Alexander, R., Campani, G., Dinh, S., and Lima, F. (2020). Challenges and opportunities on nonlinear state estimation of chemical and biochemical processes. Processes, 8, 1462. Ali, J.M., Hoang, N.H., Hussain, M.A., and Dochain, D. (2015). Review and classification of recent observers applied in chemical process systems. Computers & Chemical Engineering, 76, 27–41. Dochain, D. (2003). State and parameter estimation in chemical and biochemical processes: a tutorial. Journal of Process Control, 13, 801–818. Kurtz, M.J. and Henson, M.A. (1998). State and dis- turbance estimation for nonlinear systems affine in the unmeasured variables. Computers & Chemical Engi- neering, 22, 1441–1459. Lin, Y. and Sontag, E.D. (1991). A universal formula for stabilization with bounded controls. Systems & Control Letters, 16, 393–397. McKenna, T., Othman, S., Fevotte, G., Santos, A., and Hammouri, H. (2000). An integrated approach to polymer reaction engineering: a review of calorimetry and state estimation. Polymer Reaction Engineering, 8, 1–38. Mesbah, A., Huesman, A.E., Kramer, H.J., and Van den Hof, P.M. (2011). A comparison of nonlinear observers for output feedback model-based control of seeded batch crystallization processes. Journal of Process Control, 21, 652–666. Patwardhan, S.C., Narasimhan, S., Jagadeesan, P., Gopaluni, B., and Shah, S.L. (2012). Nonlinear bayesian state estimation: A review of recent developments. Con- trol Engineering Practice, 20, 933–953. Porru, G., Aragonese, C., Baratti, R., and Servida, A. (2000). Monitoring of a co oxidation reactor through a grey model-based EKF observer. Chemical Engineering Science, 55, 331–338. Radke, A. and Gao, Z. (2006). A survey of state and distur- bance observers for practitioners. In Proceedings of the American Control Conference, 5183–5188. Minneapolis, Minnesota. Wächter, A. and Biegler, L.T. (2006). On the implemen- tation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming, 106, 25–57. Wu, Z., Rincon, D., Luo, J., and Christofides, P.D. (2020a). Machine learning modeling and predictive control of nonlinear processes using noisy data. AIChE Journal, 67, e17164. Wu, Z., Tran, A., Rincon, D., and Christofides, P.D. (2019a). Machine learning-based predictive control of nonlinear processes. part I: Theory. AIChE Journal, 65, e16729. Wu, Z., Tran, A., Rincon, D., and Christofides, P.D. (2019b). Machine learning-based predictive control of nonlinear processes. part II: Computational implemen- tation. AIChE Journal, 65, e16734. Wu, Z., Rincon, D., and Christofides, P.D. (2020b). Pro- cess structure-based recurrent neural network model- ing for model predictive control of nonlinear processes. Journal of Process Control, 89, 74–84. Zambare, N., Soroush, M., and Grady, M.C. (2002). Real- time multirate state estimation in a pilot-scale polymer- ization reactor. AIChE journal, 48, 1022–1033. Zeitz, M. (1987). The extended luenberger observer for nonlinear systems. Systems & Control Letters, 9, 149– 156. Zhang, Z., Wu, Z., Rincon, D., and Christofides, P.D. (2019). Real-time optimization and control of nonlinear processes using machine learning. Mathematics, 7, 890.