A Causal Generative Model-Based Optimal Scheduling Method for Blast Furnace Gas System Considering Unknown Scenarios

Blast furnace gas is a significant category of byproduct energy source produced during the ironmaking process, and its rational utilization is crucial to improving energy efficiency in steel plants. However, frequent operator interventions continually create new operating scenarios, which makes it difficult for traditional methods to maintain effective scheduling. Existing scheduling methods based on optimization and generative adversarial networks (GANs) rely excessively on historical scenarios and fail to capture the explicit causal relationships among the key factors, thus restricting their applicability for scheduling under unknown conditions. To tackle such an issue, an optimal scheduling method for BFG system based on an improved causal generative model, which is capable of generating diverse and physically consistent scenarios, is proposed in this study. Each scenario is characterized by three interpretable factors, i.e. gas tank level, generation-consumption flow difference, and consumption of adjustable units. A causal conditional Wasserstein GAN (Causal-CWGAN) is then constructed by embedding a process-informed adjacency matrix and a differentiable acyclicity constraint into the WGAN-GP framework. In addition, a correction model combined with mechanism-based rationality rules is adopted to further optimize the consumption and filters unreasonable scenarios. Subsequently, the tank-level prediction is performed to update the generated scenario set and derive practical adjustment suggestions. Experimental results on real data from a steel enterprise show that, compared with the WGAN and the MGAN methods, the proposed one yields smaller Wasserstein distances, generates more rational scenarios, and provides adjustment strategies that can stably keep the gas tank level within the safety operating range.