1. Introduction 2. Modelling from Noisy Step Response Data Using Laguerre Fuctions 3. Least Squares and the PRESS Statistic using Orthogonal Decomposition 4. Frequency Sampling Filters in Process Identification 5. From FSF Models to Step Response Models 6. New Frequency Domain PID Controller Design Method 7. Tuning Rules for PID Controllers 8. Recursive Estimation from Relay Feedback Experiments; Bibliography; Index

Process engineering spans industrial applications in the manufacturing sector from petrochemical to polymer to mineral production. From Plant Data to Process Control covers the most up-to-date techniques and algorithms in the area of process identification (PID) and process control, two key components of process engineering, essential for optimizing production systems. It examines both the theoretical advances in process design and control theory, and a wide variety of implementations. A wide variety of approaches are presented for building models of dynamical systems based on observed data (process identification) and for making the output of a system behave in a desired fashion by properly selecting the process input (process control).