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Average Ratings 0 Ratings

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ease
features
design
support

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Description

A premier solution for modeling, forecasting, and enhancing processes through multivariate statistical analysis and engaging visual representations. Accelerate product development, elevate quality, and refine processes by simplifying the analysis of extensive data sets more efficiently than previously possible. Achieve quicker and more precise solutions to practical challenges by examining diverse data types to enhance product creation, quality assurance, and manufacturing operations. Utilize advanced multivariate statistical analysis techniques, specifically designed for spectroscopy and chemometrics, to gain insights. Import material, sensor, process, and spectral data in various formats, complemented by easy-to-use features for plotting, preprocessing, and modeling spectral information. Ensure superior product classification to maintain consistent quality across offerings. Enhance productivity and simplify every stage of the analytical process with streamlined project-based workflows. Additionally, utilize compliance features such as digital signatures, password protection, and audit trails to adhere to regulations including 21 CFR Part 11 and EU Annex 11, ensuring your processes meet industry standards. This comprehensive tool empowers organizations to make informed decisions, fostering innovation and operational excellence throughout.

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

Screenshots View All

Screenshots View All

Integrations

Aspen DMC3
Microsoft Excel

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

Aspen Technology

Founded

1981

Country

United States

Website

www.aspentech.com/en/products/msc/aspen-unscrambler

Vendor Details

Company Name

PiControl Solutions

Country

United States

Website

www.picontrolsolutions.com/products/columbo/

Product Features

Statistical Analysis

Analytics
Association Discovery
Compliance Tracking
File Management
File Storage
Forecasting
Multivariate Analysis
Regression Analysis
Statistical Process Control
Statistical Simulation
Survival Analysis
Time Series
Visualization

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