Prospective Environmental Assessment of Citric Acid Production: An Integrated Framework of Ex-Ante LCA and Technological Learning
クエン酸生産の将来環境評価：事前LCAと技術学習の統合フレームワーク (AI 翻訳)
Shuting Chen, Jin Wang, Ayueerguli Abuduniyazi, Mingjun Gao, Liming Dong, Guannan Liu, Suping Yu
🤖 gxceed AI 要約
日本語
本研究は、クエン酸生産における3つの回収技術（工業的CHP-IE、パイロット規模のSE、実験室規模のBMED）を比較した。技術学習曲線とエネルギー移行シナリオを組み込んだ事前LCAフレームワークを提案し、現状ではCHP-IEが最も低い温暖化係数（1.79 t CO2 eq/t CA）を示すが、2050年までに深い脱炭素化のもとでBMEDが0.78 t CO2 eq/t CAと最良の選択肢となることを示した。また、中国のトウモロコシ栽培モデルの改善による環境影響低減効果も定量化した。
English
This paper compares three citric acid recovery technologies at different readiness levels using an ex-ante LCA framework that integrates technological learning curves with energy transition scenarios. It finds that while current industrial technology (CHP-IE) has the lowest global warming potential (1.79 t CO2 eq/t CA), under deep decarbonization by 2050, the lab-scale BMED technology becomes the lowest-carbon option (0.78 t CO2 eq/t CA). Improved maize cultivation models in China can reduce acidification and eutrophication impacts by over 50%.
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
日本でも化学プロセスの脱炭素化が進む中、事前LCAと技術学習を組み合わせた本手法は、新技術導入の意思決定に有用である。ただし、ケーススタディは中国のトウモロコシを原料とするため、日本のクエン酸生産とは原料など前提が異なる点に注意が必要。
In the global GX context
This framework offers a valuable methodology for prospective environmental assessment of emerging technologies, relevant for global chemical industry decarbonization. The integration of learning curves with LCA is innovative and can inform technology selection and policy support for green supply chains.
👥 読者別の含意
🔬研究者:Provides a novel ex-ante LCA methodology that couples technological learning curves with energy scenarios, applicable to other chemical products.
🏢実務担当者:Offers insights into which recovery technology may become environmentally optimal under future decarbonization pathways, aiding investment decisions.
🏛政策担当者:Demonstrates how policy support for emerging technologies can be justified by projecting long-term environmental benefits, and highlights the importance of sustainable feedstock cultivation.
📄 Abstract（原文）
Citric acid (CA) necessitates the investigation of the environmental footprint from its production. This study compared three recovery technologies at different readiness levels, industrial calcium hydrogen salt precipitation–ion exchange (CHP-IE), pilot-scale solvent extraction (SE), and laboratory-scale bipolar membrane electrodialysis (BMED), to evaluate the life cycle environmental impacts of CA production when employing each recovery technology. SE and BMED were selected as emerging alternatives, as both are potential candidates to offer environmental or economic advantages over CHP-IE. By modeling the continuous improvement in the key production parameters as cumulative production experience increases, technological learning curves capture the efficiency gains that occur as technologies mature. This study pioneers an integrated ex-ante LCA framework that couples technological learning curves with energy transition scenarios to prospectively compare emerging CA recovery technologies against an industrialized process. Currently, CHP-IE shows the highest profit of 1078 CNY/t CA and the lowest global warming potential (GWP) of 1.79 t CO2 eq/t CA, with the latter advantage projected to persist until 2030. By 2050, under deep decarbonization, BMED becomes the lowest-carbon option with 0.78 t CO2 eq/t CA. Furthermore, with maize as the primary raw material, improved cultivation models in Northeast China reduce the environmental impacts of CA production by approximately 3% in acidification potential (AP) and eutrophication potential (EP), while diversified cropping systems in North China yield reductions of over 50% in these two categories. This paper provides an approach of comprehensive evaluation, supporting technology selection and green supply chain development in the CA industry.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18062848first seen 2026-05-06 00:08:11
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