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What is gPROMS?

Siemens' gPROMS technology revolutionizes digital process design and operations, enabling organizations in the process industry to effectively tackle complex design and operational decisions daily, which ultimately leads to sustainable value creation. By leveraging in-depth process expertise through advanced predictive models, gPROMS allows for a quick and thorough exploration of the decision-making landscape concerning processes and products. This technology captures vital insights about processes, facilitating the use of advanced mathematical techniques for detailed analysis and optimization, thereby enhancing design and operational efficiency. Not only does this approach foster innovation, but it also aids in crafting economically viable process designs while reducing technology-related risks. The gPROMS Process modeling environment integrates the entire journey from concept to operations in process design, employing state-of-the-art analysis and optimization features on high-fidelity models. As a result, it accelerates the design timeline, accurately evaluates and mitigates risks, and pinpoints the most effective process configurations that yield value throughout the plant's lifecycle. Moreover, this comprehensive strategy helps organizations maintain their competitive edge in a swiftly transforming market landscape, ensuring their long-term success. Ultimately, such capabilities position companies not only to adapt but also to thrive amidst continuous change.

What is Geminus?

Geminus leverages the power of predictive intelligence by merging artificial intelligence with physical principles through cutting-edge multi-fidelity modeling techniques. Our groundbreaking AI, rooted in fundamental principles, integrates the physical constraints of reality into resilient predictive frameworks. The Geminus platform skillfully employs limited datasets to quickly assess the dynamics of complex industrial systems, facilitating accurate predictions about the impact of crucial business decisions. By fusing models with data, Geminus's multi-fidelity approach enables the rapid development of highly precise surrogates, achieving processing speeds more than 1,000 times quicker than traditional simulations. A distinctive feature of Geminus is its capability to effectively quantify model uncertainty, providing confidence in your forecasts and the strategic decisions that arise from them. Furthermore, Geminus dramatically shortens the model development timeline from several months to just a few hours, while requiring significantly less data and computational power than conventional AI or simulation methods. The models produced by Geminus are enriched with insights drawn from the actual behaviors of real-world systems, granting organizations a clearer understanding that improves decision-making. This revolutionary methodology not only optimizes the modeling process but also equips organizations with the agility to respond rapidly to evolving circumstances, ultimately enhancing their competitive edge in the marketplace.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

Windows
Linux

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

Siemens

Date Founded

1847

Company Location

Germany

Company Website

www.siemens.com/global/en/products/automation/industry-software/gproms-digital-process-design-and-operations.html

Company Facts

Organization Name

Geminus

Company Location

United States

Company Website

www.geminus.ai/

Categories and Features

Digital Twin

Not specified

Categories and Features

AI Translation

Not specified

Digital Twin

Not specified

Simulation

Not specified

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