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Description
Atinary's Self-Driving Labs (SDLabs) platform offers a no-code solution for AI and machine learning, aimed at transforming research and development workflows by allowing conventional laboratories to move from hands-on experiments to fully autonomous experimentation. This platform enhances the design and refinement of experiments through a comprehensive closed-loop system that incorporates AI-generated hypotheses, forecasts, and decisions. Among its notable features are multi-objective optimization, efficient database management, streamlined workflow orchestration, and real-time data analysis. Users have the capability to set experimental parameters with specific constraints, enabling machine learning algorithms to determine the next steps in the process, conduct experiments either manually or with robotic aid, analyze outcomes, and update models with the latest data, thus expediting the pursuit of improved, cost-effective, and environmentally friendly products. Additionally, Atinary offers proprietary algorithms, including Emmental for tackling non-linear constrained optimization, SeMOpt for implementing transfer learning in Bayesian optimization, and Falcon, which collectively enhance the platform's functionality and effectiveness. By leveraging these advanced tools, researchers can achieve greater efficiency and innovation in their experimental processes.
Description
A closed-loop universal multivariable optimizer is designed to enhance both the performance and quality of Model Predictive Control (MPC) systems. This optimizer utilizes data from Excel files sourced from Dynamic Matrix Control (DMC) by Aspen Tech, Robust Model Predictive Control Technology (RMPCT) from Honeywell, or Predict Pro from Emerson to develop and refine accurate models for various multivariable-controller variable (MV-CV) pairs. This innovative optimization technology eliminates the need for step tests typically required by Aspen Tech and Honeywell, operating entirely within the time domain while remaining user-friendly, compact, and efficient. Given that Model Predictive Controls (MPC) can encompass tens or even hundreds of dynamic models, the possibility of incorrect models is a significant concern. The presence of inaccurate dynamic models in MPCs leads to bias, which is identified as model prediction error, manifesting as discrepancies between predicted signals and actual measurements from sensors. COLUMBO serves as a powerful tool to enhance the accuracy of Model Predictive Control (MPC) models, effectively utilizing either open-loop or fully closed-loop data to ensure optimal performance. By addressing the potential for errors in dynamic models, COLUMBO aims to significantly improve overall control system effectiveness.
API Access
Has API
API Access
Has API
Integrations
Aspen DMC3
Microsoft Excel
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Atinary
Founded
2019
Country
Switzerland
Website
atinary.com/solutions/
Vendor Details
Company Name
PiControl Solutions
Country
United States
Website
www.picontrolsolutions.com/products/columbo/