Optimize Batch Process Performance Using Multivariate Data Analysis

Course Id:  PMV121   |   Duration:  1.00 day(s)   |   CEUs Awarded:  0.7   |   Level:  Introductory


Course Objective

This course will help you prepare for the certification exam and the exam fee is waived with this course.
This course will teach you how to get actionable insights from your industrial batch data and to use that information for process optimization and troubleshooting. You will learn how to relate time-varying process data, raw material properties and initial conditions to final product quality and productivity. Using this model you can then troubleshoot and optimize your batch processes.

Note: If you are interested in this training class you need to attend Optimize 2019 and select All Access Pass
To register please go to: https://www.optimize2019.com/events/optimize-2019/registration-bea4532088f6457b866d27d8a345765f.aspx?fqp=true

Benefits

  • Gain the practical skills and knowledge to use multi-block modelling to model your batch process.
  • Improve understanding of key process relationships for batch processes.
  • Identify key contributors to poor process performance for batch processes.
  • Troubleshoot and correct recurring process problems for batch processes.
  • Optimize process performance for batch processes.

Audience

  • Engineers / scientists / statisticians responsible for process troubleshooting, control and optimizing batch processes

Approach

  • Clear guidance on fundamental topics
  • Multivariate analysis workflows
  • Hands-on software labs
  • Experienced instructor-guided demonstration

Prerequisites

PMV101: Optimize Plant Performance using multivariate data analysis
 

Class Schedule

Class Agenda

PMV121: Optimize Batch Process Performance Using Multivariate Data Analysis

Introduction to Batch Processes and Data Structure
Analysis of Historical Batch Data
  • Use of key feature (landmark) approach
  • PCA/PLS analysis based on unfolded data
  • Interpretation of score and loading plots, contributions
  • Alignment and analysis of trajectories
  • Batch data unfolding techniques
  • Trouble-shooting batch problems
Online Monitoring of Batch Processes (Multivariate SPC)
  • Detecting & diagnosing abnormal batches
  • Monitoring based on batch-wise & observation-wise unfolding
Online Prediction & Control of Batch Processes
  • Prediction of final quality
  • Mid-course correction
  • End-point determination
Optimization of Batch Processes by Latent Variable Methods
  • Important concepts for the active use of latent variable models
  • Optimization of batch recipes and trajectories to achieve a desired product
  • Model Explorer and Model Optimizer features
Workshop sessions using industrial data are held throughout the day









 
  
      
     

    Aspen Technology, Inc. awards Continuing Education Units (CEUs) for training classes conducted by our organization. One CEU is granted for every 10 hours of class participation.