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Comparative analysis of global desalination projects powered by renewable energies: technical, economic and environmental assessment

再生可能エネルギーを動力源とする世界の海水淡水化プロジェクトの比較分析:技術的、経済的、環境的評価 (AI 翻訳)

M. Azgar, El Ayachi Chater, N. Laaroussi

2026 6th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)学会2026-05-14#再生可能エネルギーOrigin: Global経営インパクト: コスト削減対象セクター: utility
DOI: 10.1109/iraset68627.2026.11538494
原典: https://doi.org/10.1109/iraset68627.2026.11538494

🤖 gxceed AI 要約

日本語

本論文は、再生可能エネルギーを動力源とする19の海水淡水化プロジェクトを比較分析。膜ベース(ED、FO、RO)と熱ベース(MVC、MSF)のプロセスを対象に、比エネルギー消費量(SEC)、統合戦略(系統連系、蓄電池/水素貯蔵、製品水貯蔵)、および水の平準化原価(LCOW)を評価。結果、補助金があれば従来法と同等の費用でグリーン淡水化が可能であり、製品水貯蔵の優先が有効と示唆。

English

This study compares 19 seawater and brackish water desalination projects powered by renewable energy, covering membrane-based (ED, FO, RO) and thermal (MVC, MSF) processes. It evaluates specific energy consumption (SEC), integration strategies (grid connection, battery/hydrogen storage, product water storage), and levelized cost of water (LCOW). Results show green desalination can be financially viable with subsidies, achieving SEC <3 kWh/m³ and LCOW of $0.359/m³, and recommends prioritizing product water storage over energy storage.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本では離島や水不足地域での再生可能エネルギー海水淡水化導入の参考となる。政策支援による初期投資低減の重要性を強調しており、日本のGX政策(特に水インフラの脱炭素化)に示唆を与える。

In the global GX context

Globally, this paper provides a benchmark for green desalination projects, highlighting the importance of integration strategies and policy support for cost reduction. It contributes to the renewable energy and water nexus, relevant for regions facing water scarcity under climate change.

👥 読者別の含意

🔬研究者:Provides a comparative framework for evaluating technical and economic performance of renewable desalination projects.

🏢実務担当者:Offers insights on cost-effective integration strategies (product water storage) and financial viability for desalination project developers.

🏛政策担当者:Emphasizes the need for subsidies and support mechanisms to make green desalination competitive, informing water and energy policy.

📄 Abstract(原文)

The global freshwater shortage caused by the increasing demand for freshwater due to the growing world population and the effects of climate change require seawater desalination to be used as an alternative source of freshwater. However, traditional desalination methods generate significant amounts of greenhouse gas emissions, with estimates suggesting around 80 million tons of CO2 annually. This study provides a comparison of 19 seawater and brackish water desalination projects using renewable energy as the power source. The projects include a number of different membrane-based processes (Electro-Dialysis (ED), Forward Osmosis (FO), and Reverse Osmosis (RO)), as well as thermal based processes (Mechanical Vapor Compression (MVC) and Multi-Stage Flash Distillation (MSF)). The focus of the comparison was on the technical aspects of each project (Specific Energy Consumption (SEC)), the integration strategies employed in each project (Grid Connection vs. Battery/Hydrogen Storage vs. Product Water Storage), and the financial viability of each project (Levelized Cost of Water (LCOW)). The results demonstrate that green desalination can be financially viable when compared to traditional methods, with optimal SECs of less than 3.0 kilowatt-hours per cubic meter and delivered water costs of 0.359 dollars per cubic meter provided that some form of subsidy or support mechanism is available to offset the higher upfront capital expenditures. The study also demonstrated that successful implementation of green desalination will depend on developing effective integration strategies (i.e., prioritizing product water storage over energy storage) as well as establishing policy frameworks that support and reduce the initial investment cost associated with green desalination.

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。