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What is INCA MPC?
Advanced Process Control (APC) serves as an effective means to boost your plant's operational efficiency without necessitating any changes to hardware. By deploying an APC application, you can achieve stable operations while optimizing either production output or energy consumption, which in turn grants valuable insights into your manufacturing workflows. This concept includes a diverse set of methodologies and technologies that enhance basic process control systems, which are mainly built upon PID controllers. For instance, technologies associated with APC feature LQR, LQC, H_infinity, neural networks, fuzzy logic, and Model-Based Predictive Control (MPC). An APC application works tirelessly to optimize plant performance every minute of every day, ensuring that operational efficiency remains consistent. Among these different techniques, MPC is particularly prominent in the industry because it employs a process model to anticipate the plant's performance in the near term, usually from a few minutes to several hours ahead, thereby offering a tactical edge in operational management. By continuously refining processes, APC not only enhances immediate efficiency but also plays a crucial role in achieving long-term sustainability objectives, making it an essential component of modern industrial practices. The integration of these advanced strategies can lead to significant improvements in both productivity and environmental impact.
What is COLUMBO?
A universal multivariable optimizer, designed for closed-loop systems, aims to improve the performance and quality of Model Predictive Control (MPC) systems. This optimizer harnesses data from Excel files derived from Dynamic Matrix Control (DMC) by Aspen Tech, Robust Model Predictive Control Technology (RMPCT) from Honeywell, or Predict Pro from Emerson, facilitating the development and fine-tuning of precise models for various multivariable-controller variable (MV-CV) pairs. This cutting-edge optimization solution does away with the need for step tests that are usually required by Aspen Tech and Honeywell, functioning entirely in the time domain to maintain user-friendliness, compactness, and efficiency. As Model Predictive Controls (MPC) often involve numerous dynamic models—sometimes tens or even hundreds—there is a significant risk of utilizing incorrect models. Inaccurate dynamic models in MPCs can introduce bias, which appears as model prediction errors, leading to inconsistencies between expected signals and actual sensor measurements. COLUMBO emerges as a robust tool to bolster the precision of Model Predictive Control (MPC) models, effectively leveraging either open-loop or fully closed-loop data to guarantee peak performance. By tackling the risks associated with errors in dynamic models, COLUMBO not only enhances the reliability of the control system but also contributes to a more efficient operational framework. Ultimately, its implementation is expected to yield substantial advancements in control system effectiveness across various applications.
Integrations Supported
Aspen DMC3
Microsoft Excel
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Inca Tools
Company Location
Netherlands
Company Website
www.incatools.com/advanced-process-control/
Company Facts
Organization Name
PiControl Solutions
Company Location
United States
Company Website
www.picontrolsolutions.com/products/columbo/