Francesco Marra Università degli studi di Salerno (Italy) Virtualization of microwave and radio-frequency heating of foods MSFS2016

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1 Francesco Marra Università degli studi di Salerno (Italy) Virtualization of microwave and radio-frequency heating of foods MSFS2016 Anacapri, NA, Italy, 8 th of June 2016

2 outline Aim of the talk and general picture of virtualization in food engineering Food processes assisted by electromagnetic fields Sources of complexity Validation and Challenges Final remarks

3 Aim of the talk stimulating discussion

4 Collaborations UniBas, Italy (t.l.: Prof. G. Ruocco; combined convective and MW drying) UCD, Ireland (t.l.: Dr J. Lyng; dielectric heating, radio-frequencies, ohmic, heat transfer, design of new beverages) UC Davis, California, USA (t.l.: Prof. R P. Singh: in-silica and in-vitro models of human stomach) Ankara University (t.l.: Prof. F. Erdogdu: RF assisted thawing, virtualization of RF heating) Northwest A&F University (t.l.: Prof. Shaojin Wang: continous RF heating, modeling of RF heating) WRRC, USDA, Albany, CA, USA (t.l.: Dr. R. Milczarek; complexity of virtualizationin food engineering) Hebrew University of Jerusalem, Israel (t.l.: Prof. S. Saguy; virtualization and open innovation) Several projects carried out for multinational companies and Italian SMEs

5 What is modelling Googling what is modeling will provide us answers such as Modeling is about building representations of things in the real world and allowing ideas to be investigated or A model is an abstraction, which allows people to concentrate on the essentials of a (complex) problem by keeping out non-essential details A simpler and better definition could be Modeling is the mathematical description of a phenomenon that can be experienced Modeling can be empirical, semi-empirical or fundamental In any case, the aim of modeling is proving tools to better understand experimental observations and to foresee effects of some process change without reproduce an experiment (the virtual lab) F. Marra - WRRC - USDA, Albany, CA 30th of August 2010

6 Virtualization: what is it? experiments design modeling dynamics and control smart prototyping optimization Food Engineering for Life - MIT, MA, USA, May 17, 2016

7 Virtualization: why? Reducing time needed for developing Shortening time needed for design Reducing time needed for equipment and process validation Lowering development costs Is this the big picture??? Accelerating time-tomarket Food Engineering for Life - MIT, MA, USA, May 17, 2016

8 Nothing is new, but everything evolves

process Process wise, multi-physics Process

9 Food: a complex world Heterogeneous Multi-component Biochemically and microbiologically unstable Subjected to structural and apparence changes during process Process wise, multi-physics Process wise, multi-disciplinary Process wise, involving chemical and physical changes

11 Coupling phenomena Example on drying: is it just mass (moisture) transfer? Heat transfer is there and maybe structural mechanics is there too! And the change in color? And any possible bio-reaction?

12 Modeling and virtualization: general complexity 12

13 General complexity in modeling and virtualization: the endless pursuit The higher the computational power (dedicated software, dedicated computers), the lower can be the modeling scale The lower the modeling scale, the better the physics description but the higher the complexity The lower the modeling scale, the higher the number of information (properties knowledge) needed The higher the number of parameters needed, the higher the experimental measurement needed The higher the number of process parameters, the higher the complexity of sensitivity analysis

14 Coming back to the topic

15 Heating of foods (cooking, drying, thawing, ) and the EM Heating of foods Conventional heating Dielectric heating Ohmic heating Air heating Water heating Infrared heating RF cooking MW cooking RF drying MW drying RF thawing MW thawing Electro heating assisted (EHA) 15

16 Some of my CAE experiences

17 Complexity of virtualization in food engineering: the Key-Words Heterogeneous domain (multi-sub-domains) Coupling phenomena (multi-physics) Looping All above Key-Words meet issues in food processes assited by electro-magnetic field (EHA)

18 What are E-H processes MW OH RF

19 Aims of E-H applications reducing processing time True improving process efficiency True increasing the heating uniformity of the food product being processed Zhao et al., 2000, J. Food Proc. Eng. Piyasena et al., 2003, Crit. Rev. Food Sci. Nutr. Marra et al., 2009, J. Food Eng. Not always true

, 2015 Space charge polarization Orientation polarization Bottom

20 RF heating systems 50Ω RF system Free-running oscillator RF system Batch heating Continuous heating Upper electrode Food product Uyar et al., 2015 Space charge polarization Orientation polarization Bottom electrode ISM frequency bands 13.46, 27.12, MHz Uyar et al., 2015 Zhou et al., 2015

postharvest processing) Post baking drying

21 Continuous RF heating system Industrial applications Improve economy and quality of mass production Handle large quantities (e.g. postharvest processing) Post baking drying disinfestation Tempering and thawingstalam, 85 kw RF Why modeling RF system? Help to understand relevant phenomena in the process Save time and money for designing and prototyping Scaling up Improve quality level of RF processed products

22 Some issues about RF heating Before designing a RF unit, some aspects must be taken into account Size (and shape ) of processable foods Fast heating >>> difficult heating control Product surface may still heat faster Chances of moisture pressure driven flow from inside to outside

23 Some issues about RF heating Before designing a RF unit, some aspects must be taken into account Size (and shape ) of processable foods Fast heating >>> difficult heating control Product surface may still heat faster Chances of moisture pressure driven flow from inside to outside

24 Temperature [ C] Comparison between Conventional and RF thawing G_p1 G_p2 RF_p1 RF_p2-14 Time [s] Uyar et al., 2016 Heating assisted by EM fields is surely fast, do not need flames, not even gravity force! Industrial use, military purposes, aerospace applications

25 Some issues of E-H applications Size of processable foods Fast heating >>> difficult heating control Product surface may still heat faster Moisture pressure driven flow from inside to outside

26 Complexity in coupling Physics and Mathematics of food cooking by E-H Are the only coupling / complex issues?

27 k (W/m-K) έ ε" Thermo-physical and dielectric properties used in model T( C) T( C) Complex permittiviy Thermal conductivity T( C) Farag et al. (2008) 27

28 Complexity in properties All thermo-physical and dielectric properties of foods are quite sensitive to temperature, first of all, and to mass composition too Substrate property Field of application HT = Heat Transfer EM = Electro-Magnetic Dependent by T = Temperature X = Moinsture content I = Ionic content Volumetric heat capacity HT T, X Thermal conductivity HT T, X, I Dielectric constant EM T, X, I Loss factor EM / HT T, X, I And by frequency of the EM field too!

29 Complexity in coupling Physics and Mathematics of food cooking by E-H are not the only coupling / complex issues

properties change EM field displacement Heat transfer Moisture transfer Ions transfer

30 An example of complex virtualization: food cooking by E-H And in the surrounding environment too In the food EM field displacement Heat transfer Moisture transfer Local properties change EM field displacement Heat transfer Moisture transfer Ions transfer Volume change Multi-component domain Properties change Crust formation Quality attributes (i.e.: color) change

31 Simplifications: do they reduce complexity? Some phenomena could be not considered in a first analysis Food properties being considered constant Ignoring the momentum transfer in the surrounding environment (and thus ignoring the role played by local heat and mass transfer due to external convection) Ignoring the thermodynamics at the interfaces DO WE LOOSE THE BIG PICTURE?

32 Example: heat transfer at boundaries simplification Heat transfer due to heat convection Heat transfer due to mass convection and moisture evaporation

33 vs an approach taking into account everything happens around the food (De Boniset al., 2010) Example: If the goal of virtualization is finding the right process conditions for a fine drying, simplification of BCs does not help

34 Thermodynamcs at boundaries Watson s equation Moisture equilibrium Antoine s equation for vapor pressure

35 How do we «read» virtual output?

36 Example: food thawing assisted by RF (27.12 MHz) Uyar et al., J Food Eng 2015 Main objective of the study was: To develop 3D mathematical model for RF thawing of foods and validate Specific objectives Study temperature distribution inside the food sample Experimental study and validation of the model

37 Material and methods

38 E 0

39 RF systems used RF system UCD, Ireland (Farag et al. 2011) RF system Mersin Uni, Turkey

40 Sample of food used in model: (Beef meat, as in Farag et al. 2008, and 2011) Experiment Sample of beef meat 40

41 Thermo-physical and dielectric properties Specific heat capacity: Apparent specific heat method [J/(kg K)] T T c c m1 p p, frozen cp, frozen cp, frozen Tm 1 T Tm 2 cp 2 T T c c m2 p p, unfrozen Density: 961 kg/m³ 1007 kg/m³ 1053 kg/m³ T T m1 T T T m1 m2 T T m2 T T m2 m1 41

42 έ ε" k (W/m-K) Thermo-physical and dielectric properties used in model T( C) Dielectric constant T( C) T( C) Farag et al. (2008) 42

43 T( o C) Determining top Voltage Experiment 1250V 1000V 2000V 1500V 1375V t(s) 43

T( ⁰C) Determining top Voltage 45 RF heating Water 40 35 30 25 20 15 10 5 0 y

44 T( ⁰C) Determining top Voltage 45 RF heating Water y = 0,0211x + 23, t(s) channel1 channel2 44

45 Model solution Solution procedure FEM based software, COMSOL 4.3a Simultaneous solution of heat conduction and quasi-static equation solved Two subdomains SD1 (rectangular food) and SD2 (air inside the RF cavity) In SD1 (both heat conduction and E-field displacement) and in SD2 (E-field displacement) were solved Measurements Temperature distribution in sample (average and at 25 points) Vertex, top surface, edge and bottom surface Effect of different phase transition intervals Power density and E-field 45

46 Temperature [ o C] Temperature [ o C] Comparison of simulation results with the experimental data obtained by Farag et al. (2011) Experimental Model predicted Hot spot at corner (model with respect to experimental) -20 Time [s] 15 Initially heated fast 10 5 Experimental Predicted 0-5 Min Average Max 46

47 47 Profile of End-point temperature distribution Difference of hot spots 9.12 ⁰C 4.70 ⁰C 1.06 ⁰C Initial 0 s Frozen 700 s P=400W f=27.12 MHz dt=1.5k Heating time 2100 s 1400 s Thawed at average T = ⁰C After 2100 s

and corners Point at high loss factor Power density, Q abs is a function of loss factor and electric field behavior Q abs

48 48 Power density at different times Power absorption inside the food (at different times) Initially 700 s Locations with higher power density [W m -3 ] 1400s 2100 s Locations with higher power density Positions with high E- field outer surfaces and corners Point at high loss factor Power density, Q abs is a function of loss factor and electric field behavior Q abs increased as heating time progress Between RF electrodes E-field deflected by edges and corners increase E-field at outer section

Temperature distribution (diagonal cut view) Portion with lower temperature Relatively higher temperature were observed at the corners and bottom surface Hot spots (max T = 13.

49 Temperature distribution (diagonal cut view) Portion with lower temperature Relatively higher temperature were observed at the corners and bottom surface Hot spots (max T = ⁰C) Power out put = 400W Frequency = MHz Thawing time = 2100 s dt=1.5k Comparison of temperature distribution at different positions Position Exp. 400 W Model Vertex Edge Top surface Bottom surface Average

50 Temperature [⁰C] Temperature [ o C] Comparison of temperature Effect of phase transition intervals on temperature distribution K 1.5 K 2 K 2.5 K Min 25 points 3- Max 25 points 2- Average 25 points 4- External vertex The effect of dt is pronounced (Hot spots) 0 dt 0 0,5 1 1,5 2 2,5 3 Run-away heating was higher in narrow interval whilst slower heating in wide range. The smaller the interval the larger the jump in values of Cp and the larger the numerical difficulty Apparent specific heat method is sensitive to dt -0,5-1 -1,5-2 -2,5 Vertex Edge Bottom surface Top surface ΔT= 0.28 C 50

51 Experimental thawing Realistic 3D model Simplified model

52 T( C) Comparison of simplified model vs realistic 3D model (just numerical results) t(s) Temperature distribution shows no significant difference between realistic and simplified model

53 T( o C) T( o C) Experimental results Temperature distribution at two different points t(s) The measurements were taken at two different points and triple data sets for each t(s)

54 Validation: another source of complexity 370 What we measure? Where we measure it? How do we measure it? Output power = 300 W Temperature [K] z [m] simulated - t = 0 sec simulated - t = 300 sec simulated - t = 600 sec simulated - t = 900 sec experimental - t = 0 sec experimental - t = 300 sec experimental - t = 600 sec experimental - t = 900 sec Temperature may be easy, but how many measurement points we need? Can this be done always «live»? Can we avoid that measuring systems are not interacting with relevant transport phenomena?

55 Validation: another source of complexity Any virtual tool needs to be assessed and validated. Validation by using data available in literature is always possible, but it may hidden some issues. The best validation is carried out by the modeler himself.

56 Validation of RF thawing of foods Temperature ( C) Exp. Data Simulation Time (s) Uyar, Bedane, Erdogdu, Palazoglu, Farag, and Marra, JFE (2015) Validation may be satisfactory or not at same time It depends on the virtualization goal

57 Validation: another source of complexity What we measure? Where we measure it? How do we measure it? Temperature measurement may be easy, but: How many measurement points we need? Can we make it on-site? How an on-site measurement system will interact with the phenomena to be observed?

58 How we validate results involving other variables? Validation of local mass transfer? Validation of EM field displacement? Validation of temperature and moisture evolution in the surrounding environment? Example: in thermal processes, nowadays we can build trustable experimental maps of temperature: will it always be enough?

59 The experiences (validation never ends!) Problem Virtualization Validation Canned food sterilization FL - CM yes MW heating (MP) FL - CM Yes and in progress Convective + MW assisted drying (MP) FL - CM Yes and in progress Ohmic heating (MP) FL - CM Yes and in progress Calendering FL - CM - RF assisted thawing (MP) CM Yes and in progress Simulation on prototypes (MP) FL - CM Yes and in progress RF heating (MP) FL - CM Yes and in progress Food packaging CM Yes and in progress Fluid-dynamics in human stomach CM Yes and in progress

60 Case study: Combined MW / convective drying: from virtualization to prototyping (Marra et al., JFoodEng, 2010; Marra et al., IFT 2011; Pace et al., IHMT, 2011)

61 Combined MW / convective drying: aims of virtualization to develop a multi-physics mathematical model, CFD based, for combined MW/convection treatment; to test the ability of the model to perform numerical analyses, for configurations closer to those found in the industrial framework, investigating also the effects of air velocity for combined MW and forced convection; to use this virtual lab to test and compare performance of different MW/convection combinations in case of an actual food substrate.

62 Combined MW / convective drying: transport phenomena MWs go inside the food sample while the surrounding air moves about it with a certain velocity and profile, thus contributing directly (heat convection) and indirectly (mass convection) to heat transfer momentum transfer had to be solved too F. Marra - WRRC - USDA, Albany, CA

63 Transport phenomena and multi-physics MW heating of solid foodstuff requires a heat conduction equation with a generation term, due to MW perturbation of the internal substrate; Solution of Maxwell s equations; Since heating will promote drying, mass transfer (and the associated heat transfer) has been considered; When drying is the goal of the MW treatment, the coupling with forced convection then becomes more important and momentum transfer has to be taken into account;

64 The practical steps Creating the domain Setting equations Analysis of relevant functions F Marra, MV De Bonis, G Ruocco (2010). Combined microwaves and forced convection heating: a conjugate approach. Journal of Food Engineering, 97, 31-39

65 Up to the VIRTUAL LAB pure convective drying: T air = 303 K, v= 1.5 m/s and Q= 0 W, after 1200 s pure MW drying: T air = T0, vp = 0 m/s and Q0 = 250 W, after 400 s combined MW/convection drying: T air = 303 K, v= 1.5 m/s and Q= 250 W, after 800 s F Marra, MV De Bonis, G Ruocco (2010). Combined microwaves and forced convection heating: a conjugate approach. Journal of Food Engineering, 97, 31-39

66 Comparison of performances, after 400 s of processing Process Moisture reduction T max pure forced convection ineffective to drying - pure MW treatment 80% > 60 C combined MW and forced convection 80% < 45 C Process performances after 400 s of drying of a potato slab F Marra, MV De Bonis, G Ruocco (2010). An integrated computational framework for combined microwave/convection treatment. In: IFT10 Chicago (USA) Institute of Food Technologists

67 The jet impingement

68 The jet impingement effetcs (Prof. G. Ruocco - UniBas)

69 The experimental concept evolution (Prof. G. UniBas lab)

70 The experimental concept evolution Q (volumetric flow rate) = m 3 /h T (jet Temperature) = 7-40 C PR (pulse ratio) = variable Q MW (MW power) = variable up to 1 kw

71 The experimental concept evolution M Pace, MV De Bonis, F Marra, G Ruocco (2011). Characterization of a combination oven prototype: Effects of microwave exposure and enhanced convection to local temperature rise in a moist substrate. International communications in Heat and Mass Transfer,

72 Challenges (some of them ) Including mass transfer in E-H application models Building trustable, established database of thermo-physical and dielectric properties Improve the knowledge of moisture and ions transfer and migration during E-H processes Use a conjugate approach when considering the effect of external convection Proposing measurement methods and tool for multi-point validation of modeling results VirProFood 2014

73 Final remarks Encourage researchers to deal with physics based mechanistic models «Modelers» talk with «measurers of physical properties» Modeling research leaders establish a platform for discussing guide-lines about possible unified approaches, as for mass transfer (i.e.: porous media models) Always consider modeling as a virtual lab, to be integrated in real labs: modeling stand alone is self-referential

74 Final key-words Curiosity Competence Community

75 acknowledgement Ph.D. student Tesfaye Faye Bedane Colleagues from around the World Prof. Ferruh Erdogdu Uni Ankara (Turkey) Prof. Shaojin Wang Northwest A&F Uni (China) Dr. Rahmi Uyar Siirt Uni (Turkey) Prof. James Lyng UCD (Ireland) Special thanks to Prof. G. Ruocco UNIBAS, Italy 75

76 Grazie

Solid Food Pasteurization by Ohmic Heating: Influence of Process Parameters

Excerpt from the Proceedings of the COMSOL Conference 2008 Boston Solid Food Pasteurization by Ohmic Heating: Influence of Process Parameters Markus Zell 1, Denis A. Cronin 1, Desmond J. Morgan 1, Francesco