed food fractions. Linear regression models were used to describe the association between quintiles of the caloric contribution of ultra-processed foods and the average content of nutrients in the diet. Consumption of ultra-processed foods represented 20.9% of the total calories in the diet of Brazilian pregnant women. There was a higher energy contribution of ultra-processed foods in the diet of pregnant women living in urban areas (22%), with higher per capita income (23.7%), and in the south region of the country (26.9%). In addition, the data showed an association between higher consumption of ultra-processed foods with reduced intake of protein, carbohydrate, fiber, potassium, iron, zinc, and folate and increased intake of total fat, saturated fat, trans fat, and free sugar. Results show that higher consumption of ultra-processed foods is associated with a reduction in the nutritional quality of the diet of Brazilian pregnant women.

Prediction and evaluation of air conditioner energy consumption of residential buildings in the Yangtze River Basin

Prediction and evaluation of energy consumption can guide energy usage behavior and promote energy saving. In view of the impact of many factors, like regional meteorological conditions, availability of passive techniques and occupant behavior, evaluation is complex. This paper focuses on the air conditioner energy consumption of residential buildings in the Yangtze River Basin. In terms of nature resources and climate conditions, A new zoning map with eight sub-zones for the adaptability of passive techniques was developed. The adaptability of single passive techniques and passive technique combinations in every sub-zone were summarized by comparing heating/cooling load, non-air-conditioning time and indoor thermal comfortable time. Moreover, the concepts of operational heating/cooling degree hours (OHDH/OCDH) and relative energy consumption index were proposed. A method to quantify and evaluate the impact of occupant behavior on the energy consumption of air conditioners was built. The occupant behavior was divided into three levels: Saving-level (S-level), Comfortable-level (C-level) and Lavish-level (L-level), based on the OCDH/OHDH. Furthermore, the energy consumption targets of diverse sub-zones are obtained, considering the indoor thermal comfort, occupant behavior and adaptive passive techniques. A case study demonstrates that the relative energy consumption index can avoid an unreasonable judgment caused by only focusing on energy consumption while neglecting indoor thermal comfort.

Environmental Sustainability in Radiation Oncology: A Scoping Review

<h3>Purpose/Objective(s)</h3> In the United States, the healthcare industry accounts for roughly 10% of greenhouse gas emissions. In recent years, interest in environmental sustainability in relation to the healthcare sector has increased. To date, little research has been conducted to assess the relationship of radiation therapy and its impact on the environment. This scoping review aims to examine and thematically organize literature regarding this subject. <h3>Materials/Methods</h3> An evaluation of evidence was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols extension for Scoping Reviews (PRISMA-ScR). Manuscripts were included that involved environmental sustainability and radiation therapy. Databases Web of Science, MEDLINE, and Scopus were queried, along with gray literature, to identify relevant publications between 1975 and 2022. Data was qualitatively analyzed and categorized. <h3>Results</h3> Of 61 articles identified, 7 met inclusion criteria, including: 5 observational studies, 1 randomized control trial and 1 case report. In relation to radiation therapy, environmental sustainability was classified by: 1) direct carbon emission/energy consumption for radiation therapy delivery; or 2) indirect carbon emission/energy consumption from sources such as patient travel, material utilization and resource lifecycle. Notably, 5 major radiation therapy companies consistently reported material, water, and energy consumption with 3 groups further differentiating between renewable and nonrenewable energy sources. From 2018 to 2021, radiation therapy companies reported decreases in total carbon emissions of 8% to 15% annually, with individual companies' global emissions ranging from 464,917 tons CO₂e to 781,865 tons CO₂e. One randomized control trial was identified which reported that single fraction radiation therapy was associated with decreased CO₂ vehicle emissions compared to conventionally fractionated radiation therapy due to minimizing treatment-related travel. Regarding direct energy usage, one case study demonstrated that with a new sustainably oriented system vs a standard linear accelerator, energy consumption decreased 70% from 4,500 kWh per month to 1,200 kWh per month, while the number of fractions delivered increased from 497 to 610. <h3>Conclusion</h3> This scoping review identified recent technologic advancements from the supplier side, with increasing emphasis on minimizing directly and indirectly related carbon emissions and energy consumption. Several gaps in the literature were identified including comparison of various treatment modalities such as single vs multifraction treatment and photon vs proton vs carbon therapy energy requirements. Further research is needed to elucidate avenues to minimize the environmental impact of radiation therapy while maintaining the highest quality patient care.

The paradigms of transport energy consumption and technological innovation as a panacea for sustainable environment: is there any asymmetric association?

Technological innovations have a great potential to develop the transportation system as more efficient, intelligent, connected, and sustainable. Therefore, transport energy consumption fundamentally transmutes how goods and people are moved with significant effects on transport demand with related energy consumption on a sustainable environment. To this end, our research aimed at investigating the environmental performance (carbon dioxide and ecological footprint) by stochastic impact by regression through a population, affluence, and technology (STIRPAT) model, and econometric approach for estimation of transport energy consumption from 1975 to 2018 for Pakistan. Moreover, our study supports the literature by exploring the association of technological innovations, financial development, carbon damage costs, and economic growth with the environment. The linear relationships of the variables are governed by the autoregressive distributive lag (ARDL) model that interestingly explored that economic growth and energy consumption, and financial development degrade the environment and resource depletion; however, technological innovations are inclined towards cleaner technologies. For asymmetric findings, we employ the non-linear autoregressive distributive lag technique recently introduced by Shin et al. (2014). The findings validate the existence of an asymmetric relationship between transport energy consumption and environmental indicators. The policymakers' prerequisites the alternative energies apart from conventional energies in the transport sector with technological innovations in transport sector energy consumption like the electronic and hybrid vehicles for a cleaner environment.

Energy-Efficient approximate compressor design for error-resilient digital signal processing

ABSTRACT Approximate computing can be considered a well-promising method for improving the critical performance criteria, such as latency and power consumption at the cost of accuracy penalty. The (4,2) compressor is a vital block at the heart of various arithmetic circuits like multipliers. This paper proposed two approximate (4,2) compressors with power and delay improvements with the error-correcting module. The simulation results based on 7 nm FinFET technology show that the proposed designs compared to the most efficient related approximate (4,2) compressors, reduce delay, power, and energy consumption. Furthermore, according to the comprehensive Monte Carlo simulations, the robustness of the proposed designs in the presence of significant FinFET process variations compared to the previous compressors is proven. In addition, an image multiplication process assesses the effectiveness of the proposed approach in the qualitative assessment of the inexact multiplier. The obtained results indicate acceptable accuracy of inexact multiplier based on the proposed approximate (4,2) compressor.

Multiagent Collaborative Learning for UAV Enabled Wireless Networks

The unmanned aerial vehicle (UAV) technique provides a potential solution to scalable wireless edge networks. This paper uses two UAVs, with accelerated motions and fixed altitudes, to realize a wireless edge network, where one UAV forwards downlink signals to user terminals (UTs) distributed over an area while the other one collects uplink data. The conditional average achievable rates, as well as their lower bounds, of both the uplink and downlink transmission are derived considering the active probability of UTs and the service queues of two UAVs. In addition, a problem aiming to maximize the energy efficiency of the whole system is formulated, which takes into account communication related energy and propulsion energy consumption. Then, we develop a novel multi-agent Q-learning (MA-QL) algorithm to maximize the energy efficiency, through optimizing the trajectory and transmit power of the UAVs. Finally, simulation results are conducted to verify our analysis and examine the impact of different parameters on the downlink and uplink achievable rates, UAV energy consumption, and system energy efficiency. It is demonstrated that the proposed algorithm achieves much higher energy efficiency than other benchmark schemes.

Hygrothermal performance assessment of a bio-based building made with date palm concrete walls

The current research falls within the general context of expanding and developing bio-based materials as insulating and low-cost construction materials. This paper aims to investigate the effect of mass transfer on hygrothermal conditions, and the energy consumption of a building made with date palm concrete (DPC). As well as, highlighting the performance of this new bio-composite material compared to other classical materials. The results were obtained by numerical simulation using EnergyPlus software. Firstly, the hygrothermal model was validated using experimental data. Afterward, the model was used to investigate the influence of moisture transport on indoor temperature, relative humidity, and energy consumption of a DPC building. The results indicate that mass transport through the DPC envelope significantly influences indoor relative humidity and building energy consumption, particularly during the summertime. On the contrary, the indoor temperature remains to be less influenced by mass transport through the building walls. Finally, the hygrothermal performances of DPC and conventional construction materials (Autoclaved Aerated Concrete, AAC) were compared in terms of thermal and hydric inertia, energy saving, and condensation risk. The results show that DPC significantly dampens and delays the temperature variation, mitigates relative humidity variations, and reduces cooling energy, especially in humid and semi-arid climates. Furthermore, DPC ensures that there is no risk of vapor condensation occurring on the envelope's surfaces.

Reduced energy consumption and enhanced CO2 desorption performance of non-aqueous ionic-liquid-containing amine blends with zeolites

• The MEA-[bmim][Ac]-hexanol blend exhibited higher absorption and desorption activities than MEA-hexanol. • Zeolite catalysts (HZSM-5, MOR, and FER) showed enhanced effect on CO 2 stripping. • Catalyzed-CO 2 desorption rate was improved by 12–14 times. • The use of zeolite catalyst can lower the heat duty by 45%–50%. • The use of the zeolite catalysts reduced the desorption time by 25%–30%. Excessive emissions of carbon dioxide (CO 2 ), a primary greenhouse gas, can cause serious environmental concerns, such as climate change and global warming. Therefore, carbon capture, utilization, and storage (CCUS) strategies are crucial for reducing the global carbon footprint. However, the considerable energy penalty in the regeneration of CO 2 -loaded solutions is a critical challenge encountered in the industrial application of amine-based post-combustion CO 2 capture technology. Therefore, an alternative pathway involving the use of zeolite catalysts was demonstrated in this study to decrease the energy requirement. The regeneration behavior of two different amine systems, namely monoethanolamine (MEA; 5 M)-1-hexanol and MEA (4 M)-1-butyl-3-methylimidazolium acetate (1 M) ([bmim][Ac])–hexanol, with three different modified zeolites (HZSM-5, H-ferrierite (FER), and H-mordenite (MOR)) were studied. The results revealed that the catalyst enhanced the CO 2 desorption performance of the non-aqueous hybrid system and reduced the relative energy consumption compared to those of the catalyst-free runs. The zeolite-enabled enhancements of the desorption performance of the rich MEA-hexanol solvent were ordered as follows: HZSM-5 > FER > MOR. HZSM-5 showed a higher catalytic performance than that of FER and MOR, and reduced the heat duty by ∼ 45% compared with that of the catalyst-free test. The zeolite catalysts decreased the desorption time for the regeneration of rich non-aqueous solutions by 25%–30% compared to that of the blank run. In summary, the results contribute toward a comprehensive understanding of the combination of non-aqueous amine blends and zeolite catalysts as an energy-efficient strategy for decreasing the energy consumed during regeneration.

CuO modified KIT-6 as a high-efficiency catalyst for energy-efficient amine solvent regeneration

Chemical absorption using an amine solution is currently the most common and effective CO2 capture technology. However, this method suffers from the high energy consumption for solvent regeneration, which hinders its industrial application. It has been proven that catalytic CO2 desorption technology using solid acid catalysts is a promising way to lower regeneration energy consumption. In this study, three transition metal oxides (CuO, NiO and Fe2O3) modified mesoporous molecular sieve (KIT-6) catalysts were prepared and then utilized to catalyze the regeneration process of a rich CO2 monoethanolamine solution. The findings demonstrated that the four catalysts enhanced the CO2 desorption rate and reduced the relative energy consumption. The relative energy consumptions (%) for the amine solvent regeneration using the prepared catalysts follows the order of: blank test (100) > KIT-6 (79.8) > Fe2O3-KIT-6 (76.5) > NiO-KIT-6 (75.4) > CuO-KIT-6 (66.6). The superior catalytic activity of the CuO-KIT-6 catalyst is attributed to its high Brønsted acid sites and large mesoporous surface area. The cyclic test results revealed that after five cycles of absorption–desorption, the catalytic activity of CuO-KIT-6 was still kept at 93.4%. In addition, a potential CuO-KIT-6 catalyst-based catalytic CO2 desorption mechanism was suggested. This study provides a new idea to design and prepare a high-efficiency catalyst to promote regeneration of the CO2-loaded amine solution, lower the regeneration energy consumption, and ultimately increase the economic viability of the catalytic regeneration method.

Construction of International Education Talents Training Mechanism Based on Data Fusion Algorithm

With the increasing trend of economic globalization and the continuous evolution of economic forms, the role of science and technology in promoting the development of human civilization has become more and more obvious. With the improvement of comprehensive national strength, the knowledge economy has brought new opportunities and challenges. With the increasing internationalization of higher education, it has not only driven the economic exchanges between countries but also led to the exchange of educational resources among transnational countries. However, China’s international education talent cultivation started late, with poor teaching awareness, imperfect discipline construction, and low quality of international education talents. A series of problems have seriously hindered the establishment of the talent training mechanism. In order to establish a reasonable and perfect talent training mechanism, optimize the international education talent training mechanism, make the international education talent training mechanism keep up with the development trend of science, technology, and education in the contemporary world, promote the internationalization of international education talent training, and strengthen the exchange of educational resources among countries. This paper uses various algorithms of fusion algorithms to accurately calculate the talent data. The algorithm results show that the optimized grouped quadratic combined Kalman filter has a shorter running time than the traditional Kalman filter, which decreased about 20%. The S-LEACH algorithm outperforms the traditional algorithm in the simulation results of the number of surviving nodes, relative energy consumption, and data transmission. In response to the national strategy of rejuvenating the country through science and education, cultivating international talents and building a modern talent training mechanism are particularly important.