Learning-Based Predictions and Soft Sensing for Process Industries: Theory, Methodology and Applications covers prediction and soft sensing in industrial processes that are subject to specific challenges with AI-empowered learning algorithms. With the aid of a data-driven modeling strategy, the book explores the problems of industrial prediction and soft sensing and formulates a series of learning-based theory, methodologies, and applications. The book introduces the basics of prediction and soft sensing backgrounds, including different categories of prediction theory. Secondly, covers the foundations of machine learning methodologies, including supervised learning prediction, semi-supervised, and self-supervised prediction. Finally, the book examines novel learning-based models/architectures.

• Covers the benefits and an explanation of recent developments in prediction and soft sensing systems
• Unifies existing and emerging concepts surrounding advanced prediction models/architectures
• Provides a series of the latest results in, including, but not limited to, supervised learning, semi-supervised learning, self-supervised learning, probabilistic learning