MARC details
| 000 -LEADER |
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| fixed length control field |
02652nam a2200241 a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
vtls000031326 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251114204413.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
080829t2008 my a f m 000 0 eng|d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0001923(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
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| Level of rules in bibliographic description |
201905131530 |
| Level of effort used to assign nonsubject heading access points |
yusri |
| Level of effort used to assign subject headings |
201107132226 |
| Level of effort used to assign classification |
VLOAD |
| Level of effort used to assign subject headings |
200908141515 |
| Level of effort used to assign classification |
VLOAD |
| Level of effort used to assign subject headings |
200908141444 |
| Level of effort used to assign classification |
VLOAD |
| -- |
200808291146 |
| -- |
ida84 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMP |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
QA76.87 .Z34 2008 rs Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Mohamad Zafarudin Mohamad |
| 245 10 - TITLE STATEMENT |
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| Title |
Estimation of vinyl acetate monomer concentration using neural network & partial least square / |
| Statement of responsibility, etc. |
Mohamad Zafarudin Mohamad |
| 246 3# - VARYING FORM OF TITLE |
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| Title proper/short title |
Estimation of vinyl acetate monomer concentration using neural network & partial least square |
| Medium |
[electronic resource] |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Kuantan, Pahang : |
| Name of publisher, distributor, etc. |
UMP, |
| Date of publication, distribution, etc. |
2008 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
70 p. : |
| Other physical details |
ill. ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 computer disc |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Project paper (Bachelor of Chemical Engineering) --- Universiti Malaysia Pahang - 2008 |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
This thesis is about the application of Artificial Neural Network (ANN) and Partial Least Square (PLS) on the chemical process plant. At the present time, the process and development in chemical plants are getting more complex and hard to measure. Therefore, the needs for a system that can help to supervise and control the process in the plant have to be accomplished in order to achieve higher performance and quality. As the emergence of Artificial Neural Network and Partial Least Square application nowadays to solve problem in various fields had given a great significant effect such as soft-sensor, lack of on-line measurement, and incorporate the safety issues while maintaining practicality and economic feasibility, both of the system are reliable to be adapted in the chemical plant. Furthermore, this thesis will be focusing more on the application of Artificial Neural Network and Partial Least Square as a estimation scheme in the chemical plant. Estimation by using Artificial Neural Network (ANN) and Partial Least Square (PLS) is popular in the present time as a mechanism to estimate the variables in the chemical plant. By implementing such system, the performance and quality of the plant will increased. For this thesis, the vinyl acetate monomer plant had been chosen as the case study to provide the necessary data and information to run the research. Vinyl acetate monomer process will provides a dependable source of data and an appropriate test for alternative control and optimization strategies for continuous chemical processes.-Author |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Neural networks (Computer science) |