Operation modeling and comparison of actual multi-effect distillation and reverse osmosis desalination plants

Abstract

Modeling actual desalination plants is often restricted by unknown parameters and system specifications that can be difficult to obtain or measure in the field. In this study, we propose an operational data recovery methodology to estimate unknown parameters and construct a simulation that accurately reproduces the operation of actual desalination systems. Furthermore, the data recovery methodology enables desalination modeling with a data-driven iterative sampling scheme to find the most plausible operation scenario. The complete models with data recovery are deployed in four case studies of desalination plants in the field: two multi-effect distillation with thermocompression (MDT) and two reverse osmosis with pressure exchange (ROX). The results show excellent agreement with actual plant operation data, reflected by the maximum difference between simulated and collected data of 5.5 % and 2.5 % for the two MDT plants as well as 6.4 % and 9.3 % for the two ROX plants. Importantly, this study introduced a new theoretical efficiency metric to define optimal operation of a desalination plant. This metric allowed to highlight two plants operating around 20 % below their theoretically achievable recovery. This efficiency calculation and complete models could help plant managers identify underperforming plants and evaluate potential upgrades.