Numerical Simulation of Red Mud Blended Raw Materials in a Precalciner
プレカルシナーにおけるレッドマッド混合原料の数値シミュレーション (AI 翻訳)
Kai Huang, H. Kao
🤖 gxceed AI 要約
日本語
本研究は、アルミナ精錬副産物であるレッドマッドをセメント原料として部分代替し、CFDモデルを用いてプレカルシナー内の熱化学環境を解析。5%までの混合率で分解率が工業許容範囲を維持し、廃棄物活用と排出削減のバランスを提案する。
English
This study uses CFD simulation to analyze red mud as a partial cement raw material in a precalciner, finding that a blending ratio up to 5% maintains decomposition rate within industrial acceptable range (85-95%), enabling waste utilization and emission reduction.
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
日本のセメント業界はカーボンニュートラル達成に向け、廃棄物活用とCO2削減の両立が急務。本研究成果は、レッドマッドの実用化における混合率の指針を数値的に提示し、SSBJやサーキュラーエコノミー政策に貢献する。
In the global GX context
Globally, cement industry decarbonization is critical. This paper provides a validated CFD model quantifying the trade-off between red mud blending, decomposition rate, and emissions, offering a replicable approach for industrial waste utilization in precalciner operations.
👥 読者別の含意
🔬研究者:Provides a validated CFD model for exploring the coupled thermo-chemical effects of alternative raw materials in cement precalciners.
🏢実務担当者:Recommends a 5% red mud blending ratio balancing waste utilization, decomposition efficiency, and emission reduction, with clear technical parameters.
🏛政策担当者:Supports policy on industrial waste utilization in cement, offering quantitative evidence for sustainable raw material substitution.
📄 Abstract（原文）
The cement industry is a major contributor to global carbon emissions. Therefore, reducing emissions while utilizing industrial wastes is critical for its sustainable development. Red mud, a solid waste byproduct of alumina smelting with main components like SiO2, Al2O3, and CaO, can partially replace limestone as a raw material in cement production. TG-DSC thermal analysis clarified red mud’s three-stage weight loss characteristic during calcination (total weight loss rate of 22.11%), and orthogonal experiments identified calcination temperature as the core factor for its CaO content, with the optimal calcination pretreatment process confirmed (0.075–0.09 mm particle size, 1373 K, 1 h residence time, CaO content up to 21.1%). Based on the results, this study uses ANSYS Fluent 2021 R1 to simulate a TTF-type precalciner, establishing a validated multi-physical field model (all relative errors < 5%) to explore red mud blending ratios of 0%, 2.5%, 5%, 7.5% and 10%. Unlike previous experimental studies, this work uses a CFD model to quantify how red mud blending ratios affect the coupled thermo-chemical environment in a TTF precalciner, revealing a mechanism-driven trade-off among decomposition rate, CO2, and NOx that experiments alone cannot capture. Results show red mud slightly alters the internal temperature field and reduces the raw meal decomposition rate. The decomposition rate remains within the industrial acceptable range of 85–95% when the red mud blending ratio is no more than 5%, while further increasing the blending ratio to 7.5% and 10% causes the decomposition rate to drop below 85%. Therefore, a blending ratio of 5% is recommended, which balances waste utilization, decomposition rate, and emission reduction, providing solid technical support for red mud’s large-scale use in cement production.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/ma19122500first seen 2026-06-16 05:14:20
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