Enhancing energy efficiency and water recovery in two-stage seawater reverse osmosis through advanced brine treatment technologies
Bibliographic record
Abstract
Two-stage reverse osmosis (2SRO) has demonstrated superior energy efficiency and higher clean water productivity compared to single-stage reverse osmosis. However, 2SRO energy consumption is still relatively high. Recently, advanced technologies for brine treatment using RO membranes have been developed such as Osmotically Assisted Reverse Osmosis (OARO), Low-Salt Rejection Reverse Osmosis (LSRRO), and Cascading Osmotically Mediated Reverse Osmosis (COMRO). These technologies help to overcome the limitation of maximum applied pressure of conventional RO. In this study, we use the concept of OARO, COMRO, and LSRRO to enhance the performance of 2SRO. We first suggested possible integration of OARO, COMRO, and LSRRO in 2SRO, then determined the specific energy consumption, the energy efficiency, and the maximum achievable water recovery of each configuration. The results demonstrated superior performance of the proposed configurations compared to 2SRO, with higher water recovery and better overall energy efficiency. Specifically, LSRRO achieved 70 % water recovery and 46 % energy efficiency, while OARO and COMRO delivered 72 % water recovery with energy efficiency of 46 % and 66 %, respectively. To decide on the feasibility of the suggested integrations, we performed a techno-economic analysis using the levelized cost of water, payback period, and return in investment as financial performance metrics. The analysis revealed a very promising result where the levelized cost of water is reduced from $1.1/m 3 to $0.79/m 3 in a payback period of 4.2 years, instead of 5.7 years for 2SRO, indicating that the proposed configurations can have a significant impact on improving the performance of existing RO desalination plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".