Advanced Engineering的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列線上看、影評和彩蛋懶人包

Model-Based Optimization for Petroleum Refinery Configuration Design

為了解決Advanced Engineering的問題，作者Khor, Cheng Seong 這樣論述：

An accessible, easy-to-read introduction to the methods of mixed-integer optimization, with practical applications, real-world operational data, and case studiesInterest in model-based approaches for optimizing the design of petroleum refineries has increased throughout the industry in recent yea

rs. Mathematical optimization based on mixed-integer programming has brought about the superstructure optimization method for synthesizing petroleum refinery configurations from multiple topological alternatives. Model-Based Optimization for Petroleum Refinery Configuration Design presents a detaile

d introduction to the use of mathematical optimization to solve both linear and nonlinear problems in the refining industry. The book opens with an overview of petroleum refining processes, basic concepts in mathematical programming, and applications of mathematical programming for refinery optimiza

tion. Subsequent chapters address superstructure representations of topological alternatives, mathematical formulation, solution strategies, and various modeling frameworks. Practical case studies demonstrate refinery configuration design, refinery retrofitting, and real-world issues and considerati

ons. Presents linear, nonlinear, and mixed-integer programming approaches applicable to both new and existing petroleum refineriesHighlights the benefits of model-based solutions to refinery configuration design problemsFeatures detailed case studies of the development and implementation of optimiza

tion modelsDiscusses economic considerations of heavy oil processing, including cash flow analysis of refinery costs and return on capitalIncludes numerical examples based on real-world operational data and various commercial technologiesModel-Based Optimization for Petroleum Refinery Configuration

Design is an invaluable resource for researchers, chemical engineers, process and energy engineers, other refining professionals, and advanced chemical engineering students.

Advanced Engineering進入發燒排行的影片

一個使用智慧型手機實現深度學習心電圖分類的心臟疾病辨識系統

為了解決Advanced Engineering的問題，作者何亞恩 這樣論述：

目錄誌謝 i摘要 iiAbstract iii目錄 v圖目錄 viii表目錄 xi第一章 緒論 11.1研究動機 11.2研究目的 21.3研究架構 2第二章 研究背景 32.1心電圖與疾病介紹 32.1.1心臟導程 32.1.2心臟疾病介紹 52.2Android系統 102.2.1 Android的基礎 102.2.2 Android系統框架 102.3相關文獻探討 11第三章 研究方法 173.1資料庫介紹 173.2訊號前處理 193.2.1小波濾波 193.2.2訊號正規化 213.3一維訊號轉二維影像 213.3.1手機螢幕上

繪製圖形 213.3.2影像儲存於智慧型手機 233.3.3資料擴增Data Augmentation 243.4深度學習架構 253.4.1多卷積核架構 253.4.2注意力模型 283.4.2.1通道注意力模組Channel attention 293.4.2.2空間注意力模組Spatial attention 303.4.2.3激活函數Activation function 303.5損失函數Loss function 313.6交叉驗證Cross validation 323.7優化訓練模型 333.8移動端應用 343.9硬體設備、軟體環境與開發環境 36

3.9.1硬體設備 363.9.2軟體環境與開發環境 37第四章 研究結果與討論 3834.1評估指標 384.2訓練參數設定 404.3實驗結果 414.3.1深度學習模型之辨識結果 414.3.1.1比較資料擴增前後之分類結果 414.3.1.2不同模型架構之分類結果 424.3.2智慧型手機應用結果 464.4相關文獻比較 48第五章 結論與未來展望 525.1結論 525.2未來展望 53參考文獻 54

Advanced Thermodynamics for Chemical Engineers

為了解決Advanced Engineering的問題，作者Fried, Joel R. 這樣論述：

Dr. Joel R. Fried is professor and past chair of the Chemical Engineering Department at the University of Louisville, Professor Emeritus of the University of Cincinnati, and Fellow of the American Institute of Chemical Engineers. He is an internationally recognized authority in the area of polymer s

cience and engineering, and is the author of all three editions of Polymer Science and Technology.

利用焙燒暨酸浸法從廢棄LED晶粒中回收鎵金屬資源

為了解決Advanced Engineering的問題，作者吳德懷 這樣論述：

LED是發光二極體(Light Emitting Diode)的簡稱。由於LED燈具有節能、無汞等特性，在照明市場之需求日益增加，LED在許多領域已經取代了傳統光源(白熾燈、螢光燈等)。LED燈之高效率白光照明主要是由LED晶粒中氮化鎵(GaN)半導體所產生。隨著LED市場的擴大，未來將產生大量的LED廢棄物。因此，回收廢棄LED中所含的鎵金屬資源對於資源的可持續利用和環境保護都具有重要意義。本研究以廢棄LED燈珠為對象，利用焙燒與酸浸法從其LED晶粒中回收鎵金屬資源，主要包括三個部分：化學組成分析、氟化鈉焙燒處理與酸溶浸漬等。探討各項實驗因子包括焙燒溫度、焙燒時間、礦鹼比、酸浸漬種類及濃度

、浸漬時間、及浸漬固液比等，對於鎵金屬浸漬率之影響，並與各文獻方法所得到的鎵金屬浸漬效果進行比較。研究結果顯示，LED晶粒中含有鎵5.21 wt.%，氟化鈉焙燒暨酸溶浸漬之最佳條件為焙燒溫度900 ℃、焙燒時間3hr、礦鹼比1:6.95、鹽酸浸漬濃度0.5 M、浸漬溫度25 ℃、浸漬時間10mins、固液比2.86 g/L，鎵金屬浸漬率為98.4%。與各文獻方法相比較，本方法可於相對低溫且常壓下獲得較高之鎵金屬浸漬效果。