Jiří Jaromír Klemeš, Petar Sabev Varbanov and Peng Yen Liew (Editors) Proceedings of the 24 th European Symposium on Computer Aided Process Engineering – ESCAPE 24 June 15-18, 2014, Budapest, Hungary.© 2014 Elsevier B.V. All rights reserved. A Novel Real-Time Methodology for the Simultaneous Dynamic Optimization and Optimal Control of Batch Processes Francesco Rossi, a Flavio Manenti, a, * Iqbal M. Mujtaba, b Giulia Bozzano a a Politecnico di Milano, Dipartimento di Chimica, Materiali e Ingegneria Chimica “Giulio Natta”, Piazza Leonardo da Vinci 32, 20133 Milano, Italy b School of Engineering & Informatics, University of Bradford, Bradford BD7 1DP, UK flavio.manenti@polimi.it Abstract A novel threefold optimization algorithm is proposed to simultaneously solve the nonlinear model predictive control and dynamic real-time optimization for batch processes while optimizing the batch operation time. Object-oriented programming and parallel computing are exploited to make the algorithm effective to handle industrial cases. A well-known literature case is selected to validate the algorithm. Keywords: dynamic optimization; batch process control; nonlinear model predictive control; optimal batch time operation. 1. Introduction Discontinuous and multi-stage processes are often (and still) managed by means of traditional and heuristic recipes, conventional controls and/or manual operations. This is mainly due to their batch nature that requires frequent manual interventions, e.g., to switch from on (operating) to off conditions, to enable/disable cooling and heating operations or loading and unloading procedures. Moreover, the control methodology adopted is often only partially effective to handle the setpoint changes dictated by the recipes of batch productions and the uncertainties typical of semi-batch operations. For these reasons, many authors focused on batch processes to find efficient solutions to make them more automatic and better controlled with the aim of improving safety and optimizing discontinuous operations: Balasubramhanya and Doyle (1997) developed a MPC algorithm for the optimal control of distillation columns where the column model was based on the wave theory, Mahadevan et al. (2001) studied a MPC procedure using the differential flatness, Abel and Marquardt (2003) worked on a scenario integrated MPC for batch reactors with the aim of avoiding the loss of control even in failure circumstances, Vallerio et al. (2014) defined the tuning rules, Logist et al. (2012) proposed some dedicated tools, and, finally, Joly and Pinto (2004) made a comparison between the efficiency of sequential and simultaneous methodologies for the dynamic optimization of discontinuous processes. Several other authors have shown the potential for applying the dynamic optimization to batch systems using either neural networks (Greaves et al., 2003) or standard simultaneous procedures (Zavala et al., 2005) or novel adaptive shooting techniques (Vite-Martínez et al., 2014). Also the importance of selecting the most appropriate control methodology to be used in dynamic optimization of discontinuous processes has been recently broached (Pahija et al., 2013b) to make it more appealing. The real problem is that the implementation of dynamic optimization has several similarities to the traditional recipe. Actually, although optimized, the