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博碩士論文 etd-0828106-170027 詳細資訊
Title page for etd-0828106-170027
論文名稱
Title
運用資料探勘技術於煉鐵製程中焦炭品質預測之研究
Prediction of Coke Quality in Ironmaking Process: A Data Mining Approach
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
62
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
2006-07-03
繳交日期
Date of Submission
2006-08-28
關鍵字
Keywords
焦炭、資料探勘、M5模式樹、倒傳遞類神經網路、高爐、煉鐵
Blast Furance, Coke, Ironmaking, M5 Model Trees, Data Mining, Backpropagation Neural Networks
統計
Statistics
本論文已被瀏覽 5754 次,被下載 4112
The thesis/dissertation has been browsed 5754 times, has been downloaded 4112 times.
中文摘要
資料探勘技術已經廣泛地被應用在各項科學研究上,也印證資料探勘從資料中所萃取潛在知識可用於科學決策輔助的可行性。高爐煉鐵製程中,焦炭在高爐所扮演之角色無法被取代,提供均質及品質佳的焦炭對提升高爐操作的穩定性及效率是相當重要的。在原料短缺及高原料成本下,要生產高爐所需要穩定品質的焦炭,為焦炭製造技術中ㄧ重要議題。
本研究目的為運用倒傳遞類神經網路技術及模式樹技術來預測焦炭強度及平均粒度,並定義混合煤物化性模式、煤種使用比例模式、煤種群組模式及延伸模式用以預測焦炭品質。經實證結果,本研究發現,煤種使用比例模式有最高之相關係數及最低之平均絕對誤差,此外模式樹技術比倒傳遞類神經網路技術有較佳之準確性及效率。
Abstract
Coke is an indispensable material in Ironmaking process by blast furnace. To provide good and constant quality coke for stable and efficient blast furance operation is very important. Furthermore, a challenging issue in the cokemaking process is the prediction of coke quality. An accurate prediction can support production planning decision and reduce business operation costs.
The objective of this thesis is to apply the backpropagation neural network and the model tree techniques for predicting the strength and meansize of coke. Specifically, we developed the coke- physical&chemical-property model, coal-usage model, coal-group-usage model, and extended model for the target prediction task. Experimentally, we found that the coal-usage model achieves the highest Correlation Coefficient and the lowest Mean Absolute Error. Moreover, the model trees technique reaches higher accuracy and better efficiency than does the backpropagation neural network technique.
目次 Table of Contents
第一章 緒論
第一節 研究背景 1
第二節 研究動機與目的 3
第三節 論文結構 5
第二章 文獻探討
第一節 傳統焦炭品質預測之方法 7
第二節 混合煤物化性與焦炭品質之趨勢關係 13
第三節 倒傳遞類神經網路 17
第四節 模式樹 22
第三章 以資料探勘技術建構焦炭品質預測模式
第一節 混合煤物化性模式 29
第二節 煤種使用比例模式 31
第三節 煤種群組模式 32
第四節 延伸模式說明 34
第四章 實證評估與結果
第一節 資料描述 35
第二節 評估準則與程序 37
第三節 實證結果分析 39
第四節 預測結果效能分析 44
第五章 結論與建議
第一節 研究結論及貢獻 46
第二節 未來研究方向 47
參考文獻 References
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