Introduction to First Principles Driven Aspen Hybrid Models
Upon successful completion of this course, you will be able to: Understand the power of first principles-driven hybrid models in industrial applications. Learn the advantages of integrating mechanistic principles with AI and machine learning techniques to create accurate, predictive models. Develop hybrid models tailored for critical processes such as distillation columns, reactors, heat exchangers, pressure changers, and separators. Additionally, use real-time plant data to enhance model accuracy to replace inadequately modeled relationships not fully captured by traditional engineering models. |
Audience:
- 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 |
Training Details
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Course Id:
CEHM102
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Duration:
0.5 day(s)
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CEUs Awarded:
0.4
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Level:
Introductory
Benefits
- 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 |
Pre-requisites
Basic knowledge of Aspen Plus/Aspen HYSYS recommended |
Subsequent Courses
EHM101: Introduction to Aspen Hybrid Models for Engineering |
Agenda
Introduction to First Principles Driven Hybrid Models Learn the 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 preprocessing available Understand the new AI training interface in the simulator 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 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 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 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 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 Additional Materials: workshop instructions will be provided for each topic as optional resources to practice after the session |
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.