Penicillin fermentation process fault isolation method based on kernel least square regression

Abstract

The invention provides a penicillin fermentation process fault isolation method based on kernel least square regression. The method comprises the following steps of: obtaining a penicillin fermentation process historical fault data set; building a kernel least square regression learning model according to the penicillin fermentation process historical fault data set, wherein the input of the model is the penicillin fermentation process historical fault data set, and the output of the model is a penicillin fermentation process fault category; collecting penicillin fermentation process data in real time and judging whether faults occur in the current penicillin fermentation process or not; and carrying out fault isolation on the real-time collected penicillin fermentation process data by utilizing a penicillin fermentation process fault isolation model based on the kernel least square regression, and determining the fault category. The penicillin fermentation process fault isolation method has the advantages that through the introduction of the kernel least square regression, nonlinear data is mapped to a linear space, so that the fault monitoring and fault diagnosis problems of a nonlinear space are solved, and the fault category can be isolated at higher precision.

Description

technical field [0001] The invention belongs to the technical field of fault detection and diagnosis, and in particular relates to a method for fault separation in a penicillin fermentation process based on kernel least squares regression. Background technique [0002] As a kind of antibiotic, penicillin has a wide range of clinical medical value; as a kind of secondary metabolite, its production equipment is a typical nonlinear and dynamic production process, which has important academic research and industrial application value. Schematic diagram of the fermentation process of penicillin figure 1 As shown, the pH value and temperature are controlled by closed-loop, while the feed is controlled by open-loop fixed value. By controlling the pH value of the process and the temperature during the fermentation period, the reaction can be carried out under the best conditions. The whole production cycle includes 4 physiological periods, the reaction lag period, the rapid growth ...

AI Technical Summary

Problems solved by technology

Under certain conditions, these risk factors make the process fail, and even cause accidents, production disruption, unsafe life, serious environmental pollution and large economic losses.

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