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Average Ratings 0 Ratings

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ease
features
design
support

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Description

MPCPy is a Python library designed to support the testing and execution of occupant-integrated model predictive control (MPC) within building systems. This tool emphasizes the application of data-driven, simplified physical or statistical models to forecast building performance and enhance control strategies. It comprises four primary modules that provide object classes for data importation, interaction with real or simulated systems, data-driven model estimation and validation, and optimization of control inputs. Although MPCPy serves as a platform for integration, it depends on various free, open-source third-party software for model execution, simulation, parameter estimation techniques, and optimization solvers. This encompasses Python libraries for scripting and data manipulation, along with more specialized software solutions tailored for distinct tasks. Notably, the modeling and optimization tasks related to physical systems are currently grounded in the specifications of the Modelica language, which enhances the flexibility and capability of the package. In essence, MPCPy enables users to leverage advanced modeling techniques through a versatile and collaborative environment.

Description

Producers like yourself possess the expertise required to maneuver through the intricate hurdles of remaining competitive in today’s market landscape. This is applicable across a wide range of sectors, including pharmaceuticals, consumer goods, food and beverage, mining, and chemicals. Therefore, embracing the latest technological innovations is essential for advancing your ongoing digital transformation efforts. Throughout your organization, from the control room to executive meetings, users of process systems consistently grapple with the challenge of optimizing productivity while managing budget limitations and resource availability, all while tackling shifting operational risks. By addressing these challenges head-on, you can unlock significant productivity enhancements across your facility with the PlantPAx distributed control system (DCS). The features of this system can greatly influence the lifespan of your plant operations, ensuring that integrated and scalable systems enhance productivity, boost profitability, and minimize operational risks. Ultimately, investing in such advanced systems can lead to a more resilient and efficient production environment.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

AADvance Control System
ControlLogix SIL 2
Python
Trusted Control System
Ubuntu

Integrations

AADvance Control System
ControlLogix SIL 2
Python
Trusted Control System
Ubuntu

Pricing Details

Free
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

MPCPy

Country

United States

Website

github.com/lbl-srg/MPCPy

Vendor Details

Company Name

Rockwell Automation

Founded

1903

Country

United States

Website

www.rockwellautomation.com/en-us/capabilities/process-solutions/process-systems/plantpax-distributed-control-system.html

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