Composite algorithm for early warning of excessive volatile fatty acids in anaerobic process

Abstract

The invention provides a composite algorithm for early warning of excessive volatile fatty acids in an anaerobic process. The composite algorithm comprises the steps of acquiring a sample data set, and establishing a decision table; performing principal component analysis according to the sample data set in the decision table so as to obtain a principal component table and an initial factor load matrix table; calculating a feature vector according to the principal component table and the initial factor load matrix table, and calculating the proportions of all indexes in the overall data; selecting the index values with high proportions in the proportion data, discretizing the breakpoints in the data set corresponding to the index values, selecting the breakpoints and forming a corresponding interval; generating an initial rule set, and the generating a final rule set of reactors according to the initial rule set and the interval; using the data participating in rough set mining as a test set, and performing test evaluation by using the final rule set so as to obtain a correct classification rate of three decision value ranges of the reactors, and carrying out the early warning of the excessive volatile fatty acids by means of a rough set.

Description

technical field [0001] The invention relates to the technical field of anaerobic early warning, in particular to a composite algorithm for early warning of excessive volatile fatty acids in anaerobic process. Background technique [0002] The anaerobic digestion process is a very complex process involving a large number of multi-step microscopic biochemical reaction processes inside microbial cells and the process of macroscopic mass transfer, heat transfer and energy transfer between solid-liquid-gas three-phase in anaerobic reactors. The fermentation process contains complex parameters, and the parameters are highly coupled and interact with each other. Monitoring and controlling the fermentation process is conducive to promoting fermentation efficiency and improving the stability and safety of the anaerobic fermentation process. Due to the existence of various buffer systems in the anaerobic reactor, the buffering effect on the pH makes the indicative indicator—volatile ...

AI Technical Summary

Problems solved by technology

The disadvantages of the above method are: 1. It is easily affected by missing data in the actual engineering application process; 2. When the data collection frequency is inconsistent, for example, the frequency of sampling test data is mostly 1 to 3 times per day, while the sensor online data The frequency is mostly every few seconds or every few minutes, which requires synchronous preprocessing of the data frequency, which is easy to introduce invalid data or loss of details, resulting in failure of prediction

[0007] Obviously, under the condition of sufficient data and accurate information, the soft sensor method can achieve the predicted data infinitely close to the detection value by establishing a mathematical model. In related research reports, this prediction method has also been verified by various experiments. However, in the actual operation of the project, various factors such as incomplete data information, missing data, dirty data, and signal interference often occur. These factors will affect the calculation of the mathematical model and lead to prediction failure in severe cases.

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