Online model-based optimization and control for the combined optimal operation and runaway prediction and prevention in (fed-)batch systems

Abstract An advanced model-based strategy for the online optimization and/or optimal control of (fed-)batch systems, the BSMBO&C, is applied to discontinuous equipment that can be subject to runaway phenomena. The aim is that of showing that it is simultaneously possible to provide profitable online optimization and/or optimal control policies, predict possible future hazardous situations in advance and automatically decide whether and when to (optimally) stop a production cycle in order to prevent safety risks (unavoidable runaways). All these actions are performed online, thus allowing for the effect of any external perturbation. A fed-batch process based on a well-known example of runaway reaction, i.e. the oxidation of 2-octanol to 2-octanone with nitric acid in aqueous solution, is selected as a benchmark to test the real attainability of simultaneous process profitability and safety.

[1]  Eugeniusz Molga,et al.  No more runaways in fine chemical reactors , 2004 .

[2]  Wolfgang Marquardt,et al.  Rigorous solution vs. fast update: Acceptable computational delay in NMPC , 2011, IEEE Conference on Decision and Control and European Control Conference.

[3]  Flavio Manenti,et al.  Model Predictive Control of a Cvd Reactor for Production of Polysilicon Rods , 2010 .

[4]  R. Nomen,et al.  Implementation of multi-Kalman filter to detect runaway situations and recover control , 2005 .

[5]  Flavio Manenti,et al.  A Novel All-in-One Real-Time Optimization and Optimal Control Method for Batch Systems: Algorithm Description, Implementation Issues, and Comparison with the Existing Methodologies , 2014 .

[6]  Venkat Venkatasubramanian,et al.  Systemic failures: Challenges and opportunities in risk management in complex systems , 2011 .

[7]  Jose M. Pinto,et al.  Optimal control of product quality for batch nylon-6,6 autoclaves , 2004 .

[8]  Dominique Bonvin,et al.  Controllability and stability of repetitive batch processes , 2007 .

[9]  Klaas R. Westerterp,et al.  Runaway behavior and thermally safe operation of multiple liquid–liquid reactions in the semi-batch reactor: The nitric acid oxidation of 2-octanol , 2002 .

[10]  Jan Van Impe,et al.  Robust optimal control of a biochemical reactor with multiple objectives , 2011 .

[11]  Jan Van Impe,et al.  Multi-Objective and Robust Optimal Control of a CVD Reactor for Polysilicon Production , 2014 .

[12]  Flavio Manenti,et al.  Defeating the Sustainability Challenge in Batch Processes through Low-Cost Utilities Usage Reduction , 2014 .

[13]  Victor M. Zavala,et al.  Fast implementations and rigorous models: Can both be accommodated in NMPC? , 2008 .

[14]  L. Gigante,et al.  Simple Procedure for Optimally Scaling-up Fine Chemical Processes. I. Practical Tools , 2009 .

[15]  Flavio Manenti,et al.  BzzMath: Library Overview and Recent Advances in Numerical Methods , 2012 .

[16]  Maria Francesca Milazzo,et al.  Comparison of criteria for prediction of runaway reactions in the sulphuric acid catalyzed esterification of acetic anhydride and methanol , 2012 .

[17]  Ergys Pahija,et al.  Assessment of control techniques for the dynamic optimization of (semi-)batch reactors , 2014, Comput. Chem. Eng..

[18]  Don W. Green,et al.  Perry's Chemical Engineers' Handbook , 2007 .

[19]  Ferenc Szeifert,et al.  Detection of Safe Operating Regions: A Novel Dynamic Process Simulator Based Predictive Alarm Management Approach , 2010 .

[20]  L. Gigante,et al.  Simple Procedure for Optimal Scale-up of Fine Chemical Processes. II. Nitration of 4-Chlorobenzotrifluoride , 2009 .

[21]  Giuseppe Maschio,et al.  A general criterion to define runaway limits in chemical reactors , 2003 .

[22]  Marcelo Embiruçu,et al.  Novel two-steps optimal control of batch polymerization reactors and application to PMMA production for the fabrication of artificial bone tissue , 2013 .

[23]  Victor M. Zavala,et al.  The advanced-step NMPC controller: Optimality, stability and robustness , 2009, Autom..

[24]  Venkat Venkatasubramanian,et al.  Fault diagnosis of a benchmark fermentation process: a comparative study of feature extraction and classification techniques , 2012, Bioprocess and Biosystems Engineering.

[25]  Marco Derudi,et al.  Safe operating conditions for semibatch processes involving consecutive reactions with autocatalytic behavior , 2010 .

[26]  Moritz Diehl,et al.  Robust NMPC for a Benchmark Fed-Batch Reactor with Runaway Conditions , 2007 .

[27]  Filip Logist,et al.  Tuning of NMPC controllers via multi-objective optimisation , 2014, Comput. Chem. Eng..

[28]  Iqbal M. Mujtaba,et al.  Neural-network approach to dynamic optimization of batch distillation: Application to a middle-vessel column , 2003 .

[29]  John W. Eaton,et al.  Model Predictive Control of Chemical Processes , 1991 .

[30]  Marco Derudi,et al.  On the divergence criterion for runaway detection: Application to complex controlled systems , 2014 .

[31]  Z. Nagy,et al.  Robust nonlinear model predictive control of batch processes , 2003 .

[32]  Eugeniusz Molga,et al.  CFD modelling and divergence criterion for safety of chemical reactors , 2005 .

[33]  Eugeniusz Molga,et al.  Runaway prevention in liquid-phase homogeneous semibatch reactors , 2007 .

[34]  Alain Vande Wouwer,et al.  Nonlinear model predictive control of fed-batch cultures of micro-organisms exhibiting overflow metabolism: Assessment and robustness , 2012, Comput. Chem. Eng..

[35]  Marco Derudi,et al.  Classification and optimization of potentially runaway processes using topology tools , 2013, Comput. Chem. Eng..

[36]  Giulia Bozzano,et al.  A Novel Real-Time Methodology for the Simultaneous Dynamic Optimization and Optimal Control of Batch Processes , 2014 .