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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 AQChemSim?

AQChemSim, an advanced cloud-based service developed by SandboxAQ, employs Large Quantitative Models (LQMs) rooted in physical and chemical principles to revolutionize the field of materials discovery and improvement. By integrating methodologies such as Density Functional Theory (DFT), Iterative Full Configuration Interaction (iFCI), Generative AI, Bayesian Optimization, and Chemical Foundation Models, AQChemSim enables accurate simulations of molecular and material behavior in practical applications. Its capabilities include predicting performance across various stress scenarios, accelerating formulations through in silico assessments, and exploring environmentally friendly chemical processes. Notably, AQChemSim has made significant strides in the realm of battery technology, reducing the prediction time for the end-of-life of lithium-ion batteries by an impressive 95%, while achieving 35 times greater precision with only a fraction of the previously necessary data. This groundbreaking progress not only enhances the efficiency of research but also opens up opportunities for more sustainable energy solutions in the future. As such, AQChemSim stands at the forefront of innovation, driving advancements that could reshape entire industries.

Media

Media

Integrations Supported

Integrations Supported

Amazon Web Services (AWS)

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

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online 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

SandboxAQ

Date Founded

2021

Company Location

United States

Company Website

www.sandboxaq.com/solutions/aqchemsim

Categories and Features

Digital Twin

Not specified

Categories and Features

Chemical

Not specified

Materials Science

Not specified

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