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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Promote circular economy efforts while addressing global economic issues, evolving market trends, and competitive forces by enhancing performance, quality, and market readiness through leading-edge simulation software tailored for chemicals, polymers, life sciences, and innovative sustainability practices. Aspen Plus stands as the premier process simulator, drawing on more than four decades of expertise and insights from leading chemical firms, complemented by a renowned physical properties database. This software offers comprehensive process modeling that integrates economic, energy, safety, and emissions evaluations, thereby enhancing time-to-market, process efficiency, and sustainability metrics. By leveraging Aspen Plus, the efficiency of chemical operations is significantly boosted, making it a valuable tool for the bulk chemicals, specialty chemicals, and pharmaceutical sectors. This advanced modeling technology not only facilitates the optimization of throughput and product quality but also reduces energy consumption across various processes, paving the way for a more sustainable future. Ultimately, Aspen Plus empowers industries to meet the challenges of modern manufacturing while driving innovation and environmental responsibility.

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

Country

United States

Website

www.aspentech.com/en/products/engineering/aspen-plus

Vendor Details

Company Name

PiControl Solutions

Country

United States

Website

www.picontrolsolutions.com/products/columbo/

Product Features

Simulation

1D Simulation
3D Modeling
3D Simulation
Agent-Based Modeling
Continuous Modeling
Design Analysis
Direct Manipulation
Discrete Event Modeling
Dynamic Modeling
Graphical Modeling
Industry Specific Database
Monte Carlo Simulation
Motion Modeling
Presentation Tools
Stochastic Modeling
Turbulence Modeling

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