Chemoinformatics-Driven Design of New Physical Solvents for Selective CO2 Absorption

Reducing aerosol and ammonia emission in post-combustion CO2 capture: Additives as key solutions

Advancement of the absorbent for CO2 capture is essential in optimizing the performance and reducing the negative environmental effects associated with this technology. Despite ammonia's promise as an absorbent, the volatility limits its practical application and creates potential environmental pollution. Therein, we assess various additives (amino acids, carbonates, and alkanolamines) for ammonia-based solvents using multi-stage circulation absorber from the viewpoints of aerosol emission, ammonia emission, and CO2 capture efficiency. Experimental findings reveal that ammonia volatilization can be inhibited by the protonation of free ammonia by carboxyl groups and the formation of hydrogen bonding between amino/hydroxyl groups and ammonia, with ammonia emission reduced by 21.7 %, aerosol emission reduced by 26.5 %, and CO2 capture efficiency increased to a maximum of 87.8 % under the condition of adding histidine. Moreover, the experiment highlights a positive correlation between total ammonia emission and aerosol concentration/diameter. Additionally, tests combining source abatement with water wash exhibit up to 50.5 % aerosol removal efficiency and up to 76.6 % ammonia removal efficiency. To further mitigate emissions, a comprehensive approach is proposed, achieving an 84.4 % reduction in ammonia emission and a 61.9 % reduction in aerosol emission. Finally, a method for recycling ammonia for desulfurization is suggested.

Enzyme-Immobilized Porous Crystals for Environmental Applications

Developing efficient technologies to eliminate or degrade contaminants is paramount for environmental protection. Biocatalytic decontamination offers distinct advantages in terms of selectivity and efficiency; however, it still remains challenging when applied in complex environmental matrices. The main challenge originates from the instability and difficult-to-separate attributes of fragile enzymes, which also results in issues of compromised activity, poor reusability, low cost-effectiveness, etc. One viable solution to harness biocatalysis in complex environments is known as enzyme immobilization, where a flexible enzyme is tightly fixed in a solid carrier. In the case where a reticular crystal is utilized as the support, it is feasible to engineer next-generation biohybrid catalysts functional in complicated environmental media. This can be interpreted by three aspects: (1) the highly crystalline skeleton can shield the immobilized enzyme against external stressors. (2) The porous network ensures the high accessibility of the interior enzyme for catalytic decontamination. And (3) the adjustable and unambiguous structure of the reticular framework favors in-depth understanding of the interfacial interaction between the framework and enzyme, which can in turn guide us in designing highly active biocomposites. This Review aims to introduce this emerging biocatalysis technology for environmental decontamination involving pollutant degradation and greenhouse gas (carbon dioxide) conversion, with emphasis on the enzyme immobilization protocols and diverse catalysis principles including single enzyme catalysis, catalysis involving enzyme cascades, and photoenzyme-coupled catalysis. Additionally, the remaining challenges and forward-looking directions in this field are discussed. We believe that this Review may offer a useful biocatalytic technology to contribute to environmental decontamination in a green and sustainable manner and will inspire more researchers at the intersection of the environment science, biochemistry, and materials science communities to co-solve environmental problems.

Insights into Aerosol Emission Control in the Postcombustion CO2 Capture Process: From Cluster Formation to Aerosol Growth

Aerosols produced in the amine carbon capture process can lead to secondary environmental pollution. This study employs molecular dynamics (MD) simulations to investigate cluster formation, amine behavior, and aerosol growth of amines, essential for reducing amine aerosol emissions. Results showed that the cluster evolution process can be divided into cluster formation and growth in terms of molecular content, and the nucleation rate for the present systems was estimated in the order of 1028 cm-3 s-1. CO2 absorption was observed alongside successful nucleation, with CO2 predominantly localizing in the cluster's outer layer postabsorption. Monoethanolamine (MEA) exhibited robust electrostatic interactions with other components via hydrogen bonding, leading to its migration toward regions where CO2 and H2O coexisted within the cluster. While MEA presence markedly spurred cluster formation, its concentration had a marginal effect on the final cluster size. Elevating water content can augment the aerosol growth rate. However, altering the gas saturation is possible only within narrow confines by introducing vapor. Contrarily, gas cooling introduced dual, opposing effects on aerosol growth. These findings, including diffusion coefficients and growth rates, enhance theoretical frameworks for predicting aerosol formation in absorbers, aiding in mitigating environmental impacts of amine-based carbon capture.

Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities

Carbon capture, utilization, and sequestration (CCUS) is a promising solution to decarbonize the energy and industrial sectors to mitigate climate change. An integrated assessment of technological options is required for the effective deployment of CCUS large-scale infrastructure between CO2 production and utilization/sequestration nodes. However, developing cost-effective strategies from engineering and operation perspectives to implement CCUS is challenging. This is due to the diversity of upstream emitting processes located in different geographical areas, available downstream utilization technologies, storage sites capacity/location, and current/future energy/emissions/economic conditions. This paper identifies the need to achieve a robust hybrid assessment tool for CCUS modeling, simulation, and optimization based mainly on artificial intelligence (AI) combined with mechanistic methods. Thus, a critical literature review is conducted to assess CCUS technologies and their related process modeling/simulation/optimization techniques, while evaluating the needs for improvements or new developments to reduce overall CCUS systems design and operation costs. These techniques include first principles- based and data-driven ones, i.e. AI and related machine learning (ML) methods. Besides, the paper gives an overview on the role of life cycle assessment (LCA) to evaluate CCUS systems where the combined LCA-AI approach is assessed. Other advanced methods based on the AI/ML capabilities/algorithms can be developed to optimize the whole CCUS value chain. Interpretable ML combined with explainable AI can accelerate optimum materials selection by giving strong rules which accelerates the design of capture/utilization plants afterwards. Besides, deep reinforcement learning (DRL) coupled with process simulations will accelerate process design/operation optimization through considering simultaneous optimization of equipment sizing and operating conditions. Moreover, generative deep learning (GDL) is a key solution to optimum capture/utilization materials design/discovery. The developed AI methods can be generalizable where the extracted knowledge can be transferred to future works to help cutting the costs of CCUS value chain.

Inhibition of Aqueous Chemical Reactions by Strong Liquid Structure Makers: Experimental Demonstration with Amides and Acid-Base Reactions

The chemical absorption of CO2 and H2S in aqueous tertiary amines is a well-known acid-base reaction. Kinetic and vapor-liquid equilibrium experiments show that the addition of an amide such as HMPA, which is known to be a strong liquid structure maker, significantly inhibits the acid-base reactions. The impact is more pronounced for CO2 than for H2S absorption. Despite the presence of water in the solvent, the absorption becomes almost physical. Due to hydrogen bonding and the hydrophobic effect, each amide molecule is involved in a cluster containing several water molecules, thus rendering the water molecules less available to participate in the reaction and to solvate HS- and HCO3- ions. This effect is absent when ethylene glycol, a weak structure maker, is added, even in large quantities. This study demonstrates the importance of solvent structure in the study of chemical reactions. State-of-the-art molecular dynamics simulations of the water-HMPA system could not reproduce the strongly negative excess volume of the mixture. This illustrates the need for more accurate force fields to simulate the structuring effect and their impact on chemical reactions.

Reduced Order Machine Learning Models for Accurate Prediction of CO2 Capture in Physical Solvents

CO2 sorption in physical solvents is one of the promising approaches for carbon capture from highly concentrated CO2 streams at high pressures. Identifying an efficient solvent and evaluating its solubility data at different operating conditions are highly essential for effective capture, which generally involves expensive and time-consuming experimental procedures. This work presents a machine learning based ultrafast alternative for accurate prediction of CO2 solubility in physical solvents using their physical, thermodynamic, and structural properties data. First, a database is established with which several linear, nonlinear, and ensemble models were trained through a systematic cross-validation and grid search method and found that kernel ridge regression (KRR) is the optimum model. Second, the descriptors are ranked based on their complete decomposition contributions derived using principal component analysis. Further, optimum key descriptors (KDs) are evaluated through an iterative sequential addition method with the objective of maximizing the prediction accuracy of the reduced order KRR (r-KRR) model. Finally, the study resulted in the r-KRR model with nine KDs exhibiting the highest prediction accuracy with a minimum root-mean-square error (0.0023), mean absolute error (0.0016), and maximum R2 (0.999). Also, the validity of the database created and ML models developed is ensured through detailed statistical analysis.

Preventing Aerosol Emissions in a CO2 Capture System: Combining Aerosol Formation Inhibition and Wet Electrostatic Precipitation

Aerosol emission from the CO₂ capture system has raised great concern for causing solvent loss and serious environmental issues. Here, we propose a comprehensive method for reducing aerosol emissions in a CO₂ capture system under the synergy of aerosol formation inhibition and wet electrostatic precipitation. The gas-solvent temperature difference plays a vital role in aerosol formation, with aerosol emissions of 740.80 mg/m³ at 50 K and 119.36 mg/m³ at 0 K. Different effects of SO₂ and SO₃ on aerosol formation are also found in this research; the aerosol mass concentration could reach 2341.25 mg/m³ at 20 ppm SO₃ and 681.01 mg/m³ at 50 ppm SO₂ with different aerosol size distributions. After the CO₂ capture process, an aerosol removal efficiency of 98% can be realized by electrostatic precipitation under different CO₂ concentrations. Due to the high concentration of aerosols and aerosol space charge generated by SO₂ and SO₃, the removal performance of the wet electrostatic precipitator decreases, resulting in a high aerosol emission concentration (up to 130.26 mg/m³). Thus, a heat exchanger is installed before the electrostatic precipitation section to enhance aerosol growth and increase aerosol removal efficiency. Under the synergy of aerosol formation inhibition and electrostatic precipitation, an aerosol removal efficiency of 99% and emission concentrations lower than 5 mg/m³ are achieved, contributing to global warming mitigation and environmental protection.