THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

The boundary layer equations for plane, incompressible, and steady flow are 

$$\matrix{ {u{{\partial u} \over {\partial x}} + v{{\partial u} \over {\partial y}} = - {1 \over \varrho }{{\partial p} \over {\partial x}} + v{{{\partial ^2}u} \over {\partial {y^2}}},} \cr {0 = {{\partial p} \over {\partial y}},} \cr {{{\partial u} \over {\partial x}} + {{\partial v} \over {\partial y}} = 0.} \cr }$$

Boundary Layer Theory

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Model-based design of experiments for parameter precision: State of the art

This review presents innovative food processing techniques and their role in promoting sustainable food industry. These techniques (such as microwave, ultrasound, pulse electric field, instant controlled pressure drop, supercritical fluid processing) in the frontiers of food processing, food chemistry, and food microbiology, are not new and were already used for> 30 years by academia and industry. We will pay special attention to the strategies and the tools available to make preservation, transformation and extraction greener and present them as success stories for research, education and at industrial scale. The design of green and sustainable processes is currently a hot research topic in food industry. Herein we aimed to describe a multifaceted strategy (innovative technologies, process intensification, bio-refinery concept) to apply this concept at research, educational, and industrial level.
Industrial relevance: Green Food Processing could be a new concept to meet the challenges of the 21st century, to protect both the environment and consumers, and in the meantime enhance competition of industries to be more ecologic, economic and innovative. This green approach should be the result of a whole chain of values in both senses of the term: economic and responsible, starting from the production and harvesting of food raw materials, processes of preservation, transformation, and extraction together with formulation and marketing.

https://hal.archives-ouvertes.fr/hal-01552142/document

Review of Green Food Processing techniques. Preservation, transformation, and extraction

Advanced model-based experiment design techniques are essential for the rapid development, refinement, and statistical assessment of deterministic process models. One objective of experiment design is to devise experiments yielding the most informative data for use in the estimation of the model parameters. Current techniques assume that multiple experiments are designed in a sequential manner. However, multiple equipment can sometimes be available, and simultaneous (parallel) experiments could be advantageous in terms of time and resources utilization. The concept of model-based design of parallel experiments is presented in this paper. Furthermore, a novel criterion for optimal experiment design is proposed:  the criterion aims at maximizing complementary information by considering different eigenvalues in the information matrix. The benefits of adopting such an approach are discussed through an illustrative case.

Model-Based Design of Parallel Experiments

The optimal model-based design of experiments aims at designing a set of dynamic experiments yielding the most informative process data to be used for the estimation of the parameters of a first-principles dynamic process model. According to the usual procedure for parameter estimation, the experiment is first designed offline; then, the experiment is carried out in the plant, and process measurements are collected; and finally, parameters are estimated after completion of the experiment. Therefore, the information gathered during the evolution of the experiment is analyzed only at the end of the experiment itself. Since the experiment is designed on the basis of the parameter estimates available before the experiment is started, the progressive increase of the information resulting from the progress of the experiment is not exploited by the designer until the end of that experiment. In this paper, a strategy for the online model-based redesign of experiments is proposed to exploit the information as soon...

Online Model-Based Redesign of Experiments for Parameter Estimation in Dynamic Systems

Hydrodynamic effects on three phase micro-packed bed reactor performance – Gold–palladium catalysed benzyl alcohol oxidation

On the development of kinetic models for solvent-free benzyl alcohol oxidation over a gold-palladium catalyst

Model-based experiment design techniques are an effective tool for the rapid development and assessment of dynamic deterministic models, yielding the most informative process data to be used for the estimation of the process model parameters. A particular advantage of the model-based approach is that it permits the definition of a set of constraints on the experiment design variables and on the predicted responses. However, uncertainty in the model parameters can lead the constrained design procedure to predict experiments that turn out to be, in practice, suboptimal, thus decreasing the effectiveness of the experiment design session. Additionally, in the presence of parametric mismatch, the feasibility constraints may well turn out to be violated when that optimally designed experiment is performed, leading in the best case to less informative data sets or, in the worst case, to an infeasible or unsafe experiment. In this article, a general methodology is proposed to formulate and solve the experiment design problem by explicitly taking into account the presence of parametric uncertainty, so as to ensure both feasibility and optimality of the planned experiment. A prediction of the system responses for the given parameter distribution is used to evaluate and update suitable backoffs from the nominal constraints, which are used in the design session to keep the system within a feasible region with specified probability. This approach is particularly useful when designing optimal experiments starting from limited preliminary knowledge of the parameter set, with great improvement in terms of design efficiency and flexibility of the overall iterative model development scheme. The effectiveness of the proposed methodology is demonstrated and discussed by simulation through two illustrative case studies concerning the parameter identification of physiological models related to diabetes and cancer care. © 2009 American Institute of Chemical Engineers AIChE J, 2010

Federico Galvanin

Papers

Model-Based Design of Parallel Experiments

Online Model-Based Redesign of Experiments for Parameter Estimation in Dynamic Systems

Hydrodynamic effects on three phase micro-packed bed reactor performance – Gold–palladium catalysed benzyl alcohol oxidation

On the development of kinetic models for solvent-free benzyl alcohol oxidation over a gold-palladium catalyst

A backoff strategy for model‐based experiment design under parametric uncertainty