Research on Soft Sensing Method of Straw Ethanol Fermentation Process Based on BSVR
Journal of Electrical and Computer Engineering(2022)
摘要
Straw fermented fuel ethanol is a complex process with multivariable, large lag, and strong nonlinearity. It is difficult to directly measure the key parameters such as ethanol concentration and cell concentration online. Aiming at the problem, a soft sensing model of straw fermented ethanol based on improved support vector regression (SVR) is proposed. Based on the analysis of the process of ethanol production from straw fermentation, the Bayesian method is used to optimize the support vector regression (BSVR). And the concepts of generation a priori and generation likelihood are introduced to optimize the data prediction model. The comparative experiment of model training and testing is carried out. The simulation results show that the proposed BSVR method is better than SVR. It can improve the generalization ability of data and the anti-interference of the model, and its prediction accuracy and stability are higher.
更多查看译文
关键词
Support Vector Machines
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要