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What is XPS (eXtended Particle Simulations)?

XPS, which stands for eXtended Particle Simulations, is an innovative simulation tool based on the Discrete Element Method, developed by RCPE and distributed globally by InSilicoTrials, specifically designed for high-accuracy simulations of particle-based processes. This software is especially advantageous for the pharmaceutical industry, as it facilitates precise predictions of the behavior of powders and granular materials, thus providing teams with critical insights and improving the management of pharmaceutical unit operations. By employing advanced contact models, XPS effectively describes the flow dynamics of granular materials and utilizes highly parallel algorithms optimized for modern GPUs, allowing for the simulation of up to 100 million particles in a fraction of the time. The unparalleled detail offered in process configuration evaluations enables pharmaceutical engineers to explore decision-making scenarios in a virtual environment, significantly reducing the reliance on costly and time-consuming physical experiments while promoting data-centric strategies for process development. Consequently, this cutting-edge software not only enhances operational efficiency but also deepens the understanding of material behaviors in pharmaceutical manufacturing settings, fostering innovation and improving overall production processes. Additionally, XPS's capabilities pave the way for future advancements in simulation technology within the industry, ensuring that teams remain at the forefront of pharmaceutical research and development.

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

Python
Ubuntu

Integrations Supported

Python
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

InSilicoTrials

Date Founded

2016

Company Location

United States

Company Website

insilicotrials.com/xps/

Company Facts

Organization Name

MPCPy

Company Location

United States

Company Website

github.com/lbl-srg/MPCPy

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