Soft Sensor Modeling Method Based on SPA-GWO-SVR for Marine Protease Fermentation Process

软传感器 MATLAB语言 支持向量机 过程(计算) 核(代数) 一般化 多元微积分 计算机科学 工程类 算法 数学 人工智能 控制工程 操作系统 组合数学 数学分析
作者
Zhu Li,Khalil Ur Rehman,Liu Wenhui,Faiza Atique
出处
期刊:Journal of Control Science and Engineering [Hindawi Publishing Corporation]
卷期号:2021: 1-10
标识
DOI:10.1155/2021/6653503
摘要

The marine protease fermentation process is a highly nonlinear, time-varying, multivariable, and strongly coupled complex biochemical reaction process. Due to the growth and reproduction of living organisms, the internal mechanism is very complicated. Some key variables (such as cell concentration, substrate concentration, and enzyme activity) that directly reflect the fermentation process's quality are difficult to measure in real-time by traditional measurement methods. A soft sensor model based on a support vector regression (SVR) is proposed in this paper to resolve this problem. To further improve the model's prediction accuracy, the grey wolf optimization (GWO) algorithm is used to optimize the critical parameters (kernel function width σ, penalty factor c, and insensitivity coefficient ε) of the SVR model. To study the influence of selecting auxiliary variables on soft sensor modeling, the successive projection algorithm (SPA) is used to determine the characteristic variables and compare them with grey relation analysis (GRA) algorithm. Finally, the Excel spreadsheet data was called by MATLAB programming, and the established SPA-GWO-SVR soft sensor model predicted crucial biological variables. The simulation results show that the SPA-GWO-SVR model has higher prediction accuracy and generalization ability than the traditional SPA-SVR model. The real-time monitoring was processed by MATLAB software for the marine protease fermentation process, which met the requirements of optimal control of the marine protease fermentation process.
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