Minimize CO2 Emissions Research Articles

Impact of Optimization Variables on Fuel Consumption in Large Four-Stroke Diesel Marine Engines with Electrically Divided Turbochargers

The objective of this study is to understand how each variable impacts the optimal configuration of a marine diesel engine equipped with an electric hybrid air-charging system that allows energy assistance and recovery. The aim is to minimize CO2 emissions by reducing fuel consumption. The hybrid system offers flexibility in adjusting parameters from both the engine and air-charging system. It is compared with the baseline engine, which uses a free-floating turbocharger. The results show a significant improvement at low engine loads, where the baseline engine struggles to provide sufficient air. While turbine speed has little influence, compressor power reduces fuel consumption at low loads. However, at mid loads, resizing the turbomachine is necessary for further improvements. At high loads, full optimization of all variables is required to reduce fuel consumption. The electric hybrid system is particularly effective in tugboat-like conditions, where low loads dominate, but less impactful for ro-pax ferries. Despite the potential of the hybrid system, a fully optimized turbocharger could provide greater benefits due to reduced losses. Future studies could explore combining the adaptability of the hybrid system with a highly efficient turbocharger to reduce emissions across all load conditions.

Fe-bearing magnesium silicate glasses for potential supplementary cementitious applications

Supplementary cementitious materials (SCMs) are used to minimize CO2 emissions associated with cement production. However, their global supply is insufficient to meet the growing market demand for cement and concrete, being essential to develop alternative SCMs based on abundant waste streams and low-cost resources. Fe-bearing Mg-based glasses are promising candidates with the potential to utilize high-volume feedstocks rich in Fe and Mg, but their effectiveness relies on deep understanding of the relationship between glass composition, reactivity, and pozzolanic properties. In this study, Fe-Mg silicate glasses with varying Fe concentrations were precisely engineered through a sol-gel route to better understand the impact of Fe on the glass structure and reactivity. While Fe3+ typically acts as a glass network former, it was observed to also function as an intermediate cation, behaving either as a network former or modifier. Glass reactivity was assessed through aqueous dissolution tests, revealing that the composition and chemical environment of Fe3+ within the glass network significantly influence the dissolution behavior. The introduction of Fe into Mg-Si glasses increased overall reactivity, potentially due to Fe-induced phase separation and the increasing of [FeO6] octahedra sites at higher Fe concentrations, which was also associated to network depolymerization. These findings deepen the understanding of the role of Fe3+ in magnesium silicate glasses, provide key insights into optimizing glass reactivity by fine-tuning the composition, and indicate the potential of these glasses as promising SCMs.

Optimization of Solar-Assisted CCHP Systems: Enhancing Efficiency and Reducing Emissions Through Harris Hawks-Based Mathematical Modeling

The increasing demand for energy, driven by technological advances, population growth, and economic expansion, has intensified the focus on efficient energy management. Tri-generation systems, such as Combined Cooling, Heating, and Power (CCHP) systems, are of particular interest due to their efficiency and sustainability. Integrating renewable energy sources like solar power with traditional fossil fuels further optimizes CCHP systems. This study presents a novel method for enhancing the CCHP system efficiency by identifying the optimal design parameters and assisting decision makers in selecting the best geometric configurations. A mathematical programming model using the Harris Hawks optimizer was developed to maximize the net power and exergy efficiency while minimizing CO2 emissions in a solar-assisted CCHP system. The optimization resulted in 100 Pareto optimal solutions, offering various choices for performance improvement. This method achieved a higher net power output, satisfactory exergy efficiency, and lower CO2 emissions compared to similar studies. The study shows that the maximum net power and exergy efficiency, with reduced CO2 emissions, can be achieved with a system having a low compression ratio and low combustion chamber inlet temperature. The proposed approach surpassed the response surface method, achieving at least a 4.2% reduction in CO2 emissions and improved exergy values.

Comparative Analysis of Solutions in the Optimal Design of Composite Flooring Systems for Different Beam Topologies

With the advancement of civil construction, there has been an increase in the utilization of composite steel and concrete systems, often employing full-web profiles for the beams. However, other solutions such as cellular beams and truss beams remain relatively unexplored. The objective of this study is to conduct a comparative analysis of the optimal design of composite floors using different beam topologies, namely: full-core beams, cellular beams, and beams composed of tubular trusses. The objective function will be to minimize CO2 emissions resulting from the materials used in the floors. To solve this optimization problem, the Ant Colony Algorithm (ACO) will be employed. A comparative analysis will be conducted between the solutions proposed in the literature for floors utilizing full-core beams and those obtained with cellular beams and beams composed of tubular trusses, to determine the most environmentally efficient solution.

Multiobjective Optimization of Steel and Concrete Composite Slabs via NSGA algorithm

With the expansion of the civil construction sector, composite steel and concrete slabs with incorporated shapes have become increasingly common due to their ease of execution and the elimination of shoring. However, optimization studies focusing on this type of structural element are still limited. The objective of this study is to propose a formulation for the multi-objective optimization of composite steel and concrete slabs, considering both the phases before and after concrete curing. Objective functions will be established to minimize CO2 emissions and slab costs, as well as to maximize the load-bearing capacity of the slab for a given span. To address this optimization problem, the NSGA algorithm implemented in the Matlab platform will be used. Comparative analyses will be conducted between examples from the literature utilizing single-objective optimization and those employing multi-objective optimization to validate the proposed formulation. Additionally, new problem instances will be examined to identify the key factors that influence the final solution.

Analysis of optimization algorithm models for the design of prestressed steel beams

Prestressing in steel beams offers significant benefits in terms of structural efficiency and material savings. The aim of this study is to present a comparative analysis of the design results of prestressed steel beams obtained through three optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and its variant, including a craziness operator for performance enhancement (CRPSO). These algorithms were used to minimize CO2 emissions and cost in the structural design process, while adhering to the constraints imposed by ABNT NBR 8800:2008. The implementation was carried out using MATLAB (2016). The validation of the methodology was conducted using examples from the literature, comparing the CO2 emissions and cost values obtained from experimental work and those derived from GA, PSO, and CRPSO. The results indicate that, although the Genetic Algorithm is widely used in optimization, more optimized results are achieved using PSO. Furthermore, the improvement obtained by CRPSO showed no significant deviation from those obtained by PSO, nor did it affect the convergence speeds of the results. It was also observed that all three implemented models yielded more optimized values of emissions and costs compared to those reported in the literature.

Genetic Algorithm-Based Optimization of Solar Photovol-taic Integration and Demand Response for CO2 Reduction in Indian Coal Power

In 2022, global coal combustion contributed significantly to global pollution, producing 15.22 billion metric tons of carbon dioxide (CO2). This research addresses the urgent challenge of mitigating CO2 emissions in Indian coal power plants by strategically deploying solar photovoltaic (PV) systems and integrating demand response mechanisms. The imperative to reduce greenhouse gas emissions from coal-based electricity generation underscores the critical context of climate change. Emphasizing the vital role of integrating renewable energy-based distributed generators into the existing coal infrastructure, this study positions solar PV technology as a promising solution. Optimal solar PV system allocation is achieved through the implementation of the genetic algorithm technique. Factors such as solar resource availability, electricity demand patterns, and the CO2 intensity associated with coal power generation are considered in this process. The primary research objective is twofold: to minimize CO2 emissions and maximize the integration of solar PV systems while mitigating power losses. The proposed approach considers the intermittent nature of solar power and the dynamic characteristics of demand. Rigorous testing on an IEEE 33-bus system powered by the studied coal power plant reveals a substantial 29.31% reduction in CO2 generation following the implementation of the proposed strategy. This research represents a decisive step towards fostering a more sustainable and environmentally friendly energy landscape. Our study's outcomes offer valuable insights for policymakers and stakeholders in the energy sector, providing a robust foundation for the advancement of environmentally conscious practices within the coal power industry.

Dedicated HVAC Technology in the Renovation of Historic Buildings on the Example of the Marshal Pilsudski Manor in Sulejówek

The article investigates HVAC (heating, ventilation, and air conditioning) technologies aimed at mitigating Primary Energy (PE) consumption in renovated buildings. This research is part of a broader initiative focused on enhancing air quality and reducing the carbon footprint within the fields of architecture and urban planning. Conducted since 2018 by a team from the Institute of Architectural Design at the Department of Contemporary Architecture, Faculty of Civil Engineering and Architecture, University of Technology in Lublin, the study exemplifies the application of these technologies at the historic Marshal Piłsudski’s “Milusin” Manor House in Sulejówek, near Warsaw. The primary objective of this research is to present HVAC solutions, particularly a free cooling and heating system, which are specifically tailored for the renovation of historic structures. This technology effectively recovers thermal energy from groundwater, achieving low energy consumption levels while simultaneously minimizing CO2 emissions.

Exploring the Potential of Coal: Electrolysis and Extraction for Chemical Recovery, Oral Presentation

Formed from the fossilized remains of ancient plants, coal is a combustible sedimentary rock classified as a nonrenewable resource due to its extremely slow formation process. The chemical structure of coal contains diverse types of carbon, including aliphatic carbons, aromatic carbons, and carbonyl carbons [1], [2]. Coal is considered the most economical source of energy; however, its potential as a repository for valuable chemical compounds remains underutilized. Although solvent extraction offers a way to extract specific components from coal, it comes with limited extraction efficiencies and significant environmental, and economic drawbacks.Coal electrolysis is a process that utilizes electricity to break down and oxidize carbon structures in diverse ways, yielding liquid-phase products [3]. One key application of coal electrolysis is the production of hydrogen gas. Traditional methods, such as Indirect coal liquefaction (ICL) and Direct coal liquefaction (DCL), rely on high-temperature and high Pressure (ranging from 200-1450°C, 100-200 atm) respectively. In contrast, the technology known as the “Continuous Coal Electrolytic Cell (CEC)” facilitates the direct conversion of coal into valuable chemicals and liquid fuels at mild temperatures (25-180°C) and low pressures (1-2 atm) [4]. Therefore, this process could potentially be more efficient and less energy-intensive than conventional methods. In the CEC, electric power is applied to a coal-slurry to directly convert the coal into pure clean hydrogen, and organic compounds (liquid form), with minimum CO2 emissions. During the CEC process, the surface of the coal particles (electrolyzed coal char) gets oxidized into lower molecular weight hydrocarbons than coal. Subsequently the electrolyzed coal char undergoes extraction to separate the liquid fuels and/or valuable chemicals. This technology has already proven that temperature and applied current are the most influential factors in maximizing hydrogen production. However, the conditions for obtaining various liquid-phase products have not yet been optimized.In this study, the effect of coal particle size during electrolysis and the subsequent extraction process was explored more in-depth specifically to determine the characteristic of the liquid extracted. Three coal particle size were electrolyzed using Pt-Ir catalyst, followed by extraction using two different solvents. The changes in the main functional groups of coal particles were studied by FTIR, while proximate analysis and carbon structure were analyzed by TGA/DSC and CHNS, SEM and XRD, respectively. Our findings reveal the distribution of sulfur and nitrogen after electrolysis and the benefits of solvent selection since it is known that the solvent influences the interaction between organic and mineral matter. By demonstrating that coal electrolysis, combined with extraction, can remove valuable chemicals, new market opportunities emerge for coal across various applications.[1] D. Jing, X. Meng, S. Ge, T. Zhang, M. Ma, and G. Wang, “Structural Model Construction and Optimal Characterization of High-Volatile Bituminous Coal Molecules,” ACS Omega, vol. 7, no. 22, pp. 18350–18360, May 2022, doi: 10.1021/acsomega.2c00505.[2] X. Gong, M. Wang, Y. Liu, Z. Wang, and Z. Guo, “Variation with time of cell voltage for coal slurry electrolysis in sulfuric acid,” Energy, vol. 65, pp. 233–239, Feb. 2014, doi: 10.1016/j.energy.2013.11.083.[3] S. Chen, W. Zhou, Y. Ding, G. Zhao, and J. Gao, “Coal-Assisted Water Electrolysis for Hydrogen Production: Evolution of Carbon Structure in Different-Rank Coal,” Energy Fuels, vol. 35, no. 4, pp. 3512–3520, Feb. 2021, doi: 10.1021/acs.energyfuels.0c04122.[4] X. Jin and G. G. Botte, “Feasibility of hydrogen production from coal electrolysis at intermediate temperatures,” J. Power Sources, vol. 171, no. 2, pp. 826–834, Sep. 2007, doi: 10.1016/j.jpowsour.2007.06.209.

Durability and hardened characteristics of cement mortar incorporating waste plastic and Polypropylene exposed to MgSO4 attack

Annual waste plastic disposal has grown, harming nature. Utilising this waste in concrete production may help preserve building resources. This study tested cement mortar with polyvinyl chloride (PVC) and polypropylene (PP) substituted for sand aggregate at 0, 5, 10, 15, and 20 %. The samples were nevertheless exposed to a 10 % and 20 % MgSO4 solution for a month. The properties of both fresh and hardened materials under these circumstances have been evaluated and contrasted with those evaluated under typical circumstances. For mixes including PP and PVC, the flow diameter increased. The rounded plastic particles provided fewer contact surfaces and less friction among mixtures, which reduced water consumption and improved workability, leading to an increase in slump flow. As the amount of plastic aggregate increases, the compressive strength decreased. Moreover, this pattern might be explained by the weakening of the bond between the surfaces of the plastic aggregate and cement paste. The hydration of cement may also be hampered by the hydrophobic properties of plastic aggregate. Like compressive strength, the splitting tensile strength decreased as the replacement level of plastic ratio increased regardless of its type under all conditions (normal and exposing to MgSO4). PP and PVC fine aggregate in mortar increases sorptivity under all situations. Following screening, those circumstances and PVC have the most impact on compressive strength. increasing PVC and PP at 10 % for each of them leads to lower values of compressive and tensile strength. An optimization process was implemented to determine the optimum value of PVC, PP, and MgSO4. It shows that using PVC of 3.9 %, PP of 10.1 %, and MgSO4 of 19.59 % leads to maximum compressive and tensile strength with the minimum cost and CO2 emissions.

Exploring the Role of Government Expenditure and Fintech in Environmental Quality: Evidence from Pakistan

The goal of achieving lower carbon emissions has recently become a hotly debated topic, and various studies have empirically assessed the repercussion of different economic variables on environment. This study examines the aftermath of GDP, government expenditure (GOE), FDI, and Fintech on CO2 in Pakistan from 1995 to 2023 by adopting the NARDL technique. The NARDL findings revealed that in positive shocks, GOE played a beneficial role in minimizing CO2 emissions, yet in negative shocks, GOE enhanced pollution levels in Pakistan. Moreover, FDI and GDP enhance environmental degradation, while Fintech mitigates CO2 emissions. These findings provide valuable insights for policy making. These findings also suggest that Pakistan should focus on reallocating government expenditures to green investments.

A Simheuristic Approach to Scheduling Sustainable and Reliable Maintenance for Bridge Infrastructure

Designing maintenance strategies for a vast portfolio of aging infrastructures requires decision-makers to ensure adequate safety levels while addressing the requirements on service interruptions, costs, and workforce availability. This study addresses the problem of scheduling maintenance interventions for a portfolio of bridges, aiming to minimize CO2 emissions while meeting minimum reliability requirements and adhering to workforce and budget constraints. To achieve this, we present a Simheuristic algorithm that combines a metaheuristic core based on the Adaptive Large Neighborhood Search metaheuristic with a Monte Carlo simulation module. This integration allows for the evaluation of optimized scheduling solutions, accounting for the inherent randomness in the structural deterioration process. The proposed approach is tested in a comparative analysis against traditional time-based and condition-based scheduling methods. Results from diverse bridge portfolios demonstrate that the proposed algorithm offers improved performance in terms of both total costs and CO2 emissions.

Thermo-environmental analysis of a renewable Allam Cycle for generating higher power and less CO2 emissions

Although CO2 emissions from burning fossil fuels have caused numerous environmental problems, the utilization of oxy-fuel cycles has proven to be an effective method for generating more power while minimizing CO2 emissions. In this study, an innovative power generation system was presented that integrates renewable energies (solar and wind) with a small-scale oxy-fuel cycle (Allam Cycle). The system underwent modeling, simulation, and optimization from energy, exergy, exergoeconomic, and environmental perspectives. Generating higher power and emitting lower CO2 emissions compared to conventional gas turbines was the main goal of this study. Additionally, to present results meaningfully, comparisons were made between two different weather conditions: St. Petersburg in Russia (cold climate) and Tehran in Iran (normal climate). In this regard, dynamic analysis was performed using TRNSYS and Engineering Equation Solver (EES) tools. Furthermore, a multi-objective optimization was conducted using the TOPSIS multi-criteria method on functional parameters. The optimization results revealed that the maximum energy efficiency and minimum total cost rate were achieved at 75 % for Tehran and 1.84 $/h for St. Petersburg. Providing power for internal components, the highest power sold to the grid was reported to be 8365 kWh in December in St. Petersburg and 9646 kWh in May in Tehran. Additionally, the flat plate collector (FPC) demonstrated maximum efficiency and stored heat of 36 % in Tehran and 2.25 kWh in St. Petersburg, respectively. Ultimately, the results of pollutant emissions indicated that the Allam Cycle achieved a significant CO2 reduction compared to a conventional gas turbine, with a reduction ratio of 117 times in St. Petersburg and 119 times in Tehran.

Carbon dioxide emission evaluation of biochar based vegetation concrete for ecological restoration projects

Vegetation concrete is increasingly being used for slope stabilization in highways, green roofing in urban developments, and erosion control in coastal areas due to its sustainable solutions for enhancing landscape aesthetics, mitigating pollution, and protecting the environment. However, the environmental and economic impacts of different mix proportions, particularly concerning CO2 emissions, remain insufficiently explored. This study aimed to identify the optimal mix proportions of vegetation concrete through laboratory testing that minimize CO2 emissions while ensuring compatibility with plant growth and cost-effectiveness. The vegetation concrete mix were prepared using different quantities of biochar (5 %, 10 %, and 15 %) and cement content (4 %, 8 %, and 12 %) by weight to evaluate their impact on porosity, unconfined compressive strength (UCS), alkalinity, plant compatibility, cost-effectiveness, and CO2 emissions from raw materials. The results indicate that increasing the biochar content from 0 % to 15 % led to a 16 % reduction in porosity and a 92 % increase in UCS of vegetation concrete. Similarly, increasing the cement content from 0 % to 12 % resulted in an 11 % decrease in porosity and a substantial 200 % increase in the UCS of vegetation concrete. Moreover, the addition of 5 % biochar had a beneficial effect on plant growth, however, increasing the biochar content beyond this level adversely impacted plant development. Based on laboratory test results, the recommended optimal mix for vegetation concrete includes a mix of 5 % biochar, 8 % low-alkaline sulfoaluminate cement, 6 % sawdust, and 6 % ferrous sulfate. The analysis of CO2 emission of the materials studied showed that cement contributed the most to CO2 emissions, followed by biochar, sawdust, water, and soil. Notably, the utilization of sulfoaluminate cement led to a 31.8 % reduction in CO2 emissions compared to Portland cement, while also lowering the overall cost of vegetation concrete by 24.7–33.8 %. This research underscores the necessity of scientifically establishing relationships between the composition of plant-based concretes and their environmental and economic performance. In summary, this study identifies optimal mix proportions for vegetation concrete, leveraging low-alkaline sulfoaluminate cement and biochar to minimize CO2 emissions, enhance landscape aesthetics, and ensure compatibility with plant growth, thus emphasizing its potential as a sustainable and cost-effective construction material.

An innovative quick-decision strategy for evaluating the applicability of extractive distillation process with a preconcentration column in separating high-concentration organic waste liquid

In the face of the high energy consumption challenge in separating high-concentration organic waste liquid through extractive distillation (ED), the concept of preconcentration provides a new strategic direction. Therefore, it has become crucial to determine the suitability of the ED process with a preconcentration column for processing such complex mixture. Taking this into consideration, the study proposes an innovative quick-decision method that utilizes thermodynamic analysis of ternary phase diagram to determine the ratio of theoretical product flowrate at the bottom of the preconcentration column to the feed flowrate, which serves as the basis for deciding whether to choose the ED process with a preconcentration column for handling such separation tasks. Three azeotropic systems with different thermodynamic characteristics are chosen as case studies: cyclohexane/ethyl acetate/ethanol (Case 1), ethyl acetate/ethanol/water (Case 2), and n-hexane/ethyl acetate/acetonitrile (Case 3). The task of separating the above three systems is accomplished through the application of both three-column extractive distillation configuration (TCED), and four-column extractive distillation configuration (FCED). The objective functions for multi-objective optimization of all separation configurations include the minimum total annual cost (TAC), the minimum entropy generation and the minimum CO2 emissions. The research findings indicate that when the ratio of theoretical product flowrate at the bottom of the preconcentration column to the feed flowrate is not less than 0.56, or when there is a significant concentration difference among the components in the feed stream, the FCED configuration exhibits clear advantages over the TCED configuration in terms of economic, environmental, and thermodynamic aspects.

The Role of Digital Technologies in Climate Change Management: Strategies for Minimizing the Environmental Impact of USA Companies

The article examines the role of digital technologies in climate change management and reducing the environmental impact of U.S. companies. Innovations such as artificial intelligence, the Internet of Things, and big data are analyzed for their ability to help companies minimize CO2 emissions, optimize resource use, and improve energy efficiency. Challenges companies face when implementing digital solutions: high initial costs, integration difficulties, and regulatory complexities, are discussed. Strategies for overcoming these barriers to achieve sustainable development are proposed.

Dynamic Network-Level Traffic Speed and Signal Control in Connected Vehicle Environment.

The advent of connected vehicles holds significant promise for enhancing existing traffic signal and vehicle speed control methods. Despite this potential, there has been a lack of concerted efforts to address issues related to vehicle fuel consumption and emissions during travel across multiple intersections controlled by traffic signals. To bridge this gap, this research introduces a novel technique aimed at optimizing both traffic signals and vehicle speeds within transportation networks. This approach is designed to contribute to the improvement of transportation networks by simultaneously addressing issues related to fuel consumption and pollutant emissions. Simulation results vividly illustrate the pronounced the effectiveness of the proposed traffic signal and vehicle speed control methods of alleviating vehicle delay, reducing stops, lowering fuel consumption, and minimizing CO2 emissions. Notably, these benefits are particularly prominent in scenarios characterized by moderate traffic density, emphasizing the versatility and positive impact of the method across varied traffic conditions.

Modern Methods of Asbestos Waste Management as Innovative Solutions for Recycling and Sustainable Cement Production

Managing asbestos waste presents a significant challenge due to the widespread industrial use of this material, and the serious health and environmental risks it poses. Despite its unique properties, such as resistance to high temperatures and substantial mechanical strength, asbestos is a material with well-documented toxicity and carcinogenicity. Ensuring the safe removal and disposal of asbestos-containing materials (ACM) is crucial for protecting public health, the environment, and for reducing CO2 emissions resulting from inefficient waste disposal methods. Traditional landfill disposal methods have proven inadequate, while modern approaches—including thermal, chemical, biotechnological, and mechanochemical methods—offer potential benefits but also come with limitations. In particular, thermal techniques that allow for asbestos degradation can significantly reduce environmental impact, while also providing the opportunity to repurpose disposal products into materials useful for cement production. Cement, a key component of concrete, can serve as a sustainable alternative, minimizing CO2 emissions and reducing the need for primary raw materials. This work provides insights into research on asbestos waste management, offering a deeper understanding of key initiatives related to asbestos removal. It presents a comprehensive review of best practices, innovative technologies, and safe asbestos management strategies, with particular emphasis on their impact on sustainable development and CO2 emission reduction. Additionally, it discusses public health hazards related to exposure to asbestos fibers, and worker protection during the asbestos disposal process. As highlighted in the review, one promising method is the currently available thermal degradation of asbestos. This method offers real opportunities for repurposing asbestos disposal products for cement production; thereby reducing CO2 emissions, minimizing waste, and supporting sustainable construction.

Artificial intelligence‐driven sustainability: Enhancing carbon capture for sustainable development goals– A review

AbstractArtificial intelligence (AI) and environmental points are equally important components within the response to local weather change. Therefore, based on the efforts of reducing carbon emissions more efficiently and effectively, this study tries to focus on AI integration with carbon capture technology. The urgency of tackling climate change means we need more advanced carbon capture, and this is an area where AI can make a huge impact in how these technologies are operated and managed. It will minimize manufacturing emissions and improve both resource efficiency as well as our planet's environmental footprint by turning waste into something of value again. Artificial intelligence could be leveraged to analyze huge data sets from carbon capture plants, searching for optimal system settings and more efficient ways of identifying patterns in the available information at a larger scale than currently possible. In addition, AI incorporated sensors and monitoring mechanisms in the supply chain can identify any operational failure at reception itself allowing for timely action to protect those areas. Artificial intelligence also helps generative design for carbon capture materials, which allows researchers to explore new types of carbon‐absorbing material, including metal–organic frameworks and polymeric materials that are important in industrial CO2, such as moisture. In addition, it increases the accuracy of reservoir simulations and controls CO2 injection systems for storage or enhanced oil recovery. Through applying AI algorithms on reservoir geology, production performance and real‐time data this study would like to facilitate the optimization of injection processes as well as minimize CO2 emissions while assuring a maximum efficiency. Artificial intelligence integrates with renewable‐based carbon capture efforts that can be employed by AI‐driven smart grid systems to improve carbon capture methods.

Mild one-pot production of glycerol carbonate from CO2 with separation from ionic liquid catalyst

Carbon capture and utilization stands as way forward in the minimization of CO2 emissions. It complements the search for clean and effective energy, as it is challenging to achieve zero to negative CO2 net emissions. The glycerol carbonate product opens a new strategy based on a double-residue approach (glycerol and CO2) to contribute, together with ionic liquids (ILs), drafting a new one-pot concept (intensified three phase reactor) at mild conditions and using homogeneous catalysts. In this work, the catalytic activity of [P666,14][Br] in the cycloaddition of CO2 to glycidol has been demonstrated to be highly effective under mild conditions of 45 °C and 5 bar. In addition, liquid–liquid extraction approach is found to be an efficient separation strategy for carbonate product and IL catalyst. 1-Decanol (DOH) is selected as efficient extracting solvent, due to its almost complete immiscibility with glycerol carbonate, as well as the high distribution ratio of the IL catalyst in DOH rich phase. It implies an easy product separation and, simultaneously, an easy catalyst recovery. A preliminary kinetic validation of the three-phase reactor performance has been properly described, enabling the intensified concept. Finally, combining COSMO/Aspen simulation and life cycle assessment (LCA) methodologies, low energy consumption (avoiding heating) and low GWP scenarios for the CO2 conversion process have been concluded. This work enables a new concept together with concluding the path to developing this CCU technology in the search of zero to negative CO2 emissions to sustainably capture and convert CO2 into value-added products.