Identification of a supercritical fluid extraction process for modelling the energy consumption

Abstract

Supercritical carbon dioxide extraction has been established as a promising and clean technology alternative to conventional separation techniques. Despite a high energy demand of extraction processes, their energy analysis has been scarcely considered. In this study, a supercritical carbon dioxide batch extraction process was modelled through system identification, forming a full simulator of its control loops affecting the energy consumption. The modelling was based on data acquired through systematic approach including experimental design and identification of dynamic process responses and energy consumption. Regression analysis and 12 identified models for subprocesses showed feasible performance during simulations with experimental data. The best local model for a subprocesses exhibited a Mean Absolute Percentage Error of 3% with independent test data. Regression model for steady-state electricity consumption showed a Mean Absolute Percentage Error of 7.6%, also suggesting the existence of nonlinearities between the response and other process variables. The identification approach reveals new information on energy consumption and dynamics of energy consumption of supercritical extraction in transient operating conditions. The models can be applied for further developments in real-time energy monitoring and optimization of supercritical extraction processes.