From assumption to application: Aligning techno-economic analysis models with operational realities in RO desalination

Desalination and Water Treatment, vol. 326, pp. 101773

Abstract

This review critically evaluates techno-economic analysis (TEA) frameworks applied to reverse osmosis (RO) desalination by examining how process-model assumptions influence cost projections under real operating conditions. Unlike conventional reviews that catalog prior studies, this work integrates process-level modeling principles with empirical observations to identify how simplified representations of energy consumption, recovery, pretreatment performance, cleaning frequency, and membrane aging introduce systematic bias in operating cost estimation. Across the literature, simulation-based TEAs commonly rely on steady-state formulations and fixed inputs, which suppress the coupled hydraulic and fouling dynamics that govern pressure evolution, permeability decline, and energy demand in full-scale systems. In contrast, empirical datasets consistently demonstrate that these parameters evolve with salinity, temperature, fouling load, and operational control strategies, leading to significant deviations in specific energy consumption, cleaning demand, and long-term OPEX. To address this gap, the review establishes an explicit process-to-cost framework that links mass transport, hydraulic behavior, and fouling progression to TEA outputs and organizes the analysis around five operational pillars representing dominant cost pathways. Building on this structure, an implementation-oriented TEA framework is proposed that incorporates dynamic parameterization, stochastic cleaning behavior, and condition-dependent membrane aging within existing simulation platforms. The results show that improving TEA reliability depends on resolving temporal variability and preserving process coupling rather than increasing model complexity alone. This work provides a pathway for transitioning TEA from a static, design-based evaluation to an operationally consistent tool that supports decision-making under realistic, variable desalination conditions.

Authors 2

1. Mohammad Tajik

   Qatar Foundation · Hamad bin Khalifa University

   Affiliation as printed

   Sustainability Division, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar

2. Peter Desmond corresponding Aachen Institute of Environmental Engineering

   RWTH Aachen University · Qatar Foundation · Hamad bin Khalifa University

   Affiliation as printed

   Institute of Environmental Engineering, RWTH Aachen University, Aachen 14, Germany

   Sustainability Division, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar