Intelligent identification method of sludge bulking based on type-2 fuzzy neural network

Patent metadata

Field	Value
Publication number	US-10919791-B2
Application number	US-201816143409-A
Country	US
Kind code	B2
Filing date	Sep 26, 2018
Priority date	Jul 18, 2018
Publication date	Feb 16, 2021
Grant date	Feb 16, 2021

Abstract

An intelligent identification method of sludge bulking based on type-2 fuzzy-neural-network belongs to the field of intelligent detection technology. The sludge volume index (SVI) in wastewater treatment plant is an important index to measure the sludge bulking of activated sludge process. However, poor production conditions and serious random interference in sewage treatment process are characterized by strong coupling, large time-varying and serious hysteresis, which makes the detection of SVI concentration of sludge volume index extremely difficult. At the same time, there are many types of sludge bulking faults, which are difficult to identify effectively. Due to the sludge volume index (SVI) is unable to online monitoring and the fault type of sludge bulking is difficult to determined, the invention develop soft-computing model based on type-2 fuzzy-neural-network to complete the real-time detection of sludge volume index (SVI). Combined with the target-related identification algorithm, the fault type of sludge bulking is determined. Results show that the intelligent identification method can quickly obtain the sludge volume index (SVI), accurate identification fault type of sludge bulking, improve the quality and ensure the safety operation of the wastewater treatment process.

First claim

What is claimed is:

1. An intelligent identification method for sludge bulking based on a type-2 fuzzy-neural-network, comprising the following steps:
(1) determine input and output variables of sludge volume index (SVI): in an activated sludge wastewater treatment process, the input variables of SVI soft-computing model include: dissolved oxygen (DO) concentration, total nitrogen (TN) concentration, organic load rate (F/M), pH, T, output values of the soft-computing model are SVI values, the sludge bulking contains the following fault types: low DO concentration, nutrient deficit, low sludge loading, low pH, and low temperature;
(2) SVI soft-computing model: establish the SVI soft-computing model based on type-2 fuzzy-neural-network, a structure of type-2 fuzzy-neural-network contains five layers: input layer, membership function layer, firing layer, consequent layer and output layer, the network is 5-M-L-2-1, including 5 neurons in the input layer, M neurons in the membership function layer, L neurons in the firing layer, 2 neurons in the consequent layer and 1 neurons in the output layer, M and L are integers larger than 2; connecting weights between the input layer and the membership function layer are assigned 1; the number of training samples is N, the input of type-2 fuzzy-neural-network is x(t)=[x 1 (t), x 2 (t), x 3 (t), x 4 (t), x 5 (t)] at time t, x 1 (t) represents DO concentration at time t; x 2 (t) represents TN concentration at time t, x 3 (t) represents an organic load rate (F/M) value at time t, x 4 (t) represents pH value at time t, and x 5 (t) represents T value at time t, the output of type-2 fuzzy-neural-network is y d (t) and an actual output is y(t); type-2 fuzzy-neural-network includes:
an input layer: there are 5 neurons in this layer, the output is:
o i ( t )= x i ( t )  (1)

where o i (t) is the ith output value at time t, i=1, 2, . . . , 5,
a membership function layer: there are M neurons in the membership function layer, the output is:

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Assignees

• Univ Beijing Technology

Inventors

• Han Honggui
• Liu Hongxu
• Li Jiaming
• Qiao Junfei

Classifications

• G06N3/043

  based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS] · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06F30/27

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title

• C02F2209/06

  pH · CPC title