A novel data-driven adaptive robust optimization framework that leverages big data in process industries is proposed. A Bayesian nonparametric model-the Dirichlet process mixture model-is adopted and combined with a variational inference algorithm to extract the information embedded within uncertainty data. Further a data-driven approach for defining uncertainty set is proposed. This machine-learning model is seamlessly integrated with adaptive robust optimization approach through a novel four-level optimization framework. This framework explicitly accounts for the correlation, asymmetry and multimode of uncertainty data, so it generates less conservative solutions. Additionally, the proposed framework is robust not only to parameter variations, but also to anomalous measurements. Because the resulting multi-level optimization problem cannot be solved directly by any off-the-shelf solvers, an efficient column-and-constraint generation algorithm is proposed to address the computational challenge. Two industrial applications on batch process scheduling and on process network planning are presented to demonstrate the advantages of the proposed modeling framework and effectiveness of the solution algorithm. (C) 2017 American Institute of Chemical Engineers