TAILIEUCHUNG - Ebook Design of experiments in chemical engineering: Part 2
(BQ) Part 2 book "Design of experiments in chemical engineering" has contents: Defining research problem, selection of the responses, screening experiments, youdens squares, statistical analysis, gradient optimization methods, simplex lattice design, extreme vertices designs,.and other contents. | 157 II Design and Analysis of Experiments Introduction to Design of Experiments (DOE) Design of experiments, like any other scientific discipline, has its own terminology, methodology and subject of research. The title of this scientific discipline itself clearly indicates that it deals with experimental methods. A large number of experiments is done in research, development and optimization of the system. This research is done in labs, pilot plants, full-scale plants, agricultural lots, clinics, etc. An experiment may be physical, psychological or model based. It may be performed directly on the subject or on its model. The model usually differs from the subject in its dimensions and sometimes in its nature. The experiment may also be done on an abstract mathematical model. When a model describes the subject precisely enough, the experiment on the subject is generally replaced by an experiment on the model. Lately, due to a rapid development of computer technology, physical models are more frequently replaced by abstract mathematical ones. An experiment takes a central place in science, particularly nowadays, due to the complexity of problems science deals with. The question of efficiency of using an experiment is therefore imposed. J. Bernal has made an estimation that scientific research is organized and done fairly chaotically so that the coefficient of its usability is about 2%. To increase research efficiency, it is necessary to introduce something completely new into classical experimental research. One kind of innovation could be, to apply statistical mathematical methods or to develop design of experiments-DOE. DOE is a planned approach for determiniing cause and effect relationships. Hereby, the following is essential: . . . reduction or minimization of total number of trials; simultaneous varying of all factors that formalizes experimenter’s activities; choice of a clear strategy that enables reliable solutions to be obtained after each sequence
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