1. Basic Concepts -- 1.1. Modeling -- 1.2. Classification of Processes -- 1.3. Process Parameters and Variables and their Classification -- 1.4. Classification of Process Models -- Capter 2. Optimizing Models -- 2.1. General Considerations -- 2.2. Objective Function — an Example -- 2.3. Designing the Objective Function -- 2.4. Objective Function as a Function of Time -- 2.5. Constrained and Unconstrained Optima -- 2.6. Objective Function — Example Revisited -- 3. Methods of Optimum Search -- 3.1. Problem Definition -- 3.2. Single Variable Search -- 3.3. Two-dimensional Search (Hill-Climbing) -- 4. Design of Experiments -- 4.1. Replication -- 4.2. Blocking of Experiments -- 4.3. Randomization -- 4.4. Factorial Design -- 4.5. Orthogonality -- 4.6. Confounding -- 4.7. Fractional Factorial Design -- 5. Dynamic Covariance Analysis -- 5.1. Dynamic Models -- 5.2. Linear Dynamic Model — Single Variable -- 5.3. End Conditions -- 5.4. Identification of Linear Model -- 5.5. Linear Dynamic Model — Multiple Variables -- 6. Principal Component Analysis -- 6.1. Reducing Number of Variables -- 6.2. Orthogonal Coordinates in Sample Space -- 6.3. Axes with Stationary Property -- 6.4. Zero-one Normalized Variables -- 6.5. Eigenvalues and Eigenvectors -- 6.6. Orthogonality -- 6.7. Mean-square Distances — Distribution of Variance -- 6.8. Numerical Example -- 6.9. Performance Variables -- 7. Regression Analysis -- 7.1. Principle of Least Squares -- 7.2. Linear Regression -- 7.3. Transformation to Linear Form -- 7.4. Choosing the Form of Model -- 7.5. Stepwise Regression -- 7.6. Non-linear Estimation -- References.
