NDLI: Syngas Compositions Prediction by Neural Estimator Based on Multi-Scale Analysis and Dynamic PCA

Content Provider	IEEE Xplore Digital Library
Author	Rong Guo Xiaojuan Wang Haijun Hu
Copyright Year	2007
Description	Author affiliation: Xian Technol. Univ., Xian (Rong Guo; Xiaojuan Wang)
Abstract	Prediction of syngas compositions, the most important parameter in determining the product's grade and quality control of raw syngas produced in coal gasification, was studied. A neural estimator model based on dynamic principal component analysis (DPCA), back-propagation (BP) networks, and multi-scale analysis (MSA) was proposed to infer the syngas compositions from real process variables. DPCA was carried out to select the most relevant process features and to eliminate the correlations of input variables; multi-scale analysis was introduced to acquire much more information and to reduce uncertainly in the system; and BP networks were used to characterize the nonlinearity of the process. A prediction of the syngas compositions in Texaco coal gasification process was taken as a case study. Research results show that the proposed method provides promising prediction reliability and accuracy, and supposed to have extensive application prospects in coal gasification processes.
Starting Page	3077
Ending Page	3082
File Size	184022
Page Count	6
File Format	PDF
ISBN	9781424408276
DOI	10.1109/ICMA.2007.4304052
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Neural networks Information analysis Power system modeling Power engineering and energy Predictive models Chemical industry Instruments Mechatronics Automation Texaco coal gasification system Dynamic principal component analysis Multi-scale analysis Neural estimator
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Texaco coal gasification quality prediction by neural estimator based on MSA and dynamic PCA

Texaco Coal Gasification Quality Prediction by Neural Estimator Based on Dynamic PCA

Prediction of Syngas Compositions in Texaco Coal Gasification Process Using Robust Neural Estimator

Inferential Estimation of Texaco Coal Gasification Quality Using Stacked Neural Networks

Prediction of C3 Concentration in FCCU Using Neural Estimator Based on Dynamic PCA

Neural Soft-Sensor of Product Quality Prediction

A data-driven soft sensor modeling for furnace temperature of Opposed Multi-Burner gasifier

An Improvement Algorithm of Principal Component Analysis

Applying PCA to establish artificial neural network for condition prediction on equipment in power plant

Syngas Compositions Prediction by Neural Estimator Based on Multi-Scale Analysis and Dynamic PCA

Similar Documents

Texaco coal gasification quality prediction by neural estimator based on MSA and dynamic PCA

Texaco Coal Gasification Quality Prediction by Neural Estimator Based on Dynamic PCA

Prediction of Syngas Compositions in Texaco Coal Gasification Process Using Robust Neural Estimator

Inferential Estimation of Texaco Coal Gasification Quality Using Stacked Neural Networks

Prediction of C3 Concentration in FCCU Using Neural Estimator Based on Dynamic PCA

Neural Soft-Sensor of Product Quality Prediction

A data-driven soft sensor modeling for furnace temperature of Opposed Multi-Burner gasifier

An Improvement Algorithm of Principal Component Analysis

Applying PCA to establish artificial neural network for condition prediction on equipment in power plant

Syngas Compositions Prediction by Neural Estimator Based on Multi-Scale Analysis and Dynamic PCA