Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks

AICHE Journal - Tập 65 Số 5 - 2019
Pascal M. Schäfer1, Adrian Caspari1, Kerstin Kleinhans1, Adel Mhamdi, Alexander Mitsos1
1AVT - Aachener Verfahrenstechnik, Process Systems Engineering RWTH Aachen University Aachen Germany

Tóm tắt

AbstractThe availability of reduced‐dimensional, accurate dynamic models is crucial for the optimal operation of chemical processes in fast‐changing environments. Herein, we present a reduced modeling approach for rectification columns. The model combines compartmentalization to reduce the number of differential equations with artificial neural networks to express the nonlinear input–output relations within compartments. We apply the model to the optimal control of an air separation unit. We reduce the size of the differential equation system by 90% while limiting the additional error in product purities to below 1 ppm compared to a full‐order stage‐by‐stage model. We demonstrate that the proposed model enables savings in computational times for optimal control problems by ~95% compared to a full order and ~99% to a standard compartment model. The presented model enables a trade‐off between accuracy and computational efficiency, which is superior to what has recently been reported for similar applications using collocation‐based reduction approaches.

Từ khóa


Tài liệu tham khảo

10.1016/j.compchemeng.2018.03.013

10.1016/j.cherd.2016.10.006

10.1016/j.desal.2010.06.041

10.1002/aic.16352

10.1016/j.compchemeng.2018.09.026

10.1021/ie070975t

10.1016/j.compchemeng.2011.09.019

10.1016/j.compchemeng.2014.01.016

10.1002/aic.14730

10.1016/j.compchemeng.2015.09.019

10.1021/acs.iecr.5b03499

10.1016/j.apenergy.2017.12.127

10.1002/aic.15408

10.1016/j.compchemeng.2018.03.009

10.1002/aic.15752

Li H, 2018, Dynamic real‐time optimization of distributed MPC systems using rigorous closed‐loop prediction, Comput Chem Eng., 10.1016/j.compchemeng.2018.08.002

10.1016/j.compchemeng.2014.09.002

10.1016/j.jprocont.2014.03.010

10.1016/j.jprocont.2008.07.006

10.1016/B978-0-444-59506-5.50153-X

10.1016/j.ifacol.2018.11.028

10.1016/B978-0-444-64241-7.50086-0

10.1002/aic.15164

10.1002/aic.690290214

10.1016/0009-2509(85)85103-4

10.1002/aic.690400508

10.1021/acs.iecr.6b02090

10.1002/aic.15753

10.1002/aic.690320703

10.1016/0098-1354(91)85006-G

10.1016/0005-1098(91)90104-A

10.1016/S1383-5866(01)00147-2

10.1016/j.jprocont.2015.05.002

10.1016/S0098-1354(00)00313-6

10.1016/j.compchemeng.2005.06.002

Marquardt W, Dynamics and Control of Chemical Reactors and Distillation Columns, 123

10.1016/S0009-2509(99)00463-7

10.1002/aic.12649

10.1016/j.jprocont.2016.11.004

10.1016/j.seppur.2005.05.001

10.1109/TCST.2009.2029087

10.1016/0098-1354(94)00109-X

10.1016/j.compchemeng.2008.09.014

Linhart A, 2010, Reduced distillation models via stage aggregation, Comput Chem Eng, 65, 3439

10.1016/S0098-1354(98)00281-6

10.1016/S0378-3812(01)00801-9

NentwichC EngellS. Application of surrogate models for the optimization and design of chemical processes. In: Proceedings from the 2016 International Joint Conference on Neural Networks (IJCNN):1291–1296IEEE; 2016.

10.1016/j.compchemeng.2018.10.007

10.1007/978-0-387-45528-0

10.1038/nature14539

Haykin SS, 2009, Neural networks and learning machines

10.1007/s10957-018-1396-0

Srivastava N, 2014, Dropout: a simple way to prevent neural networks from overfitting, J Mac Learn Res, 15, 1929

10.2307/1268522

CaspariA BremenAM FaustJMM et al.DyOS ‐ a framework for optimization of large‐scale differential algebraic equation systems. Comput Aided Chem Eng (corresponding to the proceedings of the 29th european symposium on computer aided process engineering).2018.

10.1007/BFb0006520

10.1007/978-3-642-82450-0_9

10.1137/S0036144504446096

10.1016/j.apnum.2003.07.001

10.1016/j.procs.2010.04.033

10.1002/aic.690330804

10.1016/j.jprocont.2009.02.001

10.1016/j.automatica.2008.06.011

10.1016/j.jprocont.2013.06.011