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

Zhu Li,Liu Wenhui,Faiza Atique,Khalil Ur Rehman,Yongji Wang

doi:10.1155/2021/6653503

Zhu Li, Liu Wenhui + Show 3 more

Open Access

https://doi.org/10.1155/2021/6653503

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Abstract

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.

Highlights

In recent years, marine biotechnology’s rapid development makes marine microbiological engineering occupy an essential proportion in the national economic system
In the process of marine protease fermentation, the temperature of the fermentation tank is controlled at 25°C∼28°C, the pressure of the fermentation tank is controlled at 0.03–0.05 Mpa, the dissolved oxygen is maintained at 35%∼75%, the airflow is owned at 1000 L/h, and the speed is held at 300–500 r/min. e experiment sampling period is 15 minutes, and 11 auxiliary variables are acquired through various sensors and transmitted to the computer by a distributed control system (DCS) form a database
Because the ocean they live in has a particular environment, the enzymes they produce have more unique properties than those produced by other microorganisms, such as lowtemperature resistance, alkali resistance, and pH

Summary

Introduction

Marine biotechnology’s rapid development makes marine microbiological engineering occupy an essential proportion in the national economic system. Ese excellent characteristics make it widely used in the washing industry, environmental protection, food processing, and national defence [1, 2]. In the actual fermentation process, to increase the production efficiency and product quality of enzyme preparation and reduce the economic cost, the fermentation process’s environmental variables should be controlled in a specific range. The process of cell growth, reproduction, and metabolic enzyme production is significantly reflected by the external environment. Cell concentration, substrate concentration, and enzyme activity, which are target quality variables that play a significant role in the fermentation process, are challenging to measure in real-time. Erefore, studying the real-time measurement method of the fermentation process’s key variables is of great significance for improving the quality of fermentation products, reducing the consumption of raw materials, and power energy [3] Cell concentration, substrate concentration, and enzyme activity, which are target quality variables that play a significant role in the fermentation process, are challenging to measure in real-time. erefore, studying the real-time measurement method of the fermentation process’s key variables is of great significance for improving the quality of fermentation products, reducing the consumption of raw materials, and power energy [3]

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Results

Conclusion