Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Identify patterns in operational data that can forecast deterioration and potential failures long before they occur. By employing accurate failure pattern recognition, you can reduce the frequent occurrence of false positives typically associated with traditional model-based approaches. Utilizing low-touch machine learning, you can swiftly distinguish between normal and abnormal behaviors, ensuring equipment protection starts within weeks rather than extending into months. The integration of Aspen Mtell with Aspen Cloud Connect™ provides connectivity to devices that support OPC UA. This method of recognizing operational patterns not only serves as an initial defense against asset decline but also enhances existing maintenance strategies through the deployment of AI-driven agents across various sites or throughout the entire organization. By focusing on precise failure pattern recognition, the challenge of high false positive rates in model-based solutions is effectively mitigated. Moreover, the rapid identification of operational behaviors facilitates timely equipment protection, ensuring that organizations can respond proactively to potential issues as they arise.
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
No
API Access
Has API
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Aspen Technology
Founded
1981
Country
United States
Website
www.aspentech.com/en/products/apm/aspen-mtell
Vendor Details
Company Name
PiControl Solutions
Country
United States
Website
www.picontrolsolutions.com/products/columbo/
Product Features
Preventive Maintenance
Condition Monitoring
No
Inspection Management
No
Maintenance Scheduling
No
Mobile Access
No
Predictive Maintenance
No
Purchasing
No
Reminders
No
To-Do List
No
Vendor Management
No
Work Order Management
No