Process Optimization

This is an ideal textbook for students of experimental optimization techniques used in industrial production processes. It presents a detailed treatment of Bayesian Optimization approaches and it contains a mix of technical and practical sections.

Preliminaries. - An Overview of Empirical Process Optimization. - Elements of Response Surface Methods. - Optimization Of First Order Models. - Experimental Designs For First Order Models. - Analysis and Optimization of Second Order Models. - Experimental Designs for Second Order Models. - Statistical Inference in Process Optimization. - Statistical Inference in First Order RSM Optimization. - Statistical Inference in Second Order RSM Optimization. - Bias Vs. Variance. - Robust Parameter Design and Robust Optimization. - Robust Parameter Design. - Robust Optimization. - Bayesian Approaches in Process Optimization. - to Bayesian Inference. - Bayesian Methods for Process Optimization. - to Optimization of Simulation and Computer Models. - Simulation Optimization. - Kriging and Computer Experiments. - Appendices. - Basics of Linear Regression. - Analysis of Variance. - Matrix Algebra and Optimization Results. - Some Probability Results Used in Bayesian Inference.