1. Using traditional techniques to develop a
complex and maintainable PAT system.

2.  Paul Brodbeck – Process Control Engineer
 30 Years experience.
 BS in Chemical Engineering from Case Western Reserve University
 Process Modeling/Optimization
 APL Matrix Language - MATLAB.
 Statistical based analysis at Chemical Plant: FOCUS, SAS and SPC.
 Statistical Process Control
 Machine Learning Course with Stephen Ng from Stanford
 Eigenvector Software
 Recent PAT Application with Chromatography Endpoint Detection

3.  Similarities
 Basic Process Control applied to PAT
 Case studies
 Enterprise PAT Architecture
 Manageable and maintainable system.

5.  Top Down Approach
 System Complex
 Black Box
 Build from Ground Up
 Simple Blocks to Start
 Build complexity
 Learn

6. 1. Data Management
 Collection
 Storage
 Analytical Tools/Visualization
2. Process Model Building
 MATLAB, PCA, PLS, MPC, NN, Optimization
3. Process Control
 Implementation of Real-Time Prediction Models
 Closed-Loop Control of CPPs, CQAs

7. Basic PID Block Temperature Controller
 Basic Single Loop – PID Block
 Reactor Temperature Ctrl
 Closed-Loop Feedback
 Uni-Variate Process Inputs
 Temperature, Pressure,
Flow, pH, Level, …

8. LOOP TUNING CONSTANTS
 Work Against the Error
◦ Error = Setpoint - Value
 Proportional
◦ Linear Error
 Integral
◦ Time
 Derivative
◦ Rate of Change
 Cruise Control in Car

10. Building Blocks Approach Refinery Controls

12.  Analyzers
 Bruker, Thermo, RAMAN
 MVA Packages
 CAMO, Umetrics, Eigenvector, Infometrix
 MATLAB, Mathemtica
 Process Control Systems
 PC Based – Sartorius, ABEC, GE
 PLC – ABB, Rockwell
 DCS – Siemens, Emerson, Honeywell

13.  Analysis of Critical Quality Attributes (CQA)
◦ Mass Spectrometry, Infrared Spectroscopy,
◦ Raman, FBRM, NMR, UV Spectroscopy.
 Spectral Analysis MVA
◦ Chemometrics – Principal Component Analysis (PCA) &
Partial Least Squares (PLS)
◦ CAMO, UMetrics, EigenVector
 Modeling/Optimization MVA
◦ Linear Regression, Logistic Regression, Support Vector
Machines, Neural Networks, Clustering, Linear
Programming, PCA, PLS. MATLAB, Mathematica.
 Control of Critical Process Parameters (CPP)
◦ PC Based, PLC, DCS

17.  Analyzer Interfaces
 Spectral Analysis
 Modeling Capability
 Optimization
 Process Control System Interface
 Methods
 CFR Part 11 Compliant

18.  Siemens SIMATIC
 Optimal SynTQ
 ABB XPAT
 GE Fanuc Proficy RX

19.  Basic Process Control Loops
 Complex Control Strategies
 PAT Online Analyzers CQAs
 Closed Loop Control CPPs
 Enterprise PAT
 Collect Data
◦ Data Analytics
◦ Batch Analytics
◦ Multi Variable SPC
 Modeling/Optimization