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A fermentation process fault monitoring method based on DLAE

A fermentation process and process variable technology, applied in the direction of registering/indicating the work of machines, instruments, characters and patterns, etc., can solve the problem of not considering the local structure information of data, poor robustness of hidden layer features, limiting AE fault monitoring performance, etc. question

Active Publication Date: 2019-05-10
BEIJING UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, AE is a feature extraction method based on the global structure of the data, and does not consider the local structure information of the data, and the hidden layer features are less robust and susceptible to noise interference
These greatly limit the fault monitoring performance of AE

Method used

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  • A fermentation process fault monitoring method based on DLAE
  • A fermentation process fault monitoring method based on DLAE
  • A fermentation process fault monitoring method based on DLAE

Examples

Experimental program
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Embodiment Construction

[0058] Penicillin is an important antibiotic with high efficiency, low toxicity and wide clinical application. Its production process is a typical dynamic, nonlinear, multi-stage batch production process. PenSim2.0, a penicillin simulation platform developed by the process monitoring and technology group of Illinois State Institute of Technology in the United States, provides a standard platform for the monitoring, fault diagnosis and control of penicillin intermittent production process. A series of simulations of the penicillin fermentation process can be realized on this platform. Relevant studies have shown the practicability and effectiveness of this simulation platform, and it has become an internationally influential penicillin simulation platform.

[0059] This experiment takes PenSim2.0 as the simulation research object, sets the sampling interval as 1h, and selects 10 process variables to monitor the process operation status, as shown in Table 1. 41 batches of normal...

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Abstract

The invention discloses a novel method for carrying out real-time fault monitoring on a penicillin fermentation process. The method comprises two stages of off-line modeling and on-line monitoring. The off-line modeling comprises the following steps: firstly, processing three-dimensional data of a fermentation process; then respectively calculating a Laplacian matrix of the data of each fermentation batch to represent local structure information of the data in each batch; and finally, carrying out modeling by using a noise reduction Laplace automatic encoder (DLAE) to construct monitoring statistics, and determining a control limit by using a kernel density estimation method. The on-line monitoring comprises the following steps: processing newly collected data according to a model, calculating the statistical magnitude of the data, comparing the statistical magnitude with a control limit, and judging whether the fermentation process runs normally or not. According to the method, the local structure of data in batches can be effectively utilized, and meanwhile, the training cost and the hardware requirement of the Laplace automatic encoder are reduced. Meanwhile, the robustness of the model is enhanced by adopting a noise reduction training mode, and the fault monitoring accuracy is relatively high.

Description

technical field [0001] The invention relates to the field of data-driven fault diagnosis technology, in particular to a fault diagnosis technology for batch processes. The data-driven method of the present invention is a specific application in fault monitoring of a typical batch process—penicillin fermentation process. Background technique [0002] In recent decades, batch processes have attracted much attention because they can meet the demands of producing high value-added products. However, its mechanism is complicated, its operation complexity is high, and its product quality is easily affected by uncertain factors. As a typical batch process, the penicillin fermentation process has the characteristics of strong nonlinearity, dynamics, and mixed Gaussian distribution. In order to ensure the safety and stability of the fermentation process operating system, an effective process monitoring scheme is established to detect timely Anomalies are necessary. [0003] At pres...

Claims

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Application Information

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IPC IPC(8): G06K9/62G07C3/00
Inventor 高学金徐子东
Owner BEIJING UNIV OF TECH
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