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博碩士論文 etd-0805105-001510 詳細資訊
Title page for etd-0805105-001510
論文名稱
Title
應用資料探勘技術於垃圾焚化爐作業之知識擷取
Operational Knowledge Acquisition of Refuse Incinerator Using Data Mining Techniques
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
86
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2005-07-26
繳交日期
Date of Submission
2005-08-05
關鍵字
Keywords
焚化爐、決策樹分類、特徵選取、分類分析、資料探勘
Decision Tree Classification, Refuse Incinerator, Classification Analysis, Feature Selection, Data Mining
統計
Statistics
本論文已被瀏覽 5664 次,被下載 2295
The thesis/dissertation has been browsed 5664 times, has been downloaded 2295 times.
中文摘要
對於穩定運作焚化爐來說,其中的機制是超乎想像的複雜,需要長期且完整的研究與反覆的試驗才能徹底地了解整個爐的特性。現今焚化爐皆裝配有大量的感應器,負責偵測爐內各部份的狀況並供操作人員了解爐的現況。這些大量的資料除了提供現場的監控外,理論上也可以對資料進行深入的分析,從中淬取出許多的資訊與操作經驗,提供決策參考的依據。但是受限於現階段電腦的處理能力,如此大量的資料很難直接透過電腦進行分析,因此在本研究中使用了特徵選取技巧中的Sequential Forward Floating Search algorithm (SFFS)來降低資料的維度、找出顯著的因子並避免不必要的資訊。挑選出顯著的因子後,本研究中採用了資料探勘技術中的決策樹技術,針對蒸氣產出量、NOx 產出量與SOx 產出量等三個因子建立相對應的決策樹模型,同時從決策樹模型中淬取出決策規則。本研究所建立的決策樹模型,經過驗證具有良好的預測能力,同時這些決策規則也可以配合正確率,提供相關人員進行現場操作的參考。
Abstract
The physical and chemical mechanisms in a refuse ncinerator are complex. It is difficult to make a full comprehension of the system without a thorough research and long-term on-site experiments. In addition, many sensors are equipped in refuse incineration plant and much data are collected, those data were supposed to be useful since there may be some operational experience within. But to cope with the huge data that may exceed the computation capability, sequential Forward Floating Search algorithm (SFFS) is used to reduce the data dimension and find relevant features as
well as to remove redundant information. In this research, data mining technique is applied toward three critical target attributes, steam production, NOx and SOx, to build decision tree models and extract operational experiences in the form of decision rules. Those models are evaluated by predicting accuracies, and rules extracted from decision tree models are also of great help to the on-site operation and prediction as well.
目次 Table of Contents
CHAPTER 1. INTRODUCTION 1
1.1 Research Background 1
1.2 Motivation and Objectives 4
CHAPTER 2. LITERATURE REVIEW 5
2.1 Refuse Incineration Control System 5
2.2 Feature Selection techniques 9
2.3 Data Mining Techniques 14
2.4 Decision Tree applications 19
CHAPTER 3. METHODOLOGIES 20
3.1 Feature Selection Techniques 20
3.2 Classification Analysis Techniques 30
CHAPTER 4. CONCEPTUAL MODELING AND
DATA REPARATION 35
4.1 Model Architecture 35
4.2 Data Collection 37
CHAPTER 5. RULE INDUCTION AND
KNOWLEDGE EXTRACTION 41
5.1 Feature Selection 41
5.2 Decision Tree Model 44
5.3 Rule Induction and knowledge extraction 50
CHAPTER 6. CONCLUSIONS AND SUGGESTIONS 58
6.1 Conclusions 58
6.2 Suggestions 59
REFERENCE 60
APPENDIX A: Summary of Rules for steam production 67
APPENDIX B: Summary of Rules for NOx 70
APPENDIX C: Summary of Rules for SOx 76
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