Stability criterion for the intensification of batch processes with model predictive control

Abstract Thermal runaways in batch processes can lead to significant issues for safety and performance during normal operation in industry. This is usually circumvented by running such processes at lower temperatures than necessary, hence losing the opportunity to intensify production and therefore reduce reaction time. The detection of the thermal stability of batch systems can potentially be embedded in an advanced control scheme, therefore improving the performance by being able to intensify the process, achieving higher yields while keeping a stable operation. The derivation of stability criterion K for high-order reactions is presented in this work, resulting in better control when embedded in Model Predictive Control (MPC) schemes than standard nonlinear MPC schemes, based on the work in Kahm and Vassiliadis (2018) . The non-trivial extension of stability criterion K for multi-component reactions with application to MPC systems is discussed in detail. The logic and verification of the form of the resultant Damkohler number in particular is discussed and demonstrated with case studies. A comparison of various MPC schemes is presented, showcasing that the implementation using criterion K results in intensified processes kept stable at all times, whilst reducing computational cost with regard to standard nonlinear MPC schemes. Furthermore, reaction times are reduced by at least twofold with respect to processes run at constant temperatures.

[1]  Mohd Azlan Hussain,et al.  Control of polystyrene batch reactors using neural network based model predictive control (NNMPC): An experimental investigation , 2011 .

[2]  Flavio Manenti,et al.  Online model-based optimization and control for the combined optimal operation and runaway prediction and prevention in (fed-)batch systems , 2015 .

[3]  Amyn S. Teja,et al.  Simple method for the calculation of heat capacities of liquid mixtures , 1983 .

[4]  E. Klemm,et al.  Octanol ethoxylation in microchannels , 2013 .

[5]  Fernanda Strozzi,et al.  On-Line Runaway Detection in Batch Reactors Using Chaos Theory Techniques. , 1999 .

[6]  Joseph P. Zbilut,et al.  On-line runaway detection in isoperibolic batch and semibatch reactors using the divergence criterion , 2004, Comput. Chem. Eng..

[7]  E. Obermeier,et al.  Thermal, Conductivity, Density, Viscosity, and Prandtl-Numbers of Ethylene Glycol-Water Mixtures , 1984 .

[8]  Tor Arne Johansen,et al.  Efficient Implementation of Step Response Prediction Models for Embedded Model Predictive Control , 2015 .

[9]  L. Biegler,et al.  Robust stability of economically oriented infinite horizon NMPC that include cyclic processes , 2012 .

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

[11]  Lawrence F. Shampine,et al.  Solving Index-1 DAEs in MATLAB and Simulink , 1999, SIAM Rev..

[12]  D. Mayne,et al.  Receding horizon control of nonlinear systems , 1990 .

[13]  David Q. Mayne,et al.  Model predictive control: Recent developments and future promise , 2014, Autom..

[14]  Helen Durand,et al.  A tutorial review of economic model predictive control methods , 2014 .

[15]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[16]  Kerry J. Howe,et al.  MWH's Water Treatment: Principles and Design , 2012 .

[17]  A. Hurwitz.  Ueber die Bedingungen, unter welchen eine Gleichung nur Wurzeln mit negativen reellen Theilen besitzt , 1895 .

[18]  Vassilios S. Vassiliadis,et al.  Thermal stability criterion integrated in model predictive control for batch reactors , 2018, Chemical Engineering Science.

[19]  E. J. Routh.  A Treatise on the Stability of a Given State of Motion: Particularly Steady Motion , 2010 .

[20]  V. Arnold,et al.  Ordinary Differential Equations , 1973 .

[21]  Zoltan K. Nagy,et al.  Nonlinear model predictive control of an industrial batch reactor subject to swelling constraint , 2008 .

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

[23]  Panagiotis D. Christofides,et al.  Networked and Distributed Predictive Control , 2011 .

[24]  N. N. Semenov,et al.  Thermal Theory of Combustion and Explosion , 1942 .

[25]  Andreas Potschka,et al.  Partial stability for nonlinear model predictive control , 2017, Autom..

[26]  Fernanda Strozzi,et al.  A general method for assessing the thermal stability of batch chemical reactors by sensitivity calculation based on Lyapunov exponents , 1994 .

[27]  Ming Yuchi,et al.  Online parameter estimation for MPC model uncertainties based on LSCR approach , 2017, 2017 IEEE Conference on Control Technology and Applications (CCTA).

[28]  Charles A. Desoer,et al.  An elementary proof of the Routh-Hurwitz stability criterion , 1990 .

[29]  Vassilis M. Charitopoulos,et al.  Explicit model predictive control of hybrid systems and multiparametric mixed integer polynomial programming , 2016 .

[30]  C. F. Curtiss,et al.  Molecular Theory Of Gases And Liquids , 1954 .

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

[32]  Mark E. Davis,et al.  Fundamentals of Chemical Reaction Engineering , 2002 .