REFERENCES Georgakis, C, S.-T. Chin, Z Wang, P. Hayot, L.H. Chiang, J. Wassick, and I Castillo. 2020. 'Data-Driven Optimization of an Industrial Batch Polymerization Process Using the Design of Dynamic Experiments Methodology', Ind. & Eng. Chem. Res., 59: 14868-80. Georgakis, C. 2013. 'Design of Dynamic Experiments: A Data-Driven Methodology for the Optimization of Time-Varying Processes', Ind. & Eng. Chem. Res., 52: 12369-82. Georgakis, C., S.-T. Chin, P. Hayot, J. Wassick, and L.H. Chiang. 2016. "Optimizing an Industrial Batch Process using the Design of Dynamic Experiments Methodology." In Spring AIChE Meeting. Houston: AIChE. Klebanov, N., and C. Georgakis. 2016. 'Dynamic Response Surface Models: A Data-Driven Approach for the Analysis of Time-Varying Process Outputs', Industrial & Engineering Chemistry Research, 55: 4022-34. Nie, Y. S., L. T. Biegler, C. M. Villa, and J. M. Wassick. 2013. 'Reactor modeling and recipe optimization of polyether polyol processes: Polypropylene glycol', Aiche Journal, 59: 2515-29. Valiant, Leslie. 2013. Probably approximately correct : nature's algorithms for learning and prospering in a complex world (Basic Books: New York). Wang, Z. Y., and C. Georgakis. 2017. 'New Dynamic Response Surface Methodology for Modeling Nonlinear Processes over Semi-infinite Time Horizons', Industrial & Engineering Chemistry Research, 56: 10770-82.