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

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.

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.

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.