基于机器学习的预测/甲混合物在盐水中的可溶性
Farag M A Altalbawy1, Mustafa Jassim Al-Saray2, Krunal Vaghela3
1Department of Chemistry, University College of Duba, University of Tabuk, Tabuk, Saudi Arabia. f_altalbawy@yahoo.com.
Scientific reports
|December 5, 2024
概括
预测和甲混合物在盐水中的可溶性对于地下储存至关重要. 机器学习模型,特别是LSSVM-GA和LSSVM-CSA,准确地预测溶解度,推进安全和负担得起的储存.
科学领域:
- 地质化学 地质化学
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 地下储 (UHS) 通常涉及先前存在的甲储,导致/甲混合物.
- 这些混合物在盐水中的精确可溶性数据对于安全和高效的UHS操作至关重要.
- 由于气体的腐蚀性和易燃性,实验室测量具有挑战性.
研究的目的:
- 开发精确的数据驱动智能模型,用于预测/甲混合物在盐水中的可溶性.
- 为了利用混合机器学习方法,优化了元启发算法.
- 为了研究压力,温度,混合物成分和盐水盐度对溶解度的影响.
主要方法:
- 结合自适应神经模糊推理系统 (ANFIS) 和最小平方支持矢量机器 (LSSVM) 的混合模型的开发.
- 使用粒子群优化 (PSO),遗传算法 (GA) 和合模拟化 (CSA) 来优化LSSVM模型.
- 使用实验室数据进行验证,并通过统计指标评估模型性能 (AARE%,MSE,R平方).
主要成果:
- 开发的模型在预测/甲混合物溶解度方面表现出很高的准确性.
- 灵敏度分析显示压力和分子分数是关键影响因素.
- LSSVM-GA和LSSVM-CSA模型表现出卓越的性能,具有最低的AARE%和MSE,以及最高的R平方值.
结论:
- 机器学习,特别是LSSVM-GA和LSSVM-CSA,为UHS提供了可靠和高效的方法来预测盐水中的溶性.
- 这些模型可以支持开发智能,经济高效和安全的地下储存技术.
- 这些发现有助于降低储存操作的风险,并推动经济的发展.
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