Abstract
The endpoint carbon content is an important target of converters. The precise prediction of carbon content is the key to endpoint control in converter steelmaking. In this study, a real-time dynamic prediction of the carbon content model for the second-blowing stage of the converter steelmaking process was proposed. First, a case-based reasoning (CBR) algorithm was used to retrieve similar historical cases and their corresponding process parameters in the second blowing stage, based on the process parameters of the new case in the main blowing stage. Next, a long short-term memory (LSTM) model was trained by using process parameters of similar cases from the previous moment as the input and the carbon content for the next moment as the output. Finally, the process parameters of the new case were input into the trained LSTM model to produce a real-time dynamic prediction of the carbon content in the second blowing stage. Actual production data were used for the verification, and the results showed that the prediction errors of the proposed model within the ranges of (−0.005, 0.005), (−0.010, 0.010), (−0.015, 0.015) and (−0.020, 0.020) were 25%, 54%, 71%, and 91% respectively, which were higher than the prediction accuracies of the traditional carbon integral model, cubic model, and exponential model.
Highlights
Converter steelmaking is a very complex process, involving physical and chemical changes at high temperatures
During the converter steelmaking process, the decarburisation rate is slow at the beginning and end and fast in the middle
Based on the principle of mass balance, the carbon integral model first calculates the initial carbon amount of the molten steel according to the composition of steelmaking raw materials and subtracts the amount of carbon overflowing from the off-gas in the form of CO and CO2 ; the remaining part is the amount of carbon in the molten steel in the molten pool
Summary
Converter steelmaking is a very complex process, involving physical and chemical changes at high temperatures. Prediction models for the endpoint control of converter steelmaking can be divided into static and dynamic approaches. The former can be further divided into theoretical and data-driven models. Wang et al established a data-driven real-time prediction model for the endpoint carbon content [21]. Liu et al proposed an algorithm based on off-gas analysis for dynamically calculating the carbon content of the converter bath at the second blowing stage [24]. CBR and long short-term memory (LSTM) were used to establish a model for the real-time dynamic prediction of the carbon content in the second blowing stage. The proposed model should offer improved endpoint control of the converter steelmaking process
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