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Design and Implementation of Intelligent Integrated Measuring and Controlling System for Sugar Cane Crystallization Process

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Y. Meng, J. Chen, G. Lu, et al. “Detecting syrup brix in cane sugar crystallization using an improved least squares support vector machine”, International Journal of Control and Automation, Vol. 8, 2015, pp. 171-184.10.14257/ijca.2015.8.3.19 Search in Google Scholar

Y. Meng, X. Yu, H. He, et al, “Knowledge-based modeling for predicting cane sugar crystallization state”, International Journal on Smart Sensing and Intelligent Systems, Vol. 7, 2014, pp. 942-965.10.21307/ijssis-2017-689 Search in Google Scholar

ZK. Nagy, G. Fevotte, H. Kramer, et al, “Recent advances in the monitoring, modelling and control of crystallization systems”, Chemical Engineering Research and Design, Vol. 91, 2013, pp. 1903-1922.10.1016/j.cherd.2013.07.018 Search in Google Scholar

T. Diringer, BC. Nielsen, “Importance of in-line colour measurement of sugar for product quality and factory performance”, International Sugar Journal, Vol. 34, 2012, pp. 1-9. Search in Google Scholar

W. Paengjuntuek, A. Arpornwichanop, P. Kittisupakorn, “Product quality improvement of batch crystallizer by a batch-to-batch optimization and nonlinear control approach”, Chemical Engineering Journal, Vol. 139, 2008, pp. 344-350.10.1016/j.cej.2007.08.010 Search in Google Scholar

J Yu, “A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses”, Computers and Chemical Engineering, Vol. 41, 2012, pp. 134-144.10.1016/j.compchemeng.2012.03.004 Search in Google Scholar

P. Kadlec, B. Gabrys, S. Strandt, “Data driven soft sensor in the process industry”, Computers and Chemical Engineering, Vol. 33, 2009, pp. 795-814.10.1016/j.compchemeng.2008.12.012 Search in Google Scholar

M. Kano, M. Ogawa, “The state of the art in chemical process control in Japan: Good practice and questionnaire survey”, Journal of Process Control, Vol. 9, 2010, pp. 969-982.10.1016/j.jprocont.2010.06.013 Search in Google Scholar

M. Kano, Y. Nakagawa, “Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry”, Computers and Chemical Engineering, Vol. 32, 2008, pp. 12-24.10.1016/j.compchemeng.2007.07.005 Search in Google Scholar

R. Grbic, D. Sliskovic, P. Kadlec, “Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models”, Computers and Chemical Engineering, Vol. 58, 2013, pp. 84-97.10.1016/j.compchemeng.2013.06.014 Search in Google Scholar

P. Georgieva, SF. de Azevedo, “Application of artificial neural networks in modeling and optimization of batch crystallization processes”, Electronics and Telecommunications, Vol. 4, 2006, pp. 697-706. Search in Google Scholar

P. Georgieva, SF. de Azevedo, M. J. Goncalves, “Modeling of sugar crystallization through knowledge integration”, Engineering in Life Sciences, Vol. 3, 2003, pp. 146-153.10.1002/elsc.200390019 Search in Google Scholar

LA. Suarez, P. Georgieva, SF. de Azevedo, “Nonlinear MPC for fed-batch multiple stages sugar crystallization”, Chemical Engineering Research and Design, Vol. 89, 2011, pp. 753-767.10.1016/j.cherd.2010.10.010 Search in Google Scholar

A. Andrasik, A. Meszaros, SF. de Azevedo, “On-line tuning of a neural PID controller based on plant hybrid modeling”, Computers& Chemical Engineering, Vol. 28, 2004, pp. 1499-1509.10.1016/j.compchemeng.2003.12.002 Search in Google Scholar

M. von Stosch, J. Peres, SF. de Azevedo, et al, “Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach”, BMC Systems Biology, Vol. 4, 2010, pp. 131.10.1186/1752-0509-4-131295560420863397 Search in Google Scholar

J. F. Yu, H. R. Wang, “The implement of hydraulic control system for large-scale railway maintenance equipment based on PLC”, Sensors and Transducers, Vol. 170, 2014, pp. 222-226. Search in Google Scholar

YI. Al. Mashhadany, “Design and implement of a programmable logic controller (PLC) for classical control laboratory”, Intelligent Control and Automation, Vol. 3, 2012, pp. 44-49.10.4236/ica.2012.31006 Search in Google Scholar

X. D. Miao, S. M. Li, H. Shen, “On-board lane detection system for intelligent vehicle based on monocular vision”, International Journal on Smart Sensing and Intelligent Systems, Vol. 5, No. 4, 2012, pp. 957-972.10.21307/ijssis-2017-517 Search in Google Scholar

G. Gelen, M. Uzam, “The synthesis and PLC implementation of hybrid modular supervisors for real time control of an experimental manufacturing system”, Journal of Manufacturing Systems, Vol. 33, 2014, pp. 313-326.10.1016/j.jmsy.2014.04.008 Search in Google Scholar

R. Bayindir, Y. Cetinceviz, “A water pumping control system with a programmable logic controller (PLC) and industrial wireless modules for industrial plants—An experimental setup”, ISA Transactions, Vol. 50, 2011, pp. 321-328.10.1016/j.isatra.2010.10.00621126739 Search in Google Scholar

M. Q. Vollema, H. Hoijtink, “The Multidimensionality of Self-Report Schizotypy in a Psychiatric Population: An Analysis Using Multidimensional Rach Models”, Schizophr Bull, Vol. 26, 2000, pp. 565-575.10.1093/oxfordjournals.schbul.a03347810993398 Search in Google Scholar

I. Richardson, V. Casey, F. McCaffery, “A Process Framework for Global Software Engineering Teams”, Information and Software Technology, Vol. 54, 2012, pp. 1175-1191.10.1016/j.infsof.2012.05.002 Search in Google Scholar

Y. M. Luo, P. Z. Liu, M. H. Liao, “An artificial immune network clustering algorithm for mangroves remote sensing image”, International Journal on Smart Sensing and Intelligent Systems, Vol. 7, No. 1, 2014, pp. 116-134.10.21307/ijssis-2017-648 Search in Google Scholar

Y. Q. Wang, L. Liu, “New intelligent classification method based on improved meb algorithm”, International Journal on Smart Sensing and Intelligent Systems, Vol. 7, No. 1, 2014, pp. 72-95.10.21307/ijssis-2017-646 Search in Google Scholar

N. Samanta, A. K. Chanda, C. RoyChaudhuri, “An energy efficient, minimally intrusive multi-sensor intelligent system for health monitoring of elderly people”, International Journal on Smart Sensing and Intelligent Systems, Vol. 4, No. 2, 2014, pp. 762-780.10.21307/ijssis-2017-680Search in Google Scholar

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