Case study: beer microfiltration

Ruud van der Sman1, Martijntje Vollebregt1, Gerard van Willigenburg2, Verena Eisner1

1Food Biobased Research, 2Systems & Control Wageningen Research & University

Aims

Example of “food separation process” Industrial application (Pentair Xflow):

clarification of pilsner beer (e.g. Heineken) Use of crossflow microfiltration with membranes Unfiltrated beer contains particles fouling membrane Project aim:

develop model-based control to minimize energy costs and chemical cleaning

Current process

Constant process conditions: permeate flux and cross flow flux Transmembrane pressure is measured and controlled:

mild cleaning (BW) if TMP exceeds threshold Filtration time is measured and controlled:

harsher cleaning (CIP) if too short filtration time

Pores (20-0.5μm) & selective pores (0.2-0.5 μm)

feed

permeate

Time ->

TMP

->

F BW F BW F BW CIP

Approach

Define objective function Develop first principles model (physics based) Perform scouting experiments to verify hypotheses Model reduction (scale analysis; integration) Parameter estimation from experiment Develop and verify optimization and control strategy

Objective function

Goal: minimize costs Problem:

scheduling of BF and CIP and determining process setting for cross flow and permeate flux -> mixed integer problem Assumption: identical behaviour between CIPs:

- membrane as new after CIP - feed composition the same after each CIP

Cost function and constraints

Cost function: Constraints:

● Given volume of beer must be filtered in given time ● Filtration duration is bounded ● TMP may not exceed maximum value ● Fluxes are bounded

( )Costs of one CIP cycle

: Costs of filtering beer : to be minimized (o) : Number of CIP's (CIP cycles) during full run (o): Number of backflushes in between every two

c E bf bf c

c

bf

J N J N C C

JNN

= + +

CIP's (o)

: Electricity costs pumping (o) : Costs single backflush (g)

: Costs single CIP (g)

E

bf

c

JCCo: unknown; g: given

Hypotheses for beer filtration with Xflow membranes

Cake layer is formed by yeast + permanent haze (monodisperse) Shear-induced diffusion Removable by backwash (BW)

Small aggregates screened by the cake layer, clog the membrane: support layer + pore blocking Removable by harsher cleaning (CIP)

No macromolecules fouling

Experimental setup

50 l unfiltered beer tank Variable cross flow flux Variable permeate flux Automatic back wash Temperature controlled

Sensors:

EBC for turbidity Particle counter (>1 µm) Temperature Fluxes Pressures

Ontology and experimental database

Store data in it’s context to enable: ● Human interpretation of the data ● Automated identification and use of relevant data ● Allowing access of models to data

Allow flexible set-up of ontology and database such that changes can easily be made without redesigning Experimental database:

● Save storage of experimental results

● Connect hypotheses to experiments

● Use for parameter estimation

Model development

Shear-induced diffusion state-of-the-art model using effective temperature

Vollebregt, van der Sman, Boom Soft Matter, 2010

Model reduction: scale analysis

Scale analysis = integration

Model reduction: integration x-direction

Boundary layer problem: integrate over height bulk region + boundary layer

Complete reduced model

Romero & Davis model

Fouling modes Fluid dynamics

Simulation results: sample output

Important parameter for cake formation

xcr/L ~ (Jcross/Jwall)3

Experimental design

Vary Jcross/Jwall (constant cross flow flux, or constant permeate flux) Different batches of beer (phiyeast, phiaggr) Important parameter for practice = filtration time

0 200 400 600 800 10000

0.2

0.4

0.6

0.8

1

1.2

time (minutes)

TMP

(bar

)

Beer type 1

0 100 200 300 400 500 6000

0.2

0.4

0.6

0.8

1

1.2

time (minutes)

TMP

(bar

)

Beer type 2

TMP profiles in time for two different beer types. Red: module 1, black: module 2

Filtration time for different CFV / permeate flux ratios for filtration period 1 to 7

Comparison with experiment #1

Filtration time versus cross flow

Comparison with experiment #2

Filtration at various cross flow fluxes (2.5 – 15 l/h) Permeate flux = 0.26 l/h

Comparison with experiment #3

Filtration at various permeate fluxes (0.25 – 1.5 l/h) Cross flow flux = 15 l/h

Comparison with experiment #4

TMP during multiple filtration runs

Optimization and control strategy

Minimization of number of cleanings = mixed integer problem Assumption: identical behaviour

between CIPs Perform optimization assuming

fixed number of backwashes (N); evaluate all 3<N<7 (practical range) Local minima: global NLP solver

required Solution times depends on number

of process variables and degree of freedom of process variables

Model implementation

Optimization and control strategy

Typical optimization results (on equipment scale):

Volume = 50l, time = 6 days, no of BF = 6 Degree of freedom

No of decision variables

No of CIPs

Costs Cost division

1. Constant settings over all filtration periods

2 9.55 28.52 Energy: 9.6% BF: 40.1% CIP: 50.2%

2. Variable constant settings for each filtration period

2 * no of backflushes

8.54 26.11 ( - 8.5%) Energy: 11.7% BF: 39.3% CIP: 49.1%

3. Linear varying settings for each filtration period

4 * no of backflushes

8.08 24.96 ( - 12.5%) Energy: 12.6% BF: 38.9% CIP: 48.5%

Optimization and control strategy

Typical optimization results (on equipment scale):

Cross flow flux (left) and permeate flux (right) settings in time for given assignment of filtering 50 liter beer in 6 days.

0 5 10 150

5

10

15J = 24.97, OCh = 1, Nbf = 6, Nc = 8.08, Total time: 142 h

time [h]

J cros

s [l/h

]

0 5 10 150

0.1

0.2

0.3

0.4

J = 24.97, OCh = 1, Nbf = 6, Nc = 8.08, Total time: 142 h

time [h]

J out [l

/h]

Optimization and control strategy

Optimization shows that cost reduction > 10% Control strategy:

● Start filtration assignment ● Estimate essential parameters from first filtration ● Calculate optimal process settings for next filtration

periods ● Upon too much deviation with reality: re-estimate

parameters

Conclusions

Unfiltrated beer is a complex fluid Multiple, interacting modes of membrane Backtransport by shear-induced diffusion

described by state-of-the-art model (effective temp.) Model predicts TMP(t) reasonably well Particle size distribution sensor has much promise Control problem is mixed-integer problem New paradigm: Optimal policy with varying cross flow

and permeate flux gives cost reduction > 10%

Thank you!

Contact: bastiaan.blankert@pentair .com, ruud.vandersman@wur.nl

See also:

- 21st Nov, P1.18, Crossflow microfiltration model for beer clarification using hollow fiber membranes

- 22nd Nov, P2.8, Understanding fouling development during cross flow microfiltration of raw beer