Process Performance Research Articles

Pulmonary Emphysema Disease Classification Model Using Deep Structured Techniques

Emphysema is normally employed in the sense of morphology, and thus, imaging the modalities has a significant part in detecting the disorder. Most importantly, the High-Resolution Computed Tomography (HRCT) is a trustworthy component for illustrating the emphysema pathology. Hence, the pulmonary emphysema diagnosis at the starting stage is very important and can secure the life of a human. When there is a failure in the technique, there is a possibility of the death that displays the importance of effective diagnosis. The endotracheal intubation’s delay is prevented by implementing distinct mechanisms that result in various requirements. The presented work develops a deep learning-aided pulmonary emphysema disease classification model that is implemented to defeat the above difficulties. At first, the images are accumulated from a standard dataset. Then, the collected images are provided to the phase of segmentation. Here, the newly developed Adaptive Trans-Residual Dense UNet (ATRDUNet) is employed to perform the segmentation process. Also, the recommended ATRDUNet model helps to locate the disease in the appropriate location. Moreover, the performance of the segmentation process is enriched to fine-tune the parameter using the Fitness Revised Position of Wild Horse Optimizer (FRP-WHO). While optimizing the parameters effectively, it tends to handle the complex issues to strengthen the model accurately. The segmented results from the ATRDUNet are passed to Vision Transformer-based Residual DenseNet with LSTM layer (ViTrans-RDLSTM) for getting the final classified outcome. To this end, the analysis of the suggested model is correlated to find the effectiveness of the recommended model. However, these results are examined with the help of standard performance measures. Also, the experimentation is carried out using the standard “Automatic emphysema detection using weakly labeled HRCT lung images” dataset to attain better outcomes in the developed model. The findings of the recommended approach show 95% in terms of accuracy. Also, the statistical analysis of the developed model shows 23.9%, 12.3%, 15.2%, and 13.0% better performance than BWO, CO, HBA, and WHO in terms of mean analysis to show enriched performance.

Macrokinetics of ammonium sulfite oxidation inhibited by sodium thiosulfate in wet ammonium flue gas desulfurization

AbstractThe oxidation of ammonium sulfite must be inhibited to improve the economy and cycle performance of the ammonia flue gas desulfurization process. A stirred bubbling reactor was used to investigate the macrokinetics of oxidation inhibition of ammonium sulfite by sodium thiosulfate in wet ammonia desulfurization. The effects of initial concentration, Na2S2O3 concentration, pH value, gas flow rate, and temperature on the oxidation rate were discussed. The results show that the apparent activation energy of the reaction is 37.3 kJ/mol. The macrokinetics equation of sodium thiosulfate inhibiting sulfite oxidation in the ammonia desulfurization process was established. Combined with the kinetic model, it is inferred that the rate of sodium thiosulfate inhibiting ammonium sulfite oxidation is controlled by the intrinsic chemical reaction. The results can provide a reference for the regeneration of ammonium sulfite in the wet ammonia desulfurization process.

A Carbon Capture and Utilization Process for the Production of Solid Carbon Materials from Atmospheric CO2 - Part 2: Carbon Characterization.

This article is the second part of a study reporting the results of a novel carbon capture and utilization (CCU) process, which converts atmospheric CO2 into solid carbon materials. The CCU process combines direct air capture (DAC) with catalytic methanation, which is then followed by methane pyrolysis in a reactor filled with liquid tin. While Part 1 discussed the performance of the overall process and individual process steps regarding conversions and yields, Part 2 characterizes the solid carbon products obtained under various synthesis conditions. The effects of the pyrolysis temperature and the composition of the gas mixture from the methanation step on the solid carbon product are analyzed. Carbon powder is synthesized from methane with either H2 or CO2 as most important impurity. Carbon samples are characterized using SEM, TEM, Raman spectroscopy, XPS and XRD analysis. Very thin, disordered carbon flakes, soot aggregates and large carbon onions are identified as the main solid products of the process. Their formation mechanisms in the liquid metal-filled pyrolysis reactor are discussed.

Synthesis of Black Seed Oil-loaded-, Alginate-based, pH-sensitive Beads Using Electrospraying Technique.

Black seed oil (BSO), derived from the seeds of the Nigella sativa plant, has garnered attention for its potential anti-cancer properties, particularly in the context of colon cancer. Its active compound, thymoquinone, may help inhibit cancer cell growth and induce apoptosis in colon cancer cells. Additionally, black seed oil's anti-inflammatory and antioxidant effects could contribute to a healthier gut environment, potentially reducing cancer risk. Therefore, this study synthesized pH-sensitive alginate beads to deliver BSO into the colon in a controlled-release manner without releasing the drug at pH 1.2 (stomach), thus providing a well-defined release pattern at pH 6.8. The use of electrospray technology improves process performance by making it easier to formulate small, homogeneous beads with a higher rate of swelling and diffusion in the gastrointestinal medium. The formulated beads were characterized by an ex-vivo mucoadhesive strength test, bead size, sphericity factor (SF), encapsulation efficiency (EE), scanning electron microscope (SEM), in vitro swelling behavior (SB), and in vitro drug release in acidic and buffer media. All these manufactured beads demonstrated modest sizes of 0.58 ± 0.01 mm and spherical shape of 0.03 ± 0.00 mm in this testing. The formulation showed promising floating and releasing properties in vitro. With a very low cumulative percentage of beads, the oil EE of 90.13% ± 0.93% was high, and the release study demonstrated more than 90% in pH 6.8 with good floating nature in the stomach. Additionally, the beads were evenly spaced throughout the intestine. The electrospraying approach used in this protocol can be reproducible, yielding consistent outcomes. Therefore, this protocol can be used for large-scale production for commercialization purposes.

Android Based Sports Infrastructure E-Booking Application at Provincial Youth and Sports Office Using Waterfall Method

Booking is an agreement process in the form of ordering goods or services. The Youth and Sports Service (DISPORASU) is a government agency under the auspices of the Governor of North Sumatra. DISPORASU provides sports infrastructure that can be used by the general public by rental method. In the rental process, DISPORASU still applies the direct rental method, by coming to the DISPORASU office in Medan City, so this results in a large number of tenants piling up at the same time. Therefore, a system is needed that helps tenants in carrying out the infrastructure rental process. The concept is an Android-based Sports Infrastructure E-Booking application. This application was built and designed using the waterfall model and UML (Unified Modeling Language) with several diagrams, namely Use Case Diagrams, Activity Diagrams and Class Diagrams. The programming language used is Dart, Flutter Framework and Firebase as the database. By building this sports infrastructure E-Booking application, it is hoped that it can help improve performance in future business processes and become a reference for employees/staff in this agency itself.

A serial mediation model relationship among input, process, and research performance: the role of output, productivity, and outcome

A serial mediation model relationship among input, process, and research performance: the role of output, productivity, and outcome

Nursing process performance measurement tool based on bloom’s revised taxonomy: A validity and reliability analysis

AimThis study aimed to develop a measurement tool that could evaluate educational approaches by integrating the cognitive dimensions required at each stage of the nursing process and strengthening the connection between education and clinical practice. BackgroundThe nursing process is a core component of nursing practice and supports nurses and nursing students in systematically performing the problem-solving process necessary for patient health management. DesignCross-sectional, methodological study. MethodsA nursing process performance measurement tool that could measure the cognitive dimensions required at each stage of the nursing process was developed based on Anderson’s and Bloom’s revised taxonomy. The data for this study were collected from July to August 2023 at four universities in South Korea. The reliability and validity of the measurement tool were evaluated through a survey conducted on 406 nursing students. Participants were third- and fourth-year nursing students with clinical practice experience, recruited through purposive sampling. ResultsThe developed tool was designed to systematically measure and evaluate each step of the nursing process. This tool consisted of 28 questions, each of which was divided into five assessment questions, six diagnosis questions, six planning questions, five implementation questions and six evaluation questions. Items were rated on a 5-point Likert scale, ranging from 1 (“not at all”) to 5 (“very much”). The measurement score ranged from 28 to 140 points, with higher scores indicating better nursing process performance. The cumulative variance contribution rate of the tool was 68.1 % and the Cronbach’s α value was 0.85 for all items. ConclusionsThis study provides a basis for evaluating educational approaches in nursing curricula by integrating the cognitive dimensions required at each stage of the nursing process. This approach can strengthen the connection between education and clinical practice and can be used not only as a tool to systematically evaluate the learning outcomes of nursing students but also as a standardized nursing process guide.

Effect of manufacturing new U-shaped electrode on die sinking EDM process performance

Traditional electrical discharge machining (EDM) electrodes have several limitations such as long machining time, low material removal rate MRR, hazard emissions and high energy consumption. To overcome these limitations, new electrodes (plane and frame U-shaped) were manufactured considering the electrode manufacturing classifications, such as electrode flushing mechanism, electrode cross-sectional area and electrode bottom machining area. Experiments were performed on a die-sinking EDM machine using copper electrodes and aluminum alloy workpieces. The results were compared with those obtained using the conventional electrode. The newly manufactured electrodes significantly improved the EDM process performance, reducing the machining time by more than 50%, which implies a higher MRR. The reduced machining time also led to lower costs, emissions and energy consumption for efficient EDM processes. Additionally, surface roughness measurements were performed to compare the surface quality of the machined areas using different pulse-code generator systems and electrodes. The results showed an improvement in the machined surface quality when using the new electrodes in comparison with the conventional electrode when using a roughing pulse code generator system.

Energy-efficient FDM printing of sustainable polymers: Optimization strategies for material and process performance

The integration of sustainable polymers in fused deposition modeling (FDM) 3D printing offers a promising pathway toward reducing the environmental impact of additive manufacturing. However, the energy-intensive nature of FDM processes presents a significant challenge to the overall sustainability of this technology. In this study, we explore the use of bio-based and recycled polymers in FDM printing and develop optimization strategies to reduce energy consumption without compromising material and print performance. Our results demonstrate that by systematically optimizing key printing parameters such as extrusion temperature, print speed, and layer height it is possible to achieve up to 20% energy savings. Additionally, we find that novel material formulations and advanced thermal management techniques enhance the mechanical properties of printed objects by up to 15%, all while minimizing energy use. This research not only advances the field of sustainable 3D printing but also provides a framework for the development of next-generation materials and processes that align with the principles of a circular economy.

Optimizing decentralized implementation of state estimation in active distribution networks

AbstractThe challenges facing active distribution networks have highlighted the position of the distribution system state estimation (DSSE) process in the distribution management systems as its most important function. Here, regarding the extensive scale of distribution networks and the weaknesses of centralized methods, the decentralized implementation of the DSSE process has received considerable attention. However, predefined network partitioning is supposed in previous works and zone size effects on the performance of the DSSE process have not been assessed. In response, a method for finding the optimal number of network zones and their size is proposed here. For this purpose, initially, an algorithm is used to partition the network into all possible configurations with different sizes. Subsequently, performance metrics affected by zone sizes, such as execution time, accuracy of the DSSE results, and reliability in achieving the results at the control centre, are modelled. Finally, by applying the decentralized DSSE method across all partitioning scenarios and calculating performance metrics, the most efficient and cost‐effective partitioning scenario can be identified. The performance of the proposed method is evaluated using the modified 77‐bus UK distribution network as an active test case, and the findings are subsequently presented and analysed.

Research on Stock Price Prediction Model Based on Sentiment Factor and Multi-Core Bagging Algorithm

Stock price prediction model is one of the key research points in the quantitative financial field. With the rapid development of data acquisition and storage technology, the data of emotional factors based on nonlinear structure is increasing. How to combine this part of information with common European technology factors to increase the ability of the model is a problem that needs to be solved. Based on this, this paper proposes a method of uncertainty quantification that combines European-and non-European data. Specifically, on the one hand, the Gaussian process regression model is used to capture the unknown relationship between predictive variables and price; on the other hand, the multi-kernel learning technology based on European and non- European kernel functions is used to combine the effective information of European and non- European predictive variables. Moreover, the proposed method can weigh the bias of the prediction model against the variance by using the Bagging algorithm. The simulated data analysis shows that the proposed method not only improves the prediction performance of the European Gaussian process, but also works in the presence of irrelevant predictive variables. Finally, the actual data analysis shows that the proposed method has achieved good prediction results in the task of predicting the rise and fall of stock prices.

The use of chain extenders as processing aids in the valorization of single-use polylactide (PLA) products by rotomolding

Biodegradable plastics in single-use products have increased in popularity as a way to reduce the negative environmental impact of conventional plastics and meet the tightening law regulations. However, their recyclability needs to be assessed, as the environmental behavior of single-use plastics, even if compostable, is not negligible. Polylactide (PLA) is susceptible to thermal, oxidative, hydrolytic, and mechanical degradation during reprocessing, so the conditions of such cycles must be accurately controlled. The necessity of using additives to reduce such degradations during rotational molding, a process with long cycle times and oxidizing atmosphere, has been demonstrated. Chain extenders based on a carbodiimide (Bioadimide® 100 - KI), a methylene diphenyl diisocyanate (MDI), and a polystyrene-acrylic copolymer (Joncryl® ADR-4368c - J) have been added to post-consumer PLA wastes. It has been demonstrated that these three chain extenders enabled obtaining a higher molecular weight of the reprocessed polymer, compared to the ∼50% reduction for the neat PLA. The use of carbodiimide yielded the most similar performance to that of unprocessed raw material. All samples provided adequate thermal stability and processing parameters for the rotational molding. Carbodiimide is considered the most efficient additive, as it increases the molecular mass of the polymer as it remains unchanged after processing. Similarly, KI-modified PLA rheological behavior remains unchanged after processing, which means this compound can reduce thermooxidative and hydrolytic degradation reactions. Thus contributing to the achievement of improved processability and better performance; in particular, impact strength increased from 1.06 ± 0.56 for PLA to 8.12 ± 2.28 kJ/m2 for KI-modified PLA, and toughness of 5.36 ± 1.61 to 61.49 ± 8.01 J/mm3, respectively, leading to rotomolded items without structural defects. On the opposite, the use of Joncryl® and MDI led to structural defects in the rotomolded parts despite a higher molecular weight of the polymer, which resulted in poor mechanical properties, although better than PLA without any additive. All three chain extenders resulted in an amorphous PLA structure with increased glass transition temperature and improved thermal stability, which correlated with the reduced emissions of volatile compounds compared to neat recycled PLA. The presented findings significantly contribute to a better understanding of PLA and its modifications at a molecular scale required for its efficient and possibly environmentally burden-free reprocessing. Gathered knowledge will be crucial for optimizing its environmental performance and widening its applications, particularly in a process with long cycle times and oxidative character, such as rotational molding.

Data-driven assessment of business process resilience

Process resilience represents a core competence for organizations in light of an increasing number of process disruptions, such as sudden increases in case arrivals or absences in the workforce. It reflects an organization’s ability to restore a process to its acceptable performance level after a disruption. In this regard, the first key step for organizations towards achieving resilience is to understand how resilient their processes actually are. Although recognized as important, few works focus on such resilience assessment in a data-driven manner, thus barring organizations from gaining the necessary insights into how much their processes are affected by disruptions and how long it takes them to recover. To address this problem, we propose an approach for automated resilience assessment, based on recorded event data. Our approach interprets relevant process characteristics, such as the average lead time or arrival rate, as time series, which capture the development of the process execution over time. Based on these time series, it uses statistical modeling, specifically a vector autoregressive model, to determine the inter-relations between those characteristics and assess how the process performance responds to a disruption, i.e., a significant and temporal change in one of the process characteristics. We validate our approach by comparing its accuracy with a what-if analysis using a simulation model and demonstrate its effectiveness by assessing the resilience of the same process to diverse disruptions across different organizations.

Design and Performance Analysis of Windings Considering High Frequency Loss and Connection Mode of a Starter Generator

The starter generator is the key component of the aircraft’s starter generation system. A high power density, efficiency, and reliability are essential for an aircraft’s starter generator. The configuration of the windings is closely related to the performance of the starter generator. In this paper, the winding performance analysis and design are studied. Considering the skin effect, circulation effect, and proximity effect, a calculation model of winding loss under high-frequency operation is established. The motor performance under different winding connections is compared. Through a comprehensive comparison of starting performance, generation loss, and process performance differences, the optimum configuration of windings is identified. The performance of the starter generator including winding resistance, current, and no-load loss was tested on the prototype platform to verify the rationality and correctness of the analysis and design methods.

The potential of alkaline tolerant microbial consortia for textile wastewater treatment under integrated anaerobic/aerobic conditions: Performance evaluation and microbial community analysis

Sequential anaerobic/aerobic (A/O) treatment conditions for textile wastewater (WW) are more effective than conventional biological treatment. Anaerobic treatment is essential because anaerobic microbes can first break down complex and recalcitrant compounds, which are difficult to degrade under aerobic conditions. The simpler, more degradable compounds are then further broken down by aerobic microbes. This study aimed to evaluate the performance of a sequential A/O treatment process using a pilot-scale reactor to treat real textile WW and to characterize reactor and inoculum microbial community structures. The reactors were inoculated with microbial consortia originating from a diverse alkaliphilic soda lake in the Ethiopian Rift Valley. The WW test parameters were used to evaluate the performance of the treatment process. At steady state, the removal efficiencies were 97 % for dye, 86 % for Chemical Oxygen Demand (COD), and 93 % for Total Kjeldahl Nitrogen (TKN). Amplicon sequencing revealed that Firmicutes, Proteobacteria, and Actinobacteria were the dominant phyla in all the samples. Uncategorized microorganisms, followed by Alkalibacterium, Bifidobacterium, and Clostridium were the most abundant taxon in all the samples. The microbial community detected during the treatment process was not abundant in the inoculum originating from Lake Chitu, suggesting that the communities likely originated from textile WW. The textile WW treated with the integrated A/O process effectively degraded dyes, and the inoculated microbes demonstrated resistance to the toxic chemical composition of the WW. The integrated treatment process, along with the alkaliphilic microbial consortia, has proven to be practical for treating textile WW, offering valuable insights for field-scale applications.

Moderating role of technological turbulence between green product innovation, green process innovation and performance of SMEs

Innovation in green technologies is a key factor propelling Jordanian sustainable development in the small and medium enterprises for achieving sustainability. Jordan is considered as a hub of small and medium enterprises, however, despite technological advancements and the efforts of the government, small and medium enterprises in Jordan are scarcely catering the aspect of green activities and researchers have paid less attention towards the same. Green product and green process innovation mode choices have drawn enough attention recently, but limited researchers have thoroughly examined the pathways through which green innovation is transmitted to small and medium enterprises’ performance. Green product and process innovation are the two main streams of green innovation that can enhance small and medium enterprises’ performance. The current research builds the framework for SMEs performance based on green product innovation and green process innovation following earlier research that have been conducted in the context of developed countries. It conducts an empirical analysis using responses of 384 manufacturing sector small and medium enterprises in Jordan. The findings demonstrate that small and medium enterprises can perform better when green products and processes are innovated. One significant moderating variable over the performance paths of small and medium enterprises is technological turbulence. The findings of the research contribute to a better understanding of green innovation and have implications for designing policies that promote green development.

Digitalisation of manufacturing and innovation: the role of inbound open innovation and environmental turbulence in enabling Industry 4.0-based innovation

Industry 4.0 (I4.0) technologies have demonstrated their potential to significantly drive innovation and enhance the performance of firms. However, I4.0 implementation is complex, requiring the application of diverse fields of knowledge and skill. Furthermore, although open innovation may contribute to the implementation of I4.0 technologies, the literature is still not clear about which open innovation practices best leverage the adoption of I4.0 technologies or how they might lead to improvements in product and process innovations, particularly when firms are facing turbulent environments. The present article aims to reduce this gap by investigating the relationships between open innovation, the adoption of I4.0 technologies, and product and process innovation performance, considering technological and market turbulence as moderating variables. As a research method, we carried out a quantitative survey of 246 industrial companies in Brazil. The results revealed that inbound open innovation flows can be used to support the adoption of I4.0 technologies, which in turn improves both process and product innovation performance. Our findings also indicate that in more technologically turbulent environments, the relationship between inbound open innovation and the adoption of I4.0 is stronger. Unexpectedly, the moderating role of market turbulence was not confirmed. The discussion arising from these findings addresses theoretical implications for the digitalisation of manufacturing, innovation management and contingency theory, as well as offering practical insights for managers and policymakers interested in performing I4.0-based innovation.

Application of Soft Magnetic Composite in XEV Motor Core Manufacturing: Process Effects and Performance Analysis

This study explores the application of AncorLam HR (Höganäs, Sweden), a soft magnetic composite material, in the stator core of an axial flux permanent magnet drive motor. Building on previous research that provided mechanical and thermal properties of the material, the focus is on analyzing how the manufacturing process affects the motor core’s shape. A bulk prototype was created based on case 3, which demonstrated the least deviation in density and internal stress. The prototypes were produced under the conditions of SPM 7 and 90 °C, and a heat treatment in a nitrogen atmosphere for 1 h, resulting in an average density error of 0.54%, confirming process effectiveness. A microstructural analysis using scanning electron microscopy (SEM) on Sample 2, with the highest density, confirmed consistency between simulation and prototype trends. Electron backscatter diffraction (EBSD) and X-ray diffraction (XRD) analyses revealed that the internal phase structure remained unchanged. Energy-dispersive spectroscopy (EDS) and transmission electron microscopy (TEM) identified the elimination of phosphorus (P) during molding, affecting the insulating layer, a critical factor for SMC materials. In motor simulations and actual measurements, the average torque was recorded as 37.7 N·m and 34.7 N·m at 1500 rpm and 27.7 N·m and 25.1 N·m at 2000 rpm, respectively. The torque comparison observed in the actual measurements compared to the simulation results indicates that the output loss increases in the actual measurements due to the deterioration of the insulation performance judged based on the microstructure evaluation. This study confirms the viability of using AncorLam HR in motor cores for electric vehicles and provides key data for improving the performance.

Biofilm mass transfer and thermodynamic constraints shape biofilm in trickle bed reactor syngas biomethanation

Syngas (H2, CO2, CO) produced thermochemically from lignocellulosic biomass is an underexploited source for resource recovery and valorisation through its biological conversion for the production of a wide range of chemicals and fuels. Syngas biomethanation is one such promising bioconversion pathway, displaying interesting features such as high conversion efficiency and product selectivity, as methane is the sole product of the process. The biological conversion of syngas to high purity biomethane is still typically associated with a number of challenges related to the syngas composition, CO toxicity, and gas–liquid mass transfer limitations. In this work, the syngas biomethanation process carried out in trickle bed reactors was investigated at its boundary conditions to explore the limits of the process, focusing on syngas composition and mass-transfer conditions potentially limiting process performance. The process was found to be robust when exposed to CO excess, but highly sensitive to H2 excess, which caused severe inhibition even under small amounts of excess H2. This implies that biomethane purity comparable to natural gas can be achieved by addition of renewable H2, but this requires precise control to avoid process failure. Modulating the liquid recirculation rate and gas residence time allowed for a maximum methane productivity of 9.8 ± 0.5 mmol CH4 h−1 Lreactor−1 with full conversion of H2 and CO at a gas residence time of 1 h. Nevertheless, increasing the gas–liquid mass transfer with increasing liquid recirculation rate did not lead to increased methane productivity, which suggested additional rate-limiting bottlenecks in the process. Careful investigation of other factors potentially limiting the process led to the conclusion that diffusive transport of syngas components in the biofilm was the main bottleneck of the process. This diffusive limitation leads to a scenario of severe substrate scarcity in the biofilm phase that conditions the thermodynamic feasibility of the different biochemical reactions involved in syngas biomethanation. In turn, these thermodynamic constraints were found to drive the stratification of microbial groups and sequential consumption of syngas components along the height of the reactor.

Energy-efficient heat pump-assisted pre-concentration integrated with sequential [EMIM][BF4] and ethylene glycol-based extractive distillation for enhanced recovery of ethanol and isopropyl alcohol from wastewater

Recovering ethanol and isopropyl alcohol (IPA) from wastewater offers significant economic and environmental advantages. In this work, 1-ethyl-3-methylimidazolium tetrafluoroborate ([EMIM][BF4]) and ethylene glycol (EG) are proposed as double entrainers for separating IPA/ethanol/water mixtures. The novel process sequentially uses [EMIM][BF4] to separate ethanol/water and IPA/water azeotropes, followed by EG for near-boiling mixture of ethanol/IPA. Energy-economic comparisons of extractive distillation process using single and double entrainers are performed. To address the issue of high water content in mixtures, the heat pump-assisted pre-concentration (HPP) integrated with extractive distillation processes are proposed. Subsequently, the effect of water content in the mixtures on process performances are analyzed. The results indicate that compared to using a single entrainer [EMIM][BF4] or EG process, the double entrainers process reduces energy cost and total annual cost (TAC) by 19.9% to 45.3% and 18.6% to 52.7%, respectively. The HPP scheme effectively reduces the mole fraction of water from 60% to 26.05%, further decreasing energy costs by 4.2% to 22.2%. The energy cost and TAC of the double entrainers process decreases by 4.8% to 50%, and 2.8% to 52.1% compared with the single entrainer process under HPP schemes. The double entrainers [EMIM][BF4]-EG process shows promising prospects from energy and economic perspectives.