Risk-Aware,Low-Carbon Operating Windows for PharmaceuticalWastewater Treatment via Uncertainty-Aware Bayesian Optimization
不確実性を考慮したベイズ最適化による医薬廃水処理のリスク認識・低炭素運転ウィンドウ (AI 翻訳)
Jian-yun Lu, Qing-Yi Liu, Yu-Qi Wang, Wan-Xin Yin, Jun Wei, Cong Guo, Lihong Liu, Hong‐Cheng Wang
🤖 gxceed AI 要約
日本語
医薬品廃水処理におけるエネルギー・化学物質消費と間接排出を削減するため、ガウス過程回帰サロゲートモデルと不確実性を考慮したベイズ多目的最適化を構築。データ拡張とハイブリッドサンプリングにより、除去効率を10-15%向上させつつ、エネルギー・薬剤・CO2排出をそれぞれ約12%、15%、8-9%削減。オゾン処理がリスク低減に最も有効であることを示した。
English
This study develops a Bayesian multiobjective optimization framework with Gaussian process surrogates and uncertainty-aware search to map trade-offs in pharmaceutical wastewater treatment. Optimized operating windows improve removal efficiency by 10-15% while cutting energy, chemical use, and CO2-equivalent emissions by roughly 12%, 15%, and 8-9%, respectively. Ozonation shows the strongest risk reduction, shifting antibiotic risk quotients below regulatory thresholds. The approach is transferable to other hazardous industrial effluents.
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
日本では医薬品産業の排水処理におけるカーボンニュートラル対応が課題であり、本手法はSSBJ開示や省エネ法対応に資する。AIを活用したプロセス最適化は、環境規制強化とコスト削減の両立に寄与する。
In the global GX context
This work aligns with global trends in using AI/ML for industrial decarbonization and ESG performance. It offers a transferable framework for reducing carbon-intensive inputs in wastewater treatment, relevant to TCFD/ISSB disclosure and transition finance. The methodology supports companies in meeting net-zero targets while managing environmental risks.
👥 読者別の含意
🔬研究者:Provides a novel AI-driven optimization framework for multi-objective trade-offs in industrial wastewater treatment, with transferable methods for other sectors.
🏢実務担当者:Offers actionable operating windows to reduce energy, chemicals, and carbon emissions in pharmaceutical wastewater treatment, aiding sustainability reporting and cost savings.
🏛政策担当者:Demonstrates how AI can enable low-carbon industrial processes, informing policy on technology adoption and environmental standards.
📄 Abstract（原文）
Abstract Pharmaceutical manufacturing effluents containing antibiotics and other micropollutants pose persistent challenges to environmental protection and process safety. Their treatment is increasingly constrained by high energy demand, chemical consumption and the associated carbon emissions. Here we compiled literature-sourced operational data from conventional activated sludge (CAS), membrane bioreactor (MBR) and ozonation systems treating pharmaceutical wastewater, and constructed Gaussian process regression (GPR) surrogate models for removal performance, antibiotic risk quotients, resource consumption and indirect greenhouse-gas emissions. To address the sparsity and heterogeneity of available data sets, we developed a data-augmentation-assisted Bayesian multiobjective optimization framework combining hybrid sampling with uncertainty-aware search to map trade-offs among effluent quality, risk reduction and carbon-intensive inputs. Compared with baseline conditions, optimized operating windows increased overall removal efficiency by approximately 10–15% while reducing energy consumption, chemical dosage and CO2-equivalent emissions by roughly 12, 15 and 8–9%, respectively. Ozonation showed the strongest risk-reduction capability, shifting compound-specific risk quotients below regulatory thresholds for several high-priority antibiotics, whereas CAS showed limited improvement. This workflow operationalises low-carbon strategies for pharmaceutical wastewater treatment and offers a transferable approach for other hazardous industrial effluents.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1021/acsestwater.6c00828first seen 2026-08-08 04:57:27
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