使用人工神经网络模型预测生物质-酸石颗粒的压力强度
Haoli Yan1, Xiaolei Zhou1, Lei Gao1
1Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China.
Materials (Basel, Switzerland)
|July 29, 2023
概括
酸提高了绿色石颗粒的强度,但削弱了固化颗粒. 机器学习模型预测颗粒的压力强度,帮助可持续的铁矿石颗粒技术减少排放.
科学领域:
- 材料科学 材料科学 材料科学
- 环境工程 环境工程
- 化学工程是化学工程的重要组成部分.
背景情况:
- 钢铁行业的排放需要绿色发展和减排战略.
- 铁矿石颗粒为环境保护提供了炼铁矿石的有希望的替代品.
- 高级铁矿石资源的减少凸显了石颗粒技术的重要性.
研究的目的:
- 为了研究酸对石颗粒压力强度的影响.
- 为了比较石颗粒与传统的本托尼特颗粒的性能.
- 应用人工神经网络 (ANN) 来预测颗粒的压力强度.
主要方法:
- 用不同的酸比率制备石颗粒样本.
- 对绿色和强化颗粒的压力强度的测量.
- 开发和应用ANN模型以根据粘合剂含量,颗粒直径和重量预测压力强度.
主要成果:
- 酸增加了绿色石颗粒的压力强度.
- 酸降低了加固的石颗粒的压力强度.
- 石颗粒的压力强度低于本托尼特基颗粒,ANN有效地预测了强度.
结论:
- 酸对石颗粒强度有双重影响,对绿色颗粒有益,但对硬化颗粒有害.
- 石颗粒技术需要进一步优化,以匹配基于石的颗粒性能.
- 颗粒技术与机器学习的整合,特别是ANN,促进了可持续的铁矿石加工和减排工作.
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