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机器学习技术的应用用于模拟糖醇在离子液体中的可溶性
Ali Bakhtyari1, Ali Rasoolzadeh2, Behzad Vaferi3,4
1Department of Chemical Engineering, Shiraz University, Shiraz, Iran.
这项研究使用机器学习来预测离子液体中的糖醇溶解度,发现ANFIS模型为绿色化学应用提供了高精度.
科学领域:
- 绿色化学和化学工业中的可持续加工.
- 自然物质的应用,特别是糖衍生化合物.
- 使用新型溶剂开发高效的提取工艺.
背景情况:
- 化学工业正在转向绿色加工和天然物质.
- 离子液体 (ILs) 正在成为提取的高效溶剂.
- 准确的溶解度数据对于优化基于IL的提取过程至关重要.
研究的目的:
- 使用机器学习,模拟和预测糖醇 (SAs) 在离子液体 (ILs) 中的溶解度.
- 确定影响ILs中的SA溶解度的关键参数.
- 为了比较不同机器学习模型对这个预测任务的性能.
主要方法:
- 利用了19个SA和21个IL的647个数据点的大数据集.
- 采用并比较了三种机器学习模型:人工神经网络 (ANN),最小平方支持向量回归 (LSSVR) 和自适应神经模糊推理系统 (ANFIS).
- 进行相关性分析以确定温度,密度和分子重量等有影响力的参数.
主要成果:
- 温度和融合温度被确定为对SA溶性的最有影响的因素.
- 该ANFIS模型表现出卓越的准确性,实现了7.43%的平均绝对相对偏差 (AARD%) 和0.98359.2的R2.
- 在集群中心半径为0.435的情况下,在85%的数据上训练了ANFIS的最佳性能.
结论:
- 该ANFIS模型是一个可靠和准确的工具,用于预测糖醇在离子液体中的溶解度.
- 该模型具有很高的覆盖范围和广泛的适用性,通过杆分析验证.
- 这种预测能力支持开发更有效,更可持续的绿色开采工艺.
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