A Fast and Efficient Method for Controlling the Flowing Temperature of Coal Ash

Abstract

A fast and efficient method for controlling the flow temperature of coal ash. The coal sample is ashed and the chemical composition of the coal ash is analyzed; Gray; where, A represents SiO 2 、Al 2 o 3 with TiO 2 The total mass, B means CaO, Fe 2 o 3 , MgO, K 2 O and Na 2 The total mass of O; when the coal ash is a neutral ash with 0.96≤A / B<6.72, by adjusting (m Al2O3 +m TiO2 ) / B to regulate the coal ash FT; when the coal ash is alkaline ash with A / B<0.96, by regulating (m CaO+ m MgO ) / A regulates the coal ash FT. The method proposed by the present invention not only realizes the goal of rapid and efficient control of coal ash FT, but also can be carried out in various ways such as coal blending, additive addition, and coal blending combined additive addition during the adjustment process.

Description

technical field [0001] The invention belongs to the field of coal gasification, and in particular relates to a method for fast and efficient regulation of coal ash FT. Background technique [0002] Coal gasification is an important method for efficient and clean utilization of coal resources, and it is also the basis for the development of the coal chemical industry. At this stage, entrained-bed gasification technology has become the main development direction because of its high conversion rate and large production volume. But at the same time, due to the limitation of the operating space of the gasification chamber, the time of entrained-bed gasification is generally in the "second level". Therefore, the strength of entrained-bed gasification is compared with that of fixed-bed gasification and fluidized-bed gasification. The strength is several times or even dozens of times higher. In order to effectively make up for the shortcoming of insufficient gasification reaction ...

AI Technical Summary

Problems solved by technology

However, due to the complex composition of coal ash and the large difference in the content of each component, there is generally a nonlinear relationship between coal ash FT and ash components, and this relationship is difficult to be effectively expressed by mathematical formulas.

In addition, the various components of coal ash have different occurrence forms in different coal samples and different crystal state evolution behaviors at high temperatures, which further increases the difficulty of coal ash FT prediction, resulting in the empirical relationship obtained from data regression In actual use, there are limitations of poor prediction accuracy and poor adaptability

In recent years, in order to predict the FT of coal ash more accurately through the composition of coal ash, scholars have tried to establish a predictive relationship between the composition of coal ash and FT through intelligent algorithms represented by artificial neural networks. The shortcomings of black-box operation, poor interpretation of results, and inability to make accurate mathematical descriptions have caused the actual control effect to be unsatisfactory.

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