Sewage treatment process parameter long window prediction method
A process parameter and sewage treatment technology, applied in the field of sewage treatment, can solve problems such as difficulty in establishing an accurate mathematical model, early identification of sewage purification capacity of water treatment equipment, strong time-varying external interference, etc., and achieve the effect of improving efficiency
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Embodiment 1
[0042] refer to Figure 1-3 , which is an embodiment of the present invention, provides a long-window prediction method for parameters of a sewage treatment process, including:
[0043] S1: Collect predictor variable data and feature parameter data, and construct a data set based on the predictor variable data and feature parameter data.
[0044] The predictors and their related feature parameters are selected based on the data set, and multiple long-window prediction data sets are constructed by the sliding window method.
[0045] Using the method of data dimensionality reduction, the data in the high-dimensional space is projected to the low-dimensional space through a certain mapping function, and the dimension of the feature subset is reduced to obtain a new feature set{F 1 ,F 2 ...F p }, where p is the number of features in the feature set.
[0046] The process quantity parameter x that needs to be predicted in the water treatment system and the characteristic paramet...
Embodiment 2
[0093] refer to Figure 4-7 , is another embodiment of the present invention, which is different from the first embodiment in that it provides a long-window prediction method for parameters of a sewage treatment process. The traditional technical solution and the method of the present invention are used for comparative testing, and the test results are compared by means of scientific demonstration to verify the real effect of the method.
[0094] Based on different long-window forecasting datasets and data modeling schemes, long-term time series forecasts for water treatment process parameters are carried out. The whole prediction work mainly includes the following four parts.
[0095] 1) Choose predictors and feature parameters and construct the dataset:
[0096] The predictors were derived from three types of water treatment process equipment, multimedia filters, ultrafiltration units, and reverse osmosis units, including 32 parameters such as inlet flow, differential pres...
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