Performance Assessment Techniques Research Articles

High-stakes Technical Performance Assessments Across the Continuum of Surgical Training.

The aim of this article to outline proposals for the use of high stakes technical performance assessments, defined as assessments of technical skill that take place outside of the operating theatre, across the continuum of surgical training. Technological advancements have allowed for the assessment of operative technical skill outside of the workplace environment, developed and evaluated in line with modern concepts of assessment validity. With the introduction of competency-based education principles across surgical training curricula, increasing scrutiny is being placed on the validity and reliability of assessments used to inform high-stakes training decisions regarding selection, progression through training, autonomy granting and end-of-training certification. The role of performance assessments, conducted outside of the workplace environment, is yet to be fully established. This article discusses the use of high-stakes performance assessments across the continuum of surgical training, with reference to relevant theories of surgical skill acquisition and assessment, along with contemporary evidence from the performance science literature. Assessments of increasing professional authenticity are required across the continuum of surgical training. In an attempt to capture inherent ability rather than skill at the time of trainee selection, assessments should be of deliberately low levels of professional authenticity. Assessment in early surgical training should focus on ensuring competence in core surgical skills, and then assessing for canonical competence across a series of commonly encountered procedures. Baseline competence in these procedures should ideally be assessed prior to the transition to (supervised) lead operator, with a view to ensuring patient safety and improving the efficiency of in-theatre training. Assessment later in training relies more heavily on the workplace, with simulation offering a complimentary role in ensuring a minimum competency standard in procedures less likely to be encountered in the workplace. High-stakes performance assessments can play an important role as part of a programmatic approach to surgical training and certification. They can and should be designed with an appropriate level of professional authenticity to ensure adequate and appropriate representation of the assessment construct.

The Effect of Time Pressure on Motion Economy and Smoothness of Interventional Radiology Trainee Performance in Simulated Central Venous Line Placement.

To evaluate the effect of being under time pressure on procedural performance using hand motion analysis. Eight radiology trainees performed central venous access on a phantom while recording video and hand motion data using an electromagnetic motion tracker. Each trainee performed the procedure six times: the first three trials without any prompts (control), while for the next three, they were asked to perform the task as fast as possible (time pressure). Validated hand motion metrics were analyzed, and two blinded and independent evaluators rated procedural performance using a previously validated task-specific global rating scale (GRS). Motion/time ratios and linear mixed-effect methods were used to control for time, and constants for both strategies were compared. Hand motion analysis showed that trainees completed the simulated procedure faster under time pressure (46 ± 18s vs. 56 ± 27s, p = 0.008) than during the control strategy. However, when controlling for time, trainees moved their hands 79 more centimeters (p = 0.04), made 15 more translational movements (p = 0.003) and 18 more rotational movements (p = 0.01) when under time pressure compared to at their own pace. Although trainees could perform the procedure faster under time pressure, there was a deterioration in hand motion economy and smoothness. This suggests that hand motion metrics offer a more comprehensive assessment of technical performance than time alone.

Evaluating the real-world usability of BCI control systems with augmented reality: a user study protocol.

Brain-computer interfaces (BCI) enable users to control devices through their brain activity. Motor imagery (MI), the neural activity resulting from an individual imagining performing a movement, is a common control paradigm. This study introduces a user-centric evaluation protocol for assessing the performance and user experience of an MI-based BCI control system utilizing augmented reality. Augmented reality is employed to enhance user interaction by displaying environment-aware actions, and guiding users on the necessary imagined movements for specific device commands. One of the major gaps in existing research is the lack of comprehensive evaluation methodologies, particularly in real-world conditions. To address this gap, our protocol combines quantitative and qualitative assessments across three phases. In the initial phase, the BCI prototype's technical robustness is validated. Subsequently, the second phase involves a performance assessment of the control system. The third phase introduces a comparative analysis between the prototype and an alternative approach, incorporating detailed user experience evaluations through questionnaires and comparisons with non-BCI control methods. Participants engage in various tasks, such as object sorting, picking and placing, and playing a board game using the BCI control system. The evaluation procedure is designed for versatility, intending applicability beyond the specific use case presented. Its adaptability enables easy customization to meet the specific user requirements of the investigated BCI control application. This user-centric evaluation protocol offers a comprehensive framework for iterative improvements to the BCI prototype, ensuring technical validation, performance assessment, and user experience evaluation in a systematic and user-focused manner.

Self-Gated Radial Free-Breathing Liver MR Elastography: Assessment of Technical Performance in Children at 3 T.

Conventional liver magnetic resonance elastography (MRE) requires breath-holding (BH) to avoid motion artifacts, which is challenging for children. While radial free-breathing (FB)-MRE is an alternative for quantifying liver stiffness (LS), previous methods had limitations of long scan times, acquiring two slices in 5 minutes, and not resolving motion during reconstruction. To reduce FB-MRE scan time to 4 minutes for four slices and to investigate the impact of self-gated (SG) motion compensation on FB-MRE LS quantification in terms of agreement, intrasession repeatability, and technical quality compared to conventional BH-MRE. Prospective. Twenty-six children without fibrosis (median age: 12.9 years, 15 females). 3 T; Cartesian gradient-echo (GRE) BH-MRE, research application radial GRE FB-MRE. Participants were scanned twice to measure repeatability, without moving the table or changing the participants' position. LS was measured in areas of the liver with numerical confidence ≥90%. Technical quality was examined using measurable liver area (%). Agreement of LS between BH-MRE and FB-MRE was evaluated using Bland-Altman analysis for SG acceptance rates of 40%, 60%, 80%, and 100%. LS repeatability was assessed using within-subject coefficient of variation (wCV). The differences in LS and measurable liver area were examined using Kruskal-Wallis and Wilcoxon signed-rank tests. P < 0.05 was considered significant. FB-MRE with 60% SG achieved the closest agreement with BH-MRE (mean difference 0.00 kPa). The LS ranged from 1.70 to 1.83 kPa with no significant differences between BH-MRE and FB-MRE with varying SG rates (P = 0.52). All tested methods produced repeatable LS with wCV from 4.4% to 6.5%. The median measurable liver area was smaller for FB-MRE (32%-45%) than that for BH-MRE (91%-93%) (P < 0.05). FB-MRE with 60% SG can quantify LS with close agreement and comparable repeatability with respect to BH-MRE in children. 2 TECHNICAL EFFICACY: Stage 1.

Deployment and validation of a smart bed architecture for untethered patients with wireless biomonitoring stickers.

Conventional patient monitoring in healthcare has limitations such as delayed identification of deteriorating conditions, disruptions to patient routines, and discomfort due to extensive wiring for bed-bound patients. To address these, we have recently developed an innovative IoT-based healthcare system for real-time wireless patient monitoring. This system includes a flexible epidermal patch that collects vital signs using low power electronics and transmits the data to IoT nodes in hospital beds. The nodes connect to a smart gateway that aggregates the information and interfaces with the hospital information system (HIS), facilitating the exchange of electronic health records (EHR) and enhancing access to patient vital signs for healthcare professionals. Our study validates the proposed smart bed architecture in a clinical setting, assessing its ability to meet healthcare personnel needs, patient comfort, and data transmission reliability. Technical performance assessment involves analyzing key performance indicators for communication across various interfaces, including the wearable device and the smart box, and the link between the gateway and the HIS. Also, a comparative analysis is conducted on data from our architecture and traditional hospital equipment. Usability evaluation involves questionnaires completed by patients and healthcare professionals. Results demonstrate the robustness of the architecture proposed, exhibiting reliable and efficient information flow, while offering significant improvements in patient monitoring over conventional wired methods, including unrestricted mobility and improved comfort to enhance healthcare delivery.

Non-destructive characterization techniques for battery performance and life-cycle assessment

Non-destructive characterization techniques for battery performance and life-cycle assessment

Technical and Economic Performance Assessment of Solar Milling Plants in Zambia

In 2015, the Zambian government embarked on a proactive endeavor to enhance the accessibility of economical maize meal through the inception of the Solar Powered Milling Plants (SMPs) Project. The Chinese Government and the Government of the Republic of Zambia jointly provided co-financing, supporting this audacious initiative with a significant financial commitment of US$200 million. However, there have been no documented results on the technical and economic performance of the solar milling plants. To this effect, this study aimed to establish the technical and economic performance of the milling plants installed by the Zambia Cooperative Federation. To achieve this, the study sought to: (1) establish the technical operational characteristics of the installed SMPS; (2) establish the factors that influenced the sitting of the plants; (3) determine the economic benefits of the SMPs; and (4) benchmark the technical operating conditions of the installed SMPs. The study employed a mixed methods approach, where benchmarking observations and semi-structured questionnaires were used as data collection instruments. The sample size of the study was 168, comprising cooperative leaders, solar milling plant operators, and community members from the 12 sites in Masaiti, Chikankata, Solwezi, and Kapiri Mposhi districts where the milling plants were installed. The field observations and questionnaires that were used for the study showed that all twelve sites in the Solwezi, Masaiti, Kapiri Mposhi, and Chikankata Districts needed the same amount of power to run, which was between 7.5 kW and 9.0 kW. However, only Site 3 in Solwezi and Sites 4 and 5 in Masaiti Districts strictly followed the standard operation time of 8 hours. The study has also shown that the two main factors that influenced the placement of the solar milling plants were proximity to the main roads of the communities and the availability of land near the Food Reserve Agency(FRA) shades. With regards to economic benefits, the generated results showed that the installed milling plants brought about employment creation as two operators were employed for each of the 12 sites. However, the revenues generated by the solar milling plants were not economically viable for loan repayment. The study recommended that predictive and corrective maintenance, in addition to project management training for cooperatives and operators, be carried out to improve the technical and economic performance of the solar milling plants.

Technical and economic performance assessment of blue hydrogen production using new configuration through modelling and simulation

Steam methane reforming (SMR) is the dominant process for hydrogen production, which produce large amount of carbon dioxide (CO2) as a by-product. To address concerns about carbon emissions, there is an increasing focus on blue hydrogen to mitigate carbon emissions during hydrogen production. However, the commercialization of blue hydrogen production (BHP) is hindered by the challenges of high cost and energy consumption. This study proposes a new configuration to address these challenges, which is characterized by: (a) the use of piperazine (PZ) as a solvent, which has a high CO2 absorption efficiency; (b) a more efficient heat exchange configuration which recovers the waste exergy from flue gas; (c) the advanced flash stripper (AFS) was adopted to reduce the capital cost due to its simpler stripper configuration. In addition, the technical and economic performance of the proposed energy and cost-saving blue hydrogen production (ECSB) process is investigated and compared with the standard SMR process. The detailed models of the SMR process and the post-combustion carbon capture (PCC) process were developed and integrated in Aspen plus® V11. The results of the technical analysis showed that the ECSB process with 30 wt.% PZ achieves a 36.3 % reduction in energy penalty when compared to the standard process with 30 wt.% Monoethanolamine (MEA). The results of the economic analysis showed that the lowest levelized cost of blue hydrogen (LCBH) was achieved by the ECSB process with 30 wt.% PZ. Compared to the BHP process with 30 wt.% MEA, the LCBH was reduced by 19.7 %.

Computer-Aided Diagnosis System for Automated Detection of Mri Brain Tumors

Detection and classification of brain tumors in manual or traditional way is an area which could be improved by having such automated detection and clarification system for brain tumors. In this paper, enhanced Computer-Aided Diagnosis CAD software system is introduced for brain tumor detection and classification. Total of 229 brain MRI images was taken as dataset for the purpose of this research; those dataset images include 105 normal brain MRI images, and 124 abnormal brain MRI images. Proposed CAD system is specialized for Meningioma brain tumor detection and classification, and the technique could be generalized and implemented for Glioma, and Pituitary brain tumors as well, and the whole system was implemented using MATLAB software. We started by cropping the region of interest (ROI) of dataset images. Then, feature extraction was implemented using first order statistical features, as well as using of some wavelets filters in combination with the former. T-test is used to exclude features of no statistical significance (p-value &lt; 0.05). After that, different types of classifiers were used to separate the normal set from the abnormal one. Note that, we used an iterative approach to by changing features with many runs until we got best performance, where, best accuracy results were gotten with SVM-Kernel Function (Linear), KNN-1, KNN-3, and KNN-5 classifiers. Note also that, we used convolutional neural networks (CNN) from Deep Learning toolbox of MATLAB as a control method to compare, where the images were fed directly to the CNN. The results were evaluated using performance assessment techniques which are Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, Error Rate, and Area Under the Curve (AUC) of Reciever Operator Characteristic (ROC). With SVM classifier, the best gotten accuracy results were 91 % with CNN classifier, 82% with SVM classifier, and 77 % with KNN classifier. Furthermore, it was very beneficial to find such feature extraction techniques which gave acceptable accuracy results with three different classifiers; this was the case two times as mentioned the study. All proposed CAD system areas was developed and implemented using MATLAB software.

Optimizing MS-Based Multi-Omics: Comparative Analysis of Protein, Metabolite, and Lipid Extraction Techniques.

Multi-omics integrates diverse types of biological information from genomic, proteomic, and metabolomics experiments to achieve a comprehensive understanding of complex cellular mechanisms. However, this approach is also challenging due to technical issues such as limited sample quantities, the complexity of data pre-processing, and reproducibility concerns. Furthermore, existing studies have primarily focused on technical performance assessment and the presentation of modified protocols through quantitative comparisons of the identified protein counts. Nevertheless, the specific differences in these comparisons have been minimally investigated. Here, findings obtained from various omics approaches were profiled using various extraction methods (methanol extraction, the Folch method, and Matyash methods for metabolites and lipids) and two digestion methods (filter-aided sample preparation (FASP) and suspension traps (S-Trap)) for resuspended proteins. FASP was found to be more effective for the identification of membrane-related proteins, whereas S-Trap excelled in isolating nuclear-related and RNA-processing proteins. Thus, FASP may be suitable for investigating the immune response and bacterial infection pathways, whereas S-Trap may be more effective for studies focused on the mechanisms of neurodegenerative diseases. Moreover, regarding the choice of extraction method, the single-phase method identified organic compounds and compounds related to fatty acids, whereas the two-phase extraction method identified more hydrophilic compounds such as nucleotides. Lipids with strong hydrophobicity, such as ChE and TG, were identified in the two-phase extraction results. These findings highlight that significant differences among small molecules are primarily identified due to the varying polarities of extraction solvents. These results, obtained by considering variables such as human error and batch effects in the sample preparation step, offer comprehensive and detailed results not previously provided by existing studies, thereby aiding in the selection of the most suitable pre-processing approach.

Enhancing sustainable facade design: A Belgrade case study

This research examines the potential of widespread facade systems to improve energy performance and enhance user comfort in residential buildings. Utilizing Design Builder software (Version 5.0.3.7), the study specifically aimed to reduce heating energy consumption, a significant concern in the climatic conditions of Serbia. The methodology incorporated both technical performance assessments and economic analyses, evaluating the economic viability through metrics such as Return on Investment (ROI), Net present value (NPV), and Internal rate of return (IRR). The multi-criteria evaluation framework employed allowed for a comprehensive analysis, balancing technical performance with economic and qualitative factors to identify the most favorable facade systems. By addressing both the immediate economic returns and longer-term benefits, this research contributes to a more sustainable and economically feasible building practice.

Multi-criteria decision support framework for climate change-sensitive thermal comfort evaluation in European buildings

This paper proposes a novel multi-criteria decision support framework for climate change-sensitive evaluation of building thermal comfort performance in European buildings. The proposed framework considers various comfort categories based on building use and condition, comfort models based on building operation, sensitivity to climate change, key performance indicators, and their thresholds. The proposed framework is then implemented using a passive house-certified high-performance timber dwelling near Brussels as a case study. The thermal comfort in the free-running reference timber dwelling is assessed using a whole-building energy performance simulation model with a static threshold of 26°C for the bedrooms and an adaptive threshold for the other occupied zones. The analysis found an increase in indoor overheating degree by 1.7°C and ambient warmness degree by 6.5°C during heat waves from the current scenario to the end of the century scenario. However, the reference timber dwelling effectively suppressed the impacts of climate change with varying degrees of success towards the end of the century. The proposed framework is intended to support decision-makers in effectively evaluating performance during the early stages of building design. The study highlights the need for further research on building performance assessment techniques and guidelines in a changing climate.

Machine Learning and Deep Learning Models for Predicting the Onset of Diabetes: A Pilot Study

Diabetes currently one of the most significant worldwide concerns, and its prevalence is only expected to increase in the future years. In order to monitor glucose levels in the blood and set treatment protocols for diabetes, keeping a regular schedule for checking blood glucose levels is essential. The purpose of widespread adoption of digital health in recent years has been to enhance diabetic healthcare for patients, and as a result, a massive quantity of data has been collected that may be used in the ongoing management of this chronic condition. Deep learning, a relatively new kind of machine learning, is one method that has taken advantage of this trend, and its applications seem promising. In this research, we provide a thorough analysis of how deep learning has been used in the study of diabetes thus far. We conducted a comprehensive literature search and found that this method is most often used in the following settings: diabetes diagnosis, glucose control, and the identification of diabetes-related complications. We have described the most important details regarding the learning models used, the development process, the primary outcomes, and the baseline techniques for performance assessment from the 40 original research publications that we selected based on the search. In the reviewed literature, it becomes clear that several deep learning algorithms and frameworks have outperformed traditional machine learning methods to attain state-of-the-art performance on numerous problems involving diabetes. However, we point out several gaps in the existing literature, such as a dearth of readily available data and a lack of clarity in the interpretation of models. In the near future, these obstacles may be surmounted thanks to the fast advancements in deep learning methodologies which will allow for wider application of this technology in therapeutic settings.

Technical and environmental performance assessment of VOCs inhibited asphalt binders and mixtures

Asphalt materials will emit a large amount of volatile organic compounds (VOCs) under high temperature conditions, to address the environmental impact of asphalt pavement construction, this study selected different proportions of activated carbon (AC) and expanded graphite (EG) as composite inhibitors of asphalt VOCs, the road performance and environmental performance of asphalt mixtures are explored. Furthermore, the study proposed the VOCs resistance index (VRI) and VOCs environment-technique index (ETI) to evaluate the environmental and environmental-technical performance of asphalt mixture incorporating composite VOCs inhibitors. The results show that the VOCs inhibited asphalt mixtures with 5% AC-3% EG composite inhibitors has the best comprehensive environmental performance (80.15%), and its environmental technical performance (27.59%) is also the best, which is far higher than the other three VOCs inhibited asphalt mixtures. This study provides a reference and evaluation standard for the future development of efficient asphalt VOCs inhibitors.

Implementation of an Online Intraoperative Assessment of Technical Performance for Surgical Trainees

IntroductionAssessment of surgical resident technical performance is an integral component of any surgical training program. Timely assessment delivered in a structured format is a critical step to enhance technical skills, but residents often report that the quality and quantity of timely feedback received is lacking. Moreover, the absence of written feedback with specificity can allow residents to seemingly progress in their operative milestones as a junior resident, but struggle as they progress into their postgraduate year 3 and above. We therefore designed and implemented a web-based intraoperative assessment tool and corresponding summary "dashboard" to facilitate real-time assessment and documentation of technical performance. Materials and methodsA web form was designed leveraging a cloud computing platform and implementing a modified Ottawa Surgical Competency Operating Room Evaluation instrument; this included additional, procedure-specific criteria for select operations. A link to this was provided to residents via email and to all surgical faculty as a Quick Response code. Residents open and complete a portion of the form on a smartphone, then relinquish the device to an attending surgeon who then completes and submits the assessment. The data are then transferred to a secure web-based reporting interface; each resident (together with a faculty advisor) can then access and review all completed assessments. ResultsThe Assessment form was activated in June 2021 and formally introduced to all residents in July 2021, with residents required to complete at least one assessment per month. Residents with less predictable access to operative procedures (night float or Intensive Care Unit) were exempted from the requirement on those months. To date a total of 559 assessments have been completed for operations performed by 56 trainees, supervised by 122 surgical faculty and senior trainees. The mean number of procedures assessed per resident was 10.0 and the mean number per assessor was 4.6. Resident initiation of Intraoperative Assessments has increased since the tool was introduced and scores for technical and nontechnical performance reliably differentiate residents by seniority. ConclusionsThis novel system demonstrates that an online, resident-initiated technical assessment tool is feasible to implement and scale. This model's requirement that the attending enter performance ratings into the trainee's electronic device ensures that feedback is delivered directly to the trainee. Whether this aspect of our assessment ensures more direct and specific (and therefore potentially actionable) feedback is a focus for future study. Our use of commercial cloud computing services should permit cost-effective adoption of similar systems at other training programs.

Artificial Immune System Implementation for Predicting WM Presence from MYD88 and CXCR4

Waldenstrom’s Macroglobulinemia (WM) is a rare malignancy that affects human blood cells and spreads slowly. The development of WM occurs whenever the blood cells undergo genetic changes. Better therapies can be offered by the healthcare sector to get rid of the symptoms that cannot be cured. Everyone in the healthcare sector is aware that genetic abnormalities cause WM, but they are unsure of what causes the alterations. The risk factors that increase the number of WM's aberrant cells have been found. The greatest risk variables have a fatal impact on humans. The healthcare sector is working to save lives by offering better care. Only when WM is discovered earlier when it is treatable with better care and potent medications, is it very likely. For analysing the healthcare data associated with WM, a number of prior research studies have suggested both standard and unique software models and techniques. However, the accuracy is subpar and inefficient in terms of both time and money. To analyse the genomic dataset and detect Waldenstrom's Macroglobulinemia or its symptoms, this research explored this issue and suggested an Artificial Immune System (AIS) approach. Software written in Python is used to conduct the experiment and validate the findings. by contrasting the trial outcomes with other performance assessment techniques. The analysis reveals that the suggested AIS algorithm works better than the others.

Comparative Analysis of Steel Wheel Rim &amp; Alloy Wheel Rim of a Car with Respective Materials

Abstract: This project conducts a comparative analysis of steel and alloy wheel rims for the Maruti Suzuki Baleno Sigma and Alpha variants. The aim is to assess the performance and suitability of these rim types for the specific vehicle models. Through a systematic examination of design considerations, structural analysis, and overall performance evaluation, the project provides valuable insights into the characteristics and capabilities of Carbon steel and Aluminium alloy wheel rims. CAD modeling, finite element analysis (FEA), and performance assessment techniques are employed to analyze stress distribution, strain levels, and deformation patterns. By understanding the differences and performance attributes of steel and alloy rims, the project assists in enhancing the overall performance and aesthetics of the vehicles.

Large-scale green hydrogen production via alkaline water electrolysis using solar and wind energy

The study provided a precise technique for comparing wind and solar solutions for large-scale production of green hydrogen. A one-year experimental dataset depicting the wind speed and solar irradiance with a precision of 1 min. The carbon footprint study was conducted, and indicators were applied and evaluated for the purpose of standardising the performance assessment technique for renewable-based hydrogen production systems, such as the electrolyser capacity fulfilled by wind turbines and solar photovoltaic power plants. On the basis of the considered capacities of 2.5 for wind turbines and solar photovoltaics for cost estimating findings, the obtained optimum electrolyser capacity can match the energy produced by the wind turbine power plant, which is 1.5 MW, which can produce hydrogen at a rate of about 11,963 kg/year at 8.87$/kg, and the obtained optimum electrolyser capacity can match the energy produced by the solar photovoltaics power plant, which is 2.0 MW, which can produce hydrogen at a rate of about 94,432 kg/year at 6.33 $/kg. Further, the polynomial relation between hydrogen production and electrolyser capacity has been destroyed. The method used is easily adaptable for testing large-scale applications such as industrial plants, as well as any hydrogen requirements and environmental conditions.

Technical performance and environmental assessment of an ionic liquid-based CCS process for hydrogen production

Hydrogen (H2) production combined with carbon capture and storage (CCS) is anticipated to be an important technology contributing to reduce the carbon footprint of current fossil-based H2 production systems. This work addresses for the first time the techno-environmental assessment of a CCS process based on the ionic liquid [Bmim][Acetate] for H2 production by steam methane reforming (SMR) and the comparison to conventional amine-based systems. Two different SMR plants using MDEA or [Bmim][Acetate] for CO2 capture were rigorously modelled using Aspen Plus to compute material and energy needs and emissions. Literature and simulation results were then used to perform a life cycle impact assessment (LCIA) of these processes based on the ReCiPe model. Solvent synthesis, CCS process and hydrogen production stages were considered for the cradle-to-gate analysis. Results showed that although [Bmim][Acetate] is a priori more harmful to the environment than amines (in a kg-to-kg comparison), LCIAs carried out for both CCS processes showed from 5 to 17 % lower environmental impacts values for all estimated categories when using [Bmim][Acetate] due to a 9.4 % more energy-efficient performance than MDEA, which also reduced a 17.4 % the total utility cost. Indeed, if a typical amine loss rate of 1.6 kg/tCO2 is assumed, the values of the environmental impacts increase up to 14 % for the IL-based CCS plant, but still maintaining its favorable results over MDEA. As consequence, the SMR plant with the IL-based CCS system exhibited 3–20 % lower values for most of the studied impact categories. These results contribute to shed some light on evaluating the sustainability of ILs with respect to conventional solvents for CO2 capture and to guide the synthesis of new more sustainable ILs but also, they would be used to compare the environmental burdens from the synthesis and process performance of other promising ILs for CO2 capture that are not environmentally assed yet.

Performance Assessment and Modeling Techniques for Domestic Solar Dryers

Performance Assessment and Modeling Techniques for Domestic Solar Dryers