Introduction to First Principles Driven Hybrid Models

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

Training Details

  • Course Id:


  • Duration:

    1 day(s)

  • CEUs Awarded:


  • Level:



- 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



- 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


EHY101 Introduction to Process Modeling using Aspen HYSYS

Subsequent Courses

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

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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.