Abstract
The process of sugar evaporative crystallization is a nonlinear process with large time lag and strong coupling. It is difficult to establish a reasonable mechanism model. In this paper, we use the data driving modeling method to establish an Adaptive Control model for batch boiling sugar crystallization process. First, by analyzing the main influencing factors of the evaporative crystallization process of intermittent boiling sugar, the most important two parameters, brix and liquid level, are selected as the control object. The self-adaptive differential evolution Extreme Learning Machine (SaDE-ELM) is used to construct the control model. A least squares support vector machine (LSSVM) is established and connected in the control loop to control the opening of the feed valve so that to control the feed flowrate according to the objective values of syrup Brix and liquid level. Experiments are conducted and the obtained data are used to train and verify the learning machines. Experiments indicate that the learning machines are able to realize adaptive control to key parameters of the crystallization process. Comparison of different neural networks indicates that the LSSVM performs better than BP, RBF and ELM and SaDE-ELM with prediction error of below 0.01, and training time of below 0.05 s.
Acknowledgements
Project supported by the National Natural Science Foundation of China (No. 51465003)
References
[1] Velazquez-Camilo O, Bolaños-Reynoso E, Rodriguez E, Alvarez-Ramirez J. Characterization of cane sugar crystallization using image fractal analysis. J Food Eng. 2010;100:77–84.10.1016/j.jfoodeng.2010.03.030Search in Google Scholar
[2] Menga Y, Lana Q, Qinb J, Yua S, Panga H, Zhenga K. Data-driven soft sensor modeling based on twin support vector regression for cane sugar crystallization. J Food Eng. 2019;241:159–65.10.1016/j.jfoodeng.2018.07.035Search in Google Scholar
[3] Braatz RD. Advanced control of crystallization processes. Annu Rev Control. 2002;26:87–99.10.1016/S1367-5788(02)80016-5Search in Google Scholar
[4] Nagy ZK, Chew JW, Fujiwara M, Braatz RD. Advances in the modeling and control of batch crystallizers. In: Proc. of the 7th IFAC Symp. on Advanced Control of Chemical Processes, Elsevier Scientific, Oxford, UK. 2004;83–90.10.1016/S1474-6670(17)38713-XSearch in Google Scholar
[5] Nagy ZK, Braatz RD. Advances and new directions in crystallization control. Ann Rev Chem Biomol Eng. 2012;3:55–75.10.1146/annurev-chembioeng-062011-081043Search in Google Scholar PubMed
[6] Bransom SH. Continuous crystallizer design. Chem Process Eng. 1965;46(12):647–653.Search in Google Scholar
[7] Wright PG, White ET. A mathematical model of vacuum pan crystallization. In: Proceeding of ISSCT 15th Congress. 1974:1546–60.Search in Google Scholar
[8] Fagervik K, Konstari O, Schalien R. Control of batch evaporative crystallization of sugar by means of adaptive simulation. In: American Control Conference. 1988:677–8310.23919/ACC.1988.4789805Search in Google Scholar
[9] Velazquez-Camilo O, Bolaños-Reynoso E, Rodriguez E, Alvarez-Ramirez J. Characterization of cane sugar crystallization using image fractal analysis. J Food Eng. 2010;100:77–84.10.1016/j.jfoodeng.2010.03.030Search in Google Scholar
[10] Munjal B, Bansal AK. Counter-intuitive effect of non-crystallizing sugars on the crystallization of gemcitabine HCl in frozen solutions. Int J Pharm. 2015;478:46–52.10.1016/j.ijpharm.2014.11.002Search in Google Scholar PubMed
[11] Damour C, Benne M, Boillereaux L, Grondin-Perez B and Chabriat J. Multivariable linearizing control of an industrial sugar crystallization process. J Process Control. 2011;21:46–54.10.1016/j.jprocont.2010.10.002Search in Google Scholar
[12] Bolaños-Reynoso E, Xaca-Xaca O, Alvarez-Ramires J, Lopez-Zamora L. Effect analysis from dynamic regulation of vacuum pressure in an adiabatic batch crystallizer using data and image acquisition. Ind Eng Chem Res. 2008;47:9426–36.10.1021/ie071594iSearch in Google Scholar
[13] Beyou S, Grondin-Perez B, Benne M, Damour C, Chabriat J-P. Control improvement of a C sugar cane crystallization using an auto-tuning PID controller based on linearization of a neural network. Int J Electr Inf Eng. 2009;3:1646–51.Search in Google Scholar
[14] Jha SK, Karthika S, Radhakrishnan TK. Modelling and control of crystallization process. Resour-Effic Technol. 2017;3:94–100.10.1016/j.reffit.2017.01.002Search in Google Scholar
[15] Grondin-Perez B, Benne M, Bonnecaze C and Chabriat J. Industrial multi-step forward predictor of mother liquor purity of the final stage of a cane sugar crystallisation plant. J Food Eng. 2005;66:361–7.10.1016/j.jfoodeng.2004.04.002Search in Google Scholar
[16] Lin XF, Zhang H, Li W. Optimal control for industrial sucrose crystallization with action dependent heuristic dynamic programming. In: Proceedings of the 8th World Congress on Intelligent Control and Automation. Jinan, China, 2010:656–61.10.5815/ijigsp.2009.01.05Search in Google Scholar
[17] Michal J, Kminek M, Kminek P. Expert control of vacuum pan crystallization. IEEE Control Syst. 1994;14:28–64.10.1109/37.320884Search in Google Scholar
[18] Huang GB, Zhu QY, Xsiew CK. Extreme learning machine: theory and applications. Neurocomputing. 2006;70:489–501.10.1016/j.neucom.2005.12.126Search in Google Scholar
[19] Kumar V, Prerna G, Mittal AP. Trajectory control of DC Servo using OS-ELM based controller. In: Power India Conference. IEEE Fifth, 2012:1–5.10.1109/PowerI.2012.6479497Search in Google Scholar
[20] Balbay A, Avci E, Şahin Ö, Coteli R. Modeling of drying process of bittim nuts (pistacia terebinthus) in a fixed bed dryer system by using extreme learning machine. Int J Food Eng. 2012;8:4.10.1515/1556-3758.2737Search in Google Scholar
[21] Xue X, Xiao M. Deformation evaluation on surrounding rocks of underground caverns based on PSO-LSSVM. Tunnelling Underground Space Technol. 2017;69:171–81.10.1016/j.tust.2017.06.019Search in Google Scholar
[22] Xiyun Y, Baojun S, Xinfang Z, Lixia L. Short-term wind speed forecasting based on support vector machine with similar data. Proc CSEE. 2012;32:35–41.Search in Google Scholar
[23] Baghban A, Kardani MN, Habibzadeh S. Prediction viscosity of ionic liquids using a hybrid LSSVM and group contribution method. J Mol Liq. 2017;236:452–64.10.1016/j.molliq.2017.04.019Search in Google Scholar
[24] Jebarani Evangeline S, Suresh Kumar S, Jayakumar J. Torque modeling of switched reluctance motor using LSSVM-DE. Neurocomputing. 2016;211:117–28.10.1016/j.neucom.2016.02.076Search in Google Scholar
[25] Huang G-B. An insight into extreme learning machines: random neurons, random features and kernels. Cognit Comput. 2014;6:376–90.10.1007/s12559-014-9255-2Search in Google Scholar
[26] Price K, Storn RM, Lampinen JA. Differential Evolution: A Practical Approach to Global Optimization, Springer, Berlin, 2005.10.1007/978-3-540-39930-8_6Search in Google Scholar
[27] Qin AK, 2005. Self-adaptive differential evolution algorithm for numerical optimization. In: 2005 IEEE Congress on Evolutionary Computation. Edinburgh, Scotland, UK, 2-5 Sep 2005.10.1109/CEC.2005.1554904Search in Google Scholar
[28] Goudos SK, Baltzis KB, Antoniadis K, Zaharisa ZD, Hilas CS. A comparative study of common and self-adaptive differential evolution strategies on numerical benchmark problems. Procedia Comput Sci. 2011;3:83–8.10.1016/j.procs.2010.12.015Search in Google Scholar
[29] Ghimire S, Deo RC, Downs NJ, Raj N. Self-adaptive differential evolutionary extreme learning machines for longterm solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis atmospheric products in solar-rich cities. Remote Sens Environ. 2018;212:176–98.10.1016/j.rse.2018.05.003Search in Google Scholar
[30] Zhang W, Niua P, Lia G, Lia P. Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm. Knowledge-Based Syst. 2013;39:34–44.10.1016/j.knosys.2012.10.004Search in Google Scholar
[31] Ji-Yong S, Xiao-Bo Z, Xiao-Wei H, Jie-Wen Z. Rapid detecting total acid content and classifying different types of vinegar based on near infrared spectroscopy and least-squares support vector machine. Food Chem. 2013;138:192–9.10.1016/j.foodchem.2012.10.060Search in Google Scholar PubMed
[32] Huang X, Shi L, Johan AK. Suykens. Asymmetric least squares support vector machine classifiers. Comput Stat Data Anal. 2014;70:395–405.10.1016/j.csda.2013.09.015Search in Google Scholar
[33] Suykens JAK, Vandewalle J. Least squares support vector machine classifiers. Neural Process Lett. 1999;9:293–300.10.1023/A:1018628609742Search in Google Scholar
[34] Gholami M, Bodaghi A. A robust approach through combining optimized neural network and optimized support vector regression for modeling deformation modulus of rock masses. Model Earth Syst Environ. 2017;3:22–8.10.1007/s40808-017-0303-2Search in Google Scholar
[35] Arabloo M, Ziaee H, Lee M, Bahadori A. Prediction of the properties of brines using least squares support vector machine (LS-SVM) computational strategy. J Taiwan Inst Chem Eng. 2015;50:123–30.10.1016/j.jtice.2014.12.005Search in Google Scholar
[36] Rostami A, Masoudi M, Ghaderi-Ardakani A, Arabloo M, Amani M. Effective thermal conductivity modeling of sandstones: SVM framework analysis. Int J Thermophys. 2016;37:59.10.1007/s10765-016-2057-xSearch in Google Scholar
[37] Yassin MR, Arabloo M, Shokrollahi A, Mohammadi AH. Prediction of surfactant retention in porous media: a robust modeling approach. J Dispers Sci Technol. 2014;35:1407–18.10.1080/01932691.2013.844074Search in Google Scholar
[38] Bonanno F, Capizzi G, Graditi G, Napoli C, Tina GM. A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module. Appl Energy. 2012;97:956–61.10.1016/j.apenergy.2011.12.085Search in Google Scholar
[39] Mwasiagi JI. The use of extreme learning machines (ELM) algorithms to prediction strength for cotton ring spun yarn. Fashion Text. 2016;3:23.10.1186/s40691-016-0075-8Search in Google Scholar
[40] Arabloo M, Bahadori A, Ghiasi MM, Lee M, Abbas A, Zendehboudi S. A novel modeling approach to optimize oxygen–steam ratios in coal gasification process. Fuel. 2015;153:1–5.10.1016/j.fuel.2015.02.083Search in Google Scholar
[41] Islam SM, Das S. An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Trans Syst, Man, Cybernetics—Part B: Cybernetics. 42 Apr; 2012:482–500.10.1109/TSMCB.2011.2167966Search in Google Scholar PubMed
[42] Miranian A, Abdollahzade M. Developing a local least-squares support vector machines-based neuro-fuzzy model for nonlinear and chaotic time series prediction. IEEE Trans Neural Networks Learn Syst. Feb 2013;24:207–218.10.1109/TNNLS.2012.2227148Search in Google Scholar PubMed
[43] Islam B, Baharudin Z, Nallagownden P. Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid. Nat Comput Appl. 2017;28(1):877–891.10.1007/s00521-016-2408-3Search in Google Scholar
[44] Allawi MF, El-Shafie A. Utilizing RBF-NN and ANFIS methods for multi-lead ahead prediction model of evaporation from reservoir. Water Res Manage. 2016;30:4773–88.10.1007/s11269-016-1452-1Search in Google Scholar
[45] Liouane Z, Lemlouma T, Roose P, Weis F, Messaoud H. An improved extreme learning machine model for the prediction of human scenarios in smart homes. Appl Intell. 2018;48(8):2017–2030.10.1007/s10489-017-1062-5Search in Google Scholar
[46] Barzegar R, Moghaddam AA , Adamowski J, Fijani E. Comparison of machine learning models for predicting fluoride contamination in groundwater. Stochastic Environ Res Risk Assess. 2016;31(10):2705–2718.10.1007/s00477-016-1338-zSearch in Google Scholar
© 2019 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Nutritional Value and Modelling of Carotenoids Extraction from Pumpkin (Cucurbita Moschata) Peel Flour By-Product
- Single- and Dual-Stream Foam Fractionation of Protein – Exploring a Simple and Effective System to Improve Fundamental Understanding
- Effects of Carrageenan and Chitosan as Coating Materials on the Thermal Degradation of Microencapsulated Phycocyanin from Spirulina sp.
- Heat Transfer of Power-Law Liquid Food in a Tank with Varying Stirrer Settings
- Research on the Adaptive Control in Sugar Evaporative Crystallization Using LSSVM and SaDE-ELM
- Enhancement of Heat Transfer Performance Using Ultrasonic Evaporation
- Dielectric Properties of Infant Formulae, Human Milk and Whole and Low-Fat Cow Milk Relevant for Microwave Heating
- Study of the Drying Rate and Colour Kinetics during Stepwise Air-Drying of Apricot Halves
Articles in the same Issue
- Nutritional Value and Modelling of Carotenoids Extraction from Pumpkin (Cucurbita Moschata) Peel Flour By-Product
- Single- and Dual-Stream Foam Fractionation of Protein – Exploring a Simple and Effective System to Improve Fundamental Understanding
- Effects of Carrageenan and Chitosan as Coating Materials on the Thermal Degradation of Microencapsulated Phycocyanin from Spirulina sp.
- Heat Transfer of Power-Law Liquid Food in a Tank with Varying Stirrer Settings
- Research on the Adaptive Control in Sugar Evaporative Crystallization Using LSSVM and SaDE-ELM
- Enhancement of Heat Transfer Performance Using Ultrasonic Evaporation
- Dielectric Properties of Infant Formulae, Human Milk and Whole and Low-Fat Cow Milk Relevant for Microwave Heating
- Study of the Drying Rate and Colour Kinetics during Stepwise Air-Drying of Apricot Halves