Details

Autor(en) / Beteiligte

• Wang, Bo
• Shahzad, Muhammad
• Zhu, Xianglin
• Ur Rehman, Khalil
• Ashfaq, Muhammad
• Abubakar, Muhammad

Titel

Soft-sensor modeling for l-lysine fermentation process based on hybrid ICS-MLSSVM

Ist Teil von

• Scientific reports, 2020-07, Vol.10 (1), p.11630-11630, Article 11630

Ort / Verlag

London: Nature Publishing Group

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Abstract The l -lysine fermentation process is a complex, nonlinear, dynamic biochemical reaction process with multiple inputs and multiple outputs. There is a complex nonlinear dynamic relationship between each state variable. Some key variables in the fermentation process that directly reflect the quality of the fermentation cannot be measured online in real-time which greatly limits the application of advanced control technology in biochemical processes. This work introduces a hybrid ICS-MLSSVM soft-sensor modeling method to realize the online detection of key biochemical variables (cell concentration, substrate concentration, product concentration) of the l -lysine fermentation process. First of all, a multi-output least squares support vector machine regressor (MLSSVM) model is constructed based on the multi-input and multi-output characteristics of l -lysine fermentation process. Then, important parameters ( $$\gamma$$ γ , $$\lambda$$ λ , $$\sigma$$ σ ) of MLSSVM model are optimized by using the Improved Cuckoo Search (ICS) optimization algorithm. In the end, the hybrid ICS-MLSSVM soft-sensor model is developed by using optimized model parameter values, and the key biochemical variables of the l -lysine fermentation process are realized online. The simulation results confirm that the proposed regression model can accurately predict the key biochemical variables. Furthermore, the hybrid ICS-MLSSVM soft-sensor model is better than the MLSSVM soft-sensor model based on standard CS (CS-MLSSVM), particle swarm optimization (PSO) algorithm (PSO-MLSSVM) and genetic algorithm (GA-MLSSVM) in prediction accuracy and adaptability.