Abstract
Coke is an indispensable and vital flue for blast furnace smelting, during which it plays a key role as a reducing agent, heat source, and support skeleton. Models of prediction of coke quality based on ANN are established to map the functional relationship between quality parameters Mt, Ad, Vdaf, St,d, and caking property (X, Y, and G) of mixed coal and quality parameters Ad, St,d, coke reactivity index (CRI), and coke strength after reaction (CSR) of coke. A regularized network training method based on Sigmoid function is designed considering that redundancy of network structure may lead to the learning of undesired noise, in which weights having little impact on performance and leading to overfitting are removed in terms of computational complexity and training errors. The cascade forward neural network with validation is found to be the most suitable one for coke quality prediction, with errors around 5%, followed by feedforward neural network structure and radial basis neural networks. The cascade forward neural network may play a guiding role during the coke production.
Highlights
With the growing trend of large-scale blast furnace, smelting effect of blast furnace and its economic and technical indicators are more deeply influenced by the quality and performance of coke [1]
Using the thought of coal blend with petrography, Tao Peisheng guided the coke quality prediction and determination of the coal blending ratio based on coal-rock phase composition and reflectance pattern of single coal while taking into account coal maceral and coke strength, etc
800 sets of mixed coal quality parameters and their corresponding coke quality indexes are randomly selected from the 1000 sets of preprocessed data to train Feedforward Backpropagation (FB), cascade forward backpropagation (CF), and Radial Basis Function (RBF) networks, respectively
Summary
With the growing trend of large-scale blast furnace, smelting effect of blast furnace and its economic and technical indicators are more deeply influenced by the quality and performance of coke [1]. It is of significance to study the relationship between physical and chemical properties of mixed coal and that of coke in controlling the coke quality. The above studies enrich the theory of coke quality prediction models, rare deep data rule mining is adopted for relevant data [8]. E statistical law of abundant coking testing data indicates the extremely strong nonlinear relationship between the physical and chemical properties of coke and that of mixed coal [10]. The adoption of neural network technology can decrease time consumption and reduce economic costs through rule mining of the experimental data and rationally predicting the physical and chemical properties of coke from mixed coal. Related study [15] shows that 60%-70% sulfur content from coal is transferred into coke, the inorganic sulfur in coal is transferred into coke as sulfocompounds, and the remaining part stays in ash in the form of sulfate and sulfide. e coke rate of mixed coal is 70%-80% usually, while the sulfur content in coke is 80%-90% of that in coal [16]. us, the sulfur content of the mixed coal should be constrained in the range of 0.6%-0.7%
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