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Vol. 160, Issue 12, December 2013, pp. 493-502




Soft Measurement System for Monitoring Syrup Supersaturation During a Sugar Crystallization Process Based on a Least Squares Support Vector Machine
Yanmei MENG, Haifeng PANG, Guancheng LU, Chunwa QIN, Haiping He

College of Mechanical Engineering, Guangxi University, Nanning, 530004, Guangxi, China
Tel.: +8613207713906, fax: +8607713273600 1
E-mail: panghaifenggx@163.com


Received: 18 September 2013   /Accepted: 22 November 2013   /Published: 30 December 2013

Digital Sensors and Sensor Sysstems


Abstract: It is difficult to monitor syrup supersaturation during the sugar crystallization process so we developed an online soft measurement system for syrup supersaturation analysis based on a least squares support vector machine (LSSVM). The application of a sparseness method reduced the support vector levels of the model, so the learning and modeling speed of the algorithm increased. A combined genetic algorithm (GA) and k-fold cross-validation method was used to optimize the parameters of the soft measurement algorithm, and to obtain the optimal parameters of the kernel function and the optimal penalty factor. Ultimately, this online soft measurement system was tested using a self-regulating comprehensive experimental platform that represented the sugar crystallization process, which showed that the soft measurement values were close to the actual values where the mean squared error was 0.001. Thus, we demonstrated that our system could measure the syrup supersaturation level based on the auxiliary variables.


Keywords: Syrup supersaturation, Soft measurement, Least squares support vector machine, Sparseness method, Genetic algorithm, K-fold cross-validation.


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