1. Motivation  
1.1. Overview and scope  
1.2. Soft sensing  
2. Process control  
2.1. Mathematical modeling approaches  
2.1.1. Knowledge-driven, data-driven, and hybrid modeling  
3. Soft sensors in process industries  
3.1. Motivation for soft sensing  
3.2. A quantitative overview of soft sensing  
3.3. Computational cost of soft sensors  
3.4. Industry implementation of soft sensors  
3.5. Challenges in soft sensor development  
4. Data-driven and hybrid modeling approaches for optimization and control  
4.1. Model-based optimization  
4.2. Model predictive control and real-time optimization  
4.3. Reinforcement learning  
5. Discussion  
5.1. Conclusions