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Chemical Engineering for Quality Brewing
Chemical Engineering for Quality Brewing
Nick J. Huige, Ph.D.
Nick J. Huige, Ph.D.
Nico Consulting, Waukesha, WI 53188, U.S.A. Nico Consulting, Waukesha, WI 53188, U.S.A.
ABSTRACT ABSTRACT
Chemical engineers are employed in many areas of brewing Chemical engineers are employed in many areas of brewing in-cluding operations, engineering, development, and research. cluding operations, engineering, development, and research. Chemi-cal engineering education teaches basic skills for the application of cal engineering education teaches basic skills for the application of heat, mass, and momentum transfer to optimize and control many unit heat, mass, and momentum transfer to optimize and control many unit operations that are used in brewing. This review will give examples operations that are used in brewing. This review will give examples of unit operations, along with some of their quality aspects that were of unit operations, along with some of their quality aspects that were learned on the job and through additional studies. The review will learned on the job and through additional studies. The review will also present a model to predict the loss of product shelf life during also present a model to predict the loss of product shelf life during storage and transportation at various temperatures.
storage and transportation at various temperatures.
SÍNTESIS SÍNTESIS
Se emplean ingenieros químicos en muchas áreas cerveceras, Se emplean ingenieros químicos en muchas áreas cerveceras, in-cluyendo operaciones, ingeniería, desarrollo e investigaciones. La cluyendo operaciones, ingeniería, desarrollo e investigaciones. La educación de un ingeniero químico le enseña habilidades básicas educación de un ingeniero químico le enseña habilidades básicas para la aplicación de calor, masa y transferencia de momento para para la aplicación de calor, masa y transferencia de momento para poder optimizar y controlar muchas operaciones unitarias en el poder optimizar y controlar muchas operaciones unitarias en el pro-ceso cervecero. Este repaso señalará ejemplos de operaciones ceso cervecero. Este repaso señalará ejemplos de operaciones uni-tarias, junto con algunos de sus aspectos afectando la calidad, tarias, junto con algunos de sus aspectos afectando la calidad, aprendidos en el lugar de trabajo y mediante estudios adicionales. aprendidos en el lugar de trabajo y mediante estudios adicionales. También se presentará un modelo para pronosticar la pérdida de También se presentará un modelo para pronosticar la pérdida de estabilidad al tiempo durante el almacenaje y transporte a estabilidad al tiempo durante el almacenaje y transporte a diferen-tes temperaturas.
tes temperaturas.
Introduction
Introduction
When I entered the brewing industry as research and When I entered the brewing industry as research and devel-opment manager at Schlitz Brewing Company and throughout opment manager at Schlitz Brewing Company and throughout my further career at Miller Brewing Company, I was amazed at my further career at Miller Brewing Company, I was amazed at how many opportunities there were to
how many opportunities there were to apply my chemical engi-apply my chemical neering education to the brewing process. In chemical neering education to the brewing process. In chemical engi-neering, we had studied unit operations, each of which had a neering, we had studied unit operations, each of which had a range of equipment choices, that could be
range of equipment choices, that could be used for chemical orused for chemical or biochemical reactions or for phase
biochemical reactions or for phase separations. Unit operationsseparations. Unit operations that I found useful in brewing included evaporation, that I found useful in brewing included evaporation, solid-liquid separation processes, solids handling, extraction, liquid separation processes, solids handling, extraction, adsorp-tion, absorpadsorp-tion, desorpadsorp-tion, crystallizaadsorp-tion, and membrane tion, absorption, desorption, crystallization, and membrane processes.
processes.
Chemical engineers rely on the principles of heat, mass, and Chemical engineers rely on the principles of heat, mass, and momentum transfer and reaction kinetics to design these unit momentum transfer and reaction kinetics to design these unit operations and to develop process models. They then use operations and to develop process models. They then use physical, chemical, and biochemical measurements to test physical, chemical, and biochemical measurements to test theirtheir models and to optimize the process operation. Statistics also models and to optimize the process operation. Statistics also
proved vitally important to make sure that the measurements proved vitally important to make sure that the measurements were significant. What I needed to learn most though was were significant. What I needed to learn most though was which parameters or components are
which parameters or components are important for the processimportant for the process or product quality. Some of my learning came from trial and or product quality. Some of my learning came from trial and error, but mostly I learned from suggestions and error, but mostly I learned from suggestions and recommenda-tions made by
tions made by my colleagues in brewing, engineering, researchmy colleagues in brewing, engineering, research and development, and quality assurance.
and development, and quality assurance.
Our sensory specialists taught me the importance of flavor Our sensory specialists taught me the importance of flavor and flavor stability. Many of the components that influence and flavor stability. Many of the components that influence product quality cannot be measured because not
product quality cannot be measured because not all of them areall of them are known, analytical techniques are not available, or it is not known, analytical techniques are not available, or it is not known how individual components as part of a complex known how individual components as part of a complex mix-ture affect quality parameters. Throughout the years, I have ture affect quality parameters. Throughout the years, I have in-creasingly relied therefore on sensory
creasingly relied therefore on sensory measurements whenevermeasurements whenever there was a chance of a change in quality, whether that change there was a chance of a change in quality, whether that change was good or bad. As I will show later, it has been possible to was good or bad. As I will show later, it has been possible to use sensory data to
use sensory data to build quality prediction models.build quality prediction models.
Unit Operations
Unit Operations
Below are some examples of unit operations that I Below are some examples of unit operations that I encoun-tered over the years in brewing, along with some quality tered over the years in brewing, along with some quality as-pects that I learned.
pects that I learned.
Evaporation
Evaporation
Optimizatio
Optimization of n of Brewkettle BoilBrewkettle Boil. Temperature/time, volatiles. Temperature/time, volatiles stripping, and agitation are important parameters that all need stripping, and agitation are important parameters that all need to be included in the experimental design. The conventional to be included in the experimental design. The conventional brewkettle design is not optimal to accomplish all required brewkettle design is not optimal to accomplish all required funtions effectively.
funtions effectively. Reduction of
Reduction of FouliFouling in ng in ExternExternal Calaal Calandriandria. Low temperature. Low temperature differences between wort and steam and high wort flow rates differences between wort and steam and high wort flow rates help minimize fouling.
help minimize fouling.
Process Design for Spent Grain
Process Design for Spent Grain Pressed Liquor ConcentrationPressed Liquor Concentration.. A forced-circulation evaporator is best for minimizing fouling A forced-circulation evaporator is best for minimizing fouling (3); a hydrosieve is useful in preclarifying the spent grain (3); a hydrosieve is useful in preclarifying the spent grain liquor feed. Enzymes, such as cellulose, where permitted, can liquor feed. Enzymes, such as cellulose, where permitted, can substantially reduce viscosities, as long as temperatures are substantially reduce viscosities, as long as temperatures are kept low enough to
kept low enough to avoid inactivation.avoid inactivation.
Nic
Nic HuiHuige ge stastarte rte is is rewrewing ing carecareer er at at ScSc itz itz BreBrewinwing g ComCompanpanyy in 1972. In 1978, Nick joined Miller Brewing Company, where he was in 1972. In 1978, Nick joined Miller Brewing Company, where he was manager of research engineering and planning when he retired in manager of research engineering and planning when he retired in 2002. In 1985, Nick received the MBAA Presidential Award for his 2002. In 1985, Nick received the MBAA Presidential Award for his publication on carbon dioxide recovery. His primary research publication on carbon dioxide recovery. His primary research inter-ests were in the areas of flavor stability, plastic bottles for beer, and ests were in the areas of flavor stability, plastic bottles for beer, and the application of new process technologies. A citizen of the the application of new process technologies. A citizen of the Nether- Nether-lands, Nick obtained his M.S. degree from Northwestern University lands, Nick obtained his M.S. degree from Northwestern University in
in EvEvansanstonton, , IL, IL, an an is is PP .D. .D. egreegree e in in cc emicemica engina engineerineering g romrom the Technical University of Eindhoven in the Netherlands. Nick was a the Technical University of Eindhoven in the Netherlands. Nick was a long-time member of the MBAA Technical Committee and was long-time member of the MBAA Technical Committee and was jointly
jointly responsible responsible for for initiating initiating poster poster presentations presentations at at MBAA cMBAA c on- on-ventions.
ventions.
E-mail: [email protected] E-mail: [email protected]
Award of Merit lecture presented at the 116th Convention of the Award of Merit lecture presented at the 116th Convention of the Master Brewers Association of the Americas, Milwaukee, WI, Master Brewers Association of the Americas, Milwaukee, WI, Octo-ber 2003.
ber 2003.
Publication no. T-2004-0105-01 Publication no. T-2004-0105-01
© 2004 Master Brewers Association of the Americas © 2004 Master Brewers Association of the Americas
Solid-Liquid Separation Processes
(Filtration, Centrifugation, Pressing, and Sedimentation)
Regenerable Filter Aids. Disposal of spent diatomaceous earth (DE) might become a problem in the future, but regenerable filter aids are not yet economical in the United States to re-place DE. Currently, dewatered filter aid has several economi-cal disposal options (3). Options to use various grades for vary-ing filtration loads are limited when usvary-ing regenerable filter aids.
Lautering with Recycled Spent Grain Liquor or Trub (3). Par-ticulates in the recycled liquor are bad for quality and slow down the lautering rate. Recycled liquor needs to be kept at 190°F to prevent spoilage.
Beer Recovery from Centrifuged or Pressed Yeast . Beer from centrifuged yeast gives an off-flavored product, even at a 0.5% addition rate to the main beer; however, microfiltered beer from pressed yeast is OK to use.
Whirlpool Design. Volumetric and linear tangential velocity and height-to-diameter ratio are important design parameters. The design offers a great opportunity to use momentum-transfer equations.
Solids Handling
Malt Abrasion in Conveying Systems. Abrasion causes malt dust and loose husks, which segregate in storage bins and slow down lautering, and causes brew-to-brew variability; therefore, low-density pneumatic systems, if used, require smooth, high radius curves to avoid malt abrasion.
Milling of Brewer’s Grain for Food or Specialty Feeds (3). Milled dried or wet grain can be screened to give a high-protein fraction excellent for fish, poultry, or pig feed and a high-fiber fraction excellent for human health (in low-carbohydrate diet; as cholesterol reducer).
Optimization of Brewer’s Grain Dryers (3). Design and opera-tion of dryers for low grain-exit temperature reduces air pollu-tion and reduces protein denaturapollu-tion to improve product quality.
Extraction
Optimization of Lautering for Increased Brewhouse Capacity. Increasing extract yield without knowing what is extracted
may not be good for quality. Instead, cutting off lautering early decreases time, which can increase brewhouse capacity by as much as 10% and give a better quality wort with lower levels of polyphenols, beta glucans, and silicates.
Continuous Lautering. Counter-current centrifugal lautering followed by centrifugal clarification of the extract failed in the early 1970s since the fine particles to be separated contained about 16% lipids, which caused the density difference between particles and wort to be too small for efficient separation. Con-tinuous lautering only makes sense in conjunction with con-tinuous brewing. Future use of concon-tinuous brewing is not likely as a result of brand proliferation.
Adsorption
Operating Procedures for Activated-Carbon Water Treatment . Hot water used in place of steam is an excellent option for eco-nomical and more uniform carbon-bed sterilization.
Optimization of Silica Gel Chillproofing. Dosage rate determi-nations in the laboratory are useful for determining the minimum addition rate for each brand of beer, but good dissolved-oxygen control is required. Zerogels require careful consideration since they may reduce beer flow rates.
Adsorption Protocol. In-line chillproofing with adsorbents such as polyvinylpolypyrrolidone and silica gel require batch pretreatment or proportional dosing throughout a filter run rather than dosing in a precoat or in a filter sheet, which can re-sult in overadsorption.
Crystallization
Freeze Concentration of Beer . It was first studied in the 1960s to provide concentrates that would be shipped to local distribution centers, where the product would be filtered, brought back to desired alcohol levels, and packaged, similar to soft drink products. Crystallization of water in the form of round ice crystals developed in the early 1970s (1) allows loss-free separation of ice and 15–20% beer concentrate. Freeze con-centration development was stopped in the United States be-cause of an unpopular labeling requirement stating “reconstituted product”. In ice-beer production, freezing is only temporary and dilution water is added back to the concentrate within the brewery. Temporary freezing improves product physical stability.
Membrane Processes
Membrane Cross-Flow Filtration. Economic comparison of membrane cross-flow filtration with DE filtration depends on the cost of DE and its disposal, power costs for each system, new installation or replacement, use of ceramic or polymeric membranes, simultaneous chillproofing requirements, beer loss, and fouling potential. The new process for oxidation of mem-brane foulants is useful (7). Caution is required because of the potential loss of beer components with the use of mem-branes.
Dialysis for New Products. In dialysis, a microporous sym-metric membrane is used to exchange dissolved molecules be-tween two solutions separated by the membrane. For example, these solutions can be two beers, beer and water, or beer and wort. Resulting products may be an alcohol-reduced beer or a beer with more flavor. It is useful that both product streams af-ter treatment can be used to avoid product and disposal costs.
Nanofiltration or Reverse Osmosis. These membranes sepa-rate lower-molecular-weight solutes such as ethanol from com-plex solutions such as beer. Molecular-weight cutoffs are from 150 to 500 for nanofilters and less than 150 for reverse osmosis filters. Multiple stages in combination with diafiltration with
Figure 1. Average change toward temperature equilibrium over time during warm-up or cool-down of a pallet of bottles or cans. NR =
water can be used to make near beers. Processes are slow, and frequent membrane cleaning is required.
Hydrophobic Membranes for Gas Absorption and Stripping. The driving force for absorption of gases into liquids or desorption (stripping) of dissolved gases from liquids is the dif-ference in partial pressure between the two phases. The hydro-phobic membranes are generally made of polypropylene, poly-sulfone, or polytetrafluoroethylene. They are usually arranged as bundles of hollow fibers with gas on the inside of the fibers and liquids on the outside. They have a large mass-transfer sur-face area per unit volume, similar to venturi ejectors and mo-tionless mixers. Brewery applications include oxygen removal from beer, dilution water, CO2 scrubber water, and boiler feed
water; oxygenation of wort or pitching yeast slurries (5); in-line carbonation of beer or diluent; CO2 reduction from se-lected products; CO2 removal from acid-treated hard water or
from fermenting beer; and nitrogenation of beer (6). Advan-tages are prevention of in-process foaming, simultaneous re-duction of dissolved oxygen while carbonating, easy process control, and easy scale-up from laboratory to industrial modules.
Process Modeling for Quality Assurance
As mentioned in the introduction, chemical engineers use heat and mass balances and heat-, mass-, and momentum-transfer equations to describe process steps in order to build models for process control or to be able to predict what will happen in the process when parameters such as temperatures, pressures, inlet concentrations, or mixing conditions change. An example is the model for plastic bottles that I presented at the 2002 MBAA Convention in Austin, TX (4). That model was able to predict the amount of oxygen that entered plastic bottles over time and the amount of carbonation that was lost. These changes could be calculated as a function of bottle size and wall thickness and of bottle and closure materials of
con-struction. External parameters included temperature and rela-tive humidity.
In the examples below, models will be given for the heating and cooling rates of cases of cans or bottles stacked in pallets in a warehouse or cooler or during transportation in railcars or trailers. These models will be expanded to predict how the ac-cumulative effect of temperature changes will affect product freshness over time.
Model for Individual Pallet Warm-up or Cool-down
Pallets of bottles or cans were placed in a constant-temperature cooler, and package-skin temperatures were monitored by ther-mocouples over a period of 6 days. Three different package types were monitored: 12-oz. cans (14 layers, seven cases per layer, 24 cans per case), 12-oz. nonreturnable (NR) bottles (nine layers, seven cases per layer, 24 bottles per case), and 7-oz. NR bottles (seven layers, seven cases per layer, 48 bottles per case). The thermocouples were placed in four different layers and in three locations in each layer (outside can or bottle, and the third and fifth can or bottle in). As expected, the outside packages cooled down (or warmed up) the fastest, the top-layer temperatures also changed faster than those in the bottom layer, while the middle layers changed the slowest. After deter-mining temperature profiles over time for each location in the pallet, weighted average-temperature changes were calculated. To be able to use the results for any initial temperature differ-ence between the beer and the surrounding air, the results were normalized as shown in Figure 1. The percent equilibrium is determined by dividing the average pallet temperature at time t (Tbt ) minus the initial pallet temperature (Tb0) by the initial
temperature difference between the pallet and the surrounding air (Ta0) multiplied by 100.
0 0 0 Ta Tb Tb Tb 100 m Equilibriu % t
Figure 2. Temperature of outside air and calculated average temperatures of air and beer during transportation in a railcar during cloudy weather. Average outside temperature of 80°F with no sun.
The results in Figure 1 show that the average-temperature change over time is essentially independent of package type. This is probably because the overall heat-transfer rate is con-trolled by thermal convection or conduction of beer inside the packages. This was confirmed after determining that the skin temperature on the outside of a package was almost identical to the beer temperature inside the package. The following general equation is a good approximation for the average cool-down rate of palletized beer.
0.01625 0.14
1 100 m Equilibriu % e (1)In this formula, the time t is expressed in hours. The same general formula can be used for warming rates of palletized beer as long as no condensation occurs.
Model for Warm-up or Cool-down of Pallets
in Railcars or Trailers
When palletized beer is loaded in a railcar or trailer and the railcar or trailer is exposed to temperatures that are higher or lower than the temperature of the beer, several modes of heat transfer will take place simultaneously: heat is transferred by convection from the outside air to the wall of the railcar or trailer, from there it is conducted through the walls and its in-sulation to the inside wall; the inside air then transports the heat by convection to the pallets, and warm-up of the pallets will take place with a similar mechanism as described above. When it is sunny, radiation will also play a significant role in additional heating of the outside walls. When the outside tem-perature drops below the beer temtem-perature, heat transfer will go in reverse and the palletized beer will cool down. Beer in packages on the outside of pallets and especially in the top layer will warm up faster, but they will also cool faster. It is therefore justified to work with the equation for average-pallet-temperature change, given above. The following two differen-tial equations need to be solved simultaneously. Equation 2
de-scribes the overall heat transfer from the inside air to the beer in the pallet, while Equation 3 describes the overall heat trans-fer from the outside air warming up or cooling down the con-tents of the car.
M d dtUApallet Ti Tb cppallet Tb (2)
In this equation, U is the overall heat-transfer coefficient to the pallet, A is the outside surface area of the pallet, Ti is the average temperature of the inside air, Tb is the average beer temperature, M cppallet is the mass of the pallet times the average
specific heat of the pallet, and t is the time. The constant UA / M cppallet can be calculated by differentiating Equation 1
and was found to be 0.01625 h–1.
M d dt n M d dtUAcar To Ti cpair Ti cppallet Tb (3)
In Equation 3, U is the overall heat-transfer coefficient of the railcar or trailer, while A is its surface area, To is the tempera-ture of the outside air, M cpair is the mass of the inside air times
its average specific heat, and n is the number of pallets in the railcar or trailer.
A computer spreadsheet was used to solve Equations 2 and 3 simultaneously. Three examples of model calculations are shown in Figures 2–4. For these examples, a starting beer tem-perature of 68°F was used. An average outside-air temtem-perature of 80°F was chosen; however, the actual outside-air tempera-ture varied from 71 to 91°F. The example of Figure 2 is for the transport of 49 pallets in a railcar during cloudy weather. The UA factor for an insulated railcar of 200 British thermal units (Btu)/h was used. Figure 3 is for the same railcar but in sunny weather. To compensate for the effect of sun, outside-air tem-peratures were increased by up to 1°F, depending on the time of day. To check the model, two train trips from Milwaukee, WI, to Albany, GA, were made. Thermocouples were placed at various locations in the airspace around the pallets and in the pallets themselves. Outside temperatures were also recorded.
Figure 3. Temperature of outside air and calculated average temperatures of air and beer during transportation in a railcar during sunny weather.
t
The final mean beer temperature was within 0.3°F of the mean beer temperature predicted by the model. Figure 4 gives the model prediction for pallet heat-up in sunny weather in an in-sulated trailer loaded with 22 pallets. A UA factor of 500 Btu/h was used for this trailer. With a higher UA factor, more heat is transferred per unit of time. Since this heat is also distributed over a lower number of pallets, the average beer temperature increases considerably faster during transport in trailers com-pared with that during transport by railcar. This can be seen by comparing Figures 3 and 4. On the other hand, shipping by trailer is usually considerably faster. So what mode of transpor-tation is better for product quality? To predict the loss of prod-uct freshness during transportation or during storage in a ware-house, the heat-transfer models are combined with a model that predicts the degree of product oxidation as a function of time and temperature.
Model for Product Oxidation as a Function
of Time and Temperature
The detrimental effect of high temperatures on product oxi-dation is well-known qualitatively. At close-to-freezing tem-peratures, product changes are barely noticeable, even during several months of storage, whereas at high temperatures, prod-ucts can oxidize to a considerable degree within several days to a week. By storing product isothermally at a number of differ-ent temperatures, trained sensory panels can develop freshness curves or oxidation curves for a particular beer, as shown in Figure 5. In Figure 5A and B, storage time units are left arbi-trary, since each beer has its own oxidation characteristics depending on the raw materials used and brewing and fermen-tation procedures. Methods used in brewing and packaging to reduce oxidation will also affect the rate of quality change over time (2). To develop these curves, it is important that the same product is used for all temperature exposures and that the prod-uct has received the typical care in brewing and packaging to
prevent oxidation. Since brewers are continuously learning more about product stability (as evidenced by longer electron paramagnetic resonance lag times, for example), new curves may need to be developed periodically. The curve for each temperature can usually be expressed by a mathematical equa-tion of the form
time 3 2 1 score Sensory c c e c (4)
In this equation, c1, c2, and c3 are constants. To find a unique quantitative relationship between oxidation scores or freshness and temperature, we looked at mathematical relation-ships that were available from time/temperature indicators. These indicators gradually change color over time and the rate of change is highly temperature sensitive. We found one of these color indicators to have a temperature response very similar to the sensory oxidation of an American lager that we were working with about 10 years ago. The following relation-ship appeared to apply.
Tb 036 . 0 10 4 time of unit oxidation in Increase c (5)
In this equation, c4 is a constant and Tb is the beer tempera-ture in °F. Figure 6 gives the oxidation results predicted with Equations 4 and 5 for an American lager beer stored isother-mally at various temperatures. The vertical axis gives oxidation units. A value of 1 indicates fresh beer; a value of 2, slightly oxidized; a value of 3, moderately oxidized; and a value of 4, strongly oxidized. On the horizontal axis is time in weeks.
So, what can your quality department do with this? What do these oxidation scores mean to the consumer? It is recom-mended to conduct a large consumer-acceptance study with the same beer used in the initial isothermal oxidation studies so that consumer ratings can be obtained on the acceptability of this particular beer at various levels of sensory oxidation. As-sume, for example, that the maximum oxidation score for con-sumer acceptability is 2.5. Further assume that your quality
de-Figure 4. Temperature of outside air and calculated average temperatures of air and beer during transportation in a trailer during sunny weather. Average outside temperature of 80°F with sun.
partment does not want product in the field more than 16 weeks. From Figure 6, it can be seen that this particular beer reaches an oxidation level of 2.5 after 16 weeks at 68°F. This beer stored at temperatures of 68°F or lower can, therefore, be up to 16 weeks (112 days) old and still be acceptable to the consumer.
For this particular beer it is convenient to define 1 shelf-life day as 1 day at 68°F. From this definition and Equation 5, the following relationships can be calculated.
1 day at 59°F = 0.47 days at 68°F = 0.47 shelf-life days 1 day at 68°F = 1.00 days at 68°F = 1.00 shelf-life days
(per definition)
1 day at 77°F = 2.11 days at 68°F = 2.11 shelf-life days 1 day at 86°F = 4.45 days at 68°F = 4.45 shelf-life days 1 day at 95°F = 9.38 days at 68°F = 9.38 shelf-life days 1 day at 104°F = 19.77 days at 68°F = 19.77 shelf-life days
If 112 shelf-life days are available, it then follows that when this product is stored at 59°F, it will take 112/0.47 = 238 days before the point is reached when this product becomes unac-ceptable to the consumer. On the other hand, when product is stored at 95°F, there are only 112/9.38 = 12 days before this same point is reached. It can be seen that the oxidation rates double for approximately every 9°F, rather than for every 18°F, which is expected for many chemical reactions. I expect that several reaction mechanisms must take place at once during product oxidation.
In real life, products are not stored isothermally, but it was found that product oxidation is accumulative. For example, product stored for 1 month at 75°F and then for 1 month at 85°F reached the same final oxidation score as product stored for 1 month at 85°F, followed by 1 month at 75°F, even though the scores after 2, 4, and 6 weeks were different. When product
is stored at a higher temperature followed by storage at 32°F, the sensory scores remain virtually the same during the 32°F storage period, until the temperature is raised again.
Using the above definition of shelf-life days, the temperature model discussed above can now be used to predict how quickly shelf-life days are lost during storage in a warehouse or during transportation by trailer or by railcar under a variety of weather conditions. Figure 7 shows how quickly shelf-life days are lost during transportation of 49 pallets in a railcar with a UA of 200 Btu/h and a starting beer temperature of 68°F. Generally, rail-car transport is considerably longer than truck transport and can last up to 12 days or longer. Figure 7 shows that outside tem-peratures and whether it is sunny can make a difference. Dur-ing sunny 86°F weather, for example, 36 shelf-life days can be lost during 12 days of transportation. This is more than one-third of its available shelf life of 112 days. Figure 8 shows the
loss of shelf life during transport of 22 pallets in a trailer with a UA of 500 Btu/h. Generally, trailer transport takes less than 2 days, but it can be extended if the trailer cannot be unloaded dur-ing the weekend for example. Even after 4 days, the shelf-life loss is much less in a trailer than during the much longer trip in a railcar in spite of the fact that warm-up in a trailer is much faster. During hot summer weather, trailer transport would, therefore, be preferred from a quality point of view. During cold weather, railcar transport may be better since beer will not heat up. The model can also be used to estimate when freezing may occur during various weather conditions and modes of transportation.
Figure 9 shows the results obtained by the model for staging of beer during hot 86°F weather. In this example, 10 pallets of beer are temporarily stored while they are waiting for addi-tional pallets to complete a full shipping load. The model cal-culates the shelf-life days that are lost during storage on the
Figure 7. Shelf-life days lost during 12 days of transportation in a railcar, traveling at average daily temperatures of 77 or 86°F during sunny and shady conditions.
warehouse floor or in a trailer or railcar that happens to be available for temporary storage. Figure 9 also shows the effect of the starting beer temperature (the temperature that the beer is palletized). When beer is pasteurized, it is cooled to a tem-perature above the dew point to prevent condensation, or when beer is cold-filled, it is heated to above the dew point for the same reason. This s tarting beer temperature is generally higher during the summer when dew points are high. It is important to keep the starting beer temperature as low as possible to reduce the shelf-life loss, as can be seen from the results in Figure 9 for temperatures of 68, 77, and 86°F. Figure 9 also shows that when beer is staged it is best to provide some insulation, if possible; a railcar, which is more insulated, is better than a
trailer, which in turn is better than storage on the floor, which provides no insulation.
The best solution, of course, is to stage product in tempera-ture-controlled brewery warehouses or to ship all packaged product immediately and quickly to temperatucontrolled re-gional warehouses. The quality prediction model can quantify the improvement in product quality as a result of temperature-controlled warehousing and use the results to justify its costs.
ACKNOWLEDGMENTS
I would like to thank my colleagues of Schlitz and Miller Brewing Companies for assisting me with many of my projects and for helping Figure 8. Shelf-life days lost during 4 days of transportation in a trailer, traveling at average daily temperatures of 77 or 86°F during sunny and shady conditions.
Figure 9. Shelf-life loss during staging of 10 pallets of beer for 4 days on the warehouse floor or temporarily in a trailer or railcar at 86°F. The ef-fect of beer temperature after palletizing is also shown.
me understand many of the principles of brewing and the importance of product quality. I would also like to thank the management of these companies as well as the MBAA for allowing me to present some of my learnings and findings at MBAA conventions and at MBAA short courses. There is no better way to really get to understand a subject than to present it to colleagues in your own discipline. I would like to en-courage management of all brewing companies and suppliers to have their technical specialists publish, present, and teach as much as possible.
REFERENCES
1. Huige, N. J. (1972). Nucleation and growth of ice crystals from water and sugar solutions in continuous stirred tank crystallizers. Ph.D. the-sis. Technical University of Eindhoven, Eindhoven, The Netherlands. 2. Huige, N. J. (1992). Progress in beer oxidation control. In: Beer and
Wine Production. ACS Symp. Ser. 536, pp. 64-97. B. H. Gump, Ed. American Chemical Socie ty, Washington, DC.
3. Huige, N. J. (1995). Brewery by-products and effluents. In: Handbook of Brewing, pp. 501-550. W. A. Hardwick, Ed. Marcel Dekker, Inc., New York.
4. Huige, N. J. (2002). Evaluating barrier-enhancing and scavenger tech-nologies for plastic beer bottles. Tech. Q. Master Brew. Assoc. Am. 39:218-230.
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