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, lambda, sigma) 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.
Highlights
The l-lysine fermentation process is a complex, nonlinear, dynamic biochemical reaction process with multiple inputs and multiple outputs
As computational time complexity of support vector machine (SVM) increases with the increase of the size of the dataset, least square SVM (LSSVM) solved the curse of dimensionality limitation and it is less dependent on the size of the dataset which has good generalization ability as compared to radial basis function (RBF) neural n etwork[15,16]
A hybrid Improved Cuckoo Search (ICS)-multi-output least squares support vector machine regressor (MLSSVM) soft-sensor modeling method is proposed for measuring crucial parameters of the l-lysine fermentation process
Summary
The l-lysine fermentation process is a complex, nonlinear, dynamic biochemical reaction process with multiple inputs and multiple outputs. 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. 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. Cell concentration reflects the number of bacterial cells and substrate concentration reflects the growth and reproduction status of the bacteria, which has a close relationship with fermentation metabolism and directly affects the final formation of the product Measurement of these key variables is necessary to control and optimize the fermentation process in real-time to enhance the productivity. As computational time complexity of SVM increases with the increase of the size of the dataset, LSSVM solved the curse of dimensionality limitation and it is less dependent on the size of the dataset which has good generalization ability as compared to radial basis function (RBF) neural n etwork[15,16]
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