From Then to Now and Beyond: Exploring How Machine Learning Shapes Process Design Problems.

Area of Science:

• Process Systems Engineering
• Chemical Engineering
• Data Science

Background:

• The least squares method, discovered in the early 19th century, laid the groundwork for modern data analysis.
• Machine learning (ML) has evolved from basic pattern recognition to sophisticated applications in process engineering.
• The proliferation of process data, digitalization, and computational power has accelerated ML adoption.

Purpose of the Study:

• To provide an overview of the recent history and evolution of machine learning models in process design.
• To highlight how ML has become integral to various aspects of process systems engineering.
• To explore the prospects and future directions of ML in shaping process design.

Main Methods:

• Review of recent advancements in machine learning models relevant to process engineering.
• Analysis of the impact of ML on process design problems, including optimization and fault detection.
• Exploration of the enabling factors for ML integration, such as data availability and computational power.

Main Results:

• Machine learning is now fundamental to process design and systems engineering.
• ML techniques are applied across a wide range of tasks including predictive modeling, optimization, and fault detection.
• The integration of ML is driven by increased data, digitalization, and enhanced computational capabilities.

Conclusions:

• Machine learning has transformed process design and operations.
• Continued advancements in ML promise further innovation in process systems engineering.
• The paper provides insights into the historical trajectory and future potential of ML in the field.