Coke Oven Temperature Research Articles

Heat and mass transfer analysis of coal coking process in the coking chamber

ABSTRACT To study the coking heat mass transfer process and reveal the law of industrial-scale coal pyrolysis, the heat transfer characteristics of coal material in the coking process are analyzed based on coke oven temperature measurement experiments. A mathematical model of the coking process in the coking chamber was constructed based on the distributed activation energy model. The heat transfer process of the coal feed, generation of coke oven gas, and dynamic precipitation characteristics of each component during the coking cycle were further studied. The results show that tar and water are the earliest released and fastest precipitated pyrolysis products, respectively, which are completely precipitated at 800 K. The contents of the components of the inward and outward gases are obviously different, with CH4 and H2 accounting for 73.79% of the outward gas, while H2O and tar account for 77.80% of the inward gas. When the coal reaches 1200 K, all pyrolysis products except H2 are precipitated completely. The model can effectively predict the changes of raw coke oven gas composition during the coking process at different locations in the coking chamber.

Assessment of Coke Oven Operating State Using Trend Analysis and Information Entropy

In the combustion process of a coke oven, it is crucial to evaluate the operating state to ensure control performance for the stabilization of the coke oven temperature. This paper presents an assessment method for a coke oven operating state based on the analysis of the mechanism. A coke oven, which is an integrator, is categorized into serial subsystems, which include two coking chambers and one combustion chamber. First, the raw gas temperature of every coking chamber is extracted online and is combined with the qualitative trend analysis that yields the feature point of the raw gas temperature. Subsequently, fuzzy method is presented to describe the uncertainty and evaluate the heat level of each subsystem. Finally, a comprehensive assessment of the operating state of the coke oven is performed by combining the weighted contribution of all subsystems, which is expressed by information entropy. Simulations and experiments demonstrate the validity of the method.

PID Control Optimization for Shuttle Kiln Temperature Based on Genetic Algorithm

Aiming at the coke oven temperature characteristics of great inertia pure time-delay, non-linear and time changeable, a new method that genetic algorithm is introduced to optimize PID parameters is proposed .In calculation of iteration, the optimal solutions are always reserved to guarantee all individuals to converge to the global optimization. For obtaining perfect control effect, the square of control quantity and overshoot-punishment item are set to an objective function with a time-integral to the absolute value of error. The genetic PID algorithm is adopted to control the output temperature. The simulation results of the proposed algorithm applied to a simplified oven model show the feasibility of the method

Integrated soft sensing of coke-oven temperature

This paper describes an integrated soft-sensing method of estimating the coke-oven temperature (COT). First, the characteristics of the coking process (change in direction of gas flow, coke-pushing series) are analyzed. Two variables are introduced to more precisely describe the COT: COTM is the COT on the machine side and COTC is the COT on the coke side. Next, that analysis is used to select the regenerating chambers where thermocouples should be installed; and the temperatures at the tops of the regenerating chambers on the machine side (TTRMs) and on the coke side (TTRCs) are used as auxiliary variables in soft sensing. Then, two types of linear-regression (LR) models are built, one in 12 variables and one in one variable; and supervised distributed neural networks (SDNNs) are also built to estimate COTM and COTC. The LR model in 12 variables and the SDNN models are integrated to accurately describe the nonlinear relationship between COTM (COTC) and the TTRMs (TTRCs) under normal conditions. Supervised clustering is used to divide the sample space into several subspaces, and a neural network is built for each one. The LR model in one variable [the average TTRM (TTRC)] is employed to estimate COTM (COTC) for abnormal conditions. An expert coordinator balances the outputs of these models. A model adaptive unit periodically updates the parameters of the soft-sensing model to reflect changes in the operating conditions and environment. Finally, the model was implemented in an optimization and control system for the COT. The results of actual runs demonstrate the effectiveness of the model.

Some Fundamental Aspects of Highly Reactive Iron Coke Production

It is important to develop the production and utilization technology of highly reactive coke in order to improve the efficiency of blast furnace reactions. In this study, some fundamental aspects of highly reactive iron coke produced in a coke oven chamber were investigated. First, the effects of catalytic Fe powder addition to coal before carbonization on coke strength were investigated. The addition of Fe powder decreased the coal caking property and hence the resultant coke drum index (DI15015). On the other hand it increased coke reactivity (JIS coke reactivity index and CRI) to a great extent. This means that the caking property of blended coals needs to be adjusted higher to produce iron coke with proper strength and high reactivity. Secondly, it was shown that the iron ore reacts with silica brick at 1200°C in a condition similar to that in a coke oven chamber. The iron ore and silica reacted to produce fayalite (2FeO·SiO2) and the brick was damaged. On the other hand, it was proven that the iron ore does not react with silica brick at 1100°C in the above condition. Based on this fundamental study, iron coke with proper strength and high reactivity was successfully produced in a coke oven chamber on a commercial scale by adjusting the coal blend composition and the coke oven temperature. Furthermore it was revealed that about 70% of iron in iron ore powder added to coal was reduced to metallic iron during carbonization.

高反応性フェロコークス製造技術の基礎検討

The development of production and utilization technology of highly reactive coke is significant in order to improve blast furnace reaction efficiency. In this report, some fundamental aspects of highly reactive iron coke produced in a coke oven chamber were investigated. First, the effects of catalytic Fe powder addition to coal before carbonization on coke strength were investigated. The addition of Fe powder decreased coal caking property and hence resultant coke drum index (DI15015). On the other hand it increased coke reactivity (JIS coke reactivity index and CRI) to a great extent. This means that caking property of blended coals needs to be adjusted higher to produce the iron coke with properstrength and high reactivity. Secondly, it was shown that the iron ore reacts with silica brick at 1200°C in a condition similar to that in a cokeoven chamber. The iron ore and silica reacted to produce fayalite (2FeO·SiO2) and the brick was damaged. On the other hand, it was proved that the iron ore does not react with silica brick at 1 100°C in the above condition. Based on this fundamental study, the iron coke with proper strength and high reactivity was successfully produced in coke ovens on a commercial scale by adjusting the coal blend composition and the coke oven temperature. Furthermore it was revealed that about 70% of iron in iron ore powder added to coal was reduced to metallic iron during carbonization in coke ovens.

Mathematical models of the thermal decomposition of coal: 5. Distribution of gas flow in a coke oven charge

One of the main uncertainties in analysing the coking process is the path followed by the volatile gases as they leave the oven. The objective of this study was to make a first attempt at developing a model for determining the gas flow distribution within an oven during carbonization. The model reported in this Paper consists of a combination of a coke-oven temperature history model together with a numerical implementation of a theory to describe compressible gas flow through porous media in the presence of sources and sinks. The use of the model is illustrated in the prediction of steam flow paths, together with an assessment of the influence of the bed voidage on the gas flow. Although the model involves a number of simplifying assumptions, the predictions compare well, in a qualitative sense, with earlier descriptions of gas flow in coke ovens.

Measurement of Coke Oven Temperature by Rayo Tube

要旨従来窯出ユークス温度測定に適当な設備を持つた例はほとんどなかった。今回レーヨーチューブ (米国製記録式輻射高温計) を用いて窯串コークスの温度を連続的に測定し記録することができるようになった。そのため窯出コークスの均一加熱を行うための各所の調整および補修に多大の便宜をあたえ窯出コークスめ温度差は減少してきた。レーヨーチューブによる窯出コーク家温度測定と同時に燃焼室の温度を3種類の方法, すなわち洞岡コークスエ場で採用にしている方法, 火落窯の両側のブリユーを測る方法, 広畑製鉄所沼澄夫氏の発表せる方法で測定し, レーヨーチューブによる窯出コークス温度と比較検討を行つたがいずれも大差なく, どの方法を用いてもよいことがわかった。