Mitigation approaches and techniques for combustion power plants flue gas emissions: A comprehensive review

Technological trajectories in circular economy: Bridging patent analytics with sustainable development goals

This study examines key technologies in the circular economy through patent mining and expert evaluations. We identified eleven distinct technology clusters, including Smart Fluid Management Systems, Circular Chemical Processing, and Structural Design for Circularity. Using the S-curve model, we analyzed the maturity stages of these technologies, revealing a mix of mature and emerging technologies. For example, Energy Storage and Electrical Systems have reached maturity, while Circular Chemical Processing and Waste Treatment are still in the growth phase. These findings highlight the varying levels of technological development and the potential for targeted research and investment. To assess sustainability, we used the TOPSIS method, which ranks technologies based on their alignment with the Sustainable Development Goals (SDGs). Our results indicate that Circular Chemical Processing, Thermal Management, and Structural Design for Circularity are key areas for innovation, offering significant potential to contribute to global sustainability goals. The study emphasizes the need for integrated solutions, suggesting that technological progress will be driven by synergies between different technology clusters. It calls for cross-sector collaboration to maximize the impact of these technologies. By combining insights on technological maturity and sustainability alignment, this research provides a strategic roadmap for advancing circular economy practices. It offers guidance for industry stakeholders and policymakers on prioritizing investments in technologies that support long-term sustainability and resource efficiency. In conclusion, this study provides a comprehensive framework for understanding and advancing circular economy technologies, offering valuable insights for accelerating the transition toward a sustainable, circular economy.

Capture of gaseous and fine particulate pollutants emitted in the biomass post-combustion process

PURPOSE

Emissions of gaseous pollutants, particularly carbon dioxide (CO2) and particulate matter, are increasing, primarily due to anthropogenic combustion of conventional fossil fuels. These emissions drive global warming and climate change, contributing to significant environmental and health impacts. This scenario highlights the urgent need for cleaner energy solutions, prompting industries and power plants to transition rapidly to sustainable sources such as biomass. While biomass combustion is carbon neutral regarding atmospheric CO2, it still generates particulate matter that must be controlled. Integrated strategies for CO2 capture, sequestration, storage, and utilization, along with effective particulate capture, are essential for reducing greenhouse gas emissions and minimizing health risks.

AIM

This study primarily evaluates laboratory- and pilot-scale technologies for mitigating industrial CO2 and fine particulate matter emissions from biomass combustion.

METHODS

Emissions of gaseous and particulate pollutants were evaluated using a pilot burner. Initial CO2 capture tests were performed in a laboratory absorption column, followed by biomass combustion evaluations using a Venturi scrubber for gas capture and particulate removal.

RESULTS

The results indicated low CO2 capture efficiencies without chemical reactions. When a NaOH solution was used for chemical absorption at flow rates of 1.4, 1.8, and 2.0 L/min, capture efficiencies improved to 10.3, 10.4, and 17.24%, respectively. The Venturi scrubber effectively captured particulate matter but was less effective for CO2, although it performed well in capturing CO and NOx gases.

CONCLUSION

Emissions of gaseous pollutants and PM were significantly high during the burning of biomass, such as sugar cane bagasse. The results showed high capture efficiencies for PM below 1.0 μm, reaching values above 80%. Advancements in capture technologies can help industries transition to sustainable practices, addressing both climate goals and air quality standards.

A critical appraisal of advances in integrated CO2 capture and electrochemical conversion

This perspective work examines the current advancements in integrated CO2 capture and electrochemical conversion technologies, comparing the emerging methods of (1) electrochemical reactive capture (eRCC) though amine- and (bi)carbonate-mediated processes and (2) direct (flue gas) adsorptive capture and conversion (ACC) with the conventional approach of sequential carbon capture and conversion (SCCC). We initially identified and discussed a range of cell-level technological bottlenecks inherent to eRCC and ACC including, but not limited to, mass transport limitations of reactive species, limitation of dimerization, impurity effects, inadequate in situ generation of CO2 to sustain industrially relevant current densities, and catalyst instabilities with respect to some eRCC electrolytes, amongst others. We followed this with stepwise perspectives on whether these are considered intrinsic challenges of the technologies - otherwise recommendations were disclosed where appropriate. Furthermore, technoeconomic analysis (TEA) was conducted using a net present value (NPV) model to determine the minimum selling prices (MSPs) for CO, HCOOH, CH3OH, C2H5OH, and C2H4 as target products based on cell-performance metrics from contemporary literature for SCCC, eRCC, and ACC. Additionally, sensitivity analyses were performed, focusing on cell-level parameters (voltage requirements, Faradaic efficiencies, current density), production scale factors, and other relevant variables (levelized costs of electricity and stack). This analysis sheds light on the cost-driving factors influencing commercial viability, revealing key techno-economic challenges for eRCC, particularly with liquid products. However, it also identifies optimization opportunities in current designs. By pinpointing critical areas for improvement, this work helps advance electrochemical CO2 reduction technologies towards more sustainable and economically competitive applications at different scales.

Microalgae-based flue gas CO2 sequestration for cleaner environment and biofuel feedstock production: a review

Anthropogenic CO2 emissions are the prime cause of global warming and climate change, promoting researchers to develop suitable technologies to reduce carbon footprints. Among various CO2 sequestration technologies, microalgal-based methods are found to be promising due to their easier operation, environmental benefits, and simpler equipment requirements. Microalgae-based carbon capture and storage (CCS) technology is essential for addressing challenges related to the use of industrial-emitted flue gases. This review focuses on the literature concerning the microalgal application for CO2 sequestration. It highlights the primary physiochemical parameters that affect microalgal-based CO2 biofixation, including light exposure, microalgal strain, temperature, inoculum size, pH levels, mass transfer, CO2 concentration, flow rate, cultivation system, and mixing mechanisms. Moreover, the inhibition effect of different flue gas components including NOx, SOx, and Hg on growth kinetics is discussed to enhance the capacity of microalgal-based CO2 biofixation, along with deliberated challenges and prospects for future development. Overall, the review indicated microalgal-based flue gas CO2 fixation rates range from 80 mg L-1 day-1 to over 578 mg L-1 day-1, primarily influenced by physiochemical parameters and flue gas composition. This article summarizes the mechanisms and stages of microalgal-based CO2 sequestration and provides a comprehensive review based on international interest in this green technology.

Current advances and future prospects of in-situ desulfurization processes in oxy-fuel combustion reactors

Oxy-fuel circulating fluidized bed combustion is known as one of the most potent fuel combustion technologies that capture ultra-low greenhouse gases and pollutant emissions. While many investigations have been conducted for carbon capturing, the associated in-situ desulfurization process using calcium-based sorbents should also be underlined. This paper critically reviews the effects of changes in the operating environment on in-situ desulfurization processes compared to conventional air combustion. A comprehensive understanding of the process, encompassing hydrodynamic, physical and chemical aspects can be a guideline for designing the oxy-fuel combustion process with effective sulfur removal, potentially eliminating the need of a flue gas desulfurization unit. Results from thermogravimetric analyzers and morphological changes of calcium-based materials were presented to offer an insight into the sulfation mechanisms involved in the oxy-fuel circulating fluidized beds. Recently findings suggested that in-situ direct desulfurization is influenced not only by the desulfurization kinetics but also by the fluidization characteristics of calcium-based materials. Therefore, a complex reaction analysis that incorporated oxy-combustion reactions, computational fluid dynamics modeling, in-situ desulfurization reaction models and particle behavior can provide a thorough understanding of desulfurization processes across the reactor. Meanwhile, machine learning as a robust tool to predict desulfurization efficiency and improve operational flexibility should be applied with consideration of environmental improvement and economic feasibility.

Do policies make a difference? Assessing the impact of China's air pollution prevention and control action plan on carbon emissions

This study aims to balance the dual objectives of improving environmental quality and achieving carbon peaking and carbon neutrality goals. Utilizing panel data from 274 Chinese cities between 2006 and 2020, this paper employs the difference-in-differences method to examine the impact of the Air Pollution Prevention and Control Action Plan (APPCAP) on urban carbon emissions. Empirical results indicate that the impact of the APPCAP on regional carbon emissions follows an inverted U-shaped curve, exhibiting a dual impact path. Although the implementation of the APPCAP increased the annual carbon emissions of the city by an average of 1.637 million tons, it showed a trend of inhibiting the growth of carbon emissions, although it was not significant. Specifically, the APPCAP is most effective in reducing carbon emissions during the "source prevention" stage, primarily by increasing industrial electricity consumption and reducing coal production and consumption. However, a rebound effect occurs at the policy period's end, with compensatory increases in crude oil and electricity consumption, thereby elevating regional carbon emission levels. The APPCAP's effect on carbon emissions is more pronounced in areas with lower population density and higher environmental regulation intensity. It notably raises carbon emission levels in non-industrial base and non-resource-based cities compared to other cities. Furthermore, the APPCAP positively impacts regional carbon emission intensity and economic growth, significantly driving the secondary industry while minimally affecting the primary industry.

Life cycle assessment of electrolytic manganese metal production

Manganese is an indispensable metal widely used in various fields. China ranks as the fourth-largest producer of manganese ore and the largest producer of electrolytic manganese metal (EMM). However, EMM production is linked to high energy consumption and pollution. This study conducts a life cycle assessment (LCA) of EMM production in the Manganese Triangle region of China to comprehensively evaluate its environmental impact. Results show that Human carcinogenic toxicity, mainly from electricity generation (65.3 %) and mining activities (24.4 %), is the most significant environmental impact. Chromium (VI) is identified as the predominant hazardous substance, contributing up to 91 % to Human carcinogenic toxicity. Endpoint results estimate that the production of 1 t of EMM results in 1.01E-02 DALY of harm to human health, 1.97E-05 species.yr of harm to the ecosystem, and $227.15 worth of resource depletion. Simulation scenarios demonstrate that replacing thermal power with hydropower can reduce environmental pollution by over 90 %. Finally, based on the findings, technical measures for promoting clean production of EMM were proposed.

Application of triple-branch artificial neural network system for catalytic pellets combustion

On the international level, it is common to act on reducing emissions from energy systems. However, in addition to industrial emissions, low-stack emissions also make a significant contribution. A good step in reducing its environmental impact, is to move to biofuels, including biomass. This paper examines the impact of placing a catalytic system in a retort boiler to minimize emissions of greenhouse gases, dust and other pollutants when burning pellets. The effect of platinum, and oxides of selected metals placed on the deflector as a solid catalyst was studied. Based on the experimental data, a branched artificial neural network was constructed and trained. The routing of three parallel topologies made it possible to achieve high accuracy while keeping the input data relatively simple. The system showed an average error of 3.54% against arbitrary test data. On the basis of experimental data as well as predictions returned by the artificial neural network, recommendations were shown for the catalysts used and their amounts. Depending on the biomass from which the pellet was produced, the experiment suggested the use of titanium or copper oxides. In the case of the neural network, it was able to select a better system, based on platinum, improving emission reductions by up to more than 19%, depending on the type of pellet used.

Comprehensive effect of increased calcium content in coal on the selenium emission from coal-fired power plants: Combined laboratory and field experiments

Coal combustion is the major contributor to global toxic selenium (Se) emissions. Inorganic elements in coals significantly affect Se partitioning during combustion. This work confirmed that the calcium (Ca) in ash had a stronger relationship with Se retention at 1300 °C than other major elements. Ca oxide chemically reacted with gaseous Se, and its sintering densification slightly affected Se adsorption capacities (44.45 -1840.71→35.17 -1540.15 mg/kg) at 300 - 1300 °C. Therefore, Ca in coals was identified as having potential for hindering gaseous Se emissions, and coals with increased Ca contents (2.74→5.19 wt%) were used in a 350 MW unit. The decreased Se mass distribution (3.54%→2.63%) in flue gas at air preheater inlet (320 -362 °C) confirmed the effectiveness of increased Ca content on gaseous Se emission reduction. More gaseous Se further condensed and was chemically adsorbed by fly ash when passed through an electrostatic precipitator, resulting in a significant increase in the Se content of fly ash. Additionally, the corresponding Se leaching ratio decreased from 4.88 - 35.74% to 1.87 - 26.31%, indicating enhanced stability of Se enriched in fly ash. This research confirmed the feasibility and environmental safety of sequestration of gaseous Se from flue gas to fly ash by increasing the Ca content in coals.

Machine learning regression algorithms to predict emissions from steam boilers

Currently, the modeling of complex chemical-physical processes is drastically influencing industrial development. Therefore, the analysis and study of the combustion process of the boilers using machine learning (ML) techniques are vital to increase the efficiency with which this equipment operates and reduce the pollution load they contribute to the environment. This work aims to predict the emissions of CO, CO2, NOx, and the temperature of the exhaust gases of industrial boilers from real data. Different ML algorithms for regression analysis are discussed. The following are input variables: ambient temperature, working pressure, steam production, and the type of fuel used in around 20 industrial boilers. Each boiler's emission data was collected using a TESTO 350 Combustion Gas Analyzer. The modeling, with a machine learning approach using the Gradient Boosting Regression algorithm, showed better performance in the predictions made on the test data, outperforming all other models studied. It was achieved with predicted values showing a mean absolute error of 0.51 and a coefficient of determination of 99.80%. Different regression models (DNN, MLR, RFR, GBR) were compared to select the most optimal. Compared to models based on Linear Regression, the DNN model has better prediction performance. The proposed model provides a new method to predict CO2, CO, NOx emissions, and exhaust gas outlet temperature.

Prediction Model of Lean Coal Adsorption of Power Plant Flue Gas

To minimize errors in calculating coal flue gas adsorption capacity due to gas compressibility and to preclude prediction inaccuracies in abandoned mine flue gas storage capacity for power plants, it is imperative to account for the influence of compression factor calculation accuracy while selecting the optimal theoretical adsorption model. In this paper, the flue gas adsorption experiment of a power plant with coal samples gradually pressurized to close to 5 MPa at two different temperatures is carried out, and the temperature and pressure data obtained from the experiment are substituted into five different compression factor calculation methods to calculate different absolute adsorption amounts. The calculated adsorption capacities were fitted into six theoretical adsorption models to establish a predictive model suitable for estimating the coal adsorption capacity in power plant flue gas. Results reveal significant disparities in the absolute adsorption capacity determined by different compression factors, with an error range of 0.001278-7.8262 (cm3/kg). The Redlich-Kwong equation of state emerged as the most suitable for the flue gas of the selected experimental coal sample and the chosen composition ratio among the five compression factors. Among the six theoretical adsorption models, the Brunauer-Emmett-Teller model with three parameters demonstrated the highest suitability for predicting the adsorption capacity of coal samples in power plant smoke, achieving a fitting accuracy as high as 0.9922 at 49.7 °C.

Investigating the Corrosive Influence of Chloride Ions on Slag Recovery Machine Inner Guide Wheel in Power Plants

An important method that coal-fired power plants use to realise low-cost zero discharge of desulfurisation wastewater (FGD wastewater) is to utilise wet slag removal systems. However, the high Cl- content of FGD wastewater in wet slag removal systems causes environmental damage. In this study, the corrosion behaviour of the inner guide wheel material, 20CrMnTi, was studied using dynamic weight loss and electrochemical methods. X-ray diffraction, scanning electron microscopy, and energy spectroscopy were used to analyse the organisational and phase changes on the surfaces and cross sections of the samples at different Cl- concentrations. The corrosion rate increased with the Cl- concentration up to 20 g/L, but it decreased slightly when the Cl- concentration exceeded 20 g/L. In all the cases, the corrosion rate exceeded 0.8 mm/a. The corrosion product film density initially increased and then decreased as the Cl- concentration increased. The corrosion products comprised mainly α-FeOOH, γ-FeOOH, β-FeOOH, Fe3O4, and γ-Fe2O3.