System and method for dynamic-modular-neural-network-based municipal solid waste incineration nitrogen oxides emission prediction

Patent metadata

Field	Value
Publication number	US-2024078410-A1
Application number	US-202318450945-A
Country	US
Kind code	A1
Filing date	Aug 16, 2023
Priority date	Aug 18, 2022
Publication date	Mar 7, 2024
Grant date	—

Abstract

A dynamic modular neural network (DMNN) for NOx emission prediction in MSWI process is provided. First, the input variables are smoothed and normalized. Then, a feature extraction method based on principal component analysis (PCA) was designed to realize the dynamic division of complex conditions, and the prediction task to be processed was decomposed into sub-tasks under different conditions. In addition, aiming each sub-tasks, a long short-term memory (LSTM)-based sub-network is constructed to achieve accurate prediction of NOx emissions under various working conditions. Finally, a cooperative strategy is used to integrate the output of the sub-networks, further improving the accuracy of prediction model. Finally, merits of the proposed DMNN are confirmed on a benchmark and real industrial data of a municipal solid waste incineration (MSWI) process. The problem that the NOx emission of MSWI process is difficult to be accurately predicted due to the sensor limitation is effectively solved.

First claim

What is claimed is:

1 . A method for dynamic-modular-neural-network (DMNN)-based municipal solid waste incineration (MSWI) process nitrogen oxides (NOx) emission prediction, comprising steps of:
obtaining sensor data associated with an MSWI process, the sensor data comprising a data set comprising a plurality of samples; preprocessing the sensor data to remove those of the samples that comprise noise and standardizing the data set; decomposing a task of prediction of a NOx emission associated with an MSWI process into a plurality of sub-tasks using principal component analysis, comprising applying a sliding window of a fixed size to the preprocessed sensor data set and identifying key variables of operating conditions of the MSWI process by key variables by applying a sliding window to the preprocessed sensor data set, each of the key variables associated with one of the sub-tasks: constructing a long-short-term memory (LSTM) neural network, the LTSM neural network comprising a plurality of sub-networks, wherein each of the sub-networks outputs a value for one of the sub-tasks and a key variable associated with that sub-task serves as an input for that sub-network; obtaining a further set of sensor data associated with a further MSWI process, the further sensor data comprising further data samples; comparing at least one of the further samples to at least some of the samples in the preprocessed sensor data set; activating at least some of the sub-networks based on the comparison; and using the activated subnetworks in the LTSM network to predict the NOx emission for the further MSWI process, wherein the steps are performed by at least one suitably-programmed computer and wherein a plant associated with the further MSWI process is operated based on the NOx prediction for the further MSWI process.

2 . A method according to claim 1 , wherein the comparison comprises finding a similarity between the at least one of the further samples and the at least some of the samples in the preprocessed sensor data set.

3 . A method according to claim 2 , wherein the similarity is determined using Euclidian distance.

4 . A method according to claim 1 , wherein each sub-network comprises at least one cell that comprises an input, a forget, an output, and a cell state gate.

5 . A method according to claim 1 , wherein the sensor data is obtained using one or sensors, the sensors comprising one or more a thermocouple temperature sensor, an air volume sensor, a liquid flow sensor, a continuous emission monitoring system, distributed control system and upper computer.

6 . A method according to claim 1 , wherein the sensor data comprises air flow of combustion grate left side 1-1, air flow of dry grate left side 1, temperature of primary combustion chamber, left side temperature of primary combustion chamber, right side temperature of primary combustion chamber, cumulative primary air flow, cumulative secondary air flow, accumulated urea solution flow, accumulated urea solution supply flow and a NOx emission value associated with the MSWI process.

7 . A method according to claim 1 , wherein the window moves forward along the preprocessed sensor data set by a step and the key variables are determined successively.

8 . A method according to claim 1 , wherein the NOx emission for the further MSWI process is predicted in accordance with:

y
ˆ

NOx

=

∑

r
=
1

R

y
ˆ

NOx
r

R

,

where ŷ NOx denotes the predicted value of the NOx emission, and ŷ NOx r is a sub-network output, and r=1, 2, . . . , R, R represents a number of the activated sub-networks.

9 . A method according to claim 1 , wherein the at least one suitably-programmed computer receives the further sensor data in real-time.

10 . A method according to claim 1 , wherein a denitration control system of the plant is controlled based on the for the further MSWI process.

11 . A system for dynamic-modular-neural-network (DMNN)-based municipal solid waste incineration (MSWI) process nitrogen oxides (NOx) emission prediction, comprising:
at least one computer configured to:
obtaining sensor data associated with an MSWI process, the sensor data comprising a data set comprising a plurality of samples;
preprocessing the sensor data to remove those of the samples that comprise noise and standardize the data set;
decompose a task of prediction of a NOx emission associated with an MSWI process into a plurality of sub-tasks using principal component analysis, comprising applying a sliding window of a fixed size to the preprocessed sensor data set and identifying key variables of operating conditions of the MSWI process by key variables by applying a sliding window to the preprocessed sensor data set, each of the key variables associated with one of the sub-tasks:
construct a long-short-term memory (LSTM) neural network, the LTSM neural network comprising a plurality of sub-networks, wherein each of the sub-networks outputs a value for one of the sub-tasks and a key variable associated with that sub-task serves as an input for that sub-network;
obtain a further set of sensor data associated with a further MSWI process, the further sensor data comprising further data samples;
compare at least one of the further samples to at least some of the samples in the preprocessed sensor data set;
activate at least some of the sub-networks based on the comparison; and
use the activated subnetworks in the LTSM network to predict the NOx emission for the further MSWI process, wherein a plant associated with the further MSWI process is operated based on the NOx prediction for the further MSWI process.

12 . A system according to claim 11 , wherein the comparison comprises finding a similarity between the at least one of the further samples and the at least some of the samples in the preprocessed sensor data set.

13 . A system according to claim 12 , wherein the similarity is determined using Euclidian distance.

14 . A system according to claim 11 , wherein each sub-network comprises at least one cell that comprises an input, a forget, an output, and a cell state gate.

15 . A system according to claim 11 , wherein the sensor data is obtained using one or sensors, the sensors comprising one or more a thermocouple temperature sensor, an air volume sensor, a liquid flow sensor, a continuous emission monitoring system, distributed control system and upper computer.

16 . A system according to claim 11 , wherein the sensor data comprises air flow of combustion grate left side 1-1, air flow of dry grate left side 1, temperature of primary combustion chamber, left side temperature of primary combustion chamber, right side temperature of primary combustion chamber, cumulative primary air flow, cumulative secondary air flow, accumulated urea solution flow, accumulated urea soluti

Assignees

• Univ Beijing Technology

Inventors

• Qiao Junfei
• Duan Haoshan
• Meng Xi
• Tang Jian

Classifications

• G06N3/045 Primary

  Combinations of networks · CPC title

• F23G5/50 Primary

  Control or safety arrangements · CPC title

• F23G2207/30

  Oxidant supply · CPC title

• F23G2207/60

  Additives supply · CPC title

• G06Q10/04 Primary

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title