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从那时到现在及以后:探索机器学习如何塑造过程设计问题
Burcu Beykal1,2
1Department of Chemical & Biomolecular Engineering, University of Connecticut, Storrs, CT, USA.
自最小平方法以来,机器学习和替代模型已经取得了显著的进步. 这些技术现在对于工艺设计至关重要,能够使用丰富的工艺数据进行模式识别,预测和优化.
科学领域:
- 工艺系统工程 工艺系统工程
- 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:
- 在19世纪初发现的最小平方法为现代数据分析奠定了基础.
- 机器学习 (ML) 从基本的模式识别演变为工艺工程中的复杂应用.
- 过程数据,数字化和计算能力的扩散加速了ML的采用.
研究的目的:
- 概述机器学习模型在过程设计中的近期历史和演变.
- 突出 ML 如何成为工艺系统工程各种方面不可或缺的组成部分.
- 探索机器学习在塑造工艺设计中的前景和未来方向.
主要方法:
- 审查与工艺工程相关的机器学习模型的最新进展.
- 分析ML对过程设计问题的影响,包括优化和故障检测.
- 探索 ML 集成的促成因素,如数据可用性和计算能力.
主要成果:
- 机器学习现在对过程设计和系统工程至关重要.
- 机器学习技术应用于广泛的任务,包括预测建模,优化和故障检测.
- 机器学习的整合是由增加的数据,数字化和增强的计算能力驱动的.
结论:
- 机器学习已经改变了过程设计和操作.
- 机器学习领域的持续进步有望在工艺系统工程领域进一步创新.
- 这篇论文提供了对 ML 在现场的历史轨迹和未来潜力的洞察.
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