Data-driven rolling-horizon robust optimization for petrochemical scheduling using probability density contours

Abstract In the process industry, uncertain factors, such as yield, can be quantified by analyzing industrial data generated from continuous sources. Traditional data-driven robust optimization models are mostly built on estimated probability distributions and convex uncertainty sets. As a result, the scheduling solution is only applicable to the limited sample of stochastic scenarios. We developed a rolling-horizon optimization approach to adapt the robust model to the changing environmental and operational conditions. First, a novel uncertainty set is defined by the probability density contours, covering scenarios with high possibility of occurrence. Then, we propose using new robust formulations induced by the outer-approximations of nonconvex uncertainty set. By implementing the raised model on a real-world ethylene production process using the available data, the fluctuation in fuel gas consumption can be controlled within 2%. Additionally, in agreement with our proof, the system’s total profit and consumption of fuel gas stabilize in finite steps.