DuPont Uses AI-Driven AspenTech Hybrid Models™ to Improve Polymer Quality by 10%

Polymer producers often struggle with unreliable viscosity measurements and raw material variability, leading to reactive adjustments and inconsistent product quality.

A production facility in DuPont's Electronics & Industrial business used AspenTech Hybrid Models^™ and Aspen Plus^® to combine plant data, first-principles simulation and Industrial AI, creating models that deliver real-time quality insights and proactive operating guidance.

Download the case study to learn how to:

Build an inferential viscosity sensor from historical plant data
Identify the raw material properties that most influence polymer quality
Deploy hybrid models that provide proactive operating guidance

Discover how DuPont eliminated a 10% viscosity deviation from target while improving process stability.

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Industrial Data Fabric

Asset Performance Management

Digital Grid Management

Manufacturing and Supply Chain

Performance Engineering

Subsurface Science & Engineering

DuPont Uses AI-Driven AspenTech Hybrid Models™ to Improve Polymer Quality by 10%

By submitting this form, I agree to AspenTech's Privacy and Security Policy

Are you a current user of AspenTech solutions?

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