Combination of laser-optical methods for inline dough rheology monitoring

Transcription

1 Center of Life and Food Sciences Weihenstephan Lehrstuhl für Brau- und Getränketechnologie Univ.-Prof. Dr.-Ing. Thomas Becker Combination of laser-optical methods for inline dough rheology monitoring Perez Alvarado, F., Hussein, M. A., Becker T.

2 Index Camera Camera + Laser Direct sample visual inspection (camera) Visual inspection analysis Backer master inspection Torque curve Analysis Z- Kneader analysis Spiral Industrial mixer analysis Texture feature (camera based) analysis Co-occurrence matrix extraction Homogeneity analysis Running research (camera-laser) Rheology analysis Intensity Correlation Function Particle mean square Displacement Dynamic light scattering (DLS) () + i Speckle Autocorrelation Visual recognition Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

3 Experimental Setup Visual recognition Visual recognition Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

4 Master Bakers Visual Inspection for T-160 For sample experiments in z kneader Average decision provided by 5 Master Bakers versus the one given by the power curve ±200 Average = 300 sec Mas. Bak. 1 Mas. Bak. 2 Mas. Bak. 3 Mas. Bak. 4 Mas. Bak. 5 Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

5 Decision time. Z- Kneader by Torque (Nm) mnm = 1 Nm = 500 FU After 10 experiments the maximal torque average value was obtained as decision time ±40 Exp_1 Exp_2 Exp_3 Exp_4 Exp_5 Exp_6 Exp_7 Exp_8 Exp_9 200 Average value 355,4 seconds Time to stop the process Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

6 Homogeneity analysis Image is captured every 5 sec. during the mixing process Color is converted to gray scale, and region of interest is selected Co-occurrence matrix (, ) is calculated which defines the texture according to the times a color appears in the neighbourhood of color,,, Image Color 1 Color 2 Afterwards, homogeneity (H) is calculated as a statistics measure of co-occurrence H " # ",# $"%# where ",# is the i, j element of the normalized cooccurrence matrix at row (i) and column (j) Color 8 Color 1 Color 2 Color 8 Homogeneity refers to the uniformity of dough during mixing time, emulating the decision done by backers visual inspection. Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

7 Homogeneity Analysis Homogeneity values are plotted along the mixing period Vector projecting from time - to each instantaneous homogeneity value is measured Steady-state is reached when the difference between to neighbor vectors has fixed increment Homogeneity / time (s) Steady-state time Decision time by power curve Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

8 Homogeneity Analysis The magnitude difference / between the current vector "3" and the last vector 3%1" is calculated to determine the steady-state time / 0 1 %0 1, / 1 $ 1 % 1 $ 1 Two thresholds bound the steady-state response (~ 3%) are defined [lower threshold, upper threshold] / / Predicted interval by developed regression model Steady-state time Decision time by power curve time (s) Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

9 Homogeneity Analysis A regression model is developed to predict the decision time through the information of steady-state time Decision time = fn (steady-state time) 20 reference experiments are ran and the steady-state time is calculated The decision time is obtained from the power curve of each experiment A linear relation between both times is found with , as shown :;<"=">1?"@; =?;A: =?A?;?"@; ~<=? Exp. Steady-state time (sec.) Decision time (sec.) Decision time (sec.) y = 2,4131x + 42,225 R² = 0, Steady-state time (sec.) Images during mixing Gray scale/roi Homogeneity calculation Decision time Regression model Steady-state time Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

10 Results: Offline Experiments Algorithm tested in over 23 trials to extract the curve features Validation Experiments 10 trials Experiment n Maximal torque time (s) Homogeneity analysis (s) Difference from maximal torque (s) Difference from torque Average (s) T-160_1 402,0 432,9 30,98 77,52 T-160_2 343,8 326,5-17,20-28,87 T-160_3* 310,8 439,6 128,83 84,16 T-160_4 348,0 326,5-21,40-28,87 T-160_5 367,2 343,2-23,98-12,24 T-160_6 403,8 329,9-73,88-25,54 T-160_7 355,8 393,0 37,28 37,62 T-160_8 291,0 379,7 88,78 24,32 T-160_9 376,8 343,2-33,58-12,25 Average 355,4 359,4 40,88 30,9 (*) outlier value Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

11 Decision time. Industrial spiral kneader Power (J/s) ±40 Time to stop the process Average value 564,8 seconds Time (s) Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

12 Results: Offline Experiments For sample experiments in Spiral Kneader Results obtained by homogeneity analysis vs power curve Experiment n Maximal power time (s) H. algorithm time (s) Difference from torque (s) Difference from torque Average (s) T-160_ ,07-33,92 3,27 T-160_ ,73-20,26-47,06 T-160_ ,83-5,16-3,96 T-160_ ,77 23,77 88,97 T-160_ ,17-35,82-98,62 T-160_ ,09 17,09 9,29 T-160_ ,06-24,93-23,73 T-160_8* ,66 62,66 108,86 T-160_ ,24 2,24-33,55 T-160_ ,32 14,32-3,47 Average 564,8 552,70 11,50 34,66 (*) outlier value Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

13 Camera optimum vs. Mixer optimum TA 160 Mixing Time Standard Camera 529s ± 36s 520s ± 34s spec. volume (dm 3 g -1 ) a b Crumb hardness (N) a b 0 Standard (529s) Camera (all) 0 Standard (529 s) Camera (all) Both parameter bread volume and bread crumb hardness where analyzed, and there is a significant improvement in those quality parameters by using the camera homogeneity system on baguette bread. Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

14 KameraStop vs. Knetoptimum TA 165 Standard Kamera Knetzeit 607s ± 52s 724s ± 38s spec. volume (dm 3 g -1 ) a a Crumb hardness (N) a a 0 Standard (607 s) Camera (all) 0 Standardl (607 s) Camera (all) Both parameter bread volume and bread crumb hardness where analyzed, and there is a significant improvement in those quality parameters by using the camera homogeneity system on 65% water content baguette bread. Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

15 KameraStop vs. Knetoptimum Laugengebäck (TA 155) Standard Kamera Knetzeit 463s ± 44s 501s ± 21s spec. volume (dm 3 g -1 ) a b Crumb hardness (N) a b 0 Standard (607 s) Camera (all) 0 Standardl (607 s) Camera (all) Both parameter bread volume and bread crumb hardness where analyzed, and there is a significant improvement in those quality parameters by using the camera homogeneity system on 55% water content Pretzel dough bread. Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

16 Index Camera Camera + Laser Direct sample visual inspection (camera) Visual inspection analysis Backer master inspection Torque curve Analysis Z- Kneader analysis Spiral Industrial mixer analysis Texture feature (camera based) analysis Co-occurrence matrix extraction Homogeneity analysis Running research (camera-laser) Rheology analysis Intensity Correlation Function Particle mean square Displacement Dynamic light scattering (DLS) () + i Speckle Autocorrelation Visual recognition Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

17 Camera-laser analysis Establishing the relationship between rheological properties of dough and fast on line image processing analysis. Related to: Development of an Algorithm based on a Camera-Laser Sensing System for: Estimating dough optimal mixing time (on line) Estimating rheology of dough during mixing (on line) Understanding Intermolecular kinetics Interested in what happens during deformation of the medium. Mechanical testing device. Rheometer. The frequency dependent viscoelastic modulus is measured. Where: () + i () 2πE Elastic - storage modulus Viscous - loss modulus Angular frequency Proposed Camera-Laser system. The generalized Stokes-Einstein relation provide the modulus in terms of the mean square particle displacement F G Where: P Q R HIJ Boltzmann constant Temperature K L M NA O? S T Fluid viscosity Particle radius Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

18 Mechanical testing device. Rheometer. The frequency dependent viscoelastic modulus is measured. () + i Where: () Elastic - storage modulus Viscous - loss modulus 2πE Angular frequency If a substance is purely viscous then the phase shift angle δ is 90 : 0and If the substance is purely elastic then the phase shift angle δ is zero: and 0 With the complex modulus G* one can define the frequency dependent complex viscosity S : S /V Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

19 Proposed Camera-Laser system Laser He-Ne Laser Wavelength 632,8 nm Coherent length 1m Point diameter 1mm 600 s picturing time at 333 fps Acquisition system Camera Basler View angle 45 Resolution 2 MP Grabbing at 333 fps Picturing distance: 20 cm The generalized Stokes-Einstein relation provide the modulus in terms of the mean square particle displacement F G Intensity Correlation Function HIJ K L M NA F G W X?? $? Y";Z=? Y";Z=? $? Y";Z= Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

20 Theoretical background Fick's laws of diffusion Fick s law Concentration In a complex fluid particles move from high concentration to low concentration places Slope = % l lm diffusion direction b %c d df Where: z n y Diffusion Flux \ghijgt3j^ T`kh3Ga/\]^3G_ GV`^a Particle position \]^3G_a Concentration \ghijgt3j^ T`kh3Ga Diffusion coefficient \]^3G_ /GV`^a n o Particle position n n p The diffusion coefficient is proportional to the mobility of the diffusing particles, the Boltzmann constant and the medium temperature, according to the Einstein relation (kinetic theory) : c {K L M Where: Mobility of an spherical particle P Q Boltzmann constant R Medium Temperature Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

21 Theoretical background Einstein-Stokes relation Einstein-Stokes relation The diffusion coefficient is proportional to the velocity of the diffusing particles, which depends on the medium temperature, viscosity of the fluid and the size of the particle according to the Einstein relation (kinetic theory) c {K L M Einstein-Stokes relation: P Q R Mobility of an spherical particle Boltzmann constant Temperature Stokes demonstrates that the mobility for a spherical particle with radius a is proportional to the viscosity S: { }N~A S T Fluid viscosity Particle radius The Einstein-Stokes diffusion-viscosity relation is: K LM }N~A Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

22 Theoretical background. Mean square displacement (MSD) Mean square displacement (MSD) In 1956 Einstein demonstrate that the diffusion is related to the Mean square particle displacement (MSD) in 3 dimensions by: c F G n p %n }? By solving the equality: D r t 6G k Τ 6πηa In the time domain the viscosity is related to the MSD of the particle, the temperature and the particle size. ~ K LM? NA F G Where: P Q R S T Mobility of an spherical particle Boltzmann constant Temperature Fluid viscosity Particle radius Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

23 Theoretical background. Viscoelastic modulus (s) Viscoelastic modulus The frequency dependent viscoelastic modulus is obtained by the Laplace transform of the time domain viscosity L S in the Laplace plane"j" with J V. Laplace transform of the viscosity G (J)=L S J G J L P Q Τ G ŠT F G J Using the generalized Stokes-Einstein relation provide a physical interpretation of the modulus in terms of the mean square particle displacement F G HIJ K L M NA F G The mean square particle displacement F G is deduced from the Dynamic light scattering theory. Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

24 Theoretical Background. Intensity Correlation Function Describes the rate of change in scattering intensity by comparing the intensity at time t to the intensity at a delay time (Gk $ G), providing a quantitative measurement of the flickering of the light. Mathematically, the correlation function is written as an integral over the product of intensities at some time into the mixing process and with some delay time Gk$ G, Which can be visualized as taking the intensity atg times the intensity at Gk$ G - red), followed by the same product at Gk$ G - blue, and so on W X?? $? Y";Z=? Y";Z=? $? Y";Z= Oriented particles create interference patterns, each bright spot being a speckle. The speckle pattern moves as the particle move, creating flickering. Generates a speckle pattern Various points reflect different scattering angles Where: G Lag time =? $? (ms) G - Lag time = 0 ms Camera sensor Intensity value normalized from no light 0 to high sensitivity 255. G Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

25 Deduction of mean square particle displacement The mean square particle displacement MSD is deduced from the Dynamic light scattering theory Intensity Correlation Function Dynamic light scattering (DLS) G - G - $ G Œ mž W X W c X G 1$^? G - Œ mž G - $ G Œ mž The intensity fluctuations I(t) are measured as a function of delay time (G - $G),, and the normalized intensity correlation function, (t), is calculated where the brackets indicate an average over time. Where: P is the laser wave number (15822,78j` ) 3 is the refractive index of dough (still in in research) MSD? is deduced from the equality. HIJ K L M NA F G Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

26 Experimental design To analyze the dough development 30 experiment will be done. Standard white dough (60% water content): 3000 gr Flour 1800 gr Water 20 C 30gr Yeast 45gr Salt Mixing process: 25 Hz, 30 second premixing to avoid dust problems 50 Hz, 600 second mixing (process in the graphic below) Initial temp: 21 C ± 1 C Final temperature: 32 C ± 1 C Laser He-Ne Laser Wavelength 632,8 nm Coherenz 1m Point diameter 1mm Acquisition system Camera Basler Resolution 2 MP Grabbing at 300 fps Picturing distance: 20 cm For every experiment the Rheological parameter of dough at the time point will be extracted by using the Rheometer in the Rheology lab. This results will be used for correlation and validation. Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May

27 Rheology correlation and validation Rheometer Camera-Laser-System Images during mixing Intensity features extraction Intensity Autocorrelation function Rheometer Complex modulus Correlation and validation Einstein- Stokes rheology MSD deduction For every experiment the Rheological parameter of dough at the six mixing time points will be extracted by using the Rheometer in the Rheology lab. This results will be used for correlation and validation of the Camera-Laser System. Intensity Correlation Function W X Perez Alvarado, F. - 3rd European Conference on Process Analytics and Control Technology May