Course Overview - Review definition and advantages of using First Principles Driven Hybrid Models - Develop accurate hybrid models using both mechanistic and AI/ML fundamentals - Build and deploy predictive hybrid models within process simulators to improve accuracy for several units such as distillation columns, reactors, heat exchangers, pressure changers, separators, etc. - Use plant data to replace inadequately modeled relationships not fully captured by traditional engineering models. |
Who Should Attend - Process Engineers who would like to learn how to apply AI and machine learning to improve their modeling activities. - Engineers with basic training in Aspen Plus or Aspen HYSYS |
EHM102
1 day(s)
0.7
Introductory
- Learn how to leverage plan data to enhance first-principles models using AI/ML - Gain the practical skills and knowledge to begin using First Principles Driven Hybrid Models - Review and work through different examples of different First Principles Driven Hybrid Models to get hands-on experience |
Approach - Instruction on introductory topics - Discussion about the general workflow and the key elements for building First Principles Driven Hybrid Models - Instructor-led demonstrations of features - Detailed course notes - Workshops and Solution files |
EAP101 Introduction to Process Modeling using Aspen Plus OR EHY101 Introduction to Process Modeling using Aspen HYSYS |
EHM101: Introduction to Aspen Hybrid Models for Engineering |
Introduction to First Principles Driven Hybrid Models
· Introduce general concepts and definitions of First Principles Driven Hybrid Models · Identify different types of workflows and their uses · Recognize First Principles Driven Hybrid Modeling architecture and use cases
Data Formatting and Pre-Processing
· Become familiar with the format required for building First Principles Driven Hybrid Models from plant data · Identify different types of data pre-processing available · Understand the new AI training interface in the simulator · Learn how to import raw data into the simulator · Workshop: Format and Import Plant Data
Analyzing and Conditioning Raw Data
· Understand the tools to analyze raw data · Identify trends and correlations within the data. · Learn how to apply different conditioning techniques to raw data · Workshop: Analyze and Condition Raw Data
Building the Hybrid Model
· Evaluate the need to train a hybrid model for your process · Build a hybrid model from conditioned plant data · Select dependent and independent variables to be used in the hybrid model · Identify a Neural Network Output to be trained in the hybrid model · Workshop: Evaluate and Train a Hybrid Model
Validating the Hybrid Model
· Identify best practices to validate the hybrid model · Use the snapshot feature to try different data conditioning and NN configuration · Understand key parameters to evaluate the accuracy of the hybrid model before its deployment · Workshop: Validating the Hybrid Model
Deploying the Hybrid Model
· Learn how to deploy hybrid models in the process simulator · Explore the automatic changes made to the simulation interface once the model has been deployed. · Workshop: Deploying the Hybrid Model
Using and Sustaining the Hybrid Model
· Enter the minimum input required for running the hybrid model · Recognize the usability of the model · Identify the need to re-train the model with newly updated data · Workshop: Using and Sustaining the Model
The workshops for this course can be completed in both Aspen Plus or Aspen HYSYS |
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.