Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Advanced Process Control (APC) provides a highly efficient solution for enhancing your plant's performance without requiring any hardware modifications. By implementing an APC application, you can stabilize operations while simultaneously optimizing production or energy usage, leading to a deeper insight into your production processes. This term encompasses a wide array of methods and technologies that complement fundamental process control systems, which are primarily constructed using PID controllers. Some examples of APC technologies include LQR, LQC, H_infinity, neural networks, fuzzy logic, and Model-Based Predictive Control (MPC). An APC application continually optimizes plant operations every minute, round-the-clock, seven days a week, ensuring consistent efficiency. Among these technologies, MPC stands out as the most widely adopted within the industry, as it utilizes a process model to forecast the plant's behavior for the near future, typically ranging from a few minutes to several hours ahead, thus providing a strategic advantage in operational planning. Through the continual refinement of processes, APC not only improves efficiency but also contributes to long-term sustainability goals.
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.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Python
No
Ubuntu
No
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
No
Chromebook
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
Customer Support
Business Hours
Yes
Live Rep (24/7)
Yes
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)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Inca Tools
Country
Netherlands
Website
www.incatools.com/advanced-process-control/
Vendor Details
Company Name
MPCPy
Country
United States
Website
github.com/lbl-srg/MPCPy