SolACE: an open source package for nolinear model predictive control and state estimation for (bio)chemical processes. n spite of the wide spread use of Nonlinear Model Predictive Control (NMPC) in large chemical companies, the small and medium enterprises (SMEs) remain oblivious of its potential mostly due to the large investment costs and in-house expertise required. This paper presents an open source python based simulation environment - SolACE, which can aid SMEs in realizing the full potential of the advanced control techniques. The syntax to introduce the controller and plant models is straightforward, enabling even non-experts to easily formulate the control (and estimation) problems. With the developed package, SMEs can consider the implementation of NMPC on their processes without any overlaying costs or large technical know-how. From a research perspective, the current package can be used as a building block to develop toolkits for advanced control strategies like robust or economic NMPC. It also provides researchers a way to test various models in an NMPC framework without the hustle of having to write the discretization and optimization routines themselves
References in zbMATH (referenced in 1 article )
Showing result 1 of 1.
- Nimmegeers, Philippe; Lauwers, Joost; Telen, Dries; Logist, Filip; van Impe, Jan: Identifiability of large-scale non-linear dynamic network models applied to the ADM1-case study (2017)