Fermentation process fault monitoring method based on just-in-time learning local model

A fermentation process and fault monitoring technology, applied in program control, electrical program control, comprehensive factory control, etc., can solve problems such as unsteady working point, false alarm, etc., to overcome multi-stage problems, reduce false alarm rate, improve The effect of accuracy

Active Publication Date: 2016-06-08
BEIJING UNIV OF TECH
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Problems solved by technology

[0003] However, the fermentation process often has no steady-state operating point, and often changes from one stable state to another, with dynamic and time

Method used

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  • Fermentation process fault monitoring method based on just-in-time learning local model
  • Fermentation process fault monitoring method based on just-in-time learning local model
  • Fermentation process fault monitoring method based on just-in-time learning local model

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Embodiment Construction

[0033] The Pensim simulation platform used in this method was developed by the Process Modeling, Monitoring and Control Research Group of the Illinois Institute of Technology (IIT) with Professor Cinar as the subject leader. A series of simulations of the penicillin fermentation process can be realized on this platform, and it has become an internationally influential penicillin simulation platform.

[0034]It provides a standard platform for the monitoring, fault diagnosis and quality prediction of fermentation production. At present, there have been many research results based on Pensim2.0. Pensim2.0 can simulate the microbial concentration, CO2 concentration, pH value, penicillin concentration, oxygen concentration and heat generated during the penicillin production process. The initialization parameters that need to be set include: reaction time, sampling time, biomass, fermentation environment, temperature control parameters, and pH control parameters. The simulation pla...

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Abstract

The invention discloses a fermentation process fault monitoring method based on a just-in-time learning local model and relates to the field of data driving fault diagnosis. Global modeling can not overcome the problems of multiple stages of production process, seasonal effects and material quality effects during actual production, and a large number of false alarms will be produced during time-varying process. A local modeling method based on a just-in-time learning strategy is put forward to solve the problem of model mismatching during actual fault monitoring, and faults are monitored through a local partial least square model. Information entropy is introduced into the just-in-time learning strategy, and similar sample points are automatically selected for modeling. Due to the fact that the local model can represent the current system state, stage identification is not needed, the calculated amount is reduced, and problems which are brought to monitoring by time-variant characteristics in the fermentation process are overcome. The false alarm rate is effectively reduced, the fault monitoring accuracy rate is increased, and safety and economy of production are guaranteed.

Description

technical field [0001] The invention relates to the technical field of data-driven fault diagnosis, in particular to a fault monitoring technology for a multi-stage fermentation process. The data-driven method of the present invention is a specific application in fault monitoring of penicillin fermentation process. Background technique [0002] The process scale and complexity of modern fermentation industry are constantly expanding, and people pay more and more attention to the safety and reliability of process production. The process data of the process industry has problems such as high dimensionality, high coupling, collinearity, data loss, and noise pollution, while principal component analysis (PCA), principal component regression (PCR) and partial least squares (PLS) are the core technologies The multivariate statistical process monitoring (MSPM) method can better solve the above problems. Among them, partial least squares has been widely used because it can decompo...

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Application Information

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IPC IPC(8): G05B19/418
CPCY02P90/02G05B19/41865G05B2219/24024
Inventor 李亚芬张亚堃高学金王锡昌王普
Owner BEIJING UNIV OF TECH
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