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Integrated Data-Driven Optimisation of LNG Hot Section for Energy Efficiency and Decarbonization

エネルギー効率と脱炭素化のためのLNGホットセクションの統合データ駆動最適化 (AI 翻訳)

Aisha Al-Hammadi, Dr Tareq Al-Ansari, Dr Ahmed AlNouss, Abdul Aziz Shaikh

Systems and Control Transactions📚 査読済 / 学会2026-06-19#省エネ経営インパクト: コスト削減対象セクター: energy
DOI: 10.69997/sct.169972
原典: https://doi.org/10.69997/sct.169972

🤖 gxceed AI 要約

日本語

LNG処理のホットセクション（受入、酸ガス除去、脱水）のエネルギー消費を最小化するデータ駆動型最適化フレームワークを開発。HYSYSシミュレーションデータを用いて重要変数を特定し、Pareto最適化により硫黄回収率96.9%の「knee」点を発見。エネルギー効率向上と環境基準遵守の両立を図る。

English

This study develops a data-driven optimization framework to minimize energy consumption across interdependent LNG hot section units (inlet receivers, acid gas removal, dehydration). Using HYSYS simulation data, it identifies critical variables and applies Pareto frontier analysis to find a knee point at 96.9% sulfur recovery, balancing production and heating loads. The findings offer control coefficients for maximizing yield while meeting environmental limits.

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

日本のLNG受入基地や石油精製・化学プラントにおける省エネルギーと脱炭素化の取り組みに直接関連。SSBJ開示やカーボンニュートラル目標に向けたエネルギー効率改善の具体的手法として参考になる。

In the global GX context

This work contributes to global efforts in LNG value chain decarbonization by demonstrating a data-driven optimization approach that reduces energy intensity. It aligns with the growing emphasis on operational efficiency as a key lever for Scope 1 and 2 emission reductions, complementing disclosure frameworks like TCFD and ISSB.

👥 読者別の含意

🔬研究者:Provides a methodological framework for integrated optimization of energy-intensive processes, applicable to broader industrial decarbonization research.

🏢実務担当者:Offers actionable insights and control coefficients for optimizing LNG plant operations to reduce energy costs and emissions.

🏛政策担当者:Highlights the potential of data-driven optimization in industrial energy efficiency, supporting policy incentives for digitalization in the energy sector.

📄 Abstract（原文）

In today’s competitive LNG market, reducing energy consumption is critical for enhancing both profitability and sustainability. The hot section of the LNG processing, which includes inlet receivers, acid gas removal, and dehydration units, is the most thermally demanding. Previous optimisation methods targeted each unit separately. On the other hand, this work details the development of a data-driven optimisation framework to minimise energy across these interdependent units. Preliminary application of the framework has yielded encouraging results. Utilising HYSYS process simulation data, the study successfully identifies critical operating variables—such as reboiler duty, amine circulation rate, and air-to-furnace stoichiometry—that drive production efficiency and energy consumption. Results indicate that a baseline condensate mass flow of 2, 048.71 kg/h is achieved at a stripper bottom temperature of 137.74 °C, while the AGRU produces sweet gas with 0.18 ppm H2S. Optimisation using Pareto frontier analysis reveals a "knee" point in the SRU at 96.9% recovery efficiency, balancing elemental sulfur production (7, 622.24 kg/h) against heating loads. The findings provide mathematical coefficients for plant control to maximise yield while maintaining strict environmental stack limits.

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