通过人工智能驱动的实验方法逆向设计大豆油的化学酶性环氧化
Nipon Sarmah1, Vazida Mehtab2, Kashmiri Borah3
1Chemical Engineering & Process Technology, CSIR-Indian Institute of Chemical Technology, Hyderabad 500007, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India; Department of Chemical and Environmental Engineering, School of Engineering, RMIT University, Melbourne VIC - 3001, Australia.
人工智能优化了使用脂酶的大豆油环氧化. 这种由人工智能驱动的方法实现了高环氧转换,提供了一个一般的过程优化策略.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 生物技术是生物技术.
- 人工智能的人工智能
背景情况:
- 大豆油的化学酶性环氧化对于生产环氧化合物至关重要.
- 优化反应参数是最大化氧化氧含量 (OOC) 的关键.
- 传统的优化方法可能是资源密集型和耗时的.
研究的目的:
- 开发和介绍一个人工智能驱动的反向设计方法来优化大豆油环氧化.
- 为了提高化学酶氧化过程的效率和产量.
- 为加强流程优化建立一个可通用的框架.
主要方法:
- 利用Box-Behnken设计对五个因素进行系统的实验.
- 训练了各种人工智能 (AI) 模型,包括支持向量回归 (SVR).
- 在粒子集群优化 (PSO) 中使用SVR作为一个适应性函数,用于参数优化.
主要成果:
- 确定SVR是预测OOC最准确的AI模型.
- 达到7.19%的最大OOC (大约98.5%的相对转换).
- 证明了拟议的人工智能驱动的方法比现有优化技术的优越性.
结论:
- 由人工智能驱动的反向设计方法显著增强了大豆油的化学酶性环氧化.
- 开发的框架为流程优化提供了一个高效和资源最小的战略.
- 这种方法广泛适用于各种化学和生物过程.
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