Selective Catalytic Reduction Denitrification Research Articles

Composition, characteristics, and treatment technologies of condensable particulate matter present in flue gas emitted by coking plants in China

To study the total particulate matter (TPM) in flue gas emitted by coking plants, a sampling system that could be used to collect filterable particulate matter (FPM) and condensable particulate matter (CPM) was designed and developed based on Method 202 recommended by the U.S. Environmental Protection Agency in 2017 and HJ 836-2017 issued by China. Using this system, FPM and CPM in flue gas emitted by four coking furnaces named A, B, C, and D were tested in China. Further, 9 water-soluble ions, 20 elements, and organic matter present in the CPM were simultaneously examined to determine their formation mechanisms. Statistical data suggested that the FPM emission level in the coking flue gas was low and the average mass concentration was less than 10 mg/m3. However, with high CPM and TPM emission levels, the TPM mass concentrations of A, B, C, and D were 130 ± 11.1, 84.4 ± 6.36, 35.1 ± 17.0, and 63.8 ± 13.0 mg/m3, respectively. The main component of TPM was CPM, and the average mass concentration of CPM accounted for 98%, 95%, 68%, and 95% of TPM in furnaces A, B, C, and D, respectively. Water-soluble ions were the important components of CPM, and the total concentration of water-soluble ions accounted for 70%, 87%, 42%, and 66% of CPM in furnaces A, B, C, and D, respectively. Toxic and harmful heavy metals, such as Mn, Cr, Ni, Cu, Zn, As, Cd, and Pb, were detected in CPM. The formation mechanism of CPM was analyzed in combination with flue-gas treatment. It was shown that the treatment process “activated carbon– flue-gas countercurrent-integrated purification technology + ammonia spraying” used in furnaces A and B was less effective in removing CPM, water-soluble ions, metals, and compounds than that of “selective catalytic reduction denitrification + limestone–gypsum wet desulfurization (spraying NaOH solution)” in furnaces C and D. Hence, different flue-gas treatment technologies and operation levels played vital roles in the formation, transformation, and removal of CPM from flue-gas. Organic components in CPM discharged from furnace A were determined via gas chromatography–mass spectrometry, and the top 15 organic components in CPM were obtained using the area normalization method. N-alkanes accounted for the highest proportion, followed by esters and phenols, and most of them were toxic and harmful to humans and ecosystems. Therefore, advanced CPM treatment technologies should be developed to reduce atmospheric PM2.5 and its precursors to improve ambient air quality in China.

A novel hybrid forecasting approach for NOx emission of coal‐fired boiler combined with CEEMDAN and self‐attention improved by LSTM

AbstractThe precise prediction of NOx generation concentration in coal‐fired boilers serves as the foundational cornerstone for the judicious optimization and control of selective catalytic reduction denitrification (SCR) systems. Owing to the intricate nature of the denitrification process within SCR, there exists a temporal delay in regulating the ammonia injection rate based on the monitored data of NOx concentration at the SCR inlet. Such delays can give rise to ammonia leakage and subsequent obstruction of the air preheater. In light of this, a predictive model, CEEMDAN‐LSTM‐SA, is proposed as an amalgamation of data decomposition and the LSTM (long short‐term memory) fusion self‐attention mechanism within a deep learning network, which is introduced to forecast the NOx emission concentration at the SCR inlet of coal‐fired units. To mitigate the impact of data outliers on the training effectiveness of the model, a clustering method coupled with a statistical testing strategy is initially applied to refine the dataset first. CEEMDAN data decomposition technology is leveraged to facilitate the breakdown of data, alleviating its non‐stationary and intricate characteristics. Subsequently, through spectral analysis, the decomposed components are grouped and aggregated to form novel data elements, which are then subjected to prediction by the constructed LSTM‐SA deep learning network. The ultimate NOx emission concentration prediction value is derived through a process of fusion. Upon scrutinizing and comparing the predictions derived from various models using coal‐fired power plant data, it is evident that the performance metrics of CEEMDAN‐LSTM‐SA predictions exhibit a mean absolute error of 7.425, mean absolute percentage error of 2.415%, root mean square error of 9.715, R‐squared (R2) value of .789, mean absolute relative error of 2.109%, and a Theil's information criterion of .016. In contrast to other models, including traditional self‐attention networks, LSTM, and LSTM‐SA combination networks, CEEMDAN‐LSTM‐SA proposed in this study demonstrates superior prediction accuracy and enhanced generalization capabilities. Consequently, this predictive model stands poised to furnish an efficacious framework for the SCR ammonia injection strategy within thermal power units.

Research on structural strengthening technology for regenerative denitration catalysts.

The cost of replacing failed selective catalytic reduction (SCR) catalysts and their disposal as hazardous solid waste is high. If failed catalysts are recovered and regenerated into new SCR denitration catalysts, the cost of flue gas denitration can be effectively reduced. However, regenerated SCR catalysts have relatively low structural strength and activity and cannot yet form an effective replacement. In this study, aluminum dihydrogen phosphate, aluminum nitrate, and aluminum sulfate were used as structural strengthening agents in the regeneration of SCR catalysts, and over-impregnation, drumming-assisted impregnation, and ultrasonic-assisted preparation techniques were compared. The corresponding regenerated SCR catalysts were then prepared and analyzed for compressive strength, wear strength, H2-TPR, NH3-TPD, and in situ IR. Factors influencing the structural strength, physical properties, and catalytic activity of the regenerated catalysts were investigated. The best results were obtained as follows: compressive strength of 4.57 MPa, wear rate of 0.088% kg-1, and denitration of 58% after 10 min of drumming-assisted impregnation in an aluminum sulfate solution with a concentration of 16%. Based on this, a synergistic method for catalyst activity and structural strengthening was explored to support the design of better SCR catalysts for regeneration.

Modified Active Disturbance Rejection Control Design Based on Gain Scheduling for Selective Catalytic Reduction Denitrification Processes

Modified Active Disturbance Rejection Control Design Based on Gain Scheduling for Selective Catalytic Reduction Denitrification Processes

Enhancement of SCR denitrification control strategy considering fluegas temperature fluctuation: Fundamental principle and performance evaluation

Nitrogen oxides (NOx) constitute a primary pollutant in coal-fired power plants, and are mitigated through the application of selective catalytic reduction (SCR). Coal-fired power plants should frequently change their output power to help the power grid to accommodate high penetration of intermittent renewable power. However, NOx emission transient overshoot often occurs in coal-fired power plants when coal-fired power plants dynamically change their output power. To address this issue, dynamic models for both coal-fired power plants and SCR denitrification were established. The study identifies fluctuations in fluegas temperature as a critical factor contributing to transient NOx overshoots. The change law of SCR reaction temperature during load cycling processes is obtained, and its prediction model based on neural network algorithm is developed. An enhanced control strategy, accounting for the temperature variations at the SCR inlet, was subsequently introduced. Performance comparisons indicate that the enhanced control strategy effectively eliminates transient NOx overshoots during load cycling processes. Specifically, the maximum NOx emissions during loading down and loading up processes under the enhanced control strategy are 48.62 and 49.44 mg m−3, representing reductions of 7.36 and 4.63 mg m−3 compared to the original control strategy. Additionally, when the enhanced control strategy is adopted, the cumulative ammonia (NH3) consumption decreases by 0.48 and 11.84 g m−3 during loading down and loading up processes, respectively.

Specific resistance and adsorption performance of acid-modified fly ash for escaped ammonia in flue gas

Escaped ammonia emission following Selective Catalytic Reduction denitrification significantly influences subsequent flue gas treatment processes. This study investigates the adsorption capabilities of acid-modified fly ash concerning escaped ammonia (NH3) and its consequential impact on specific resistance. Furthermore, the adsorption mechanism of acid-modified fly ash on NH3 was explained. Acid activation facilitated the dissolution of a portion of Fe and Al constituents within the fly ash, the contents of Fe and Al in SFA decrease by 4.91% and 5.64%, respectively. In addition, the specific surface area and porosity of fly ash are obviously improved. The specific surface areas of HFA and SFA increased from 1.83 (OFA) to 4.69 and 7.71 m2/g, respectively. Adsorption kinetics adhered to the pseudo-first-order model. SFA showed the best adsorption performance, with NH3 adsorption up to 10.65 mg/g, which was 4.27 times higher than OFA. The creation of a surface liquid film during NH3 adsorption led to decreased specific resistance values across all fly ash samples after-adsorption. The highest specific resistance values recorded for original fly ash (OFA), hydrochloric acid-modified fly ash (HFA) and sulfuric acid-modified fly ash (SFA) were 6.21 × 1012, 3.37 × 1011 and 5.02 × 1010 Ω·cm, respectively. Sulfuric acid activation makes fly ash have stronger adsorption capacity for escaping ammonia, and SFA maintains good specific resistance characteristics, which has good application prospects in electrostatic precipitation and air pollution control.

Multi‐model predictive control of SCR flue gas denitrification system in coal‐fired power plant based on kernel fuzzy c‐means clustering and integrated model

AbstractA multi‐model predictive control strategy based on kernel fuzzy c‐means (KFCM) clustering and integrated model is proposed for the complex problem of rapid and accurate control of ammonia injection in selective catalytic reduction (SCR) denitrification systems of coal‐fired power plants under a wide range of variable load conditions. First, the SCR data samples are clustered using the KFCM clustering algorithm, and the number of clusters is determined by introducing the Xie‐Beni index. Second, the prediction model of the SCR denitrification system is established by an integrated modelling approach, and the sub‐learners of the integrated model are the genetic algorithm optimized back propagation (GA‐BP) neural network model and the least squares support vector machine (LSSVM) model. Third, a multi‐model prediction controller based on the particle swarm optimization (PSO) algorithm and the integrated model is designed and developed. To ensure the stability of the system, a model‐switching strategy based on the minimum Euclidean distance is proposed. Finally, simulation verification and industrial field application verification are fulfilled by comparing with proportion integral differential (PID) control and single model predictive control (MPC). The results show that the multi‐model predictive control method proposed in this paper can obtain higher control accuracy and better control stability and meet the control requirements for the long‐term operation of the SCR denitrification system.

Low-temperature CO selective catalytic reduction denitrification and sulfuric acid regeneration mechanism of Cu-Ce/AC catalyst poisoned with zinc salt

To explore Cu–Ce/activated carbon (AC) catalyst poisoned of with zinc salt and its mechanism of low-temperature CO selective catalytic reduction (CO-SCR) denitrification following sulfuric acid regeneration, this paper uses ZnCl2 and ZnSO4 to poison Cu–Ce/AC catalyst and H2SO4 pickling to regenerate the poisoned catalyst. Not only the catalyst denitrification activity was investigated but also the surface morphology, pore structure, variation of active component phases and elemental valence, surface functional groups, reduction characteristics and adsorption mechanism of catalyst before and after regeneration were systematically characterized. The low-temperature CO-SCR denitrification mechanism of the poisoned Cu–Ce/AC catalyst was revealed. After being poisoned with zinc salt, the oxide on the catalyst surface aggregated, causing the blockage of the pore, thereby inhibiting the adsorption capacity of the reaction gas and the synergistic reaction between Cu-Ce. Moreover, zinc salt not only occupies the active site, but also destroys the -C=O and –OH oxygen-containing functional groups. This not only reduces the chemically adsorbed oxygen (Oβ) and oxygen vacancy but also leads to the reduction of Cu+–CO species, the reduction of catalyst redox capacity, and the reduction of NO conversion rate. The regeneration mechanism of the sulfuric acid method was as follows. First, the solid particles deposited on the catalyst surface are removed, thereby restoring its specific surface area and pore structure, and increasing the adsorption area of reaction gas. Additionally, the content of Cu and Ce active components was increased, which promoted the synergistic reaction, and the content of Cu+–CO species, oxygen vacancy, and Oβ was increased, resulting in enhanced redox capacity. Finally, sulfuric acid impregnation generated S2O82− groups, which sulfated the surface of the catalyst, and its S=O reacted with Zn to form S–O–S, which prevented zinc salt from destroying the oxygen-containing functional groups and improved the reducing capacity and CO adsorption capacity of the catalyst, thereby restoring the denitrification activity of the catalyst.

Carbon monoxide+ammonia coupling denitration at low temperatures over manganese–cerium/activated carbon catalysts

To identify the mechanism of the low-temperature CO + NH3 coupling selective catalytic reduction (SCR) denitration process, a series of Mn–Ce/activated carbon catalysts differing in the Ce content were prepared and characterized via SEM, EDS, XRD, XPS, FTIR and evaluated in the CO + NH3 coupling SCR denitration. The results show that the denitrification rates of Mn-Ce/AC catalysts are in the order of 7Mn-3Ce/AC > 7Mn-5Ce/AC > 7Mn-1Ce/AC at a temperature range of 150 °C–300 °C and an oxygen content of 9%. A moderate Ce content provides a smooth catalyst surface with a homogeneous distribution of metal particles and pores, resulting in the optimization of the physical and chemical performance of the Mn-based catalyst. As a result, more active oxygen species and lattice defects are formed, which promoted the dispersion of active components on the catalyst surface, increased the adsorption sites of CO and NH3, and provided more active sites for denitration reaction. Mn4+ and Ce3+ both increased to varying degrees, and oxygen-containing functional groups carboxyl and lactone groups increased, which promoted the synergistic reaction between MnCe, enhancing the NO adsorption and conversion rate. At 150 °C–250 °C, the CO + NH3 coupling denitration is mainly governed by physical adsorption, whereas chemical adsorption is the predominant mechanism at 250 °C–300 °C. The denitration process includes four stages: reaction gas adsorption, adsorption molecular dissociation, catalytic denitration of metal active components and product desorption.

One-pot synthesis of bimetallic CeCu-SAPO-34 for high-efficiency selective catalytic reduction of nitrogen oxides with NH3 at low temperature

NH3 selective catalytic reduction (SCR) has been widely recognized as a promising technique for reducing nitrogen oxides from diesel vehicle exhausts. High-efficiency SCR catalysts that could perform at low temperatures are essential to denitration. In this work, a series of bimetallic CeCu-SAPO-34 molecular sieves were synthesized by one-step hydrothermal method. The CeCu-SAPO-34 maintained good crystallinity and a regular hexahedron appearance of Cu-SAPO-34 after introducing Ce species, while exhibiting a higher specific surface area and pore volume. The as-prepared CeCu-SAPO-34 with 0.02% (mass) Ce constituent exhibited the best catalytic activity below 300 oC and a maximum NOx conversion of 99% was attained; the NOx removal rates of more than 68% and 94% were achieved at 150 °C and 200 °C, respectively. And the introduction of cerium species in Cu-SAPO-34 improves the low-temperature hydrothermal stability of the catalyst towards NH3-SCR reaction. Additionally, the introduced Ce species could enhance the formation of abundant weak Brønsted acid centers and promote the synergistic effect between CuO grains and isolated Cu2+ to enhance the redox cycle, which benefit the NH3-SCR reaction. This work provides a facile synthesis method of high-efficiency SCR denitration catalysts towards diesel vehicles exhaust treatment under low temperature.

Quantitative discrimination of surface adsorbed NOx species on CeO2 via spectrophotometry for SCR denitration investigation

CeO2-based catalysts are widely investigated for selective catalytic reduction (SCR) of NO with NH3. Interaction of NO/O2 with CeO2 generally produces two surface species, i.e., nitrates and nitrites. However, the explicit quantification of these two species is still unresolved. Herein, we reported that spectrophotometry characterization was effective in determining surface adsorbed NOx species on CeO2 by measuring the corresponding ions (NO2– and NO3–) dissolved in aqueous solution. Experimental results show that both nitrate (–NO3) and nitrite (–NO2) species can be quantitatively evaluated and the accuracy is verified by calibrating with NOx-TPD result. Exclusive transfer of adsorbed NOx from catalyst surface to aqueous solution is confirmed and the dissolution process can be accelerated by ultrasonic treatment. Moreover, useful information related to evolution of surface NOx species under various conditions (O2 treatment, different adsorption temperature and duration) and over different catalysts (Fe2O3, MnO2 and MnOx–CeO2) are provided. The result of present study demonstrates the potential of spectrophotometry for quantitative discrimination of surface NOx species on CeO2 and other oxide-based materials, which is conducive to mechanism analysis of SCR reaction.

A New Regularized Spatiotemporal Attention-Based LSTM with Application to Nitrogen Oxides Emission Prediction.

The data collected from complex process industries are usually time series with considerable nonlinearities and dynamics, as well as excessive redundancy. Moreover, there are temporal and spatial correlations between input variables and key performance variables. These characteristics bring great difficulties to data-driven modeling of the key performance variables. To overcome the problems, a new regularized spatiotemporal attention (STA)-based long short-term memory (LSTM) was developed. First, a standard LSTM network with an STA module was trained to capture the dynamic relationship between input and target variables. Second, the least absolute shrinkage and selection operator was introduced to optimize the STA module. Third, the hyperparameter representing the regularization strength of the algorithm was determined using a moving window cross-validation strategy. Finally, the proposed algorithm was compared to other state-of-the-art algorithms using artificial data, and then it was used to predict the nitrogen oxide emissions of a selective catalytic reduction denitration system. Simulation results showed that the proposed algorithm achieved more accurate predictions than the other algorithms. Furthermore, the statistics and analysis of the importance of the variables are consistent with known chemical-reaction mechanisms and observations of field experts. Thus, the proposed method can provide technical support for the predictive control and optimization of such systems.

Synthesis of Cu-Ce-BTC catalyst and its catalytic performance for denitration and synergistic mercury removal

Nitrogen oxides (NOx) and mercury (Hg0) produced by coal combustion are harmful to the environment. It is the most direct, simple and economic technology to use the existing SCR (Selective catalytic reduction) denitration process to achieve efficient mercury removal. Therefore, it is of practical significance to develop a high-performance SCR denitration synergistic mercury removal catalyst. Based on this, Cu-Ce-BTC catalyst was synthesized by one pot hydrothermal method. Its physicochemical properties were studied by various characterization methods, and its denitration and synergistic mercury removal was investigated. The results showed that: (1) Cu-Ce-BTC retains both Cu-BTC and Ce-BTC characteristic, indicating that Ce successfully enters the Cu-BTC skeleton structure. (2) Ce promotes the catalytic activity, when the Cu/Ce ratio is 2:1, it shows a good synergistic performance. The removal rates of NO and Hg0 reach 88.38% and 77.14% at 240 ℃. (3) The physical and chemical properties of Cu-Ce-BTC are still stable before and after the reaction. Also, Cu2+ participates in the removal of NO and Hg0, and some Cu2+ is converted into Cu+. Ce exists in two valence states of Ce3+ and Ce4+, and some redox reactions occur between them.

Mn-Ce Oxide Nanoparticles Supported on Nitrogen-Doped Graphene for Low-Temperature Catalytic Reduction of NOx: De-Nitration Characteristics and Kinetics

Selective catalytic reduction (SCR) of NOx with NH3 as the reductant has been proven an efficient and cost-effective technology to remove NOx pollutants in industries. Traditional SCR catalysts usually operate above 300 °C and suffer from intoxication and limited lifetime. Nano-catalysts are attractive for their high catalytic activities at reduced operating temperatures. We have recently developed a series of nitrogen-doped graphene-supported Mn-Ce oxides (MnCeOx/NG). The influences of reaction temperature, space velocity, mole ratio of NH3/NO and O2 concentration on SCR de-nitration activity were assessed. The novel catalyst with optimal Mn/Ce ratio, at appropriate processing conditions, can achieve a NO conversion efficiency of 99.5% at a temperature of 180 °C, and 93.5% at 150 °C. The kinetics of the SCR reaction on this novel catalyst were also established, exhibiting first-order with respect to NO, zero-order to NH3, and nearly 0.5-order to O2 at low temperatures. In the presence of sufficient O2 content, the apparent activation energy of the NH3-SCR on MnCeOx/NG is 37.6 kJ/mol, which is promising for low-temperature applications.

Predictive control of SCR denitrification system in thermal power plants based on GA‐BP and PSO

AbstractAiming at the characteristics of strong non‐linearity and large inertia in the reaction process of a selective catalytic reduction (SCR) denitrification system, a predictive control algorithm based on a back propagation neural network optimized by genetic algorithm (GA‐BP) and particle swarm optimization (PSO) is proposed. First, the prediction model of a SCR denitrification system is established by GA‐BP. Second, output feedback and bias correction are used to reduce the prediction error. Third, the optimal inlet ammonia concentration is obtained by PSO. At the same time, in order to solve the problems of high dimension, large noise, and strong coupling in the original data of the SCR system in the process of establishing the prediction model, the least absolute shrinkage selection operator (LASSO) algorithm and the local outlier factor (LOF) detection algorithm are used to screen important variables and samples in the original data set of the SCR system to remove redundant variables and outliers. Finally, the simulation results show that the prediction model has good prediction accuracy and that the proposed predictive control method can achieve accurate control of ammonia injection concentration. This method improves the denitrification efficiency and reduces the NOx emission concentration, which can provide good guidance for on‐site production.

A multi-criteria group decision making framework for sustainability evaluation of sintering flue gas treatment technologies in the iron and steel industry

The iron and steel industry (ISI) is at a critical stage of upgrading to ultra-low emission technologies in China. It is imperative to construct a life cycle sustainability evaluation framework as a guide for the selection of ultra-low emission technologies for the ISI. This research developed a novel multi-criteria group decision making framework. Four mature treatment technologies for sintering flue gas (SFG) were selected for analysis from an environmental, economic, and technological multi-dimensional perspective. The results of the life cycle assessment and life cycle costing are incorporated into the evaluation framework. The sustainability of ultra-low emission technologies was evaluated by combining Bayesian and hesitant fuzzy set theories for the first time. The results revealed that of the four alternatives considered, the most sustainable technology consists of semi-dry flue gas desulfurization, bag filter, and selective catalytic reduction denitrification. Triple sensitivity analysis and comparative analysis verified the consistency, robustness, and superiority of the proposed methodology. The interrelationship among criteria showed that technical maturity is key to enhancing the sustainability of ultra-low emission technologies, as it has a significant impact on costs and improves the environmental impact of the system. The conclusions provide optimization suggestions for further improving the sustainability of ultra-low emission technologies for SFG of ISI.

Mixing performance of preheated flue gas entering selective catalytic reactor under low loads

The reduced inlet flue gas temperature of the selective catalytic reduction (SCR) denitrification device is a significant problem during the low-load operation of boilers. In this study, an experimental system for turbulent thermal mixing of high and low-temperature flue gases was built to study the thermal mixing method in bypass flue gas technology and the effect of the temperature difference and flow rate variation of high and low-temperature flue gases at low load on the thermal mixing characteristics. The experiments evaluated the thermal mixing characteristics by measuring the temperature field distribution, calculating the inhomogeneity parameters ζT, and investigate the multi-jet distribution characteristics. The investigation was carried out more accurately by combining the simulation results. The results show that the tube wall jet combined with the bottom jet method provides the highest warming effect and possesses the lowest mixing non-uniformity parameter. At the location which is 1.25 times the hydraulic diameter of the duct away from the jet pipe, the temperature difference between the mainstream and the jet drops from 270 °C to 180 °C, causing ζT to drop from 0.46 to 0.37. Decreasing the jet velocity and increasing the main flow velocity promote a decrease in ζT, indicating that the ratio of jet velocity to main flow velocity and the velocity ratio UJUm are crucial factors affecting the thermal mixing characteristics, which directly affects the heat transfer between hot and cold fluids. The ζT decreases from 0.52 to 0.42 as UJUm changes from 3.58 to 2.15. This study provides a reference for ensuring the safe and stable operation of SCR denitrification system during the low-load operation of coal-fired boilers.

NOx concentration prediction in coal-fired power plant based on CNN-LSTM algorithm

Measuring the nitrogen oxides concentration accurately at the inlet of the selective catalytic reduction denitrification system plays an important role in controlling the nitrogen oxides concentration for coal-fired power plants, and a coupling relationship exists between nitrogen oxides concentration and multiple operational variables. Here, a modeling method based on feature fusion and long short-term memory network is proposed to mine the spatial and temporal coupling relationship between input variables for improving the prediction accuracy. First, the collected data were converted to image-like sequences. Then, the high-dimensional features of image-like sequences were fused by a convolutional neural network, and the spatial coupling features among the variables were mined. Finally, the constructed fusion features were input into the long short-term memory network to further explore the time coupling characteristics among the variables and complete the prediction of nitrogen oxides concentration at the inlet of the selective catalytic reduction denitrification system. The simulation results show that the prediction error of nitrogen oxides concentration at the inlet of selective catalytic reduction denitrification system based on CNN-LSTM model is 15.15% lower than that of traditional LSTM model.

A Model for Predicting the Erosion Rate Induced by the Use of a Selective Catalytic Reduction Denitrification Technology in Cement Kilns Flue Gas

Selective catalytic reduction (SCR) is a technology by which nitrogen oxides are converted with the aid of a catalyst into diatomic nitrogen and water. It is known that the catalyst can be easily eroded if a cement kiln with a high-dust content is considered. To understand this process, numerical simulations have been carried out considering a single catalyst channel in order to study the collision and erosion of fly ash and catalysts at meso scale. Based on a response surface methodology, the effects of five factors on the erosion rate have been studied, namely, the catalyst particle velocity, the particle size, the particle concentration, the incidence angle and the catalyst porosity. The results show that the influence of particle velocity, particle size and particle concentration is statistically significant and the particle size and incidence angle have a significant effect on the erosion rate. A quadratic polynomial prediction model for the erosion rate of honeycomb catalysts in cement kiln SCR reactors is finally proposed to support the future optimization of these systems.

Performance Analysis and Optimization for Static Mixer of SCR Denitration System under Different Arrangements

In order to solve the poor flow performance issues of selective catalytic reduction (SCR) denitration systems, the effect of the static mixer on the flow field was studied using computational fluid dynamics (CFD) numerical simulations. Based on the analysis of the original SCR denitration system, two static mixers were selected to explore their influence on system performance. The results show that both static mixers can effectively improve the denitration performance under different conditions. The static mixer with a rotating arrangement showed a better performance in the uniformity of concentration. The pressure loss without a static mixer is 834 Pa, and the pressure loss increases by 94 Pa and 73 Pa for rotating and X-arranged static mixers, respectively. Meanwhile, a static mixer will increase energy loss. Therefore, power plants can choose the layout of their static mixers according to the actual situation to achieve the optimal performance.