Select an Action
Intelligent systems in process engineering. Part II, Paradigms from process operations
Title:
Intelligent systems in process engineering. Part II, Paradigms from process operations
Author:
Stephanopoulos, George.
ISBN:
9780080565699
9780120085224
Publication Information:
San Diego : Academic Press, 1995.
Physical Description:
1 online resource (xxxi, 313-625 pages) : illustrations
Series:
Advances in chemical engineering, 22
Advances in chemical engineering ; 22.
Contents:
Front Cover; Advcances in Chemical Engineering, Volume 22; Copyright Page; Contents; Contributors to Volume 22; Prologue; Chapter 1. Nonmonotonic Reasoning: The Synthesis of Operating Procedures in Chemical Plants; I. Introduction; II. HierarchicalModeling of Processes and Operations; III. Nonmonotonic Planning; IV. Illustrations of Modeling and Nonmonotonic Operations Planning; V. Revamping Process Designs to Ensure Feasibility of Operating Procedures; VI. Summary and Conclusions; References; Chapter 2. Inductive and Analogic Learning: Data-Driven Improvement of Process Operations.
I. IntroductionII. General Problem Statement and Scope of the Learning Task; III. A Generic Framework to Describe Learning Procedures; IV. Learning with Categorical Performance Metrics; V. Continuous Performance Metrics; VI. Systems with Multiple Operational Objectives; VII. Complex Systems with Internal Structure; VIII. Summary and Conclusions; References; Chapter 3. Empirical Learning through Neural Networks: The Wave-Net Solution; I. Introduction; II. Formulation of the Functional Estimation Problem; III. Solution to the Functional Estimation Problem.
IV. Applications of the Learning AlgorithmV. Conclusions; VI. Appendices; References; Chapter 4. Reasoning in Time: Modeling, Analysis, and Pattern Recognition of Temporal Process Trends; I. Introduction; II. Formal Representation of Process Trends; III. Wavelet Decomposition: Extraction of Trends at Multiple Scales; IV. Compression of Process Data through Feature Extraction and Functional Approximation; V. Recognition of Temporal Patterns for Diagnosis and Control; VI. Summary and Conclusions; References.
Chapter 5. Intelligence in Numerical Computing: Improving Batch Scheduling Algorithms through Explanation-Based LearningI. Introduction; II. Formal Description of Branch-and-Bound Framework; III. The Use of Problem-Solving Experience in Synthesizing New Control Knowledge; IV. Representation; V. Learning; VI. Conclusions; References; Index; Contents of Volume in This Serial.
Abstract:
Volumes 21 and 22 of Advances in Chemical Engineering contain ten prototypical paradigms which integrate ideas and methodologies from artificial intelligence with those from operations research, estimation andcontrol theory, and statistics. Each paradigm has been constructed around an engineering problem, e.g. product design, process design, process operations monitoring, planning, scheduling, or control. Along with the engineering problem, each paradigm advances a specific methodological theme from AI, such as: modeling languages; automation in design; symbolic and quantitative reason.
Genre:
Electronic Access:
ScienceDirect https://www.sciencedirect.com/science/book/9780120085224 ScienceDirect https://www.sciencedirect.com/science/bookseries/00652377/22Available:*
Shelf Number | Item Barcode | Shelf Location | Status |
|---|---|---|---|
| TP155.75 .I58 EB VOL. 22, PT. 2 | 1179296-1001 | Elsevier E-Book Collections | Searching... |
On Order
Select a list
Make this your default list.
The following items were successfully added.
There was an error while adding the following items. Please try again.
:
Select An Item
Data usage warning: You will receive one text message for each title you selected.
Standard text messaging rates apply.