Oxygen Content In Flue Gas Research Articles

Prediction of oxygen content in flue gas of power plant with stacked target-enhanced autoencoder and attention-based LSTM

Accurate and robust measurement of the oxygen content in the flue gas of power plant is significant to optimal the coal combustion process. Conventional hard measurement algorithms have low accuracy, short lifetime and high cost. Therefore, it is urgent to establish a precise and reliable soft measurement model. However, how to extract effective feature representations from complex process data is still the difficult and hot spot in the soft measurement field. The paper develops a novel soft measurement model, which integrates stacked target-enhanced autoencoder (STAE) with attention mechanism-based long short-term memory (ALSTM) in view of the above issues. The data cleaning strategy is firstly designed to filter the redundant data from the original data. Then STAE is developed to extract the useful features, which introduces target output into each autoencoder to ensure the feature representation is output-related and stacks several autoencoder units to learn high-level information. The extracted hierarchical features can not only represent the input data, but also have high correlation with the target output. A three-layer-based attention network is designed to allocate different weights on the different levels’ features. Finally, due to the dynamic characteristic of the problem, a LSTM-based prediction network is dexterously designed to learn the potential relation from extracted features to target output, i.e., oxygen content. Numerous real-word data is collected from a 300 MW power plant and extensive experiments are conducted. The maximum prediction errors of A and B are 0.42 and 0.41. The 95.1 % prediction errors of A side are within [−0.2, 0.2], and the 96.2 % prediction errors of B side are within [−0.2, 0.2].

A Hybrid Soft Sensor Model for Measuring the Oxygen Content in Boiler Flue Gas.

As an indispensable component of coal-fired power plants, boilers play a crucial role in converting water into high-pressure steam. The oxygen content in the flue gas is a crucial indicator, which indicates the state of combustion within the boiler. The oxygen content not only affects the thermal efficiency of the boiler and the energy utilization of the generator unit, but also has adverse impacts on the environment. Therefore, accurate measurement of the flue gas's oxygen content is of paramount importance in enhancing the energy utilization efficiency of coal-fired power plants and reducing the emissions of waste gas and pollutants. This study proposes a prediction model for the oxygen content in the flue gas that combines the whale optimization algorithm (WOA) and long short-term memory (LSTM) networks. Among them, the whale optimization algorithm (WOA) was used to optimize the learning rate, the number of hidden layers, and the regularization coefficients of the long short-term memory (LSTM). The data used in this study were obtained from a 350 MW power generation unit in a coal-fired power plant to validate the practicality and effectiveness of the proposed hybrid model. The simulation results demonstrated that the whale optimization algorithm-long short-term memory (WOA-LSTM) model achieved an MAE of 0.16493, an RMSE of 0.12712, an MAPE of 2.2254%, and an R2 value of 0.98664. The whale optimization algorithm-long short-term memory (WOA-LSTM) model demonstrated enhancements in accuracy compared with the least squares support vector machine (LSSVM), long short-term memory (LSTM), particle swarm optimization-least squares support vector machine (PSO-LSSVM), and particle swarm optimization-long short-term memory (PSO-LSTM), with improvements of 4.93%, 4.03%, 1.35%, and 0.49%, respectively. These results indicated that the proposed soft sensor model exhibited more accurate performance, which can meet practical requirements of coal-fired power plants.

Catalytic reduction behaviors of nitrogen oxides and its use in biomass combustion

How to effectively apply NOx control technology to the biomass combustion process has become a current research hotspot. Aiming at the characteristics that the nitrogen oxides produced in biomass combustion process are mainly from the nitrogen element of biomass fuel, this paper adopts the fuel classification - zone reduction method to control NOx. Firstly, the reaction behavior, kinetic characteristics and optimal reduction parameters of NO reduction by coke were explored in a fixed-bed reactor. And then, the elemental analysis, pore analysis, scanning electron microscopy and other characterization methods combined with reduction experiments were used to study the changes of physical property parameters, reduction capacity and denitrification efficiency of coke after loading Fe based catalyst. By understanding the NO-carbon heterogeneous reduction process, the optimal conditions for enhancing reduction efficiency will be revealed, such as temperature, oxygen content in flue gas, and catalyst loading. The findings will provide a theoretical basis and technical support for implementing carbon reduction in low-nitrogen biomass combustion.

Data-Driven Optimal Control for Municipal Solid Waste Incineration Process

Municipal solid waste incineration (MSWI) is a dynamic industrial process involving complex physical and chemical reactions. Due to the uncertain municipal solid waste (MSW) composition and dynamic operation conditions, it is difficult to guarantee optimal operation for the MSWI process. To solve this problem, a data-driven optimal control scheme is proposed with a hierarchical control structure. In the set points optimization stage, an online adaptive fuzzy neural network (OAFNN) is designed to construct the objective functions, including combustion efficiency and NOx emission concentration. An adaptive mutation particle swarm optimization (AMPSO) algorithm is developed to obtain the optimal set points of oxygen content in flue gas. In the control stage, a double long short-term memory neural networks-based model predictive control (DLSTM-MPC) strategy is exploited. A self-organizing long short-term memory (SOLSTM) network is employed to predict the oxygen content in flue gas with a compact structure. To reduce the influences from dynamic disturbances and further improve the tracking control performance, another long short-term memory (LSTM) neural network is established to correct the prediction results. Finally, the set points optimization method and DLSTM-MPC strategy are combined to realize the optimal operation of the MSWI process. The effectiveness of the proposed optimal control scheme is verified by real industrial data. The experimental results demonstrate that the optimal control scheme can achieve promising tracking control performance of oxygen content in flue gas and improve the operational performance of the MSWI process.

Adaptive multi-objective competitive swarm optimization algorithm based on kinematic analysis for municipal solid waste incineration

Multi-objective optimization for the municipal solid waste incineration process is considered as a valuable technique to improve energy recovery and reduce pollutant emission. However, the complex mechanism analysis and multimodal problem of the municipal solid waste incineration process set challenges for both the modeling and optimization studies. To overcome this problem, an adaptive multi-objective optimization for the municipal solid waste incineration process is proposed in this paper. First, a bi-objective model of the municipal solid waste incineration, the basis for optimization, is established based on mass balance and energy balance, which takes furnace temperature and flue gas oxygen content as decision variables to mathematically deduce the generated heat and exhaust gases. Second, an adaptive multi-objective competitive swarm optimization algorithm is proposed for the optimization of the municipal solid waste incineration process. Two-step competition and multi-strategy learning are designed to provide a clear division of labor for particles and a novel idea for detecting evolutionary environment is proposed based on kinematic analysis of particles. Finally, relevant experiments are conducted on benchmark instances and the municipal solid waste incineration optimization model. The proposed algorithm shows promising convergence, diversity, fastness by comparing with several representative and state-of-the-art algorithms. The proposed algorithm achieves the optimization effects with 4.36% improvement of the available heat for power generation and 4.13% reduction of the exhaust gas assessment in the optimization of the municipal solid waste incineration process.

Adoption of Pareto Charts in the Analysis of Input Variables Interaction and Selection of Input-Output Pairings in a Refinery Fluid Catalytic Cracking Unit

This paper presents a study of the effects of input variables (main factors) and their couplings on key process variables(responses) in a refinery fluid catalytic cracking (FCC)unit using pareto analysis. Five responses namely, Riser temperature(Trx),Regenerator temperature(Trg) ,flue gas oxygen concentration, (Od ),Gasoline yield(y2 ), Light gases yield(y3 )) and four main factors, namely gasoil feed rate(Fgr), regenerated catalyst flow rate(Frc),combustion air flow rate(Fa ), combustion air temperature(Ta )and were studied in a regular two-layer experimental design that generated 32 numerical experiments. A combination of Design Expert software and an in-mouse FCC unit simulator was used to conduct the numerical experiments from which pareto plots were generated as a tool for response-factor analysis. Results from this study show that Frc and Fgr are the only significant main factors with respect to riser temperature response. While the significance is of the order Frc> Fgr, Frc produced negative effect while Fgr produced positive effect on riser temperature while the interaction factor (Fgr+Frc) is the only cross-coupling .Cross -coupling of variables is more significant in the regenerator as two interaction factors (Fgr+Frc) and (Frc+Fa ) featured prominently and produced significant effects on regenerator temperature and flue gas oxygen concentration, respectively. The ranking of the effects on regenerator temperature is Frc>Fa > Fgr while that on flue gas oxygen concentration is Frc>Fgr>Fa . Results further show that while Frc, Fgr and Fa produced positive effects on regenerator temperature, only Fa produced negative effects in flue gas oxygen concentration. Moreover, the four main factors in this study and their couplings did not dhow direct effects on gasoline yield and light gases yield respectively. The results are in tandem with FCC unit behaviour and thus assert the merit in the adopted tool.

Prediction of Oxygen Content in Boiler Flue Gas Based on a Convolutional Neural Network

As one of the core pieces of equipment of the thermal power generation system, the economic and environmental performance of a boiler determines the energy efficiency of the thermal power generation unit. The oxygen content in boiler flue gas is an important parameter reflecting the combustion status of the furnace, and accurate prediction of flue gas oxygen content is of great significance for online boiler optimization. In order to solve the online prediction problem of the oxygen content in boiler flue gas, a CNN is applied to build a time series prediction model, which takes the time series samples within a fixed time window as the input of the model and uses several feature extraction modules containing convolutional, activation, and pooling layers for feature extraction and compression, and the model output is the oxygen content in boiler flue gas. Since the oxygen content in boiler flue gas is not only correlated with other variables but also influenced by its own historical trend, the input of the CNN model is improved, and an oxygen content in boiler flue gas time series prediction model (TS-CNN) is established, which takes the historical values of the boiler flue gas oxygen content as the input of the model. The comparison test results show that the R2 and RMSE of the TS-CNN model are 0.8929 and 0.1684, respectively. The prediction accuracy is higher than the CNN model, LSSVM model, and BPNN model by 18.6%, 31.2%, and 54.6%, respectively.

Investigation on self-sustained combustion, NOx emissions, and ash characteristics of coal slime in a lab-scale fluidized bed

Incineration of coal slime in the fluidized bed boiler is an efficient means of energy recovery and environmental protection. In this study, combustion behaviors and NOx emissions of coal slime were studied using a lab-scale fluidized bed reactor. Besides, the fundamental characteristics of coal slime and its ash were explored coupled with a particle size analyzer, X-Ray Diffraction (XRD), thermogravimetric analysis (TGA), automatic specific surface, porosity analyzer, etc. It revealed that the range of 350–640 °C was the main mass loss stage with more than 92% of the total loss during combustion through the thermogravimetric analysis, guaranteeing good burnout performance. The challenges of fluidization and burnout of C-type particles accounting for 36.59% of coal slime were solved by mixing the water with coal slime to enhance its agglomeration and thermal explosion. Through the coupling of multiple operating parameters such as primary and secondary air rate, secondary air height, the oxygen content of flue gas, etc., reasonable furnace temperature and reduction zone distribution can be achieved. In addition, it was found that the migrations of most heavy metals are correlated with the proportion of fine particles in coal slime during combustion. The high content of alkaline substances of coal slime leads to a slightly slagging propensity below 900 °C due to the prevention of eutectics formation. Pore analysis implies that bottom ash and fly ash show different trends in the specific surface area and pore volume compared to the coal slime during combustion.

The prediction of the oxygen content of the flue gas in a gas-fired boiler system using neural networks and random forest

<p><span lang="EN-US">The oxygen content of the gas-fired boiler flue gas is used to monitor boiler combustion efficiency. Conventionally, this oxygen content is measured using an oxygen content sensor. However, because it operates in extreme conditions, this oxygen sensor tends to have the disadvantage of high maintenance costs. In addition, the absence of other sensors as an element of redundancy and when there is damage to the sensor causes manual handling by workers. It is dangerous for these workers, considering environmental conditions with high-risk hazards. We propose an artificial neural network (ANN) and random forest-based soft sensor to predict the oxygen content to overcome the problems. The prediction is made by utilizing measured data on the power plant’s boiler, consisting of 19 process variables from a distributed control system. The research has proved that the proposed soft sensor successfully predicts the oxygen content. Research using random forest shows better performance results than ANN. The random forest prediction errors are mean absolute error (MAE) of 0.0486, mean squared error (MSE) of 0.0052, root-mean-square error (RMSE) of 0.0718, and Std Error of 0.0719. While the errors using ANN are MAE of 0.0715, MSE of 0.0087, RMSE of 0.0935, and Std Error of 0.0935.</span></p>

Significant energy saving in industrial natural draught furnace: A model-based investigation

• Model based simulation is carried out for industrial scale natural drought cabin furnace. • Aspen Hysys and exchanger design rigorous (EDR) program used for modelling and simulation. • Sensitivity analysis was carried out to predict the impact of several parameters on the furnace efficiency. • The study shows potential of significant savings in energy, operational cost and carbon footprint. In all industrial petrochemical plants and refineries, the furnace is the source of heat resulting from fuel combustion with air. The model-based furnace simulation is considered one of the efficient methods help to reduce the energy loss and maintain fixed refinery revenues, conserving energy, and finally reducing external fuel consumption and total fuel cost. In this paper, a model-based simulation is carried out for a natural air draught industrial scale furnace related to Liquified Petroleum Gas (LPG) production plant in Libya to thoroughly investigate the most responsible factors in lowering the furnace butane exit temperature, which is supposed to be two degrees Fahrenheit higher than inlet temperature. Therefore, to resolve this industrial problem, Aspen Hysys V10, coupling with EDR (exchanger design and rating) is used to carry out rigorous model-based simulation. This is specifically used to assess the impact of heat loss from inside the firebox to the surrounding medium and heat loss from the furnace stack and walls, besides the effect of excess air on the furnace efficiency. Furthermore, this research intends to verify whether the operating conditions, such as furnace tubes inlet flow rate, temperature and pumping pressure, are conforming to the upstream process design specifications or need to be adjusted. The results confirm that increasing furnace outlet temperature two degrees Fahrenheit from off specification 190 °F instead of 184 °F is successfully achieved by decreasing upstream stream flowrate 25% below the operating value and cutback excess air gradually until 20%. Also, the results clarify the necessity of increasing the flue gas temperature by 7% over design condition, to gain a significant reduction of heat loss of 31.6% and reach as low as 35.5 MBtu/hr. This improvement is achieved using optimum operating conditions of an excess air of 20%, and flue gas oxygen content of 3.3% delivered to stack. Accordingly, the furnace efficiency has been increased by 18% to hit 58.9%. Furthermore, the heat loss from the furnace walls can be also reduced by 68% from 5.41 MBtu/hr to 1.7 MBtu/hr by increasing the refractory wall thickness to 6 in., which entails an increase in the furnace efficiency by 3.66% to reach 58.96%. Decreasing the heat loss fraction through the refractory wall, pip doors, expansion windows and refractory hair cracks would also increase the efficiency by 21% to reach a high of 59.7%. Accordingly, a significant reduction in daily fuel consumption is observed, which costs 1.7 M$ per year. The outcomes of this research clearly show the potential of reducing the operation and maintenance costs significantly.

Decoupling Control Strategy of Alkali Recovery Furnace

In order to analyze the correlation between the variables in the combustion process of the alkali recovery furnace, this paper uses the control variable method to design the combustion process experiment of the alkali recovery furnace in the actual field. Through the analysis of the experimental results, it is shown that the first two of the three key parameters of furnace temperature, flue gas oxygen content, and furnace negative pressure have a strong coupling relationship, while the interaction between furnace temperature, flue gas oxygen content and furnace negative pressure is relatively weak. For this reason, this paper introduces the linear active disturbance rejection control strategy into the control of the alkali recovery furnace, regards the mutual influence between variables as disturbances, designs disturbance observers for estimation, and designs error feedback control laws to compensate. The results of simulation and actual operation show that, compared with the traditional PID controller, the control strategy can effectively eliminate the mutual interference between variables, and obtain a better control effect, to ensure that the alkali recovery furnace can run more safely, stably, and efficiently.

Effect of Flue Gas Oxygen Content to Gas-Gas Heater Requirement in Limestone Forced Oxidation Desulfurizer System

To comply the regulation, the sulfur dioxide (SO2) concentration should be reduced to designated standard of emission which was stated in the regulation. It causes many coal fired power plant (CFPP) need to procure a new or upgrade their desulfurization equipment. In this study the desulfurization method focused on the usage of Limestone forced oxidation (LSFO). The study is based on subcritical 300 MW CFPP and the specification of coal for the calculation was taken from coal properties of Palu 3 CFPP. There are two cases and two configurations in this study. The first case is flue gas oxygen content of 3.5% and the second is 7%. Then in the configuration of with or without gas-gas heater (GGH). The oxygen content condition in the flue gas have an influence to the presence of GGH within LSFO desulfurizer system. The lower of oxygen content in the flue gas then increase the tendency to use GGH.

Alternative fuels co-fired with natural gas in the pre-calciner of a cement plant: Energy and material flows

Cement-making is an energy-intensive industrial process that contributes 8% of the global CO2 emissions. This study develops a thermal energy flow model (TEF) for 4200 tonnes of clinker per day, a natural gas-fired cement plant in which 50% of the pre-calciner energy requirements can be supplied by alternative fuels (AF) including biomass, plastics, etc. The TEF shows that the lower heating value (LHV dry), oxygen content (dry basis) and moisture content of the AF, as well as the flue gas oxygen concentration [O2] needed for complete combustion, affect the thermal energy intensity (TEI) and total air demand (AD). Compared to the reference system fueled by natural gas (NG) that burns completely at 1% [O2] in flue gas, all AFs require more total air and thermal energy demand, especially when the solid AFs need a higher [O2] in the flue gas (typically 3%) to ensure complete combustion. Due to the higher carbon (C) intensity, co-firing AFs could increase the total CO2 emissions by 1% to 18%. For the wood dust with 100% biogenic C, 50% NG replacement in pre-calciner could avoid 55.5 and 43.1 kgCO2/t clinker at 1% and 3% [O2] in flue gas respectively, an equivalent of 7.5% and 5.8% decrease in total GHG emissions relative to the reference case. Results of the TEF model from 24 diverse AFs were used to generate regressions that link fuel properties and flue gas oxygen requirement with TEI, making it possible to compare highly diverse AFs for their likely performance in the clinker-making.

Prediction of Oxygen Content Using Weighted PCA and Improved LSTM Network in MSWI Process

The accurate and real-time measurement of oxygen content in flue gas is the cornerstone of high incineration efficiency and economic benefits for municipal solid waste incineration (MSWI) plants. However, conventional hardware oxygen analyzers are difficult to obtain the oxygen content in flue gas timely and precisely. In this article, a weighted principal component analysis (WPCA) algorithm combined with improved long short-term memory (ILSTM) network is proposed for oxygen content prediction. First, to reduce the model complexity, a WPCA is developed to calculate mutual information correlation coefficients between principal components and the quality variable. Second, the LSTM network is exploited to establish a prediction model, and its hyperparameters are obtained with the particle swarm optimization (PSO) algorithm to improve the generalization ability of the prediction model. Finally, the effectiveness of the proposed prediction method is validated by a benchmark simulation and the real industrial data. And the comparison results with other methodologies demonstrate the considerable prediction performance of the proposed WPCA-ILSTM model.

Output-Relevant Common Trend Analysis for KPI-Related Nonstationary Process Monitoring With Applications to Thermal Power Plants

Operation safety and efficiency are two main concerns in power plants. It is important to detect the anomalies in power plants, and further judge whether they affect key performance indicators (KPIs), such as the thermal efficiency. These two goals can be achieved by KPI-related nonstationary process monitoring. Although the thermal efficiency cannot be accurately measured online, it can be strongly characterized by some online measurable variables, including the exhaust gas temperature and oxygen content of flue gas. These critical variables closely related to the thermal efficiency are termed as output variables. Inspired from nonstationary common trends between input and output variables in thermal power plants, the output-relevant common trend analysis (OCTA) method is proposed, in this article, to model the input–output relationship. In OCTA, input and output variables are decomposed into nonstationary common trends and stationary residuals, and the model parameters are estimated by solving an optimization problem. It is pointed out that OCTA is a generalized form of partial least squares (PLS). The superior monitoring performance of OCTA is illustrated by case studies on a real power plant in Zhejiang Provincial Energy Group of China. Compared with the other PLS-based recursive algorithms, OCTA can effectively detect the anomalies in power plants and accurately determine whether they have an impact on the thermal efficiency or not.

Modeling on Thermal Efficiency and Nox Emission of Alkali Recovery Furnace Depending on Oxygen Content of Flue Gas

Modeling on Thermal Efficiency and Nox Emission of Alkali Recovery Furnace Depending on Oxygen Content of Flue Gas

Dynamic modeling of municipal solid waste incineration

In this work, a comprehensive dynamic model of a moving grate Waste-to-Energy plant is developed using MATLAB Simulink. The objective is to develop a reliable and flexible model which can reproduce the dynamic behavior of combustion chamber and boiler. For this purpose, an extensive number of process data is used both in model development and for validation. Contrary to previous works in literature, fluctuations in both waste properties and operational set points are taken into account. The validated model is then used to study the dynamic response of the plant to changes in important process parameters. As expected, the dynamic response of the plant is faster for changes in primary and secondary air than for changes in grate speed and waste flow. The steam production response is from 1 to 4 min slower than the flue gas oxygen concentration response. Moreover, the response time depends to a large extent on the properties of the waste; as an example, an increase in waste humidity from 25% to 35% results in a 21 min increase in the steam production response time. Such characterization of the dynamic response of the plant is fundamental to develop improved control strategies.

LSTM-based soft sensor design for oxygen content of flue gas in coal-fired power plant

To address problems of high cost, complicated process and low accuracy of oxygen content measurement in flue gas of coal-fired power plant, a method based on long short-term memory (LSTM) network is proposed in this paper to replace oxygen sensor to estimate oxygen content in flue gas of boilers. Specifically, first, the LSTM model was built with the Keras deep learning framework, and the accuracy of the model was further improved by selecting appropriate super-parameters through experiments. Secondly, the flue gas oxygen content, as the leading variable, was combined with the mechanism and boiler process primary auxiliary variables. Based on the actual production data collected from a coal-fired power plant in Yulin, China, the data sets were preprocessed. Moreover, a selection model of auxiliary variables based on grey relational analysis is proposed to construct a new data set and divide the training set and testing set. Finally, this model is compared with the traditional soft-sensing modelling methods (i.e. the methods based on support vector machine and BP neural network). The RMSE of LSTM model is 4.51% lower than that of GA-SVM model and 3.55% lower than that of PSO-BP model. The conclusion shows that the oxygen content model based on LSTM has better generalization and has certain industrial value.

Evaluation of Co and Nox Emissions in Real-Life Operating Conditions of Herbaceous Biomass Briquettes Combustion

Abstract The issue of carbon monoxide and nitrogen oxides emissions into the atmosphere is very current. This article thus focuses on the assessment of elemental composition of selected herbal biomass species and emission concentrations during combustion in a commonly available grate combustion device for briquetted fuel. In tests, emission concentrations were evaluated in contrast to the oxygen concentration in flue gas and flue gas temperatures. Samples of camelina (Camelina sativa), giant miscanthus (Miscanthus gigantheus), reed canary grass (Phalaris arundinacea L.) and sorghum (Sorghum bicolor) were used. Elementary and stoichiometric combustion analyses were conducted for these samples (LECO AC-600 semi-automatic calorimeter, CHN628 + S elemental analyser and LECO TGA-701 analyser). Analyses of C, H, N and S concentrations and calorific values showed that samples of briquetted herb biomass had insignificant differences and could be used for energy purposes without limitation. The limiting factor was the high amount of ash, which amounted to 6.59% of dry weight in reed canary grass briquette sample. Furthermore, a high percentage (1.91% wt.) of nitrogen was observed in reed canary grass briquettes. Such a high amount of nitrogen during combustion tests resulted in an increase in NOx emission levels in flue gas of the Phalaris arundinacea L. sample, in which it reached the maximum concentration of 375.20 mg·m−3. The combustion tests showed that even under steady-state conditions, high concentrations of carbon monoxide could not be avoided for tested briquetted biofuels without active regulation of the combustion process.

Innovative NOx reduction from cement kiln and pilot-scale experimental verification

Nitrogen oxide emissions from cement industry have greatly promoted the formation of haze weather. Current denitration technologies can hardly meet the increasingly stringent emission requirements. Here we proposed an innovative method called “rear independent reduction” (RIR) to reduce nitrogen oxide emissions from the cement kiln. A pilot-scale experimental setup was put in place to verify its feasibility. Reduction efficiencies of 61.2% and 48.6% were obtained in the absence and presence of cement raw meal, respectively. The effects of amount of reduction pulverized coal, flue gas temperature, the addition of cement raw meal, and oxygen content in flue gas on the NOx reduction efficiency were comprehensively investigated. Increasing the amount of reduction pulverized coal cannot only enhance the reduction efficiency, but also promote the rise of flue gas temperature. The ideal flue gas temperature is around 900 °C. Oxygen content in flue gas determines the impact of cement raw meal on the reduction efficiency, and the optimal value is below 1.0%. The significant reduction of NOx in flue gas should be attributed to the reaction between reductants and nitrogen oxides. This work provides a new way to address the problem of NOx emission in cement industry.