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AspenTech University Industrial AI Curriculum

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Last Updated: 17-Mar-2025

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AspenTech University Industrial AI Curriculum

Introducing the Industrial AI Curriculum: Transforming Industries with Agility, Automation and Guidance

We're excited to announce availability of Industrial AI Curriculum, designed to equip you and your organization with the solutions to leverage artificial intelligence in the industrial sector to enhance manufacturing efficiency, streamline workflows and solve complex challenges. With a focus on agility, automation and guidance, our Curriculum includes-

Industry-Relevant Content: Developed by experts with deep knowledge in both AI and industrial applications.
Hands-On Training: Practical workshops enable you to apply AI solutions directly to real-world challenges.
Delivered by Experts: Learn directly from experts on how you can start leveraging Industrial AI solutions.

Harness the power of Industrial AI. Take your career—and your organization—to the next level with the following courses:

Industrial AI Key Focus	Product	Course Name	Course Description
Industrial AI Fundamentals	All Products	IAI101: Industrial AI Fundamentals: Unlocking Opportunities Through Agility, Guidance and Automation ^{Coming soon! (Want to be notified when this training is available? Click here!)}	The Industrial AI Fundamentals course provides a comprehensive understanding of how Industrial AI can be leveraged to optimize and transform industrial processes. This course provides an understanding of what differentiates Industrial AI from off the shelf machine learning methods and exemplifies problems that it solves in various industrial settings to drive agility, guidance and automation. The course also includes case studies on real world examples of AI implementation.
Agility	Aspen AI Model Builder	EHM101: Combine Domain Expertise and AI to Model Complex Processes	This course provides introduction to Aspen Hybrid Models^™, focusing on their applications in process simulation and optimization. Learn the fundamentals of hybrid modeling, including an overview of different modeling approaches, their advantages and disadvantages and how these models can be leveraged to solve complex engineering problems. Understand various workflows for building and deploying Aspen Hybrid Models, with an emphasis on both AI-driven and reduced-order sensor workflows. Gain practical experience in creating and applying hybrid models using Aspen Plus^® and/or Aspen HYSYS^®, focusing on real-world applications
Agility	Aspen Hybrid Models	EHM102: Enhance Model Accuracy in Industrial Applications with First Principles-Driven Hybrid Models	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.
Guidance	Aspen DMC3	APC024: Boost Controller Uptime and Performance with Industrial AI in Aspen DMC3^™	Learn how to apply latest DMC3 AI/ML innovations to your existing applications to improve controller uptime and performance with Aspen Deep Learning, Aspen Maestro and Aspen Virtual Advisor.
	Aspen Unified PIMS	AUP101: Maximize Margins with Aspen Unified PIMS^™ and AI-Driven Optimization Analysis	In this course, learn Aspen Unified PIMS (AUP), a powerful platform for integrated process modeling and optimization to maximize margins. The course covers the full spectrum of AUP features, from initial setup to advanced model management, optimizing model configurations, and managing model life cycles. Throughout the course, users will engage in workshops that reinforce each key topic, providing hands-on experience with tasks such as model migration, case management, price catalog creation and blending model inputs.
	Aspen Mtell	MPM101: Implement Predictive Maintenance with Machine Learning	Learn how to implement the Aspen Mtell^® project. You will understand System Architecture and the Installation procedure, learn how to conduct technical reviews for defining assets and sensor groups, set up security and connect to adapters, import data, build Machine Learning Agent, setting up alerting and use Aspen Mtell Alert Manager to monitor alerts. You will practice automatic agent tuning for the best accuracy and earliest detection of excursions from normal behavior, and the precise patterns that match degrading conditions that lead to failure.
	Aspen Mtell	MPM121: Design and Build Machine Learning Agents	Build Agents for use in Aspen Mtell real-time monitoring solution. You will learn the Aspen Mtell implementation methodology to create Machine Learning Agents for a single asset, practice agent tuning to create the most accurate agent with the earliest detection of failures. You will use Aspen Mtell Failure Agents to analyze past failure data and the precise patterns that match degrading conditions that lead to failure. Additionally, you will learn to deploy machine learning agents to fully specify your input data to create agents ready for continuous 24/7 protection of critical plan assets
Automation	Aspen SeisEarth	SEI111: Rock Type Classification using Machine Learning in Aspen SeisEarth	This course teaches how to use a unique machine learning approach to predict rock facies away from the wellbore using lithology logs and seismic data. Learn about the theory behind the Rock Type Classification workflow (which uses a probabilistic Democratic Neural Network Association) and understand how to define and QC the parameters to ensure the best results.
	Aspen SeisEarth	SEI112: Attribute Clustering using Unsupervised Machine Learning in Aspen SeisEarth	Learn how to use unsupervised machine learning for subsurface characterization using seismic data with Aspen SeisEarth^™. Gain knowledge on the Attribute Clustering Workflow in Integrated Canvas, which uses a self-growing neural network method to create facies and probability volumes from poststack and/or prestack seismic data.
	Aspen SeisEarth	SEI113: Waveform Classification using Machine Learning in Aspen SeisEarth	In this course, learn how to use a unique machine learning approach to delineate subtle variations in the seismic response associated with reservoir characteristics in Aspen SeisEarth^™. Understand the Waveform Classification workflow in Integrated Canvas, which uses a neural network approach to create facies volumes from input seismic attributes.
	Aspen Geolog	GEO207: Electrofacies Analysis Using Machine Learning	Learn Facimage functionality in Aspen Geolog^™ and gain hands-on experience with electrofacies analysis and data modeling. Understand Facimage MRGC (Multi Resolution Graph Based Clustering) and KNN (K-Nearest Neighbor) approach for simple, fast, and effective integration of all types of petrophysical and geological information including conventional logs, array and image logs, core measurement and core description.
	Aspen Tempest	TEM102: Assisted History Matching and Uncertainty Assessment Using Machine Learning	Learn how to perform history matching, prediction uncertainty and optimization workflows using Aspen Tempest ENABLE in conjunction with the reservoir simulator. Additionally, analyze the simulation results using the analytic dashboard

In addition, we offer training course on bringing your own model in any language hosted anywhere to learn how to implement custom agents in Aspen Mtell MPM221: Bring Your Own Model.