Search Results (92)

The electrochemical capture and transformation of carbon dioxide (CO₂) (ECC) has recently emerged as a transformative alternative to conventional sorbent-based processes, enabling fully reversible operation under mild conditions and direct compatibility with renewable energy sources. This review focuses on redox-active metal–organic frameworks (MOFs) as electrosorbent materials for the electrochemical capture of CO₂. Rather than encompassing all electrochemical CO₂ capture technologies, we use molecular, polymeric, and COF-based systems as a framework to define what makes a MOF truly “redox-active” for CO₂ electrosorption and how its performance can be assessed. This includes capacitive versus faradic electrosorption mechanisms and design strategies based on the redox chemistry associated with metal nodes, π-conjugated ligands, and strongly redox-active units such as tetrathiafulvalene, viologen, and ferrocene. The way in which defects affect hybrid MOF composites was highlighted, and in situ and operando spectroscopic techniques have improved the understanding of the reaction mechanism in carbon dioxide capture and release under controlled potential. Research comparing carbonaceous materials, redox polymers, and hybrid structures has highlighted both the opportunities and limitations of MOFs, particularly in terms of energy efficiency, scalability, structural robustness, and reproducibility. From a broader perspective, redox-active MOFs occupy a unique position at the intersection of coordination chemistry, electrochemistry, and materials engineering for large-scale applications. In this review, we analyze how redox activity in MOFs—at the metal nodes, ligands, and extended structures—can be harnessed to design energy-efficient, cyclic electrochemical CO₂ capture systems. Furthermore, we propose cross-cutting metrics and design rules that enable meaningful comparisons between materials and device architecture. Full article

The article describes oxygen gasification installation for waste biomass in waste-to-fuel technology, in which the final product is liquid methanol (the reference case). A comprehensive techno-economic model integrates oxygen-based gasification of wet sludge with three waste-heat recovery technologies—expander, Stirling engine, and organic Rankine cycle—and directs the recovered electrical power to a PEM electrolyzer for additional hydrogen production. The model captures full material flows, thermodynamic efficiencies, CO₂ balances, and an economic analysis over a 20-year horizon. A comparison of the use of an expander, Stirling engines, and ORC modules to power the electrolytic hydrogen generation installation was proposed. The produced hydrogen is an additional substrate for the methanol reactor, which will consequently increase the methanol yield from the entire installation and reduce the specific CO₂ emissions. Oxygen from the electrolysis process can be used in the gasifier, which will reduce the energy consumption of the Air Separation Unit, and thus increase the efficiency of the entire gasification system. In addition to the technical evaluation, an economic analysis was carried out to assess the profitability of the proposed concepts, showing that process integration can significantly improve both energy performance and economic feasibility of methanol production in waste-to-fuel systems. Results show that proposed modifications have the potential to increase overall efficiency from 75.498% (reference scenario) to even 82.545% (best scenario), while specific emissions of carbon dioxide drop from 1.746 kg CO₂/kg CH₃OH (reference scenario) to 1.468 kg CO₂/kg CH₃OH (best scenario), with an increase in methanol yield of about 9.4% (from 0.255 kg CH₃OH/kg Bio in reference scenario to 0.279 in best scenario). Full article

This study addressed the persistent limitation of discontinuous and labor-intensive compost monitoring procedures by developing and field-validating a low-cost sensor system for monitoring oxygen (O₂), carbon dioxide (CO₂), and methane (CH₄) under tropical windrow conditions. In contrast to laboratory-restricted studies, this framework integrated rigorous calibration, multi-layer statistical validation, and process optimization into a unified, real-time adaptive design. Experimental validation was performed across three independent composting replicates to ensure reproducibility and account for environmental variability. Calibration using ISO-traceable gas standards generated linear correction models, confirming sensor accuracy within ±1.5% for O₂, ±304 ppm for CO₂, and ±1.3 ppm for CH₄. Expanded uncertainties (U₉₅) remained within acceptable limits for composting applications, reinforcing the precision and reproducibility of the calibration framework. Sensor reliability and agreement with reference instruments were statistically validated using analysis of variance (ANOVA), intraclass correlation coefficient (ICC), and Bland–Altman analysis. Validation against a reference multi-gas analyzer demonstrated laboratory-grade accuracy, with ICC values exceeding 0.97, ANOVA showing no significant phase-wise differences (p > 0.95), and Bland–Altman plots confirming near-zero bias and narrow agreement limits. Ecological interdependencies were also captured, with O₂ strongly anticorrelated to CO₂ (r = −0.967) and CH₄ moderately correlated with pH (r = 0.756), consistent with microbial respiration and methanogenic activities. Nutrient analyses indicated compost maturity, marked by increases in nitrogen (+31.7%), phosphorus (+87.7%), and potassium (+92.3%). Regression analysis revealed that ambient temperature explained 25.8% of CO₂ variability (slope = 520 ppm °C⁻¹, p = 0.021), whereas O₂ and CH₄ remained unaffected. Overall, these findings validate the developed sensors as accurate and resilient tools, enabling real-time adaptive intervention, advancing sustainable waste valorization, and aligning with the United Nations Sustainable Development Goals (SDGs) 12 and 13. Full article

There is an urgent need to reduce greenhouse gas emissions, particularly carbon dioxide (CO₂). Currently, numerous research initiatives are underway to develop CO₂ Capture and Storage (CCS) technologies aiming for net-zero emissions, especially in sectors that are difficult to decarbonize, such as fossil fuel power generation. Integrating solar thermal energy into CO₂ capture facilities (CCFs) for fossil fuel-based power plants offers a promising approach to reduce the high operational costs associated with CO₂ capture processes. However, a comprehensive systematic review focusing on the integration of solar thermal energy with CCFs in fossil fuel power generation is currently lacking. To address this gap, this study systematically evaluates the technological frameworks involved, including (a) various generation technologies such as coal-fired Rankine cycle plants, natural gas combined cycle plants, and cogeneration units; (b) concentrated solar power (CSP) technologies, including parabolic trough collectors, linear Fresnel reflectors, solar power towers, and Stirling dish systems; and (c) post-combustion CO₂ capture systems. Additionally, this research analyzes relevant projects, patents, and scholarly publications from the past 25 years that explore the coupling of CSP technologies with fossil fuel power plants and post-combustion CO₂ capture systems. This literature review encompasses diverse methodologies, such as innovative patents, conceptual models, evaluations of solar collector performances, thermal integration optimization, and various system configurations. It also investigates technical advancements aimed at improving efficiency, reliability, and flexibility of fossil fuel power plants while mitigating the inherent challenges of CO₂ capture. Beyond the energy-focused aspects, we explore complementary circular economy strategies—such as by-product valorization and material substitution in sectors like mining, cement, and steel manufacturing—that can reduce embodied emissions and enhance the overall system benefits of solar-assisted CO₂ capture. The review employs a bibliometric approach using digital tools including Publish or Perish, Mendeley, and VOSviewer to systematically analyze the scholarly landscape. Full article

Supply chains are a primary contributor to global greenhouse gas (GHG) emissions, rendering their decarbonization an essential dimension of sustainable development. Artificial intelligence (AI) provides a transformative pathway by facilitating proactive emission avoidance through operational efficiency, transparency, and resilience, in contrast to post-emission mitigation approaches such as carbon capture. This study explores the potential of AI to support indirect carbon dioxide removal (CDR) via supply chain decarbonization, adopting a comparative case study methodology. Empirical evidence is drawn from Tunisian agri-food, textile, and port logistics sectors, based on multi-source datasets spanning 6–12 months and covering fleet sizes ranging from 40 to 250,000 units. Methodological robustness was ensured through the use of pre-intervention baselines, statistical imputation for missing data (<5%), and validation against 20% out-of-sample test sets. Results indicate that AI-enabled interventions achieved annual avoided emissions between 500 and 1500 tCO₂ and reduced fuel consumption by 12–15%, with sensitivity analyses incorporating ±8–12% error margins. Among the approaches tested, hybrid models integrating operational and strategic layers demonstrated the most pronounced impact, aligning immediate efficiency gains with long-term systemic decarbonization. Furthermore, AI facilitates renewable energy integration, digital twin applications, and compliance with international sustainability frameworks, notably the Paris Agreement and the United Nations Sustainable Development Goals. Nevertheless, challenges related to data quality, computational demands, limited expertise, and organizational resistance constrain scalability. The findings underscore AI’s dual role as a technological enabler and systemic driver of supply chain decarbonization, advancing its positioning within global environmental sustainability transitions. Full article

At present, enhancing the first- and second-law efficiencies of power generation cycles is no longer the sole objective of engineers. Increasing attention is now being paid to reducing carbon emissions in the environment and minimizing the time required to recover the costs of the power plant, in addition to improving work output and first- and second-law efficiencies. The present analytical study compares the power generation cycle with and without a carbon capture unit. The combined cycle selected is the reheat gas turbine cycle using an ammonia–water mixture and transcritical carbon dioxide as working fluids in the bottoming cycle. The comparison of both the configurations depicts that at a cycle pressure ratio of 40, an ambient temperature of 303 K, and a turbine inlet temperature of 1600 K, the configuration incorporating the maximum number of ammonia–water turbines in the bottoming cycle yields the highest work output, amounting to 952.3 kJ/kg. The payback period is found to be the longest—approximately 8 years and 4 months for the configuration utilizing transcritical carbon dioxide as the working fluid. The integration of a carbon capture unit results in a reduction in carbon emissions ranging from a minimum of 15% to a maximum of 22.81%. However, a higher operating separation temperature for ammonia and water is observed to degrade the thermodynamic performance across all configurations analyzed. Full article

The Allam power cycle is a groundbreaking elevated-pressure power generation unit that utilizes oxygen and fossil fuels to generate low-cost electricity while capturing carbon dioxide (CO₂) inherently. In this project, we utilize the CO₂ generated from the Allam cycle as feedstock for a newly envisioned industrial complex dedicated to producing renewable hydrocarbons. The industrial complex (FAAR) comprises four subsystems: (i) a Fischer–Tropsch synthesis plant (FTSP), (ii) an alkaline water electrolysis plant (AWEP), (iii) an Allam power cycle plant (APCP), and (iv) a reverse water-gas shift plant (RWGSP). Through effective material, heat, and power integration, the FAAR complex, utilizing 57.1% renewable energy for its electricity needs, can poly-generate sustainable hydrocarbons (C₁–C₃₀), pure hydrogen, and oxygen with near-zero emissions from natural gas and water. Economic analysis indicates strong financial performance of the development, with an internal rate of return (IRR) of 18%, a discounted payback period of 8.7 years, and a profitability index of 2.39. The complex has been validated through rigorous modeling and simulation using Aspen Plus version 14, including sensitivity analysis. Full article

Enhanced Rock Weathering (ERW) is a promising carbon dioxide removal (CDR) strategy that accelerates mineral dissolution, sequestering atmospheric CO₂ while improving soil health. This study builds on prior applications of soil calcimetry by investigating its ability to resolve short-term carbonate fluxes and rainfall-modulated weathering dynamics in wollastonite-amended croplands. Conducted over a single growing season (May–October 2024) in temperate row-crop fields near Port Colborne, Ontario—characterized by fibric mesisol soils (Histosols, FAO-WRB)—this study tests whether calcimetry can distinguish between dissolution and precipitation phases and serve as a proxy for weathering flux within the upper soil horizon, under the assumption that rapid pedogenic carbonate cycling dominates alkalinity retention in this soil–mineral system. Monthly measurements of soil pH (Milli-Q and CaCl₂) and calcium carbonate equivalent (CCE) were conducted across 10 plots, totaling 180 composite samples. Results show significant alkalinization (p < 0.001), with average pH increases of ~+1.0 unit in both Milli-Q and CaCl₂ extracts over the timeline. In contrast, CCE values showed high spatiotemporal variability (−2.5 to +6.4%) without consistent seasonal trends. The calcimetry-derived weathering proxy, log (Σ ΔCCE/Δt), correlated positively with pH (r = 0.652), capturing net carbonate accumulation, while the kinetic dissolution rate model correlated strongly and negatively with pH (r ≈ −1), reflecting acid-promoted dissolution. This divergence confirms that the two metrics capture complementary stages of the weathering–precipitation continuum. Rainfall strongly modulated short-term carbonate formation, with cumulative precipitation over the previous 7–10 days enhancing formation rates up to a saturation point (~30 mm), beyond which additional rainfall yielded diminishing returns. In contrast, dissolution fluxes remained largely independent of rainfall. These results highlight calcimetry as a direct, scalable, and dynamic tool not only for monitoring solid-phase carbonate formation, but also for inferring carbonate migration and dissolution dynamics. In systems dominated by rapid pedogenic carbonate cycling, this approach captures the majority of alkalinity fluxes, offering a conservative yet comprehensive proxy for CO₂ sequestration. Full article

This paper estimates potential changes in the total system cost (TSC) of decarbonization of two regional transmission organizations (RTOs) in the United States (U.S.)—Midcontinent Independent System Operator-North (MISO-N) and Southwest Power Pool (SPP) RTO West. In particular, the study serves to highlight potential differences in technology costs between two decarbonization pathways at carbon reduction rates close to 100% (relative to 2019 levels) while maintaining system reliability. In Pathway A, decarbonization is achieved by replacing fossil energy (FE)-fired thermal power plants with variable renewable energy (VRE) technologies coupled with energy storage (ES). Pathway B considers retrofitting fossil fuel-fired units with carbon capture and storage (CCS) and the addition of VRE and ES. The results show that including CCS technologies in the path to decarbonization has a significant benefit from a system cost perspective. When summing up all system costs and avoided emissions over 30 years of operation of the decarbonized systems, the pathway that includes CCS is significantly more cost-effective. TSCs for MISO-N are at least USD 1279 billion (B) and at most USD 910 B under Pathways A and B, respectively. For SPP RTO West, Pathway A TSCs are at least USD 230 B, and Pathway B TSCs are at most USD 153 B. TSCs of Pathway A are 1.4–8 times larger than the total system costs of Pathway B. When CCS is not included, the cost per ton of carbon dioxide (CO₂) avoided is estimated to be USD 124–489/ton for MISO-N and USD 248–552/ton for SPP RTO West. When CCS is included, the cost of avoided CO₂ is projected to decrease by 29–87% (mid-point estimate of 73%) with values varying between USD 64 and 114/ton and USD 74 and 164/ton for MISO-N and SPP RTO West, respectively. These differences highlight the need for consideration of all low-carbon-intensive technology options in cost-optimal approaches to deep decarbonization and the value of CCS technologies in the energy transition. Full article

The United Arab Emirates has become increasingly concerned about carbon dioxide emissions due to their impact on climate change and the environment, as it is one of the top ten world oil producers. This reflects its recognition of the need for sustainable development. Therefore, this research aims to study the dynamic impact of economic growth, urbanization, energy consumption, and economic openness on CO₂ emissions, during the period 1975–2022. To capture these effects, a novel dynamic ARDL is employed to separate the impact of each variable separately. The results indicate that the effect of GDP per capita on carbon emissions is negative, as a 1% increase in economic growth leads to a decrease in carbon dioxide emissions by 0.6%. Moreover, the findings confirm that the UAE economy does not apply to the Kuznets curve in developing countries. Furthermore, the impact of energy consumption, urbanization, and trade openness is positive on CO₂ emissions, as a 1% increase in each raises CO₂ by 0.17%, 11.6%, and 1.2%, respectively. These findings are important for decision makers in the environmental field to make decisions to reduce carbon emissions by altering the impact of economic variables and spread awareness towards reducing carbon emissions. Full article

Unmanned aerial vehicles, also termed as unarmed aerial vehicles, are used for various purposes in and around the environment, such as delivering things, spying on opponents, identification of aerial images, extinguishing fire, spraying the agricultural fields, etc. As there are multi-functions in a single UAV model, it can be used for various purposes as per the user’s requirement. The UAVs are used for faster communication of identified information, entry through the critical atmospheres, and causing no harm to humans before entering a collapsed path. In relation to the above discussion, a UAV system is designed to classify and transmit information about the atmospheric conditions of the environment to a central controller. The UAV is equipped with advanced sensors that are capable of detecting air pollutants such as carbon monoxide (CO), carbon dioxide (CO₂), methane (CH₄), ammonia (NH₃), hydrogen sulfide (H₂S), etc. These sensors present in the UAV model monitor the quality of air, time-to-time, as the UAV navigates through different areas and transmits real-time data regarding the air quality to a central unit; this data includes detailed information on the concentrations of different pollutants. The central unit analyzes the data that are captured by the sensor and checks whether the quality of air meets the atmospheric standards. If the sensed levels of pollutants exceed the thresholds, then the system present in the UAV triggers a warning alert; this alert is communicated to local authorities and the public to take necessary precautions. The developed UAV is furnished with cameras which are used to capture real-time images of the environment and it is processed using the YOLO V3 algorithm. Here, the YOLO V3 algorithm is defined to identify the context and source of pollution, such as identifying industrial activities, traffic congestion, or natural sources like wildfires. Full article

Carbon capture and sequestration have been recently presented as a viable option to reduce atmospheric carbon dioxide emissions and mitigate global climate change. The concept entails the capture, compression, transportation, and injection of the gas into a medium suitable for storage. This paper examines the thermodynamic and transport properties of carbon dioxide that are pertinent to its sequestration and storage, describes the various methods that have been recommended for its separation from the mixture of the flue gases, and determines the mechanical power and heat rate required for the capture of the gas. The power required for the compression and transportation of the gas by a pipeline is also determined, as well as the effect of the ambient temperature on the transportation power. Calculations for the total power required are performed for two cases, one a cement production unit and the second a coal power plant. The mechanical power needed for the sequestration of CO₂ is substantial in both cases, with the cement unit needing less power because of the availability of high-temperature waste heat. In both cases, the equivalent mechanical work needed for the sequestration and storage of this gas is on the order of 1 MJ per kg CO₂ sequestered. Full article

Direct air capture (DAC) technology is increasingly recognized as a key tool in the pursuit of climate neutrality, enabling the removal of carbon dioxide directly from the atmosphere. Despite its potential, DAC remains in the early stages of development, with most installations limited to pilot or demonstration units. The main barriers to its widespread implementation include high energy demands and significant capture costs. This literature review addresses the most critical research directions related to the development of this technology, focusing on its challenges and prospects for deployment. Particular attention is given to studies aimed at developing new, cost-effective, and efficient sorbents that could significantly reduce the energy intensity and costs of the process. Alternative technologies, such as electrochemical and membrane-based processes, show promise but require further research to overcome limitations, such as sensitivity to oxygen presence or insufficient membrane selectivity. The economic feasibility of DAC remains uncertain, with current estimates subject to significant uncertainty. Governmental and regulatory support will be crucial for the technology’s success. Furthermore, the location of DAC installations should consider factors such as energy availability, options for carbon dioxide storage or utilization, and climatic conditions, which significantly affect process efficiency. This review highlights the necessity for continued research to overcome existing barriers and fully harness the potential of DAC technology. Full article

Chemical absorption carbon capture systems use solutions with complex compositions to further reduce energy consumption and improve performance. Modeling and simulation are essential methods for studying the characteristics of these systems and optimizing them. However, existing methods cannot be used to build models of systems with complex or unknown solutions. This study proposes a hybrid modeling method integrating mechanism modeling and operational data for a chemical absorption carbon capture system. This method interprets the physical and chemical properties of solvents under various operating conditions based on operational data. To validate the effectiveness of this method, it is applied to a real-life post-combustion carbon dioxide capture system in a 1000 MW coal-fired power unit, which has an annual capture capacity of 10,000 tons. The results of the case study indicate that the proposed method can obtain values of key property parameters of solvents, including absorption heat, cyclic carbon capacity, and heat capacity. The average relative error between operational data and simulation data ranges from 0.2% to 8.0%. Full article

With the rapid development of industrial automation and intelligence, the consumption of resources and the environmental impact of production processes cannot today be ignored. Today, natural gas, as a commonly used energy source, produces significantly lower emissions of carbon dioxide, sulphur dioxide, and nitrogen oxides from combustion than coal and oil, and can be further purified to remove the small amount of impurities it contains, such as sulphur compounds. Therefore, purified natural gas (hereinafter referred to as purified gas), as a clean energy source, plays an important role in realising sustainable development. At the same time, It becomes more and more important to dispatch purified gas resources reasonably and accurately, and the paramount factor is that the load of purified gas needs to be predicted accurately. Therefore, this paper proposes a Transformer–GRU-based hourly prediction model for purified gas. The model uses the Transformer model for data fusion and feature extraction, and then combines the time series processing capability of the Gate Recurrent Unit (GRU) model to capture long-term dependencies and short-term dynamic changes in time series data. In this paper, the purified gas load data of Chongqing Municipality in 2020 was first preprocessed, and then divided into daily and hourly load datasets according to the measurement step. Meanwhile, considering the influence of temperature factor, the experimental dataset is subdivided according to whether it includes temperature data or not, and then the Transformer–GRU model was built for prediction, respectively. The results show that, compared with the Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN) and the Transformer and GRU models alone, the Transformer–GRU model exhibits good performance in terms of the coefficient of determination, the average absolute percentage error, and mean square error, which can well meet the requirement of hourly prediction accuracy and has greater application value. Full article