Abstract

Natural gas serves as the primary energy source for heating slabs in the Pusher Furnace prior to their rolling into steel plates. The required quantity of natural gas for a 24-hour operational period must be estimated in advance. This study employs multifactor regression analysis to develop a straightforward yet effective predictive equation for natural gas consumption, incorporating key variables such as projected production, the number of slabs to be heated, effective operating time, and the average dimensions of the slabs over a 24-hour workday. Extensive data collected over an extended period formed the matrix for determining the regression coefficients. The resulting model demonstrates a high correlation coefficient (R² = 0.99), underscoring the accuracy of the regression equation. This indicates that the model reliably predicts natural gas consumption with minimal deviation from actual usage during a 24-hour workday.