Emission Concentrations Of SO2 Research Articles

Co-firing characteristic prediction of solid waste and coal for supercritical CO2 power cycle based on CFD simulation and machine learning algorithm

The co-firing technology of combustible solid waste (CSW) and coal in the supercritical CO2 (S-CO2) circulating fluidized bed (CFB) can effectively deal with domestic waste, promote social and environmental benefits, improve the coal conversion rate, and reduce pollutant emission. This study focuses on the co-firing characteristics of CSW and coal under S-CO2 power cycle, and simulations are conducted by employing Multiphase Particle-in-cell (MP-PIC) method integrated with the comprehensive chemical reaction models in a 300 MW S-CO2 CFB boiler. Effects of operating parameters including fuel mixture proportion and first stage stoichiometry on the gas emission characteristics are further analyzed. Based on training and testing database based on the simulation results, a novel Improved Whale Optimization Algorithm and Bi-dictionary Long Short-Term Memory (IWOA-BiLSTM) algorithm model is established to predict CFB temperature, NOx emission concentration, and SO2 emission concentration, respectively. Results show that CO and SO2 decrease with the coal mass ratio of the fuel mixture increasing, while NOx increases. With the increase of first stage stoichiometry, CO increases, NOx declines, and the change of SO2 is not obvious. Compared with two other basic algorithm models, the prediction error of the proposed algorithm model for the three targets is minimal with the average relative error of 0.032 %, 0.231 %, and 0.157 %, respectively, which can meet the prediction requirements with acceptable accuracy.

Enhanced SO2 and CO2 synergistic capture with reduced NH3 emissions using multi-stage solvent circulation process

NH3-based SO2 and CO2 synergistic capture technology shows promise in reducing the costs associated with current flue gas desulfurization and CO2 capture systems. However, it faces challenges such as NH3 slip and high energy consumption. In this study, we proposed an advanced Multi-Stage Solvent Circulation (MSC) process that incorporates a desulfurization-washing solution circulation, which involved partitioned absorption according to different functions of SO2 capture, CO2 capture, and NH3 emission control. The experimental results demonstrated that using desulfurization solution in place of water for washing reduced the NH3 emissions from the absorber and desorber by 10.6 % and 7.9 %, respectively. Additionally, the recovered NH3 enhanced SO2 capture, resulting in a 61.8 % decrease in SO2 emission concentration. A pilot-plant trial model for SO2 and CO2 synergistic capture was further developed using Aspen Plus. The impact of operational time and parameters on capture efficiency and energy consumption were analyzed. Under typical flue gas conditions, the process achieved SO2 capture efficiency of > 99 %, regeneration energy consumption of 2.42 GJ/t CO2, NH3 emissions of < 5 ppm. This study presents a novel approach for designing a SO2 and CO2 synergistic capture system, which has the potential to facilitate the economic and effective implementation of post-combustion flue gas pollutant control management and carbon emission reduction.

Prediction of SO2 emission concentration in industrial flue gas based on deep learning: The ammonia desulfurization system of the Yunnan aluminum carbon plant as the research object

The desulfurization efficiency of flue gas desulfurization (FGD) systems is affected by many operation parameters, and predicting SO2 emission concentration through mechanism models requires a significant number of resources, limiting the process optimization of the overall desulfurization efficiency. Taking the desulfurization system of Yunnan aluminum carbon plant as the research object, based on the inlet flue gas, process control parameters, and pollutants of the discharged flue gas, we constructed the NARX (nonlinear auto-regressive model with external inputs) neural network and TCN (temporal convolutional network) models. Predicted the concentration of SO2 in the discharged flue gas in the next moment from the above three types of historical data, optimized the industrial control parameters, and reduced the concentration of the discharged SO2. The results show that both TCN and NARX models have strong nonlinear dynamic descriptive ability, and both are capable of accurately predicting the SO2 concentration in the emitted flue gas. The TCN neural network model can control the mean absolute percentage error and root mean square error of the dataset within 1.46% and 1.86, respectively. The response relationship established by the model has an important role in guiding the regulation of SO2 emission and process operation parameters, such as reducing the flue gas pressure of the absorber tower inlet or increasing the pressure of the concentration section of the absorber tower, which can effectively improve the desulfurization rate.

Innovation driver and county air pollution: cost-benefit analysis perspective.

Innovation is the first power to drive the county's green and low-carbon. It is crucial to explore the impact of innovation on air pollution from the perspective of counties at the bottom of the administrative division hierarchy. The article is aimed at exploring the direct impact effects, spatial spillover effects, impact mechanism pathways, non-linear relationships, and cost-benefits of innovation drive on air pollution in counties. To this end, based on the collection of county-level data from 2007 to 2020 in mainland China, the article constructs a fixed-effects model, a dynamic panel model, and a spatial Durbin model for analysis. For every 1% increase in the quantity of innovation, the county SO2 emission concentration decreases by 0.2% on average; for every 1% increase in the quality of innovation, the county SO2 emission concentration decreases by 0.3% on average. When the county innovation quantity driver increases by one standard deviation, the county SO2 concentration decreases by an average of 0.29%; when the county innovation quality driver each standard deviation increases, the county SO2 concentration is reduced by 0.33% on average. The significant entry of high-end factors, the increased frequency of regulation by the environmental protection department, and the increasing efficiency of energy use are the important mechanism pathways for innovation-driven reduction of air pollution in counties. There is no significant "(inverted) U-shaped" relationship between innovation-driven air pollution in the county samples. There is a negative spatial spillover effect of the innovation quality drive on air pollution control in all Chinese county samples. Innovation to drive the declining size of the county's sulfur dioxide can bring about one billion yuan (about 139.81 million U.S. dollars) in comprehensive economic benefits. In the coming period, county governments should build a new pattern of "blue sky and white clouds" with neighboring regions in terms of spatial agglomeration of high-end elements, green transformation and utilization of energy, and intelligent monitoring and supervision of pollution.

Co-disposal kinetics and characteristics of sewage sludge and municipal solid waste incineration fly ash

Sewage sludge (SS) and municipal solid waste incineration fly ash (MSWIFA) have characteristics of high yield, scattered distribution, and strong harm. Co-disposal of SS and MSWIFA in the existing cement kilns is a prospective way to deal with these two types of solid waste. In this study, the pyrolysis kinetic, thermogravimetric analysis, heat and mass transfer characteristics and pollutant release during SS and MSWIFA co-disposal were investigated through a series of experiments and simulations. In the experiment, pulverized coal (PC) was used as fuel to imitate the real environment of a cement kiln. According to the results, the interaction of the two solid wastes was observed mainly during the devolatilization and combustion periods. At an average blending ratio (60%) and 25 ℃/min heating rate, the blend actual mass loss rate has the largest difference compared to the linearly calculated mass loss rate (0.74%/min), with higher comprehensive combustion characteristic index (3.54), and the blends have better overall heat transfer, internally converging to 350 K. Meanwhile, when SS proportion in PC-SS-MSWIFA is between 20% and 30%, the emission concentration of SO2 and NO was low, and it has good environmental benefits. This research aims to offer theoretical support for the co-disposal of SS and MSWIFA.

Clean Preparation of Formed Coke from Semi-coke by the Carbonated Consolidation Process.

In order to address the low thermal efficiency of low-rank coal combustion and the accompanying serious environmental issues, formed coke was prepared using a carbonization consolidation method with low-rank coal semi-coke. The test for briquetting and carbonation consolidation conditions revealed that the optimal parameters were a briquetting pressure of 93.63 MPa, moisture content of 16%, Ca(OH)2 binder amount of 10%, and a CO2 concentration of 30% at 20 °C. Under these conditions and a carbonation consolidation time of 60 min, high-quality formed coke was produced, exhibiting a compressive strength of 1256.2 N/a, redrying strength of 286.2 N/a, and a dropping strength of 10.6 number/a. The combustion characteristics of the prepared formed coke were investigated, revealing that ignition temperatures (345.39 °C), burnout temperatures (495.57 °C), and peak of the maximum weight loss rate temperatures (437.93 °C) are slightly higher than those of bituminous coal. The low calorific value of the briquette was 20.4 MJ/kg. During the combustion process, the emission concentrations of SO2, NOX, and solid particles from the formed coke were significantly lower than those of bituminous coal, indicating that it is a cleaner energy source. Moreover, adding Ca(OH)2 effectively reduced SO2 emissions and achieved sulfur fixation and emission reduction.

Multiobjective economic model predictive control using utopia-tracking for the wet flue gas desulphurization system

Efficient control of the desulphurization system is challenging in maximizing the economic objective while reducing the SO2 emission concentration. The conventional optimization method is generally based on a hierarchical structure in which the upper optimization layer calculates the steady-state results and the lower control layer is responsible to drive the process to the target point. However, the conventional hierarchical structure does not take the economic performance of the dynamic tracking process into account. To this end, multi-objective economic model predictive control (MOEMPC) is introduced in this paper, which unifies the optimization and control layers in a single stage. The objective functions are formulated in terms of a dynamic horizon and to balance the stability and economic performance. In the MOEMPC scheme, economic performance and SO2 emission performance are guaranteed by tracking a set of utopia points during dynamic transitions. The terminal penalty function and stabilizing constraint conditions are designed to ensure the stability of the system. Finally, an optimized control method for the stable operation of the complex desulfurization system has been established. Simulation results demonstrate that MOEMPC is superior over another control strategy in terms of economic performance and emission reduction, especially when the desulphurization system suffers from frequent flue gas disturbances.

Real-time prediction of SO2 emission concentration under wide range of variable loads by convolution-LSTM VE-transformer

In this paper, a sulfur dioxide (SO2) emission concentration real-time dynamic prediction model is proposed to achieve the control target of ultra-low SO2 emissions and economical desulfurization system operation at a large-scale coal-fired power plant. First, a vision-expansion self-attention strategy is adopted on the Transformer algorithm structure for better time-series information feature extraction. Second, to adapt to the vision-expansion self-attention strategy, convolution and long short-term memory (LSTM) are combined to process input information position encoding, which can obtain long time-series information absolute position encoding. Then, a Convolution-LSTM VE-Transformer model is constructed by combining the above two proposed parts. The mutual information method and the differential evolution algorithm are combined to calculate correlations between variables and obtain the optimal delay time. Finally, a real-time dynamic prediction model of SO2 is established. In this model, the structure of the input and output variables is determined by the delay time and the influence of coal quality on the SO2 concentration. The quantitative comparison results show the superior accuracy and generalization ability of the proposed prediction model to related popular methods, indicating that the model has strong potential for use in further controller design and applications.

Model NOx, SO2 Emissions Concentration and Thermal Efficiency of CFBB Based on a Hyper-Parameter Self-Optimized Broad Learning System

At present, establishing a multidimensional characteristic model of a boiler combustion system plays an important role in realizing its dynamic optimization and real-time control, so as to achieve the purpose of reducing environmental pollution and saving coal resources. However, the complexity of the boiler combustion process makes it difficult to model it using traditional mathematical methods. In this paper, a kind of hyper-parameter self-optimized broad learning system by a sparrow search algorithm is proposed to model the NOx, SO2 emissions concentration and thermal efficiency of a circulation fluidized bed boiler (CFBB). A broad learning system (BLS) is a novel neural network algorithm, which shows good performance in multidimensional feature learning. However, the BLS has several hyper-parameters to be set in a wide range, so that the optimal combination between hyper-parameters is difficult to determine. This paper uses a sparrow search algorithm (SSA) to select the optimal hyper-parameters combination of the broad learning system, namely as SSA-BLS. To verify the effectiveness of SSA-BLS, ten benchmark regression datasets are applied. Experimental results show that SSA-BLS obtains good regression accuracy and model stability. Additionally, the proposed SSA-BLS is applied to model the combustion process parameters of a 330 MW circulating fluidized bed boiler. Experimental results reveal that SSA-BLS can establish the accurate prediction models for thermal efficiency, NOx emission concentration and SO2 emission concentration, separately. Altogether, SSA-BLS is an effective modelling method.

The changes in spatial layout of steel industry in China and associated pollutant emissions: A case of SO2

The spatial layout of the steel industry has an impact on the regional atmospheric environment. In this study, the steel industry evolution model and the driving force analysis model were combined to analyze the evolution of spatial layout of the steel industry in China and the driving factors of this evolution. In addition, the WRF-SMOKE-CMAQ model was used to analyze the spatial dynamics of SO2 emissions from the steel industry. Our analysis presents the evolution of the steel industry in China in four stages: policy-determining, resource-oriented, economic promotion and market-oriented stage. The change in the spatial layout of the Chinese steel industry resulted in a continuously decreasing trend of pollutants in temporal characteristics and a decreasing share of emissions in North China and a continuous growth in East China in spatial characteristics. Our simulation shows that, by 2025, the pollutant SO2 emission concentration will migrate to the southeast, subject to market-oriented factors.

Analysis of Air Pollutant Emission Differences and Emission Reductions of Coal-fired Stoves of Different Residents

In order to explore the differences in air pollutant emissions of different types of stoves, this study selected five common new types of residential coal-fired stoves (square coal stoves, baffle heat exchange stoves, kang heating and cooking integrated stoves, Gasification forward burning and gasification reverse burning stoves) Simulated combustion and atmospheric pollutant emission level monitoring in the laboratory to quantitatively evaluate the environmental effects of different stoves, identify the influencing factors and causes of differences in pollution emission levels, and propose furnaces Suggestions for improvement of pollution reduction such as R & D and design. The results show that: ① The emission of air pollutants differs greatly among different stoves, and the intensity of pollution emitted by the stoves with kang heating and cooking is the largest. The emission intensity of the three types of atmospheric pollutants (SO2, NOx and particulate matter) is 2.9 kg t-1 It is 1.6 times the average value; stoves using square coal technology have the lowest air pollutant emission intensity, which is 65% of the average value. ②The emission of gaseous pollutants in different furnaces at different combustion stages also showed obvious differences and characteristics. During the fire stage, the NOx emission concentration of the square coal stove is 0.49mg ⋅ m-3, which is 45% ∼ 72% lower than that of other types of stoves; the SO2 emission concentration of the gasification anti-burning stove is 1.38mg ⋅ m- 3. Compared with other types of stoves, the maximum reduction is 28%. ③The factors that affect the emission of atmospheric pollutants of the stove include the application technology and the type of combustion. The stoves with square coal and gasification anti-burning technology have better environmental benefits, but the economic costs of the two types of stoves are 20,000 yuan and 18,000 Yuan, significantly higher than other types of stoves. ④Considering the differences in pollution emissions from different stoves, it is recommended to adopt differentiated economic policies and strict emission and product standards, promote the use of energy-saving and environmentally friendly stoves, and reduce the emission of atmospheric pollutants from coal-fired stoves.

Investigation on the Emission Difference of Air Pollutants from Common Residential Coal Stoves and Suggestions for Emission Reduction

To explore the difference in the emission of air pollutants from coal stoves, five new and common residential coal stoves (square briquette, baffled heat exchange, heating and cooking integrated gasification positive burning, and gasification reverse burning stove) were selected. The emission level of major air pollutants was determined using simulated combustion in the laboratory for quantitatively evaluating the environmental effects of different coal stoves. Moreover, the factors and reasons for the differences were identified, and suggestions for the reduction of emission were proposed. The results showed that: ① the emissions of air pollutants from different coal stoves were significantly different; the heating and cooking integrated stove had the largest emission intensity (2.9 kg ·t-1) of air pollutants (SO2, NOx, and TSP), which was 1.6 times of the average value, and the square briquette stove had the lowest emission level, which was 65% of the average value. ② The emissions of gaseous pollutants from the coal stoves in different combustion stages also showed evident differences. In the high-fire stage, the NOx emission concentration of the square briquette stove was 0.49 mg ·m-3, which was 45%-72% lower than that of the other stoves; the SO2 emission concentration of the gasification reverse burning stove was 1.38 mg ·m-3, which was 28% lower than that of the other stoves. ③ The application technology and combustion type were the main factors affecting the emission levels of the coal stoves. The stove with square briquette technology and gasification reverse burning technology could have great environmental benefit. However, the cost of the two stoves was 20 and 18 thousand yuan, respectively, which was clearly higher than that of the other types of stoves. ④ Considering the difference in the emission of air pollutants from the different stoves, it was suggested to adopt differentiated economic policy and strict emission and product standards, promoting the use of energy-saving and environment-friendly coal stoves to reduce the emission of air pollutants from coal-fired stoves.

Conversion of water hyacinth to value-added fuel via hydrothermal carbonization

Hydrothermal carbonization (HTC) of water hyacinth (WH) was investigated to elucidate the effects of reaction temperature, residence time and pH (acid and alkali catalysts) on the chemical properties, combustion behavior and emission properties of hydrochar. Results found that high reaction temperature, long residence time and catalysts were beneficial to ameliorate the fuel properties of hydrochar in terms of calorific value and energy densification, albeit the yield and energetic recovery efficiency got deteriorated. The lower H/C, O/C and N/C ratios of hydrochar reflected more severe dehydration, decarboxylation and denitrogenation reactions within HTC process. SEM images represented that HTC could lead to the fragmentized structure of hydrochar. As HTC progressed, the vibration of hydroxyl and carboxyl groups in hydrochar weakened, which was conductive to improving the hydrophobicity of hydrochar. The combustion characteristics of hydrochar got remarkable upgraded after HTC, whose combustibility index S and combustion stability index Rw were both superior to that of WH. The hydrochar obtained from the addition of alkali catalyst (NaOH) possessed lower emission concentration of SO2 and NOX during combustion, thus demonstrating better emission properties. Overall, HTC was a feasible way to bridge the gap from WH to alternative renewable fuel.

Feasibility Study of Using Carbide Slag as In-Bed Desulfurizer in Circulating Fluidized Bed Boiler

Carbide slag is a waste residue during the production of acetylene. Due to its high content of Ca(OH)2, carbide slag becomes a potential alternative to limestone as the in-bed desulfurizer of circulating fluidized bed (CFB) boilers. In this study, the calcination and sulfation characteristics of carbide slag were investigated by three different facilities, thermogravimetric analyzer (TGA), 1 MWth pilot CFB boiler, and 690 t·h−1 CFB boiler. Pore structures and sulfation behaviors of carbide slag and limestone were investigated for the sake of comparison. The results showed that carbide slag has a lower calcination temperature than limestone. Its calcined product has a better pore structure and desulfurization activity. The carbide slag exhibited a higher desulfurization efficiency than the limestone in the pilot tests. The SO2 emission concentration showed a downward trend with the increase of molar fraction of carbide slag in the desulfurizer. Meanwhile, carbide slag had a better sintering-resistance property, which makes it possible to effectively reduce SO2 emissions even at high combustion temperatures (&gt;910 °C). While the field test results were similar to that of the pilot tests, the desulfurization efficiency of carbide slag with the same Ca/S mole ratio was higher than that of limestone. The fine size of carbide slag particles and the lower separation efficiency of the cyclone on the 690 t·h−1 boiler left the carbide slag with insufficient residence time in the furnace. Therefore, it is necessary to ensure a high separation efficiency of the cyclone if the carbide slag is used as an alternative desulfurizer in furnace.

Evaluation of Ultralow Emission Performance of Magnesium Oxide (MgO) Wet Flue Gas Desulfurization Unit for Coal-fired Units

Performance tests and evaluations were carried out on the magnesium oxide (MgO) wet flue gas desulfurization unit being used in the ultra-low emission reformation of coal-fired units. It is of reference for units that have not yet carried out ultra-low emission transformation. Taking a magnesium oxide (MgO) wet flue gas desulfurization device of a 350MW coal-fired unit that has completed ultra-low emission modification as the research object, The main performance indexes such as desulfurization efficiency, GGH air leakage rate and reducing agent consumption of the device were tested, and the system operation status were mastered. The test proves that when the magnesium oxide (MgO) wet flue gas desulfurization device used in the desulfurization ultra-low emission reform of the unit is operated under design conditions, It can maintain high desulfurization efficiency and low SO2 emission concentration for a long time, and the main design indicators can meet the design value requirements. The effect of GGH leakage rate on SO2 emission concentration is obvious, resulting in system desulfurization efficiency slightly lower than that of absorption tower desulfurization efficiency.

Effect of Flow-pattern Reconstruction on Combustion Efficiency and Pollutant Emission in CFB Boiler

The effect of flow-pattern reconstruction on combustion efficiency and pollutant emission in CFB boilers was investigated with a 1-D CFB combustion model. The results from the 1-D model agree well with the field test data. It was found that, the mass fraction of fine coal particles should be increased at lower bed pressure drop to achieve flow-pattern optimization. As the bed pressure drop decreases, the carbon content decreases first and then increases, both in fly and bottom ash. The change of bed pressure drop is the main factor in the influence of flow-pattern optimization on SO2 emission. The higher the bed pressure drop is, the lower the SO2 emission concentration is. Bed pressure drop and coal size distribution are the chief factors of NOx emission concentration in flow-pattern optimization. With the decrease in bed pressure drop and increase in mass fraction of fine particles, NOx emission concentration decreases first and then increases. SO2 emission concentration can be decreased below 100 mg/m3 by increasing Ca/S, while the original emission concentration of NOx increases. Therefore, other assistant adjustments are also needed to achieve flow pattern optimization, such as the secondary air proportion and CaO particle size distribution.

Economic Operation Analysis of Environmental Protection Facilities in Coal-fired Power Plants under the New Environmental Protection Policy

With the transformation of ultra-low emissions and the increasingly stringent emission standards for environmental pollutants, power plants have to seek the best economic operation mode to meet the requirements of the policy. In this paper, taking a power plant in North China as an example, the economic operation mode of denitration and desulphurization under the new local policy is considered. When the emission concentration of NOX at the outlet of selective catalytic reduction reactor is controlled in 20 mg/Nm3 and 15 mg/Nm3, the power plant has a significant economic effect. However, the desulfurization system is inappropriate if reduces the SO2 emission concentration.

How can Indian power plants cost-effectively meet the new sulfur emission standards? Policy evaluation using marginal abatement cost-curves

The first-ever SO2 emission concentration standards for Indian coal-power plants were notified on December 7, 2015. In light of the new stringent standards, this paper conducts a techno-economic policy evaluation of SO2 abatement options by building a system-wide marginal abatement cost curve (MACC) for India. An abatement cost model is developed, to estimate retrofit costs for three end-of-process (EOP) SO2 abatement technologies. A system-wide SO2 MACC is derived based on cost optimal allocation of EOP abatement technology to each boiler. Compliance with the new stricter emission standards is evaluated at 75% pollution reduction (≈ 4600 kt-SO2 reductions per year). Compliance with the new standard corresponds to a marginal abatement cost of INR 23,500 per ton of SO2 (≈USD 368.50 per ton of SO2) and total system-wide abatement cost of INR 75 billion (≈USD 1.2 billion) per year. Reduction in pollution is estimated to save 46,000 lives per year at the cost of about INR 1.63 million per life per year. Sensitivity analysis of the MAC curve shows that plant capacity utilization has the most significant impact on total lifetime compliance costs followed by equipment fixed cost, sorbent cost, and water cost in that order.

Simultaneous volatilization characteristics of arsenic and sulfur during isothermal coal combustion

Six coals that with different rank and sulfur contents were chosen for the isothermal combustion experiments at 900°C and 1300°C. By changing the residence time, the volatilization ratio and rate of arsenic with time were obtained. Effects of residence time, temperature, coal type and sulfur content on arsenic volatilization characteristics were discussed. The emission concentration of SO2 was also obtained through online monitoring by flue gas analyzer. Furthermore, the curves of arsenic volatilization, SO2 emission and mass loss of coal were compared together to study the simultaneous volatilization characteristics of arsenic and sulfur. Results show that coals with higher sulfur content tend to have a larger proportion of sulfide bound arsenic, which could have a positive effect on arsenic volatilization. With the increase of residence time, the volatilization ratio of arsenic increases gradually with various volatilization rate. One volatilization peak of arsenic is observed for all the coals. The volatilization peak of arsenic moves forward with higher temperature and lower coal rank. The peak rate of arsenic increases with higher temperature while varies much in different coals. At 900°C, coals with lower coal rank tend to have a higher peak rate of arsenic, while at 1300°C, coals with larger proportion of sulfide bound arsenic tend to have a higher peak rate of arsenic. As for SO2, all the coals have one emission peak at 900°C while two emission peaks at 1300°C, which is similar to the mass loss characteristics of coals. The simultaneous releasing behavior of arsenic and sulfur is observed at 900°C due to the moderate mass loss of coal and at 1300°C due to the decomposition of As-bearing sulfides.

Novel technology of reducing SO2 emission in the iron ore sintering

Iron ore sintering is the main SO2 emission process in iron and steel industry. The flue gas of iron ore sintering has been cleaned by desulfurization equipment, which causes high capital and operation cost. A novel technology of reducing SO2 emission of flue gas during iron ore sintering process by adding urea into granules bed was investigated in this paper. The results show that the emission concentration of SO2 in flue gas decreased significantly by adding urea loaded in the bed. The desulfurization rate higher than 85% was obtained when 0.05% urea was added in the bed at the height of 70–150mm from the bed bottom, and there is no adverse influence on sintering indexes and final sinter quality.