The Building Energy Systems Simulator
BESS is a user-friendly software package that can be used to simulate building energy systems using solid oxide fuel cells (SOFCs), compressed air energy storage (CAES), and renewable energy (solar, wind, etc.). You enter key design parameters and demand profiles for power and heating services for your building and it uses models developed at the MACC to determine the system performance on a hour by hour basis for an entire year of use, along with many key performance metrics. Suitable for anyone interested in using SOFCs for building-scale or community-scale applications.
McMaster Statistical Package for Multivariate Data Analysis
McMaster researchers have developed new methods for analyzing correlated process data for Statistical Process Control, inferential modelling, and process diagnosis. MacStat was developed to enable member companies to evaluate and apply the evolving technology. The main features are:
- Data pretreatment
- Multiple linear regression
- Principle components analysis (PCA)
- Partial least squares (PLS), including linear, non-linear, and multiblock.
- Create models and project new data using created models.
CO2 Capture Simulation Software
Examples in Aspen Plus, Aspen HYSYS, Pro/II, and ProMax
Solvent-based process for capturing CO2 from major energy conversion processes such as power plants and synthetic fuels processes have been in use for decades and are now experencing even greater interest due to their potential use in mitigating climate change. However, simulating these processes in major commercial software packages is notoriously difficult, even for expert users of the software. To address this, MACC researchers have created tutorials with 20 example simulations showing how to successfully simulate solvent-based capture processes using both chemical and physical solvents in four major commercial software packages. The examples cover a wide range of solvents in numerous real process such as traditional combustion power plants, advanced gasification-based power plants, and synthetic fuels processes.
MACC Multivariate Image Analysis
MACCMIA has been developed to illustrate the concept of Multivariate Image Analysis (MIA). The package comprises a userfriendly MATLAB interface that directs the user through the process of analyzing an image using multivariate statistics. Now supporting 64-bit Windows and Mac.