List of Aspen Hybrid Models Customers
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Since 2010, our global team of researchers has been studying Aspen Hybrid Models customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Aspen Hybrid Models for Process Simulation from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Aspen Hybrid Models for Process Simulation include: Mitsubishi Chemical Japan, a Japan based Oil, Gas and Chemicals organisation with 13249 employees and revenues of $25.66 billion, Tupras, a Turkey based Oil, Gas and Chemicals organisation with 6208 employees and revenues of $19.30 billion, Corteva Agriscience, a United States based Manufacturing organisation with 22000 employees and revenues of $16.91 billion, Nissan Chemical Corporation, a Japan based Oil, Gas and Chemicals organisation with 2737 employees and revenues of $1.81 billion and many others.
Contact us if you need a completed and verified list of companies using Aspen Hybrid Models, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Corteva Agriscience | Manufacturing | 22000 | $16.9B | United States | AspenTech | Aspen Hybrid Models | Process Simulation | 2022 | n/a |
In 2022, Corteva Agriscience implemented Aspen Hybrid Models embedded in Aspen Plus to improve reboiler and heat-exchanger predictions for solvent distillation columns. The deployment targeted the "" Apps Category and was used to guide proactive maintenance and operational decision making in the United States.
Aspen Hybrid Models were configured to generate fast-value predictive models for heat-exchange and reboiler behavior, with individual models created in less than a day. Functional capabilities emphasized hybrid modeling workflows, combining first principles simulation from Aspen Plus with data-driven model components to produce near real-time predictions for column thermal performance and fouling risk.
Integration was centered on embedding Aspen Hybrid Models inside Aspen Plus process flowsheets, enabling operators and maintenance teams to consume model outputs within existing simulation and engineering workflows. Operational coverage focused on solvent distillation columns and associated heat-exchanger trains, informing maintenance planning to reduce the likelihood of unscheduled shutdowns.
Governance and rollout emphasized rapid model provisioning and operational adoption, with models reported to deliver fast insights for maintenance crews and process engineers. The implementation was reported in the vendor case study to help prevent unscheduled shutdowns, and the case study noted estimated annual avoided maintenance and sales losses.
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Mitsubishi Chemical Japan | Oil, Gas and Chemicals | 13249 | $25.7B | Japan | AspenTech | Aspen Hybrid Models | Process Simulation | 2023 | n/a |
In 2023, Mitsubishi Chemical Japan implemented Aspen Hybrid Models with Aspen Plus in its polymer manufacturing operations in Japan, Apps Category . The deployment targeted product quality prediction and prevention to reduce manual on-site sampling and to accelerate operator time-to-value.
The implementation combined first-principles process simulation with machine learning, leveraging Aspen Hybrid Models and the Aspen Plus module for process model coupling and quality prediction. Functional capabilities implemented included model-based quality prediction, automated anomaly detection, and prevention workflows that provide operator decision support and run-time quality forecasts.
Integration scope explicitly included Aspen Plus as the simulation engine, with hybrid models consuming process variables and delivering forecasts and alerts into operations and quality control workflows. Governance emphasis centered on maintaining hybrid model fidelity and embedding predictions into operator workflows to drive faster operator time-to-value, and the Japan case reported reduced on-site sampling alongside improved detection and avoidance of product quality issues.
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Nissan Chemical Corporation | Oil, Gas and Chemicals | 2737 | $1.8B | Japan | AspenTech | Aspen Hybrid Models | Process Simulation | 2021 | Aspen Technology |
In 2021, Nissan Chemical Corporation deployed Aspen Hybrid Models embedded in Aspen Plus to improve steam-reformer modeling for its ammonia plant in Japan. The deployment used Aspen Hybrid Models in Apps Category "" to reproduce real plant data and produce a faster, more accurate steam-reformer model.
Aspen Hybrid Models were implemented as first principles driven hybrid models within Aspen Plus process simulation flowsheets, combining physics based reactor modeling with data driven calibration and parameter identification. Configuration emphasized steam-reformer specific model parameterization and dynamic calibration against field measurements to align simulation outputs with plant behavior.
The project was developed working with Aspen Technology as implementation partner, integrating Aspen Hybrid Models outputs into process engineering workflows and operator model libraries. Operational coverage targeted the ammonia plant site in Japan and impacted process engineering, operations, and plant performance modeling functions.
Governance and delivery were partner led, with model validation cycles against on site data and iterative calibration. The implementation reproduced real plant data at twice the execution speed of the conventional model and delivered measurable accuracy and speed improvements, identifying up to approximately 1% potential operating cost reduction through optimized steam input.
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Oil, Gas and Chemicals | 6208 | $19.3B | Turkey | AspenTech | Aspen Hybrid Models | Process Simulation | 2015 | n/a |
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