Coke Ratio Research Articles

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 Collaborative Optimization Strategy for Energy Reduction in Ironmaking Digital Twin

The expenses of pig iron manufacturing stay in a relatively high level due to the large consumption of coke and ore. There are a variety of methods to optimize the ironmaking process, including multi-indexes method, production-oriented approach, and cost control strategy. In order to reduce the production cost, we optimize the raw materials supply schedule of an ironmaking plant with five sintering machines and seven blast furnaces. The ironmaking plant used to fix the feeding system, leading to an inefficient operation and mismatched connection between these two procedures. At first, the physical characteristics of each blast furnace is captured by linear regression model. Then, a self-adaptive genetic algorithm with variable population size is constructed under distribution system. Finally, the improved genetic algorithm is applied to optimize the total production cost represented by the aggregation of seven coke ratios on a cloud computing platform. With the application of this cloud collaborative optimization framework, the mean coke ratio of the ironmaking factory has decreased 13.96kg/t Fe.

Thermogravimetric analysis of combustion characteristics of coal gangue and petroleum coke mixture

In order to improve the comprehensive combustion efficiency of the steam coal and reduce waste of resources, combustion experiments of petroleum coke and coal gangue were carried out by thermogravimetric analyser at different proportions and heating rates. Combustion characteristic curves (TG, DTG, DTA) and combustion characteristic parameters of the blended samples were analysed by thermogravimetric analysis and thermogravimetric analysis. The optimum combustion ratio and combustion characteristic parameters of the blended samples were explored. Experiments show that the combustion performance of petroleum coke and coal gangue is the best when the mixture ratio of petroleum coke and coal gangue is 4:1 in air atmosphere and the flow rate is 30ml/min, and the combustion characteristics of petroleum coke and coal gangue are better than that of single sample. The burning effect of the mixed fuel is the best. The exothermic area of 15°C/min and 25°C/min is greater than that of 20°C/min, which indicates that the heating rate of 20°C/min is not conducive to exothermic.

An All-Factors Analysis Approach on Energy Consumption for the Blast Furnace Iron Making Process in Iron and Steel industry

The blast furnace iron making process (BFIMP) is the key of the integrated steel enterprise for energy saving due to its largest energy consumption proportion. In this paper, an all-factors analysis approach on energy consumption was proposed in BFIMP. Firstly, the BFIMP composition and production data should be collected. Secondly, the material flows and energy flows analysis models could be established based on material balance and the thermal equilibrium. Then, the all influence factors (mainly including material flows, energy flows and operation parameters) on energy consumption were obtained. Thirdly, the main influence factors, which influenced the coke ratio (CR) and the pulverized coal injection ratio (PCIR), were obtained by using the partial correlation analysis (PCA) method, because CR and PCIR were the key energy consumption performance in BFIMP. Furthermore, anall-factors analysis result could be achieved by a multivariate linear model (MLR), which was established through these main influence factors. The case study showed that the PCIR was the most effective parameter on CR; when it was increased by 1% (0.84 kg/t), the CR would reduce by 0.507 kg/t. Therefore, the increase in PCIR consumption is the key measure to realize energy saving for BFIMP. The results showed that the improvement of some material flows, energy flows and operation parameters could increase the amount of PCIR, such as sinter size, ore grade, sinter grade, M10, blast volume, blast temperature and especially for sinter alkalinity. Moreover, theall-factors analysis approach on energy consumption can widely be used in various BFIMPs, too.

Intelligent Integrated Control for Burn-Through Point to Carbon Efficiency Optimization in Iron Ore Sintering Process

The iron ore sintering process is an important step in preparing raw material for ironmaking. How to reduce carbon consumption while ensuring the stable running of the sintering process is an urgent problem to be solved. In this brief, an intelligent integrated control strategy for the burn-through point (BTP) to carbon efficiency optimization in the sintering process is presented. The comprehensive coke ratio (CCR) is employed as a measure of carbon efficiency, and the BTP is a measure of the stability of the sintering process. First, a short time scale model is established to predict the CCR, and the carbon efficiency is optimized by using the particle swarm optimization algorithm. This yields an optimal carbon efficiency and one control quantity of strand velocity. Another control quantity of strand velocity is obtained by a BTP expert-fuzzy controller. Both control quantities are integrated by a well-designed intelligent integrated controller, so that the optimal strand velocity, as the final control input, is determined. An experiment is carried out to verify the effectiveness of the proposed strategy. The experimental results show that the proposed strategy improves the carbon efficiency while ensuring the stable running of the sintering process, which has a good application prospect in the industrial site.

Numerical Simulation of Effects of Different Operational Parameters on the Carbon Solution Loss Ratio of Coke inside Blast Furnace

Carbon solution loss reaction of coke gasification is one of the most important reasons for coke deterioration and degradation in a blast furnace. It also affects the permeability of gas and fluids, as well as stable working conditions. In this paper, a three dimensional model is established based on the operational parameters of blast furnace B in Bayi Steel. The model is then used to calculate the effects of oxygen enrichment, coke oven gas injection, and steel scrap charging on the carbon solution loss ratio of coke in the blast furnace. Results show that the carbon solution loss ratio of coke gasification for blast furnace B is almost 20% since the results of a model are probably only indicative. The oxygen enrichment and the addition of steel scrap can reduce the carbon solution loss ratio with little effect on the working condition. However, coke oven gas injection increases the carbon solution loss ratio. Therefore, coke oven gas should not be injected into the blast furnace unless the quality of the coke is improved.

Interaction reaction between the degradation of coke and the reduction of iron ore in oxygen blast furnace under hydrogen-enriched operation

ABSTRACTThe interaction reaction between the degradation of coke and the reduction of iron ore was experimentally investigated under two blast furnace (BF) atmospheres. The one is full oxygen blast furnace (FOBF) under hydrogen-enriched operation. The other is traditional BF. The results indicated that the reaction temperature has a great important impact on the gasification solution loss reaction of coke. Moreover, with increasing the ratio of iron ore to coke, the solution loss reaction of coke is enhanced while the reduction index of sinter tends to be slightly decreased. In addition, under the atmosphere of FOBF, the reduction index of sinter is no less than 91.3% at 1000°C for 180 min. Nevertheless, the solution loss ratio of coke is no more than 3.7%. Under the atmosphere of BF, the reduction index of sinter is 54.3%, suggesting that the further reduction of sinter needs to sequentially occur at high temperature zone.

Effects of clay minerals on the low-temperature oxidation of heavy oil

In situ combustion (ISC) is an efficient method to recover heavy oil, in which low-temperature oxidation (LTO) is an important process. A fixed-bed reactor was used to conduct ramped temperature oxidation experiments on a mixture of heavy oil from China, silica and clay minerals, including montmorillonite, kaolinite, illite and chlorite. The four clay minerals promoted the LTO of heavy oil, which was reflected in the increase in the total amount of O2 consumption with the decrease in the peak temperature and the change in the composition and yield of gas, liquid and solid products. The total amounts of CO and CO2 release increased, and the peak temperature decreased. The total amount of hydrocarbons in the gas phase decreased, whereas the peak temperature and main components didn’t change. The presence of clay minerals showed little effect on the composition of produced oil, whereas the presence of montmorillonite decreased the yield of produced oil. The yield and O/C ratio of coke increased, the heat value of coke decreased and the oxidation activity of coke improved in the presence of clay minerals. Montmorillonite showed the most notable effect. The mechanism of the effects of clay minerals on the LTO of heavy oil was analyzed by combining the change in the distribution proportion of O and C in products. Montmorillonite had large specific surface area and adsorption capacity and catalyzed decarbonylation, decarboxylation and polycondensation. The other three clay minerals mainly affected the LTO of heavy oil through the catalysis of decarbonylation, decarboxylation and polycondensation.

Effect of coke breeze content on sintering mechanism and metallurgical properties of high-chromium vanadium-titanium magnetite

ABSTRACT The coke ratio has an important effect on the behaviour of high-chromium vanadium–titanium magnetite (HCVTM) sintering. The effect of coke breeze content on sintering properties and mechanism of HCVTM was investigated in an experimental-scale sintering pot. And the softening-melting test was carried out to study the metallurgical properties of sinter. The results showed that with increasing coke breeze content from 4.0% to 6.0%, the mineral phase grains of HCVTM sinter refined. Further, the mineral content of FeO, silicate, perovskite, and magnetite increased while calcium ferrite and haematite decreased. The vertical sintering speed (VSS), tumble index (TI), productivity, and reduction disintegration index (RDI) increased while yield and reducibility index (RI) of HCVTM sinter decreased, and the softening-melting behaviour of HCVTM sinter improved with the increasing of coke breeze content.

Multi-model ensemble prediction model for carbon efficiency with application to iron ore sintering process

Iron ore sintering is one of the most energy-consuming process in steel industry. Accurate prediction of carbon efficiency for this process is beneficial to energy savings and consumption reduction. Considering the sintering process exhibits strong nonlinearities, multiple parameters, multiple operating conditions, etc., a multi-model ensemble prediction model based on the actual run data is developed to achieve the high-precision prediction of carbon efficiency. It takes the comprehensive coke ratio (CCR) as a metric (index) of carbon efficiency in the sintering process. First, an affinity propagation clustering algorithm is used to realize the automatic identification of multiple operating conditions. Then, different models are established under different operating conditions by using the proposed least squares support vector machine (LS-SVM) with hybrid kernel modeling method. Finally, a partial least-squares regression method is employed as an ensemble strategy to combine the different models to form the multi-model ensemble prediction model for the CCR. The simulation results involving the actual run data demonstrate that the proposed model can predict the CCR accurately when compared with other prediction methods. The results of actual runs show that the coefficient of determination for the proposed model is 0.877. The proposed model satisfies the requirements of actual sintering process and enables the real-time prediction.

Effect of Nut Coke Addition on Physicochemical Behaviour of Pellet Bed in Ironmaking Blast Furnace

One of the primary causes that limit the blast furnace productivity is the resistance exerted to the gas flow in the cohesive zone by the ferrous burden. Use of nut coke (10–40 mm) together with ferrous burden proves beneficial for decreasing this resistance. In present study, effect of nut coke addition on the olivine fluxed iron ore pellet bed is investigated under simulated blast furnace conditions. Nut coke mixing degree (replacement ratio of regular coke) was varied from 0 to 40 wt% to investigate the physicochemical characteristics of the pellet bed. Three distinct stages of bed contraction are observed and the principal phenomena governing these stages are indirect reduction, softening and melting. It is observed that nut coke mixing enhances the reduction kinetics, lowers softening, limits sintering and promotes iron carburisation to affects all three stages. In the second stage, the temperature and displacement range is reduced by 60°C and 24%, respectively upon 40 wt% nut coke mixing. Addition of nut coke exponentially increases the gas permeability (represented by pressure drop and S-value). A higher degree of carburisation achieved on the pellet shell (iron) is suggested to be the principal reason for decrease in the pellet melting temperature. The pellets softening temperature increases by approximately 4°C, melting and dripping temperature drops by 11°C and 12°C, respectively, for every 10 wt% nut coke addition. Consequently, the nut coke addition shortens the softening, melting and dripping temperature ranges, which shows improved properties of the cohesive zone.

Hybrid modeling and online optimization strategy for improving carbon efficiency in iron ore sintering process

Iron ore sintering is the second most energy consuming process in ironmarking, and the main energy consumption comes from the combustion of carbon. In order to improve the utilization of carbon in the process, taking a comprehensive coke ratio (CCR) as a metric of carbon efficiency, we develop a hybrid prediction model for CCR. On this basis, we present an online optimization strategy. Through the analysis of the sintering mechanism, the key sintering parameters affecting CCR are determined by a Pearson’s correlation coefficient analysis. Then, multiple operating conditions of the process are identified by a Fuzzy C-Means clustering. Multiple models for different operating conditions are built by least squares support vector machine, and combined by using a Takagi–Sugeno fusion scheme (i.e., T-S rule-based model) so that the CCR prediction model is formed. To achieve CCR optimization based on this model, an online optimization strategy based on a chaos particle swarm optimization is presented. The simulation involving actual run data verifies that the proposed modeling method exhibits high prediction accuracy. Moreover, the results of actual runs show that the proposed optimization method satisfies the requirements of the actual sintering production, where the CCR is reduced by 1.327 kg/t (on average).

Estimation of Particle Segregation Behavior in Ore-coke Mixed Layer Using Screening Layer Model

To achieve low RAR operation by coke mixed charging, it is important to control coke segregation behavior in mixed layer at blast furnace top. In this study, a numerical simulator based on screening layer model was developed to estimate the distribution of mixed coke ratio in mixed layer. The results are summarized as follows:

Preparation of MgO added iron ore pellets and effects on a pilot scale blast furnace operation

The preparation of MgO bearing iron ore pellets in the mini and pilot scales, and the application for 4070m3 blast furnace were conducted in this paper. The results show that with increasing the MgO content, the compressive strength of fired pellets decreased significantly, while the metallurgical performances, such as RI (reducibility index), RDI (reduction degradation index) and RSI (reduction swelling index) were improved. The scanning electron microscope with energy dispersive spectrometry was used to test the microstructures and the compositions of iron oxide, and the slag of pellets. With increasing the MgO content, MgO exists in the form of magnesium ferrite and monticellite, which presents the high melting temperature, decreasing the content of liquid phase. The pilot scale experiment was performed on a commercial running grate-rotary kiln with the annual output of 2.4 million tons of pellets, which agrees with the results obtained in the mini scale experiment. The application of MgO-bearing pellets was carried out in the 4070 m3 blast furnace of Meisteel China, and the production indexes improved with increasing the yield and reducing the coke ratio, and the total benefits are 7067 USD/d, which presents the superior economic benefits.

Multi-Objective Optimization of Cost Saving and Emission Reduction in Blast Furnace Ironmaking Process

Due to the increasing environmental pressures, one of the most direct and effective way to achieve emission reduction is to reduce the CO2 emissions of the blast furnace process in the iron and steel industry. Based on the substance conservation and energy conservation of ironmaking process and the engineering method, the carbon loss model was firstly established to calculate the amount of solution loss. Based on this model, the blast furnace emission reduction optimization mathematical model with the cost and CO2 emissions as objective functions was then established using the multiple-objective optimization method. The optimized results were obtained by using the GRG (Generalized Reduced Gradient) nonlinear solving method. The optimization model was applied to the B# blast furnace of BayiSteel in China. The optimization model was verified by comparing the optimized results with the actual production data. The optimization model was then applied to analyze the effects of coke ratio, coal rate, blast temperature and other factors on the cost, CO2 emission and solution loss, and some measures to save cost, reduce emissions and reduce solution loss have been proposed.

A Multilevel Prediction Model of Carbon Efficiency Based on the Differential Evolution Algorithm for the Iron Ore Sintering Process

The iron ore sintering process is the second most energy-consuming process in iron and steel production, where the main energy consumption results from the combustion of carbon. In order to optimize the utilization of carbon, it is necessary to predict the carbon efficiency first. Thus, this paper develops a multilevel prediction model to predict the carbon efficiency. First, the comprehensive coke ratio (CCR) is employed as an index to measure the carbon efficiency. Based on the mechanism analysis, the sintering parameters affecting the CCR are determined. Next, an improved fuzzy C-means (FCM) clustering method is used to automatically identify the optimal number of working conditions for the sintering parameters. Then, the least-squares support vector machine (LS-SVM) submodels are established for different working conditions. To improve the generalization ability of the model, a differential evolution algorithm is developed to optimize the parameters and the weights of the LS-SVM submodels. Finally, the multilevel prediction model for the CCR is established by aggregating LS-SVM submodels. The simulation results based on actual run data demonstrate that the prediction accuracy of the multilevel prediction model is higher than that of a backpropagation neural network model and an FCM-LSSVM model, and the proposed modeling method satisfies the requirements of the actual sintering production.

The ignored emission of volatile organic compounds from iron ore sinter process

Iron ore sintering is a major source of gaseous and particulate pollutants emission in iron smelt plant. The aim of present study is to characterize the volatile organic compounds (VOCs) emission profiles from iron ore sintering process. Both sinter pot test and sinter simulation experiment were conducted and compared. Out results showed that sinter process produced large quantity of VOCs together with NOx and SO2. VOCs and NO were produced simultaneously in sinter pot test from 3 to 24 min after ignition, flowed by SO2 production from 15 min to the end of sintering. Total VOCs (TVOC) concentration in sinter flue gas was affected by the coal and coke ratio in sinter raw material. The maximum TVOC concentration was 34.5 ppm when using 100% coal as fuel. Sinter simulation experiments found that the number of VOCs species and their concentrations were found by sinter temperature. The largest VOCs species varieties were obtained at 500 °C. Benzene, toluene, xylene and ethylbenzene were major VOCs in sinter flue gas based on the results from both simulation test and sinter pot. It thus demonstrated that in addition to NOx, SO2 and metal oxide particles, sinter flue gas also contained significant amount of VOCs whose environmental impact cannot be ignored. Based on our work, it is timely needed to establish a new VOC emission standard for sinter flue gas and develop advanced techniques to simultaneously eliminate multi-pollutants in iron ore sinter process.

Exergy Analysis and Optimization of Sintering Process

Iron ore sintering is an important and large energy‐consumption segment in the iron and steel industry. It is thus essential to save energy in the sintering process considering both quality and quantity. This paper takes exergy loss minimization as an optimization objective, and a sintering optimization model is established based on material balance and exergy balance. Optimized results are obtained by solving constrained multidimensional nonlinear programming problems. Two types of sintering processes, in which solid fuels consist of coke and coke/coal powder, are calculated and analyzed. The effects of fuel ratio, fuel preheat temperature, and sinter basicity on exergy losses are also studied. The results show that the exergy losses obtained from optimizations for two types of solid fuels decrease by 5.0% and 8.1%, respectively. Exergy loss has a negative correlation with the coal ratio and fuel preheat temperature and a positive correlation with the coke ratio and sinter basicity. Exergy efficiencies increase from 29.7% to 35.0% and 35.5%, for each optimization. The research in this paper is highly significant to exploring of the sintering process, guiding steel production, and reducing energy consumption.

Microwave-enhanced pyrolysis of Yunnan lignite for improving rich hydrogen syngas production

ABSTRACTThis study aimed to obtain the maximum possible gas yield and the high-quality syngas production from microwave pyrolysis of lignite. The microwave pyrolysis experiments have explored the influence of the using of microwave stir device and different coke ratios on gas production rate and gas yield. The results show that the influence of microwave stir device on both gas production rate and gas yield was substantial. Adding appropriate semi-cokes can accelerate the lignite microwave pyrolysis rate, and increase the production of gas.

An experimental study on effect of coke ratio on SO2 and NOx emissions in sintering process

By using the sinter cup experiment, the effects of different coke ratios of 0%, 25%, 50%, 75%, and 100% on the formation and total emissions of SO2 and NOx in the sintering process were studied with the Testo350 flue gas analyzer. The experimental results show that the emissions of SO2 and NOx are closely related to sintering process. With the increase of the coke proportion, the sintering temperature changes and the maximum peak time appears earlier. SO2 concentration has a bimodal distribution and NOx concentration has a triple peak. Besides, the both maximum peaks appear at the end of sintering. In addition, due to the increasing of the S and N contents in the fuel with the coke ratios from 0% to 100%, the amounts of SO2 and NOx emissions are raised respectively at 10.82 mg, 11.42 mg, 13.84 mg, 13.69 mg, 20.36 mg and 3.11 mg, 3.39 mg, 4.44 mg, 4.31 mg, 6.16 mg.