A Scheduling Method for Blast Furnace Gas Systems Based on Conditional Generative Adversarial Networks and Process Rules

Blast furnace gas (BFG), a vital byproduct energy resource in the ironmaking process, can be efficiently managed through proper scheduling to minimize emissions and augment productivity. A novel scheduling method for BFG systems based on an improved Conditional Generative Adversarial Network (CGAN) and process rules is proposed. Based on the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and the process production mechanism under ideal conditions, a scenario generative model is constructed to constrain the training process with the rules of the changes among scenario factors. Labels are used as condition information for CGAN to generate the corresponding labeled scenarios. Then, by matching real-time operational data with the generated scenarios, the scheduling solution is derived for the given condition. Using the actual data collected from a large steel enterprise, the validation experiments confirm that the scheduling scenarios generated by the proposed method are consistent with actual operational patterns. Moreover, it delivers viable and rational scheduling solutions for new operating conditions, safeguarding the safe operation of the BFG system.