Gas Utilization Ratio Research Articles

Effect of coke reactivity on softening-melting and dripping behaviors of sinter

ABSTRACT The use of high reactivity coke in blast furnaces is an important direction for low-carbon development. In this study, we investigated the effect of coke reactivity on the softening-melting behaviors of the sinter. The results show that the gasification temperature of coke decreased with increasing coke reactivity. With increasing coke reactivity from 24.75% to 45.11%, the initial melting temperature rose from 1294°C to 1312°C, the dripping temperature dropped from 1522°C to 1507°C and the melting region decreased from 228°C to 195°C, respectively. The cohesive zone shifted downward and the thickness narrowed. The permeability index and highest differential pressure dropped from 1219.67 KPa·°C to 607.61 KPa·°C and from 7.0962 KPa to 4.8523 KPa, respectively. Coke with high reactivity can improve the gas permeability of burden. The gas utilization ratio in the softening-melting and dripping processes of the sinter raised by about 2% with increasing coke reactivity.

Development of Alternative Reductant using Biomass for Reducing CO<sub>2</sub> in Ironmaking Process

Technologies to reduce CO<sub>2</sub> emissions in the steel industry have been actively developed since the early 1990s in order to deal with global climate change. In particular, the utilization of various types of biomass including wood, bamboo, grass, food and agricultural by-product are being attempted as carbon neutral fuels in the blast furnace process. In this study, an alternative reducing agent for coke and pulverized coal using biomass was developed and the effect on blast furnace performance and CO<sub>2</sub> reduction was evaluated. The first investigation was whether a burden material made of torrefied biomass mixed with iron ore and coal (Iron-bearing Biomass Coke, IBC) could be used as a substitute for coke in the form of a carbonized briquette. In addition, a method of mixing torrefied biomass with pulverized coal (Bio-PC) to inject through tuyere in a blast furnace was examined. As a result, when coke was replaced with 10% of IBC, the reducibility of the sintered ore was improved in association with an increased CO gas utilization ratio, hence CO<sub>2</sub> emissions decreased by 2.3%. Furthermore, the combustion efficiency of Bio-PC mixed with 10% of torrefied biomass was improved by 13.2%, thus, the amount of CO<sub>2</sub> emissions was calculated to decrease by 4%. These results suggest that the possibility of using IBC and Bio-PC in blast furnaces, as they could effectively reduce CO<sub>2</sub> emissions in the ironmaking process.

Experimental Study on Physical Simulation of Large-Scale Interfracture Injection and Production with Different Injection Media in Tight Oil Reservoirs

Abstract Given the difficulty of water injection and effective displacement system establishment in Changqing oilfield, this research carried out the interfracture injection and production experiment of a large outcrop model with two types of injection fluid medium, natural gas and surfactant, and studied the interfracture injection and production law of different displacing medium and the principal contradiction existing in the different displacing medium. The results show that natural gas drive in tight oil reservoir is beneficial to reduce the Jamin resistance and form a rapid oil production. Still, the gas drive process is easy to develop gas breakthrough, leading to a decrease in gas utilization ratio, and its oil displacement efficiency is 17.25%. Additionally, due to the strong adsorption of surfactant, the seepage capacity of a porous medium in the process of surfactant-oil displacement is reduced, and blockage is formed at the injection end, resulting in the poor oil displacement effect of surfactant. However, compared with natural gas injection, the surfactant has higher oil displacement efficiency, up to 22.05%. Therefore, for tight oil reservoirs, rational utilization of the advantages of different mediums and controlling their disadvantages are essential for the development of such reservoirs.

Techno-economic and environmental impact analysis of tuyere injection of hot reducing gas from low-rank coal gasification in blast furnace

Our goal is to find a strategy to reduce coke use during operation of a blast furnace (BF). This study focuses on the techno-economic and environmental impact analysis of tuyere injection of hot reducing gas (HRG) from low-rank coal (LRC) gasification in the BF. LRC gasification by entrained-flow gasifier is introduced to make gaseous fuel, and the cleaning process of the raw syngas is implemented using Aspen Plus V10. The effects of HRG injection are evaluated using a BF model derived from the Rist operating diagram. Integrated results from raw fuel to the BF were analyzed considering economic, energy, and environmental aspects. By injecting rich syngas (>95 vol %) into the BF at 1150 K, coke replacement ratio is estimated to be 0.354 kg/Nm 3 without adverse impact on the BF. The most sensitive economic factor is coke price. The energy efficiency of raw syngas cleaning process, relative energy intensity of LRC, gas utilization ratio are 57.5%, 90.6%, and 50.0% respectively. Utilization of HRG can reduce CO 2 emission by 21.88 kt CO2 /yr compared to the reference BF operation. • The hot reducing gas injection is proposed as a coke-saving strategy. • In technical aspect, the coke replacement ratio is estimated to be 0.354 kg/Nm 3 . • The suggested process is economically feasible. • The annual potential abatement of CO 2 is 21.88 ktCO 2 /yr.

Estimation of the H2 Gas Utilization Ratio Using a BF Shaft Inner Reaction Simulator

The effects of hydrogen gas on the reduction rate of ore and reactivity of coke were estimated by using the Boris type movable reaction furnace shaft inner reaction simulator (SIS). The temperature profile was controlled to reproduce the shaft region in the actual blast furnace, and the gas composition was self-controlled inside the reaction tube. The apparent gas utilization ratio was also affected by carbon activity.

Burden Surface Decision Using MODE With TOPSIS in Blast Furnace Ironmkaing

Burden surface distribution plays a key role in achieving an energy-efficient status of blast furnace (BF). However, actual adjustment of burden surface usually depends on the operator’s experience when the production status changes. Meanwhile, due to the characteristics of high dimension, strong coupling, and distributed parameters, it is difficult to establish the accurate mechanism model for BF ironmaking process. Considering the aforementioned issues, this paper proposes an integrated multi-objective optimization framework for optimizing burden surface distribution based on the analysis of BF operation characteristics. Firstly, data-driven models are constructed for two objectives, i.e., gas utilization ratio (GUR) and coke ratio (CR), and two constraints using adaptive particle swarm optimization (APSO) based extreme learning machine (ELM), named APSO-ELM. Multi-objective optimization is subsequently carried out between GUR and CR using the multi-objective differential evolution algorithm (MODE) to generate the Pareto optimal solutions. Finally, TOPSIS is applied to select a best compromise solution among the Pareto optimal solutions for this optimization problem. Comprehensive experiments are presented to illustrate the performance of the proposed integrated multi-objective optimization framework. The experimental results demonstrate that the proposed framework can give a reasonable burden surface profile according to the production status changes to guarantee the BF operation more efficient and stable.

A Soft Sensing Scheme of Gas Utilization Ratio Prediction for Blast Furnace Via Improved Extreme Learning Machine

Gas utilization ratio (GUR) is an important indicator reflecting the operating state and energy consumption of blast furnace (BF). Skilled operators usually refer to changing trends of GUR to guide the next step of production. For these reasons, this paper establishes a soft sensing scheme based on an improved extreme learning machine (ELM) to predict GUR. In order to enhance the modeling capability of ELM for industrial data, an improved ELM, named GR-ELM, is proposed based on grey relational analysis (GRA) and residual modification mechanism. In GR-ELM, considering the different effective information contained in each input attribute for modeling, the input attribute optimization is proposed combining with GRA and entropy weight method. Then, because the modeling capability of ELM is limited and the data collected from industrial process are usually contaminated, the residual modification mechanism is implemented to improve the reliability of the model. In addition, considering the influence of time delay in BF ironmaking process, generalized correlation coefficient method based on mutual information is used for time delay analysis to eliminate the influence. The real data collected from a BF are applied and validated the performance and effectiveness of the proposed soft sensing scheme. The experimental results show that the proposed soft sensing scheme is available and can achieve better performance than some state-of-the-art algorithms. The soft sensing scheme can provide effective decision support and guidance for further optimization operation.

Residual compensation extreme learning machine for regression

Abstract Extreme learning machine (ELM) was proposed for training single hidden layer feedforward neural networks (SLFNs), and can provide an efficient learning solution for regression problem. However, the prediction error of ELM is unavoidable due to its limited modeling capability, and the nonlinear and stochastic nature of the regression problem. In this paper, a novel ELM, residual compensation ELM (RC-ELM), is proposed for regression problem by employing a multilayer structure with the baseline layer for building the feature mapping between the input and the output, and the other layers for residual compensation layer by layer iteratively. Two real world applications, device-free localization (DFL) and gas utilization ratio (GUR) prediction in blast furnace, are used for experimental testing of the proposed RC-ELM. Experimental results show that RC-ELM has better generalization performance and robustness than other machine learning approaches, including the classic ELM, weighted K-nearest neighbor (WKNN), support vector machine (SVM), and back propagation neural network (BPNN).

The Prediction of the Gas Utilization Ratio based on TS Fuzzy Neural Network and Particle Swarm Optimization.

Gas utilization ratio (GUR) is an important indicator that is used to evaluate the energy consumption of blast furnaces (BFs). Currently, the existing methods cannot predict the GUR accurately. In this paper, we present a novel data-driven model for predicting the GUR. The proposed approach utilized both the TS fuzzy neural network (TS-FNN) and the particle swarm algorithm (PSO) to predict the GUR. The particle swarm algorithm (PSO) is applied to optimize the parameters of the TS-FNN in order to decrease the error caused by the inaccurate initial parameter. This paper also applied the box graph (Box-plot) method to eliminate the abnormal value of the raw data during the data preprocessing. This method can deal with the data which does not obey the normal distribution which is caused by the complex industrial environments. The prediction results demonstrate that the optimization model based on PSO and the TS-FNN approach achieves higher prediction accuracy compared with the TS-FNN model and SVM model and the proposed approach can accurately predict the GUR of the blast furnace, providing an effective way for the on-line blast furnace distribution control.

Gas–liquid mass transfer and flow phenomena in the Peirce–Smith converter: a water model study

A water model with a geometric similarity ratio of 1:5 was developed to investigate the gas-liquid mass transfer and flow characteristics in a Peirce-Smith converter. A gas mixture of CO2 and Ar was injected into a NaOH solution bath. The flow field, volumetric mass transfer coefficient per unit volume (Ak/V; where A is the contact area between phases, V is the volume, and k is the mass transfer coefficient), and gas utilization ratio (eta) were then measured at different gas flow rates and blow angles. The results showed that the flow field could be divided into five regions, i.e., injection, strong loop, weak loop, splashing, and dead zone. Whereas the Ak/V of the bath increased and then decreased with increasing gas flow rate, and eta steadily increased. When the converter was rotated clockwise, both Ak/V and eta increased. However, the flow condition deteriorated when the gas flow rate and blow angle were drastically increased. Therefore, these parameters must be controlled to optimal conditions. In the proposed model, the optimal gas flow rate and blow angle were 7.5 m(3)center dot h(-1) and 10A degrees, respectively.

A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces.

Gas utilization ratio (GUR) is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs). In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM) named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF), depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS) to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation.

Analysis of MPL Impact in PEFC with in-Situ Temperature Measurement in through-Plane Direction

Water management is a key issue to the practical operation of PEFC. Drying up in polymer electrolyte membrane (PEM) increase the ionic resistance and rises IR overvoltage. On the other hand, supplying excessive water causes flooding and increases the concentration overvoltage, and hence cell performance deteriorates. Therefore, appropriate water management is required. Micro porous layer (MPL) has been developed to control the water and gas flow in cell. MPL is a hydrophobic component which consists of carbon black and PTFE on the surface of gas diffusion layer (GDL) by the CL side. MPL can control liquid water and gases transport, and keep high performance even when severe flooding or drying-up condition. Recently, MPLs are improved with adding hydrophilic layer for further robustness in PEFC operation. However, the impact of MPL on cell performance is not sufficiently understood, and this situation becomes an obstacle in the development and improvement of MPL. Impacts of the structure and hydrophobicity in MPL are usually evaluated with IV characteristics. It is difficult to elucidate the change of water and gases transport only from IV characteristics. Local water behavior, temperature and current should be clarified in advance to efficiently optimize MPL. Some methods have been reported to measure the internal state inside cell in-plane and through-plane direction. For example, neutron imaging discloses in-situ liquid water distribution in the cell, contributing to the understanding the relationship between operation parameters and local water behavior. Not only water behavior but also temperature may change with MPL, because thermal conductivity in MPL is different from cell components, such as GDL and CL. Temperature distribution especially in through-plane direction may change as MPL is employed and water behavior changes. These backgrounds motivate us to challenge to measure the temperature distribution by fabricating ultrafine in-line thermocouples and placing them in a cell. The tailor-made thermocouples are accurately positioned under rib or channel and also placed in through-plane direction, so that temperature distribution measurement in the cell with and without MPL is conducted. Though the temperature difference between under the rib and channel did not vary from presence of MPL, the distribution in the through-plane direction changed considerably, suggesting that MPL impacts on water behavior significantly. This study discusses the impact of MPL on water behavior with the in-situ measured temperature distribution. Ultrafine in-line thermocouples are fabricated in the following manner. Butt-welded K-type thermocouple wires in diameter 50 μm formed an in-line thermocouple. Electrical insulation is required to insert the thermocouple into the cell. For this purpose, the thermocouple was coated with polyimide. The fabricated thermocouple is precisely placed into cell with the fixing tool which has a squire frame shape. In-line thermocouples were stretched across the tools, with an adjustment so that the thermocouple junctions are positioned under rib or channel. Four sets of the fixing frames are arrayed in piles, and temperature is measured along through-plane direction on the four points: anode GDL, anode CL, cathode GDL and cathode CL. Air and hydrogen were supplied into the cathode and anode respectively with relative humidity of 20 % and the utilization ratio of 0.1 at 1A/cm2. The cell voltage difference between the cases with and without MPL was about 0.1 V. The temperature around CL with MPL was slightly higher than that without MPL. The distribution without MPL was homogeneous, however, the MPL case tended to have the maximum temperature at cathode CL; it might be caused by the low thermal conductivity of MPL. Cell voltage in the case with MPL was 0.42 V higher than that without MPL, while cell was operated under relative humidity of 95 % and gas utilization ratio of 0.4. Although it is expected that flooding occurs at cathode CL and cell performance decreases in this condition, MPL promotes to drain the bulk liquid water. This is the reason that the performance with MPL became higher. Temperature with MPL was also higher than that in the case without MPL. The cell without MPL tends to accumulate water at CL, and water evaporates there. Thus, the latent heat with the evaporation decreases the temperature in the cell without MPL. From the above, water behavior differences between with and without MPL is explained through the measured temperatures. It is suggested that the developed temperature measurement method can contribute to cell performance improvement with water management by MPL.

Application of in-seam directional drilling technology for gas drainage with benefits to gas outburst control and greenhouse gas reductions in Daning coal mine, China

Gas outburst disasters are becoming more serious as the underground coal mines become deeper in China, and a thick zone of deformed coal provides conditions favorable to coal and gas outbursts. The Daning coal mine’s main mining seam is the No. 3 coal seam with coal and gas outburst hazard, which often contains two normal coal sub-layers and one deformed sub-layer. Considering both the geological conditions of the coal seam and applications of the in-seam directional longhole drilling technology, a new schematic diagram of in-seam directional longholes for gas drainage is developed. The two borehole layout models of longwall panel and main entries for gas outburst disasters control have been successfully applied. The gas drainage rates of both models are >70 %, and the residual gas contents are both <8 m3/t, which can be considered that the gas outburst disasters were effectively controlled. To better guide gas drainage, gas drainage normal and failure modes have been obtained. Although in-seam directional longhole technology has been successfully applied for regional gas drainage with benefits to gas outburst control, there are also some problems that are detrimental to greenhouse gas reductions in gas drainage and gas utilization. The three main problems are air leakage failure in gas drainage, decreasing gas concentration and a low gas utilization ratio. To address the problems mentioned above, five improvements are suggested.

Flow Field Model Experiment Studies of a Bipolar Plates PEMFC

The mode experiment is respectively done under condition of different amount of gas supply, different shape of gas admission room, different cross-section shape of flow channel. Results show that pressure descend of U shape cross-section slot is smaller than the rectangle cross-section slot in the equal fault stream section situation, so flow distribution characteristic of U shape cross-section slot is better. The experiment results are consistent with numerical simulation results. In addition, numerical simulation results also indicate that the mass of gas diffusing from U shape cross-section slot to the diffusion layer is over greater, and U shape cross-section flow channel has a higher gas utilization ratio.

Energy consumption in smelting reduction (SR) processes

In contrast, conventional processes use coke and hematite/sinter in the blast furnace, in SR processes, other alternative fuels and iron ore sources, like charcoal and fine iron ores, can be used to produce sponge iron. The use of these alternative sources, by SR processes, can reduce environmental impacts and lower production costs. At first, the concepts of the theoretical gas utilization ratio, the smelting heat of the iron ore and the effective calorific value of coal were introduced. Then, the reason for gas utilization ratio and its performance in the shaft as a reducer in the smelting process are discussed and calculated. The relationship between coal consumption and iron ore reduction in the fluidized bed are also discussed. Finally, the influence of post-combustion on coal consumption in an iron bath furnace are calculated and discussed.

Improvement of Gas Utilization Ratio in the Gaseous Reduction of Iron Oxide and Suppression of Dioxins Formation in Combustion Processes by the Enhancement of Carbon Deposition

Improvement of Gas Utilization Ratio in the Gaseous Reduction of Iron Oxide and Suppression of Dioxins Formation in Combustion Processes by the Enhancement of Carbon Deposition

Energy Consumption in Smelting Reduction

The concepts of theoretical gas utilization ratio, smelting heat of iron ore and effective calorific value of coal were introduced in this work. The practical gas utilization ratio and the gas consumption in the shaft of a reduction unit for smelting reduction were discussed. The Corex process was optimized and the energy consumption was minimized according to the relationship of gas production in the smelting unit and the degree of iron ore reduction in the reduction unit. It was proven that the most important factor for saving energy in smelting reduction process is to use coal with suitable effective calorific value for the smelting heat of iron ore.

Development of a performance test method for PEFC stack

It is important to obtain repeatable I–V characteristics in order to evaluate the steady-state performance of a PEFC stack. A performance test method would be useful for this purpose; so, we conducted experiments with several PEFC stacks to collect data. In this study, we set two goals. One was to stabilize the PEFC stack output, and the other was to clarify the operating conditions and to determine the accuracy required to control the parameters that affect the I–V characteristic curve, including stack-operating temperature (coolant outlet temperature), gas operating pressure, gas utilization ratio (gas stoichiometry) and gas dew point. Through experiments, we found a means of obtaining repeatable data with a stack voltage accuracy of ±1%.

97/04957 Experimental study on optimizing operation of smelting reduction: Fang, J. and Chen, M. Gangtie, 1997, 32, (2), 14–18. (In Chinese)

Simulating tests of iron ore reduction in fluidized bed and shaft were made to investi- gate the influences of reduction conditions on gas utilization ratio and essential gas require- ments. The necessary coal quantity for smelting under different pre-reduction parameters is dis- cussed. The optimum conditions of reduction and smelting for minimum energy consumption are analysed.

Effects of Geometrical Factors on Liquid Mixing in a Gas-Induced Agitated Tank

An agitated tank having two 45° pitched blade downward turbines in series and four thin baffles which were narrower than the conventional ones was studied. At higher impeller speeds, a gas and liquid vortex induces the gas above the liquid surface vertically downward into the turbines, which in turn cuts the gas stream into bubbles which then circulate in the liquid. Consequently gas-induction increases the gas utilization ratio as well as simplifying the ancillary equipment otherwise required to recycle the escaped gas. The relationship between tank geometrical factors and the liquid mixing time was also investigated. The experimental variables investigated were the impeller speed, the baffle width, the turbine diameter, the space between two impellers, the liquid level, and the clearance between the lower turbine and the bottom of the tank. The results showed that all but the clearance between the lower turbine and the bottom of the tank had significant effects on the mixing time under normal gas-induction, revealing results in contrast to the conventional agitated tank. © 1997 SCI.