27-01-2022 дата публикации
Номер: US20220027706A1
Принадлежит:
An intelligent warning method based on knowledge-fuzzy learning algorithm is designed for membrane fouling with high accuracy. A multi-step prediction strategy, using the least-squares linear regression model, is developed to predict the characteristic variables of membrane fouling Meanwhile, the knowledge of membrane fouling category, which is extracted from the real wastewater treatment process, can be expressed as the form of fuzzy rules. Moreover, a knowledge-based fuzzy neural network is designed to establish the membrane fouling warning model, thus deal with the problem of difficult warning of membrane fouling. The results reveal that the intelligent warning method can improve the ability to solve the membrane fouling, mitigate the deleterious effect on the process performance and ensure the safety operation of the wastewater treatment process. 1(1) determine characteristic variables of membrane fouling: for a sewage treatment process of membrane bioreactor, sewage treatment process variables are analyzed and the characteristic variables of membrane fouling are selected, including water permeability (P), permeability decay (PD), water flow (WF), gas washing size (GWS), sludge concentration (SC), transmembrane pressure (TMP), water turbidity (WT) and permeability recovery rate (PR); {'br': None, 'i': x', 'n+', 'a', '+a', 'x', 'n', 'a', 'x', 'n+', 'a', 'x', 'n+', 'a', 'x', 'n+, 'sub': d', 'd0', 'd1', 'd', 'd2', 'd', 'd3', 'd', 'd4', 'd, '(4)=()+(1)+(2)+(3),\u2003\u2003(1)'}, '(2) predict the characteristic variables of membrane fouling: the characteristic variables are obtained by a multi-step prediction strategy based on a least-squares linear regression model; values of a characteristic variable with first four moments are used to predict the characteristic variable at a fifth moment; the estimation of a characteristic variable using the least-squares linear regression model can be expressed as. A membrane fouling warning method based on knowledge-fuzzy ...
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