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What is PlantPAx?

Producers like you have the necessary skills to navigate the complex challenges of maintaining competitiveness in today’s market environment. This necessity spans numerous industries, such as pharmaceuticals, consumer products, food and beverage, mining, and chemicals. As a result, adopting cutting-edge technological advancements is crucial for propelling your digital transformation initiatives forward. Throughout your organization—ranging from control rooms to board meetings—users of process systems continually face the difficulty of maximizing productivity while adhering to budget constraints and managing available resources, all while addressing evolving operational risks. By confronting these issues directly, you can achieve substantial productivity gains throughout your facility using the PlantPAx distributed control system (DCS). The capabilities of this system can significantly impact the longevity of your plant operations, ensuring that integrated and scalable solutions enhance productivity, increase profitability, and reduce operational risks. Ultimately, investing in such sophisticated systems not only fosters a more resilient production environment but also positions your organization for future growth and success. Embracing innovation will empower you to stay ahead of the competition in an ever-changing industry landscape.

What is MPCPy?

MPCPy is a Python-based library specifically created to facilitate the testing and implementation of occupant-integrated model predictive control (MPC) in building systems. This innovative tool focuses on utilizing data-driven, simplified physical or statistical models to predict the performance of buildings and improve control methodologies. It consists of four key modules that offer object classes for tasks such as data importation, engagement with either real or simulated systems, estimation and validation of data-driven models, and optimization of control inputs. While MPCPy acts as a comprehensive integration platform, it relies on a variety of free, open-source third-party software for executing models, conducting simulations, implementing parameter estimation techniques, and optimizing solvers. This includes Python libraries for scripting and data manipulation, as well as specialized software solutions designed for specific functions. Importantly, the tasks involving modeling and optimization of physical systems are currently based on the requirements of the Modelica language, which significantly enhances the package's flexibility and capabilities. Overall, MPCPy empowers users to harness sophisticated modeling methods within a dynamic and cooperative environment, ultimately fostering improved building system performance. Furthermore, it opens up opportunities for researchers and practitioners alike to experiment with cutting-edge control strategies tailored to real-world scenarios.

Media

Media

Integrations Supported

AADvance Control System
ControlLogix SIL 2
Python
Trusted Control System
Ubuntu

Integrations Supported

AADvance Control System
ControlLogix SIL 2
Python
Trusted Control System
Ubuntu

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Free
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

Rockwell Automation

Date Founded

1903

Company Location

United States

Company Website

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

Company Facts

Organization Name

MPCPy

Company Location

United States

Company Website

github.com/lbl-srg/MPCPy

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