Abstract
BackgroundAiming at the characteristics of nonlinear, multi-parameter, strong coupling and difficulty in direct on-line measurement of key biological parameters of marine low-temperature protease fermentation process, a soft-sensing modeling method based on artificial bee colony (ABC) and multiple least squares support vector machine (MLSSVM) inversion for marine protease fermentation process is proposed.MethodsFirstly, based on the material balance and the characteristics of the fermentation process, the dynamic “grey box” model of the fed-batch fermentation process of marine protease is established. The inverse model is constructed by analyzing the inverse system existence and introducing the characteristic information of the fermentation process. Then, the inverse model is identified off-line using MLSSVM. Meanwhile, in order to reduce the model error, the ABC algorithm is used to correct the inverse model. Finally, the corrected inverse model is connected in series to the marine alkaline protease MP fermentation process to form a composite pseudo-linear system, thus, real-time on-line prediction of key biological parameters in fermentation process can be realized.ResultsTaking the alkaline protease MP fermentation process as an example, the simulation results demonstrate that the soft-sensing modeling method can solve the real-time prediction problem of key biological parameters in the fermentation process on-line, and has higher accuracy and generalization ability than the traditional soft-sensing method of support vector machine.ConclusionsThe research provides a new method for soft-sensing modeling of key biological parameters in fermentation process, which can be extended to soft-sensing modeling of general nonlinear systems.
Highlights
Aiming at the characteristics of nonlinear, multi-parameter, strong coupling and difficulty in direct online measurement of key biological parameters of marine low-temperature protease fermentation process, a softsensing modeling method based on artificial bee colony (ABC) and multiple least squares support vector machine (MLSSVM) inversion for marine protease fermentation process is proposed
Suffice it to say that the inverse system method greatly facilitates the soft-sensing modelling of highly nonlinear systems in engineering practices
In order to make the experiment closer to the production process, the experiment scheme is designed as follows: 1) The high-yield low-temperature alkaline protease strain YS-80 isolated from Huang Hai water samples of China is selected as the strain
Summary
Aiming at the characteristics of nonlinear, multi-parameter, strong coupling and difficulty in direct online measurement of key biological parameters of marine low-temperature protease fermentation process, a softsensing modeling method based on artificial bee colony (ABC) and multiple least squares support vector machine (MLSSVM) inversion for marine protease fermentation process is proposed. The inverse system method faces two problems in soft-sensing of the marine low-temperature alkaline protease MP fermentation process. The neural network, inspired by the asymptotic theory, is based on the unrealistic assumption that the number of samples is infinite, but the number of samples in the actual problem is often limited, especially the strong coupling, large lag complex nonlinear system as the marine alkaline protease MP fermentation process, it is extremely difficult to obtain accurate sample data. In the case of small samples, the research of inverse soft-sensing methods suitable for the marine alkaline protease MP fermentation process and easy to implement in engineering has become the key problem to be solved urgently in the marine low-temperature alkaline protease MP fermentation process
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