CO2 capture is a critical technology for mitigating climate change by reducing greenhouse gas emissions from industrial and power generation sources. This study presents a comprehensive techno-economic assessment of the CO2 absorption–desorption process using the CESAR 1 solvent (27 wt% AMP and 13 wt% PZ). The analysis was conducted in Aspen Plus v14 using a standard process configuration (Case A) as the base scenario.Several process configurations were investigated to improve performance, including the addition of an intercooling section in the absorber column (Case B), splitting the lean solvent stream (Case C), and splitting the rich solvent stream (Case D). The impact of these configurations on carbon capture level (CCL), energy requirement, and economic performance was systematically evaluated.Results show that a simple solvent switch (Case A) performs poorly (6.22 MJ/kg CO2) due to column height limits under dilute gas conditions; however, advanced configurations fully unlock the energy-saving benefits of the mixed-amine system. Case B achieved the highest CO2 removal efficiency of 98.48 % with a reboiler duty of 5.37 MJ/kg CO2. Case C demonstrated the lowest energy requirement with a reboiler duty of 4.94 MJ/kg CO2 and a capture efficiency of 95.20 %. Case D further improved system performance with a removal efficiency of 97.22 % and a reboiler duty of 5.38 MJ/kg CO2. Economic and sensitivity analyses estimated the cost of CO2 capture, including compression to 30 bar, to range between 77.33 and 85.07 USD/tCO2. Overall, the results demonstrate process intercooling and solvent splitting, can significantly improve the efficiency and economic viability of CO2 capture in NGCC systems.
Pseudomonas aeruginosa is recognized as a critical-priority pathogen due to its major role in healthcare-associated infections (HAIs) and the rapid global rise of multidrug resistance. It is responsible for approximately 7.1–7.3
Cryptocurrencies, with their transformative potential in diversification and market growth, have captivated global investors and researchers. This study breaks new ground by uncovering long-term relationships between macroeconomic factors and cryptocurrency returns, Bitcoin (BTC), Ethereum (ETH), Tether (USDT), and Binance Coin (BNB), using an advanced ARDL model from 2017 to 2023. Unlike prior short-term analyses, we reveal how indicators such as the consumer price index, foreign direct investment, gross domestic product, industrial production index, and interest rates uniquely influence crypto returns, with varying impacts across assets. These insights empower investors to craft resilient portfolios and guide policymakers in regulating dynamic crypto markets.
Despite growing recognition of the participation of autistic people as collaborators in research, this field continues to face several significant challenges. Key issues include a lack of guidance for researchers on how to implement participatory methods effectively and a shortage of evaluation tools to support best practice. Meaningful participation has the potential to redress power imbalances, reduce stigma, and ensure that research accurately reflects the lived experiences of autistic individuals, leading to more relevant and effective services and support. To address these challenges, researchers codeveloped and refined a standardized evaluative framework to promote meaningful autistic participation across all research stages and evaluate good practice in establishing a Participatory Action Research (PAR) Group while enhancing transparency regarding the extent of involvement. Draft tools were developed by a neurodivergent lead researcher, with the development and refinement process led by autistic researchers who facilitated consultation with participatory researchers, ensuring that the tools reflected diverse perspectives and lived experiences. The refined evaluation framework consists of the Evaluating Levels of Participation in Autism Research Tool which promotes participation of autistic people as collaborators across the stages of research and PAR group checklist which evaluates inclusive and accessible interactions with autistic collaborators. Both tools support researchers in addressing power dynamics, provide specific guidance on how to operationalize participatory approaches, and serve as frameworks for evaluating and enhancing the meaningful participation of autistic individuals in research. These tools can support equitable and impactful collaborations between researchers and the autistic community.
The growing integration of renewable energy into modern power systems presents significant challenges for optimal distributed energy resource (DER) planning in interconnected microgrids due to the stochastic nature of wind and solar generation. To address this, we propose a robust multi-objective planning framework that combines Vine Copula-based spatiotemporal scenario generation with a Multi-Objective Antlion Optimizer (MOALO). The Vine Copula approach captures complex, non-Gaussian dependencies among renewable sources, generating realistic and correlated scenarios. The planning model aims to minimize both the annualized total cost and source-load mismatch, thereby enhancing economic efficiency and operational reliability. Simulation results on a three-microgrid system demonstrate that the proposed method achieves an annualized cost of 13,070 $, a source-load deviation of 3.75 x 107 kW2, and over 50 % renewable energy penetration, outperforming traditional methods such as MOPSO and NSGA-II. These findings validate the framework as an effective and resilient solution for cost-efficient microgrid planning under uncertainty.
