HAL Id: hal-02790956
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Formulating preliminary design optimization problem of an agrifood process using expert knowledge: Application
to milk microfiltration
Maellis Belna, a L Marie, F Sfiligoï-Taillandier, Amadou Ndiaye, Geneviève Gésan-Guiziou
To cite this version:
Maellis Belna, a L Marie, F Sfiligoï-Taillandier, Amadou Ndiaye, Geneviève Gésan-Guiziou. Formu- lating preliminary design optimization problem of an agrifood process using expert knowledge: Ap- plication to milk microfiltration. journées scientifiques de l’école doctorale, Jul 2019, Rennes, France.
2019. �hal-02790956�
FORMULATING PRELIMINARY DESIGN OPTIMIZATION PROBLEM OF AN AGRIFOOD PROCESS USING EXPERT KNOWLEDGE:
Application to milk microfiltration
BELNA M.
(1,2,3), MARIE, A-L.
(1), SFILIGOI-TAILLANDIER F.
(4,*), NDIAYE A.
(2), GESAN-GUIZIOU G.
(3)www6.rennes.inra.fr/stlo
labellisé
(1) Boccard, ResearchAnd Development, 35360, Montauban-de-bretagne, France (2) I2M, INRA, Université De Bordeaux, 33400 Talence, France
(3) STLO, INRA, AGROCAMPUS OUEST, 35000, Rennes, France
(4) I2M,Université De Bordeaux, 33400, Talence, France
(*) New Adress : Recover, Irstea, Aix-marseille Université, 13080, Aix-en-provence, France Contact adress : xxxxxxxxxxxx
• Complexity of the food product itself
• Lack of knowledge concerning mechanisms limiting process performances
• Heterogeneity of involved variables
(ordinal, cardinal, discrete or continuous variables)
Milk
(Product state variables)
Characteristics of obtained products
(permeate & retentate)(composition, purity...)
Costs
(Investment, operating cost)
Env. impacts
(water, energy…)
Product
Conflicting objectives
Membrane
(polymeric, ceramic...)
Processing designs
(UTP or without, diafiltration…)
Operating conditions
(Transmembrane pressure,
temperature…)
• Mainly based on know-how of operators and available expert knowledge
• Does not take into account links between product, multiple designs and conflicting objectives
Optimisation
CONTEXT
METHOD
RESULTS
CONCLUSION
HOW TO FORMULATE A DESIGN OPTIMIZATION PROBLEM USING EXPERT KNOWLEDGE ELICITATION ?
Example of an agrifood process : skim milk 0.1 µm crossflow microfiltration
(Talbi et al.,2009)
(Hobbalah et al., 2018)
Knowledge modeling
Modeling of the
optimization objectives
Problem to solve
Knowledge model
Objective functions Experts Interviews
Knowledge elicitation
• Skim milk crossflow microfiltration pore diameter 0.1 µm (MF 0.1 µm) is used in dairy industry to separate casein micelles (retentate) from serum proteins (permeate). This fractionation method allows the preservation of
« native » properties of dairy proteins and offers many ways of valorization for the two products.
• In the literature, optimization of MF 0.1 µm is only partial and empirical.
• Many links between the process operating variables, product state variables and optimization objectives are not formalized. Knowledge elicitation can be coupled with optimization in order to solve the problem.
Multiple designs but no existing rules to guide the design of this process.
123 variables - 218 influence relations
• The optimization problem studied is complex with conflicting objectives and heterogeneous variables.
• The integration of expert knowledge allows the identification of relevant objectives regarding to industrial issues. The MF process is considered from conception to performances with the identification of lack of knowledge in order to plan new experiments.
• Causal maps allow an easy to understand representation of the influences between the variables and make it possible to establish which variables influence which objectives. Based on these maps, the objective functions will be formalized allowing to perform the optimization.
Optimization
• The problem domain was divided into domains of knowledge : easier way to identify experts and collect knowledge.
• Influence relations between variables and objectives were represented as causal maps.
• After validations by experts, maps of the same knowledge domain were merged, discussed and validated by the group of the domain experts.
• The final map represent the knowledge model to use for formulating the optimization objectives as functions.
Membrane properties Permeate
fraction
Retentate fraction Performance of the filtration Costs
Environmental impacts
Operating variables Diafiltration
Process design
Domains of knowledge : MF 0,1 µm Experts and domains
Semi-structure interviews Intermediate maps (exemple) Final map
Different types of experts :
Industrialists, scientists, equipment manufacturers
• 11 experts
• 36 interviews
• 14h30 of audio recording
0 1 2 3 4 5 6 7 8
1 2 3 4 5 6 7 8 9 10 11 12
Number of experts by domains
Domains
Important to setup :
• shared glossary
• generic process scheme
Validation of domain map by the domain expert group
Expert 1 Expert 2 Expert 3
Domain 1 Domain 2 Domain 3
Merged by domain
Individual validations by expert on their maps
=
Discussion and Validation of the whole map by the whole group of experts
