农业工程学报Issue(19)：268-276,9.DOI:10.3969/j.issn.1002-6819.2013.19.033
基于动态数据交换技术的海洋蛋白酶发酵过程GD-FNN软测量
Soft sensor of generalized dynamic fuzzy neural network for marine protease fermentation process based on dynamic data exchange
摘要
Abstract
The crucial biological variables (such as biomass concentration, substrate concentration, and product concentration, and so on) of the microbial fermentation process are difficult to measure online, which has a great influence on the quality of fermentation production. In this paper, a soft sensing method based on a generalized dynamic fuzzy neural network (GD-FNN) was proposed. The configuration software windows control center (WinCC) possesses the advantages of powerful practicality and flexible configuration. A complex interactive graphical interface can be generated by WinCC, but its ability to perform data processing is weak. So it is unable to achieve soft sensor modeling of biological parameters and estimate the value of biological parameters by WinCC. MATLAB is professional software for mathematical analysis and engineering operations. It has the characteristic of powerful data processing capabilities and an open application programming interface, but direct data communication can not be realized between MATLAB and the industrial control equipment. In order to solve this problem, combining MATLAB with WinCC to achieve respective advantage, taking Excel as the middle bridge, the real-time communication between MATLAB and WinCC was established by dynamic data exchange (DDE,DDE is the message mechanism based on Windows, two Windows applications carry on DDE Conversation through mutual transfer DDE message, and thus complete the data request, response, and transmission).Finally the real-time display and monitoring of crucial biological variables was realized. In this paper, the typical microbial fermentation process (the marine protease fermentation process) was taken as an example. First, in MATLAB, a soft sensor model based on GD-FNN(The algorithm of GD-FNN was based on an elliptical basis function. In the algorithm, fuzzyε-completeness was used as the distribution mechanism of on-line parameters, the importance of fuzzy rules and input variables were evaluated, and this algorithm which has salient advantages in the aspect of learning efficiency and performance was established by using the training sample set for the fermentation process. The established model was verified by the test sample set. Second, the real-time collection data was transferred from configuration software WinCC to Excel by DDE technology. The data of Excel was called by MATLAB programming, and crucial biological parameters were predicted by the established model and the value transferred back to Excel. Finally, the real-time display and monitoring of the biological parameters were realized by DDE settings and the friendly human-machine interface of WinCC, and the intelligent monitoring system of a marine protease fermentation process based on WinCC was established. The application results showed that the prediction accuracy of soft sensor modeling based on GD-FNN is higher,and connecting MATLAB and WinCC by DDE technology has the characteristic of efficient programming, convenient use, and good general performance. The real-time monitoring was processed by WinCC for the marine protease fermentation process, which met the requirements of optimal control of the marine protease fermentation process and enhanced the automation level of the fermentation process and improved the product yield and economic benefit. These lay the foundation for the industrial production of a marine protease fermentation process.关键词
微生物/发酵/神经网络/动态数据交换/软测量Key words
microforms/fermentation/neural networks/dynamic data exchange/soft sensing分类
化学化工引用本文复制引用
黄永红,孙丽娜,孙玉坤,刘国海,聂文惠..基于动态数据交换技术的海洋蛋白酶发酵过程GD-FNN软测量[J].农业工程学报,2013,(19):268-276,9.基金项目
江苏高校优势学科建设工程资助项目（苏政办发〔2011〕6号）；“十二五”国家863重点科技项目（2011AA09070301）；江苏省科技计划项目（BE2010354）；江苏大学高级专业人才科研启动基金项目 （）
