Multiobjective optimization of skimmed milk microfiltration 0.1µm
| Autor: | BELNA, Maellis |
|---|---|
| Přispěvatelé: | Science et Technologie du Lait et de l'Oeuf (STLO), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Société Boccard, Partenaires INRAE, Université de Bordeaux (UB), Dr Laurent Bazinet Full Professor Institute of Nutrition and Functional Foods (INAF), Université Laval, Dr Sergey Mikhaylin Professor Institute of Nutrition and Functional Foods (INAF), Université Laval, Dr Jen-Yi Huang Purdue University United States, Dr Pascal Dhulster Charles Viollette Institute, Université de Lille France, Dr Charis Galanakis ISEKI Food Association Austria, Dr Alain Doyen INAF, Université Laval Canada, Dr Ozan Ciftci University of Nebraska - Lincoln United States, Cristina Marques CRIBIQ Canada, Dr Oleksii Parniakov Elea Technology Germany, Dr Geneviève Gésan-Guiziou INRAE - Science and technology of milk and egg France, Claudie Aspirault INAF, Université Laval Canada, Giboulot, Anne |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
algorithm
dairy industry [SPI.GPROC] Engineering Sciences [physics]/Chemical and Process Engineering skim milk [SDV.IDA]Life Sciences [q-bio]/Food engineering microfiltration [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering [SDV.IDA] Life Sciences [q-bio]/Food engineering native casein micelle food process optimization protein of milk |
| Zdroj: | International Congress-Green Food Tech 2021: Sustainable Processing for Tomorrow's Food, Virtual conference-Morning EST (GMT 5 International Congress-Green Food Tech 2021: Sustainable Processing for Tomorrow's Food, Virtual conference-Morning EST (GMT 5, Dr Laurent Bazinet Full Professor Institute of Nutrition and Functional Foods (INAF), Université Laval; Dr Sergey Mikhaylin Professor Institute of Nutrition and Functional Foods (INAF), Université Laval; Dr Jen-Yi Huang Purdue University United States;Dr Pascal Dhulster Charles Viollette Institute, Université de Lille France; Dr Charis Galanakis ISEKI Food Association Austria; Dr Alain Doyen INAF, Université Laval Canada; Dr Ozan Ciftci University of Nebraska-Lincoln United States ; Cristina Marques CRIBIQ Canada; Dr Oleksii Parniakov Elea Technology Germany; Dr Geneviève Gésan-Guiziou INRAE-Science and technology of milk and egg France; Claudie Aspirault INAF, Université Laval Canada, Apr 2021, VIRTUAL CONFERENCE, Canada |
| Popis: | International audience; Modelling and optimizing food processes is a complicated task due to the high complexity of the food product, the lack of knowledge about mechanisms limiting process performances, and the heterogeneity of the involved variables (ordinal, cardinal, discrete or continuous variables). This is the case for skim milk crossflow microfiltration with 0.1 µm pore size (MF 0.1 µm). This operation is commonly used in dairy industry to separate proteins: native casein micelles (retentate) are used in cheese making while serum proteins (permeate) are mainly used in food formulations for specific populations (elderly people, infants, etc.). Despite the high interest in MF 0.1 µm in the dairy sector, this process has not been optimized yet regarding stakeholder's objectives. The choices of membrane configurations, processing designs and operating conditions are mainly based on the know-how of equipment manufacturers and the available expert knowledge. In the literature, the optimization of MF 0.1 µm is performed as mono-objective empirical problem or as the specific influence of one variable of interest on a group of chosen variables. This work aims to optimize skim milk crossflow MF 0.1 µm from design to product composition with conflicting optimization objectives. The considerate objectives are to maximize the composition of retentate and permeate fractions, to maximize the protein recovery in permeate fraction and to minimize the economic costs. The applied methodology can be divided in three parts. First, the acquisition of scientific and expert knowledge about the objectives of the optimization, second, the modeling of objectives as computable functions, then the solving of the optimization problem by using an adapted metaheuristic multiobjective optimization algorithm. |
| Databáze: | OpenAIRE |
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