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PROBABILISTIC SIMULATION OF BATCH TRAY DRYING USING MARKOV CHAINS AND THE MONTE CARLO TECHNIQUE
Author(s) -
CRONIN KEVIN
Publication year - 1998
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/j.1745-4530.1998.tb00464.x
Subject(s) - tray , markov chain , monte carlo method , probabilistic logic , markov chain monte carlo , water content , computer science , process engineering , moisture , markov process , simulation , mathematics , biological system , materials science , statistics , mechanical engineering , engineering , artificial intelligence , machine learning , geotechnical engineering , composite material , biology
The objective in food dehydration is to dry the product to a specified uniform moisture content. In practice, a distribution in final moisture content is unavoidable, arising from the intrinsic variability of food properties and the probabilistic nature of the food drying process. A methodology is outlined whereby the inherent stochastic dynamics of batch tray dehydration in cabinet or tunnel dryers can be modeled efficiently using Markov chains. By simulating the drying process with the model, the progressive transformation of the moisture distribution of the trays throughout the drying cycle can be quantified. Output from the model is validated with experimental drying results to test its accuracy. In addition the output of the Markov approach is compared with that of an equivalent Monte Carlo simulation model to provide inter‐model comparisons. Markov analysis can predict the variation in final moisture content and thus has the potential to help maximize the value of the dried product.