Application of Feed Forward Neural Networks in Modeling and Control of a Fed-Batch Crystallization Process

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Neural Networks in Modeling and Control of a Fed-Batch Crystallization Process</title> <meta name="description" content="Application of Feed Forward Neural Networks in Modeling and Control of a Fed-Batch Crystallization Process"> <meta name="keywords" content="Feed forward neural network, process modelling, model predictive control, crystallization process."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Application of Feed Forward Neural Networks in Modeling and Control of a Fed-Batch Crystallization Process"> <meta name="citation_author" content="Petia Georgieva"> <meta name="citation_author" content="Sebasti茫o Feyo de Azevedo"> <meta name="citation_publication_date" content="2007/12/21"> <meta name="citation_journal_title" content="International Journal of Chemical and Molecular Engineering"> <meta name="citation_volume" content="1"> <meta 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class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Petia%20Georgieva">Petia Georgieva</a>, <a href="https://publications.waset.org/search?q=Sebasti%C3%A3o%20Feyo%20de%20Azevedo"> Sebasti茫o Feyo de Azevedo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This paper is focused on issues of nonlinear dynamic process modeling and model-based predictive control of a fed-batch sugar crystallization process applying the concept of artificial neural networks as computational tools. The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, considered as the control input. A feed forward neural network (FFNN) model of the process is first built as part of the controller structure to predict the process response over a specified (prediction) horizon. The predictions are supplied to an optimization procedure to determine the values of the control action over a specified (control) horizon that minimizes a predefined performance index. The control task is rather challenging due to the strong nonlinearity of the process dynamics and variations in the crystallization kinetics. However, the simulation results demonstrated smooth behavior of the control actions and satisfactory reference tracking.</p> <iframe src="https://publications.waset.org/11491.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Feed%20forward%20neural%20network" title="Feed forward neural network">Feed forward neural network</a>, <a href="https://publications.waset.org/search?q=process%20modelling" title=" process modelling"> process modelling</a>, <a href="https://publications.waset.org/search?q=model%20predictive%20control" title=" model predictive control"> model predictive control</a>, <a href="https://publications.waset.org/search?q=crystallization%20process." title=" crystallization process."> crystallization process.</a> </p> <p class="card-text"><strong>Digital Object Identifier (DOI):</strong> <a href="https://doi.org/10.5281/zenodo.1077387" target="_blank">doi.org/10.5281/zenodo.1077387</a> </p> <a href="https://publications.waset.org/11491/application-of-feed-forward-neural-networks-in-modeling-and-control-of-a-fed-batch-crystallization-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/11491/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/11491/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/11491/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/11491/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/11491/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/11491/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/11491/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/11491/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/11491/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/11491/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/11491.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1875</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] J. B. Rawlings, S. M. Miller, and W. R. Witkowski., "Model identification and control of solution crystallization processes: a review", Ind. Eng. Chem. Res., 32(7):1275-1296, July 1993. <br>[2] Nagy Z. K. and R. D. Braatz,. Robust nonlinear model predictive control of batch processes. AIChE J., 49:1776-1786, 2003. <br>[3] Fujiwara, M. Z. K. Nagy, J. W. Chew, and R. D. Braatz First-principles and direct design approaches for the control of pharmaceutical crystallization. J. of Process Control, 15:493-504, 2005 (invited). <br>[4] Feyo de Azevedo, S., Chor鈹溍簅, J., Gon鈹溌篴lves, M.J. and Bento, L., 1994, On-line Monitoring of White Sugar Crystallization through Software Sensors. Int. Sugar JNL., 96:18-26. <br>[5] S. Haykin S. (1994), Neural Networks: A comprehensive foundation, Prentice Hall, UK. <br>[6] Russell N.T. and H.H.C. Bakker (1997), Modural modelling of an evaporator for long-range prediction, Artificial Intelligence in Engineering, 11, 347-355. <br>[7] Zorzetto L.F.M., R. Maciel Filho and M.R. Wolf-Maciel (2000), Process modelling development through artificial neural networks and hybrid models, Computers and Chemical Engineering, 24, 1355-1360. <br>[8] Georgieva P., Meireles, M. J. and Feyo de Azevedo, S., 2003, KBHM of fed-batch sugar crystallization when accounting for nucleation, growth and agglomeration phenomena. Chem Eng Sci, 58:3699-3711. <br>[9] Ditl, P., Beranek L., & Rieger, Z. (1990). Simulation of a stirred sugar boiling pan. Zuckerind., 115, 667-676. <br>[10] A.D. Randolph, M.A. Larson, Theory of Particulate Processes - Analyses and Techniques of Continuous Crystallization, Academic Pres, 1988. <br>[11] P. Lauret, H. Boyer, J.C. Gatina, Hybrid modelling of a sugar boiling process, Control Engineering Practice, 8, 2000, 299-310. <br>[12] Hagan M. T., M. Menhaj (1994), "Training feedforward networks with the Marquardt algorithm," IEEE Transactions on Neural Networks, vol. 5, no.6, pp. 989-993. <br>[13] D.C. Psichogios, L. H. Ungar, A hybrid neural network - first principles approach to process modelling, AIChE J., 38(10), 1992, 1499-1511. <br>[14] Graefe, J., Bogaerts, P., Castillo, J., Cherlet, M., Werenne, J., Marenbach, P., & Hanus, R. (1999). A new training method for hybrid models of bioprocesses. Bioprocess Engineering, 21, 423-429. <br>[15] V. Galvanauskas, P. Geogieva, S. Feyo de Azevedo, Dynamic optimization of industrial sugar crystallization process based on a hybrid (mechanistic +ANN) model, submitted for publication to IJCNN, Vancouver, Canada, June 2006. </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>

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Application of Feed Forward Neural Networks in Modeling and Control of a Fed-Batch Crystallization Process