Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
Yes
API Access
Has API
No
Integrations
AADvance Control System
No
ControlLogix SIL 2
No
Python
Yes
Trusted Control System
No
Ubuntu
Yes
Integrations
AADvance Control System
Yes
ControlLogix SIL 2
Yes
Python
No
Trusted Control System
Yes
Ubuntu
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
Yes
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
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
No
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