期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 171, 期 -, 页码 170-181出版社
ELSEVIER
DOI: 10.1016/j.chemolab.2017.10.018
关键词
Biomass concentration; Fermentation; Input weighted; Empirical model; Neural network; Cuckoo search
类别
资金
- Fundamental Research Funds for the Central Universities [N162504013]
- Specialized Research Fund for the Doctoral Program of Higher Education [20120042120014]
Biomass concentration (BC) is considered as one of the most important biochemical parameters. Its reliable online estimation is crucial in the real-time status monitoring, and quality control of fermentation processes. Considering that each input variable may have different influence on BC in actual fermentation processes, a novel input-weighted empirical model based on the radial basis function neural network (RBFN) and a new peer learning cuckoo search (PLCS) algorithm, is proposed in this paper to predict BC. The determination of input variable weights and RBFN parameters for the proposed BC prediction model is framed as one and the same optimization problem. Inspired by a common social phenomenon that the mutual learning between team members (peers) would be extremely helpful for their team to accomplish a work efficiently, a PLCS algorithm is proposed to solve the resulting optimization (RO) problem, and thereby accomplish the development of the proposed BC prediction model. The effectiveness and superiority of this new prediction model is validated using the production data from a lab-scale nosiheptide fermentation process. Moreover, the performance of PLCS is also demonstrated on the RO problem with these data and some benchmark functions.
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