624 pages | 210 B/W Illus.
With the advancement of computers, the use of modeling to reduce time and expense, and improve process optimization, predictive capability, process automation, and control possibilities, is now an integral part of food science and engineering. New technology and ease of use expands the range of techniques that scientists and researchers have at their disposal making it increasingly important for the user to be aware of and have a good working knowledge of the alternatives.
Unique in its scope, the Handbook of Food and Bioprocess Modeling Techniques provides a comprehensive overview of the modeling options available to today’s researcher. The book covers a wide range of topics including transport processes, reaction kinetics, probabilistic modeling, data mining, neural network and genetic algorithms. Both mesoscale and macroscale modeling are covered. Each chapter is complete with a clear, succinct description of a specific modeling technique, followed by detailed examples of the utilization, application, benefits, and limitations of the technique described. By having both physics-based and observation-based models explained in one place, the researcher can find not only the most appropriate tool or combination of tools for the application, but also those that best suit the technical expertise of the personnel involved. The book emphasizes problem formulation and explains the choice and structure of the modeling technique from an application point of view, making it exceedingly practical and easy-to-use. The international panel of authors and contributors ensures the quality of the individual chapters and the usefulness of the information across wide-ranging food products and processes.
An indispensable resource for the full range of contemporary modeling techniques, the Handbook of Food and Bioprocess Modeling Techniques provides food and bioprocess researchers in industry and academia with an invaluable comprehensive working reference.
Mathematical Modeling Techniques in Food and Bioprocesses: An Overview, A.K. Datta and S.S. Sablani
Part I: Physics-Based Models
Lattice Boltzmann Simulation of Microstructures, R.G.M. van der Sman
Fluid Flow and Its Modeling Using Computational Fluid Dynamics, A. Kumar and I. Dilber
Heat Transfer, A.K. Datta
Mass Transfer: Membrane Processes, D. Hughes, T. Taha, and Z. Cui
Simultaneous Heat and Mass Transfer, X.D. Chen
Reaction Kinetics, M.C. Giannakourou and P.S. Taoukis
Probabilistic Modeling, B.M. Nicolaï, N. Scheerlinck, and M.L.A.T.M. Hertog
Part II: Observation-Based Models
Experimental Design and Response-Surface Methodology, S. Nakai, E.C.Y. Li-Chan, and J. Dou
Multivariate Analysis, I.S. Arvanitoyannis
Data Mining, G. Holmes
Artificial Neural Network Modeling, S.S. Sablani
Genetic Algorithms, T. Morimoto
Fractal Analysis, M.S. Rahman
Fuzzy Modeling, H.M.S. Lababidi and C.G.J. Baker
Part III: Some Generic Modeling Techniques
Monte Carlo Simulation, K. Cronin and J.P. Gleeson
Dimensional Analysis, L.C. Lim, S.S. Sablani, and A.S. Mujumdar
Linear Programming, E. Feinerman and S. Saguy Index