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

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

Similarities

Basic Process Control applied to PAT

Case studies

Enterprise PAT Architecture

Manageable and maintainable system.

Top Down Approach

System Complex

Black Box

Build from Ground Up

Simple Blocks to Start

Build complexity

Learn

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

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, …

LOOP TUNING CONSTANTS

Work Against the Error◦ Error = Setpoint - Value

Proportional◦ Linear Error

Integral◦ Time

Derivative◦ Rate of Change

Cruise Control in Car

Building Blocks Approach Refinery Controls

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

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

Analyzer Interfaces

Spectral Analysis

Modeling Capability

Optimization

Process Control System Interface

Methods

CFR Part 11 Compliant

Siemens SIMATIC

Optimal SynTQ

ABB XPAT

GE Fanuc Proficy RX

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