Data-driven rolling-horizon robust optimization for petrochemical scheduling using probability density contours. (12th July 2018)

• Record Type: Journal Article
• Authors: Zhang, Yi; Feng, Yiping; Rong, Gang
• Is Part Of: Computers & chemical engineering. Volume 115(2018)
• Page Start: 342
• Page End: 360
• Publication Date: 2018-07-12
• Subjects: Robust optimization -- Rolling-horizon -- Data-driven -- Probability density contour -- Petrochemical scheduling; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2018.04.013
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A novel uncertainty set is defined by probability density contours. Outer-approximations are constructed to reformulate the nonconvex robust model. A rolling-horizon scheduling strategy is proposed for robust scheduling. In a real-world case, the fluctuation in the fuel gas consumption is controlled within 2%. The stability and bottlenecks of the rolling-scheduling strategy are proved. 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.