Improve Model Accuracy using AI driven process simulation with AI Model Builder
Accelerate innovation, gain insights, connect with industry leaders, and boost your skills through our premier worldwide user Optimize 26 exclusive trainings, register here today! 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. Use real-time plant data to enhance model accuracy to replace modeled relationships not fully captured by traditional engineering models. |
Audience:
- Control Engineers who are interested in using Machine learning/AI to improve quality control - Process Engineers who are interested in using Machine learning/AI to build complex models* today that are accurate and run quickly - Engineers and Managers involved in digitalization strategies for their companies - Anyone interested in Aspen Hybrid Models *Complex models: Simulations models for equipment that cannot be modeled today through first principles, Building digital twins for online deployment |
Training Details
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Course Id:
EHM026
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Duration:
1 day(s)
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CEUs Awarded:
0.7
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Level:
Introductory
Benefits
- Gain the practical skills and knowledge to begin using Aspen Hybrid Models - Learn differences between various workflows and information needed to create Aspen Hybrid models - Review and work through examples of different types of Aspen Hybrid Models to get hands-on experience |
Approach
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Pre-requisites
For Control Engineers: - APC105 Model and Build Aspen DMC3 controllers using Aspen DMC3 Builder For Process Engineers: - EAP101 Introduction to Process Modeling using Aspen Plus OR - EHY101 Introduction to Process Modeling using Aspen HYSYS |
Subsequent Courses
EHM101, EHM102 |
Agenda
Introduction to Aspen Hybrid Models - Understand what Aspen Hybrid Models and their applications are - Review different types of modeling approaches and their advantages and disadvantages - Recognize the importance of using hybrid modeling and how it can help solve complex problems Creating Aspen Hybrid Models - Understand what Aspen AI Model Builder is - Identify the workflow of Aspen AI Model Builder - Discuss how to access Aspen AI Model Builder - Understand how to create a new project in Aspen AI Model Builder Workshop 1: Create an AI Driven Hybrid model for a membrane using plant data Data Importing and Configuration - Review data format required for building the Hybrid Models - Understand how to import data from external sources to the Aspen AI Model Builder - Discuss how to configure the imported variables Workshop 2: Configure the project to build the hybrid model Analyzing and Cleaning Raw Data - Understand the tools to analyze raw data - Discuss different options available for data cleaning - Learn how to apply different conditioning techniques to raw data Building the Hybrid Model - Build a hybrid model from conditioned plant data - Select dependent and independent variables to be used in the Hybrid Model - Identify Machine Learning methods Workshop 3: Conditioning the Raw data and building the Hybrid Model Analyzing and Deploying the Hybrid Model - Analyze results for the model built using parity and coefficient plots as well as accuracy and predictability values - 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 4: Analyzing and Deploying Hybrid Model Introduction to Aspen Multi-Case - Review of what is Aspen Multi-Case - Discuss the advantages of using Aspen Multi-Case for building Reduced Order Models - Demonstrate steps involved in using Aspen Multi-Case Workshop 5: Use simulation data to build a Reduced Order Sensor to improve quality control in a distillation column for Aspen DMC3 Workshop 6: Create simulation data for a Cumene Production Process using Aspen Plus or Natural Gas Dehydration with TEG using Aspen HYSYS Additional Workshop Workshop 7: Create an Aspen Hybrid Model for improving quality control in a distillation column for Aspen DMC3 Workshop 8: Create an Aspen Hybrid Model for a Cumene Production Process using Aspen Plus or Natural Gas Dehydration with TEG using 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.