Research on soft sensing method of photosynthetic bacteria fermentation process based on ant colony algorithm and least squares support vector machine

Abstract

Photosynthetic bacteria wastewater treatment is an efficient water pollution treatment method, but photosynthetic bacteria fermentation is a multivariable, non-linear, and time-varying process. So it is difficult to establish an accurate model. Aiming at the difficulty of online measurement of key parameters, such as bacterial concentration and matrix concentration in photosynthetic bacteria fermentation process, an improved ant colony algorithm least squares support vector machine (AC-LSSVM) soft sensing model method is proposed in this paper. Firstly, the virtual sensing subsystem of the photosynthetic bacteria fermentation process is proposed, with measurable parameters as input and unmeasurable key parameters as output, and the left inverse soft sensing model of virtual sensing is constructed. Then, the ant colony algorithm can quickly find the shortest path to optimize the parameters of the traditional PI regulation, to improve the dynamic performance and accuracy of parameter measurement in the fermentation process. After that, the ant colony algorithm is used to optimize penalty parameters C and kernel parameters σ of LSSVM, which effectively avoids the local optimization and improves the computing power and global optimization ability. Finally, the soft sensing prediction model of the photosynthetic bacteria fermentation process based on AC-LSSVM is established. Compared with SVM and LSSVM prediction models, the root mean square error of bacterial concentration and matrix concentration based on the AC-LSSVM model are 0.468 and 0.126, respectively. The simulation analysis shows that this model has less error and better prediction ability, and it can meet the needs of online prediction of key parameters of photosynthetic bacteria fermentation.

Keywords:

• Ant colony
• fermentation
• least squares support vector machine
• photosynthetic bacteria
• soft sensing

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study is supported by the Zhenjiang Key R&D Project (SH2020005) and the Natural Science Foundation of Jiangsu Province (BK20191225).