Best Advanced Process Control (APC) Systems for Linux

Epicor Connected Process Control (CPC) helps discrete manufacturers improve process control across production and assembly operations. Using digital work instructions, operator guidance, assembly verification, error proofing, and real-time data collection, CPC helps organizations standardize processes, improve product quality, increase visibility, and reduce variation. By connecting people, equipment, and production data, manufacturers can ensure critical processes are executed consistently while supporting traceability and continuous improvement initiatives.

MPCPy is a Python package that facilitates the testing and implementation of occupant-integrated model predictive control (MPC) for building systems. The package focuses on the use of data-driven, simplified physical or statistical models to predict building performance and optimize control. Four main modules contain object classes to import data, interact with real or emulated systems, estimate and validate data-driven models, and optimize control input. While MPCPy provides an integration platform, it relies on free, open-source, third-party software packages for model implementation, simulators, parameter estimation algorithms, and optimization solvers. This includes Python packages for scripting and data manipulation as well as other more comprehensive software packages for specific purposes. In particular, modeling and optimization for physical systems currently rely on the Modelica language specification.