PATENT CLAIM ANALYSIS

Application Number: 15891175
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-06
Patent Classification: ["702", "025000"]

Abstract:
In various implementations, methods and systems are designed for predicting effluent total phosphorus (TP) concentrations in an urban wastewater treatment process (WWTP). To improve the efficiency of TP prediction, a particle swarm optimization self-organizing radial basis function (PSO-SORBF) neural network may be established. Implementations may adjust structures and parameters associated with the neural network to train the neural network. The implementations may predict the effluent TP concentrations with reasonable accuracy and allow timely measurement of the effluent TP concentrations. The implementations may further collect online information related to the estimated effluent TP concentrations. This may improve the quality of monitoring processes and enhance management of WWTP.

Claim (Index 22):
A method for monitoring a value of effluent TP of wastewater in a real-time manner, the method comprising:\n measuring, by a plurality of sensors, real-time information of variable parameters of a wastewater facility comprising an influent tank, an anaerobic tank, an aerobic tank, a second setting tank, and an effluent tank, the variable parameters comprising:\n a value of ORP in the anaerobic tank and the effluent tank, \n a temperature in the effluent tank, \n pH in the effluent tank, \n a value of DO in the aerobic tank, \n a value of COD in the effluent tank, \n a value of TN in the effluent tank, and \n a value of TSS in the aerobic ad tank; and \n training, by the one or more processors, a SORB neural network using pre-measured training data consistent of values of influent TP, values of redox potential, values of DO, temperatures, values of ORP, values of COD, values of TN, and values of TSS as well as corresponding values of effluent TP; and predicting, by the one or more processors, a value of current effluent TP using the trained SORBF neural network based on the received real-time information of variable parameters.

Metadata:
- Claim Count in Document: 17.0
- Percentile: 88.0
- Lexical Diversity: 1.64384
- Patent Class: 702.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['14668836', '13985482', '14234955', '15389755', '11996384']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1767050593310167
- 35 USC 102 Novelty (BERT): 0.5234078766972817
- Combined Prediction Score: 0.2113753410676432
- Mean Citation Score: 243.30053
- Max Citation Score: 318.0135
- Similarity Product: 220.3833424589038

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 0
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test