A data-driven strategy for industrial cracking furnace system scheduling under uncertainty

Abstract

Cyclic scheduling of ethylene cracking furnace system (CSECFS) has a significant impact on raising economic performance of ethylene plants. However, in actual plants, optimal results of deterministic models may be suboptimal or ineffective because of various uncertainties. This paper proposes a novel data-driven adaptive robust optimization (DDARO) strategy that effectively bridges robust optimization and machine learning methods. A mixed-integer nonlinear programming (MINLP) model for CSECFS is developed initially. Second, data-driven uncertainty sets are generated using the historical data of processing flow rates: maximally correlated principal component analysis (MCPCA) is employed to partition the data into two subspaces, which are then delineated by support vector clustering (SVC) and generalized norm approaches, respectively. Third, a multi-stage DDARO model is established using the derived uncertainty sets and afterward transformed as a tractable single-stage model. Finally, a real-world case is performed to exemplify the effectiveness of the proposed framework.