Mechanistic mathematical models are an essential tool for the study, simulation and optimisation of processes in chemical engineering, allowing for a quantitative description of observed phenomena through the definition of laws and correlations. Development of these models are often costly and time-consuming, whilst the validation and statistical assessment of the model structure, and the precise estimation of model parameters, may require extensive experimentation. In response, model building procedures have been proposed for developing, improving and validating mechanistic models in more efficient ways by managing and guiding the information obtained from experimental activities. These procedures heavily rely on the use of efficient computational techniques for model identification based on the use of optimal design of experiments techniques. This book guides the reader through statistical tools and methods for building mechanistic mathematical models in chemical engineering using design of experiment techniques. Relevant chemical engineering case studies are used throughout the book to provide a practical approach to this complex topic. Ideal for experimenters, who will find useful tips for driving experiments, and modellers who will find useful information on model development, selection and validation, this book is essential for chemical engineers across academia and industry.