Table of Contents
New Directions in Model Based Data Assimilation
Outline of Presentation
What is the Chemical Industry?
Models are Crucial in Chemical Engineering
Data Need: Model Verification / Discrimination
Other Dimensions of the Need for Data
High Bandwidth Data Assimilation -- Mixing
Measurements are Vital – Historical View
Success will Depend On...
Opportunities for New Algorithms/Theory
Overall Goal – From Bench to Plant Scales
A Simple Example – Linear Balance Equations
Solution to State Estimation Problem
Assumptions underlying Solution
Simple Problem
Solution in the Presence of Uncertainty
Key Message – Outcomes are Important
Example: Where to Allocate Resources
How do Uncertain Inputs effect the Outputs?
Attributes of an Uncertainty Analysis System
Incorporating Uncertainty at the Beginning
Curse of Dimensionality
Example of Uncertainty Analysis
Simple Reaction Sequence
Effect of Parameter Uncertainty on Variance
AMAT Centura Chemical Vapor Deposition Reactor
TCS Lower Wall Deposition Rate (ANOVA)
Research Opportunities in Uncertainty
Success will Depend On...
Opportunities in Computational Systems
Role of Computing in Data Assimilation
Visions for Software Architecture
Chemical Engineer’s Workbench
Need for Multi-scale Models and their Integration
Multi-Scale Integration of Software Systems
Data Structures and XML Representations
Reaction Mechanism Manager
Benefits of Model Based Process Controllers
Interacting with “Black Box” Models
Example -- Aspen Process Simulation
Hierarchical Information – Physical Properties
Environments to help Build Models
Virtual Plant Walkthrough
Opportunities from Computational Systems
Conclusions
John von Neumann
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