Type-2 fuzzy neural network-based cooperative control method for wastewater treatment process

Patent metadata

Field	Value
Publication number	US-11530139-B2
Application number	US-201916694911-A
Country	US
Kind code	B2
Filing date	Nov 25, 2019
Priority date	Sep 23, 2019
Publication date	Dec 20, 2022
Grant date	Dec 20, 2022

Abstract

A cooperative fuzzy-neural control method is designed in this present invention. Due to the difficulty for cooperatively controlling the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process, a cooperative fuzzy-neural control method is investigated. In this proposed method, firstly, a interval type-2 fuzzy neural network is employed to construct the cooperative fuzzy-neural controller. Secondly, a parameter cooperative strategy is proposed to cooperatively optimize the global and local parameters of the cooperative fuzzy-neural controller to meet the control requirements. This proposed cooperative fuzzy-neural control method can cooperatively control the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process. The results illustrate that the proposed cooperative fuzzy-neural control method can achieve the high control accuracy and guarantee the normal operations of wastewater treatment process under the different operation conditions.

First claim

What is claimed is:

1. A type-2 fuzzy neural network-based cooperative control method for controlling dissolved oxygen (DO) and nitrate nitrogen (NO 3 —N) concentrations of wastewater treatment process (WWTP), wherein aeration value and internal backflow value are used as control variables, the DO and NO 3 —N concentrations are used as controlled variables, the method comprising the following steps:
(1) design a type-2 fuzzy neural network (T2FNN) for controlling the DO and NO 3 —N concentrations, the T2FNN contains five-layers: an input layer, a membership layer, a rule layer, a consequent layer and an output layer, wherein:
the input layer contains 4 input neurons and an input vector is:
X(t)=[x 1 (t), x 2 (t), x 3 (t), x 4 (t)] T   (1)

where X(t) is the input vector of the T2FNN at time t, x 1 (t) is an error between a set-point and a measured value of DO concentration at time t, x 2 (t) is an error variation between the set-point and the measured value of DO concentration at time t, x 3 (t) is an error between a set-point and a measured value of NO 3 —N concentration at time t, x 4 (t) is an error variation between the set-point and the measured value of NO 3 —N concentration at time t, T represents a revolution of the weight matrix of the input layer and the input vector;
the membership layer contains P membership neurons and a neuron represents an interval type-2 membership function:

m
_

ij

(

x
i

(
t
)

)

=

{

e

-

1
2

⁢

(

x
i

(
t
)

-

c
_

ij

(
t
)

σ
ij

(
t
)

)

2

,

x
i

(
t
)

≤

(

c
_

ij

(
t
)

+

c
_

ij

(
t
)

)

/
2

e

-

1
2

⁢

(

x
i

(
t
)

-

c

Assignees

• Univ Beijing Technology

Inventors

• Han Honggui
• Li Jiaming
• Wu Xiaolong
• Qiao Junfei

Classifications

• G06N3/043

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

• G06F17/16

  Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} · CPC title

• G06N3/08

  Learning methods · CPC title

• C02F2209/22

  O2 · CPC title

• C02F3/305

  characterised by the denitrification · CPC title