Energy consumption and energy efficiency of high-pressure reverse osmosis: Effect of water recovery, number of stages, and energy recovery
Bibliographic record
Abstract
Reverse osmosis (RO) brine is becoming a concern due to its environmental impact. One of the proposed solutions to manage RO brine is to treat it using high-pressure reverse osmosis (HPRO) to increase potable water production and achieve near-Zero Liquide Discharge (n-ZLD). However, HPRO energy consumption is considerably high compared to conventional RO which urges the need to optimize it and make brine desalination economically feasible. In this paper, we aim to discuss possible pathways to minimize HPRO energy consumption towards n-ZLD. First, we present the impact of the recovery rate ( RR ) of each RO stage on the energy consumption and the energy efficiency of HPRO for a targeted total RR = 90 %. To investigate the opportunity of n-ZLD, we compared the energy consumption of 2-stage RO and 3-stage RO for high water recoveries ( RR = 90 % with 2-stage RO versus RR = 95 % with 3-stage RO). Our analysis revealed that, depending on the values RR of each RO stage, the energy consumption of 3-stage RO can be lower than that of 2-stage RO where it reaches a minimum of 4.62 kWh m —3 (compared 5.41 kWh m —3 for conventional two-stage RO with RR = 80 %). To decide on the feasibility of 3-stage RO for high water recovery, we performed an economic analysis to estimate the levelized cost of water (LCOW). Results showed that the choice of RR in each RO stage is critical to achieve minimum energy consumption, thereby, lowering the LCOW. Finally, we investigated further improvement of the system by introducing pressure retarded osmosis (PRO) to recover the osmotic energy from the HPRO brine. The energetic and economic analysis revealed that the viability of the process is strongly dependent on the performance of the PRO membrane, PRO feed water concentration, fouling mitigation strategy, and the choice of PRO pretreatment. This study offers insights for a better energy efficient and cost-effective RO desalination process. • Energy consumption and efficiency of high-pressure reverse osmosis was investigated. • The choice of the water recovery in each stage is critical for optimum efficiency. • The energy efficiency 2-stage RO and 3stage-RO were compared for ZLD. • PRO can help reduce the energy consumption of high-pressure RO.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".