作为一种可持续的抑制剂来缓解酸性溶液中的腐蚀:实验,机器学习模拟和多目标优化
Mahya Olfatmiri1, Mohammad-Bagher Gholivand1, Mohammad Mahdavian2
1Department of Analytical Chemistry, Faculty of Chemistry, Razi University, Kermanshah 6714414971, Iran.
Langmuir : the ACS journal of surfaces and colloids
|December 4, 2024
概括
艾迪安木-维尼里斯 (ACV) 提取物有效地抑制了轻钢在酸中的腐蚀. 机器学习优化了ACV度,达到88%的抑制,并确认其作为可持续腐蚀抑制剂的潜力.
科学领域:
- 材料科学 材料科学 材料科学
- 电化学 电化学 电化学
- 绿色化学 绿色化学
背景情况:
- 合成有机腐蚀抑制剂对环境构成风险.
- 绿色腐蚀抑制剂提供了可持续的替代品.
- 艾迪安木-威尼里斯 (ACV) 提取物正在探索用于温和钢的保护.
研究的目的:
- 为了评估ACV提取物作为盐酸中温钢的绿色腐蚀抑制剂的有效性.
- 使用机器学习优化ACV度和暴露时间.
- 为了确认防腐蚀抑制机制和性能.
主要方法:
- 电化学阻抗光谱 (EIS) 和极化技术.
- 对EIS数据进行浅层神经网络建模.
- 多目标遗传算法用于优化.
- 表面分析技术用于表面表征.
主要成果:
- 在800ppm时,ACV提取物达到88%的抑制效率.
- ACV作为混合类型的抑制剂,降低了腐蚀电流密度从105到44μA/cm2.
- 机器学习模型准确地预测了耐腐蚀性.
- 通过帕雷托前线分析确定了最佳的ACV性能.
结论:
- ACV提取物是一种可行的和有效的绿色腐蚀抑制剂,用于温钢.
- 机器学习显著提高了对腐蚀抑制剂性能的优化和预测.
- 这种方法提高了开发可持续腐蚀解决方案的概括能力.
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