Prediction of the outlet temperature of the converter dry-type dust removal evaporative cooler based on LAOA-SCN

Abstract

In the converter dry-type dust removal system, controlling the outlet temperature directly impacts the efficiency of flue gas treatment. To ensure high-precision control of the outlet temperature, this study utilized improved Arithmetic Optimization Algorithm for Optimizing Stochastic Configuration Networks. This resulted in the establishment of the outlet temperature prediction model, LAOA-SCN, for the converter dry-type dust removal evaporative cooler. To assess the predictive performance of model, a comparative analysis was conducted with algorithms such as Back Propagation (BP), Radial Basis Function (RBF), and Twin Support Vector Regression (TSVR). Finally, the model was applied to practical production verification, confirming its high prediction accuracy. This underscores its potential to provide theoretical guidance for the control of outlet temperature in converter dry-type dust removal evaporative coolers.

Keywords

• steel
• converter
• outlet temperature
• dust removal
• LAOA-SCN