A systematic sensitivity analysis approach is proposed to assess the robustness of thermodynamic model predictions employed in ABR processes. A sensitivity matrix is developed which incorporates the derivatives of multiple ABR performance indicators (e.g. coefficient of performance, generated cooling, mass flowrate of working fluid etc.) with respect to multiple thermodynamic property parameters propagated through different thermodynamic prediction models. The dominant eigenvector direction of the sensitivity matrix is identified and used to explore the ABR process behaviour as indicated by the change of ABR performance indicators under simultaneous, multiple and finite thermodynamic parameter variations. This enables the robust mapping of ABR performance toward the direction of maximum variability in the multiparametric space and hence the identification of a thermodynamic model which supports robust predictions regardless of variability. The approach is illustrated in a single effect ABR process system using the NH3/H2O mixture. Among various thermodynamic models we find that the Schwartzentruber-Renon with eNRTL are the most robust combination to parameter variability.
ASJC Scopus subject areas
- Chemical Engineering(all)