Dynamic Simulation of Plant Operation in the Wine Industry

Copyright © IFAC Control Applications in Post-Harvest and Processing Technology, Ostend, Belgium, 1995

DYNAMIC SIMULATION OF PLANT OPERATION IN THE WINE INDUSTRY

Pierre Grenier (1), Philippe Vilette (2), Christophe Durand (1), Jean-Michel Roger (1) , Francis Sevila (1), Yvan Racault (3)

(1) Cemagref, BP 5095, 34033, Montpellier cedex 1 (2) IMECA, BP 94, 34800, Clermont-L 'Herault (3) Cemagref, BP 3, 33611 Gazinet cedex

Abstract: A model of simulation of wineries operation has been proposed. Each elementary set of knowledge used in the model has been defmed as an entity gathering constant data, variable parameters, and expert calculation functions . The model has been structured hierarchically. It has been implemented in object oriented programming. This expert simulation software produces dynamic balances of resources. This paper presents an application of the software to estimation of water consumption and organic matter rejections. At the winery Caves du Sieur d' Arques in Limoux, both predictions proved to be accurate with a maximum error of 20 % for the 10-day period of the peak of activity of grape-picking. Keywords: Dynamic modelling, Object-oriented programming, Food processing, Water pollution

1. INTRODUCTION

In order to simulate the operation of a winery, flexibility is required. It is necessary to integrate the winemaker's expertise for going into more detail. As the Enologist directs winemaking, implementing his knowledge and reasorming can make the simulation more realistic. In addition, from one winery to another, the enological conditions may change and one must be able to modify the simulator by a simple change in the winery configuration or in the implemented expertise. The techniques which permit to maximize the flexibility of programming and to facilitate expertise implementation are relevant to Artificial Intelligence (AI).

During the period of grapepicking, Enologists have to manage resources as varied as human power, grapes, process equipments, and refrigeration power. A simple idea for optimising the use of these resources is the dynamic simulation of the winery operation (Grenier et ai, 1992). In the winery, refrigeration is one of the few resources being taken care. But even so, the management rules remain quite rough. An inputs calendar is translated into refrigeration needs by making the hypothesis of an average fermentation activity with an imposed temperature setpoint. Cautiously the refrigeration power is overdesigned. Graphics permit to determine the refrigeration requirements. Recent softwares contain more precise calculations. But they do not simulate process lines.

Hitzmarm et al ( 1991) insist on the interest of implementing expertise by means of expert systems methology mixed to other algorithmic approaches that might seem more natural. According to Konstantinov and Yoshida (1992), the results so far achieved in the field of knowledge based control of fermentations lead to the conclusion that modern 65

expert systems should typically integrate various methodologies including rule-based approaches, fuzzy sets, mathematical models, and neural nets. AI is consequently interesting for our simulation scope.

Wineries are taken in this research as an example representative of a large sector of the food industry, with a high seasonality. The whole food industry should benefit from this research especially for environmental aspects, as it represents in France 24 % of the industrial rejections of suspended solids, 45 % of the organic matters, and 36 % of the matters contammg nitrogen (Ministere Delegue a I'Environnement et a la Prevention des Risques Technologiques et Naturels Majeurs, 1991).

In the Food Industry, optimization of the plant as a whole is a new concern while chemical engineering has more experience about it. For instance, the software SpeedUp permits to instanciate complex plant situations (Lucas et ai, 1988; Daubas et ai, 1991). But the cost of decision aiding systems developped for manufacture or chemical plants is too high for most food plants, especially wineries. An original dynamic simulation methodology has been implemented by building a knowledge based simulator (Niviere et ai, 1994). This generator has been structured around a hierarchical model as defmed by Oussalah (1988) and Mozetic (1990). A special attention has been dedicated to winemaking, and especially to equipments use and energy expenditure.

2. BIBLIOGRAPHY At present time, no software has been presented in the litterature for aiding decision about waste reduction prior to depollution, excepted the suggestion made by Niviere et al (1994). But many engineering solutions have been proposed for achieving such reduction.

Since 1994, wineries processing more than 20000 hI/year need an authorization for carrying on their activity, and this authorization depends on strict environmental conditions. In addition, such wineries will have to be equipped with a depollution unit before the end of the year 2000, either individually or within an industrial community. Present associations with urban depollution units will be prohibited. Wineries processing less than 20000 hi/year will just have to declare their activity. But they will have to pay increased environmental taxes (a seven-fold increase from 1990 to 1994) unless they treat their wastes.

Racault (1992) suggested separating rain from used waters, saving water, and fractionning water between categories of waste waters more or less charged with pollution. Adams (1986) proposed to retain non soluble solids with grids and filters, to rinse barrels with wine prior to water, to recycle refrigeration water, and to save water through pressure rinsing, ultrasounds, automatic washing of presses. Mayer et al (1992) has compared various solutions for reduction of water consumption. By an automatic washing of drains in presses, Rochard could reduce the water consumption from 600 liters to 80 liters (1994). The same author showed that muds and lees represent up to 50 % of a winery pollution and that they can be recuperated.

These constraints push winemakers to invest in depollution. In order to reduce investment costs, it is necessary to look for reducing rejections on one hand, and saving water on the other hand. This corresponds to the general notion of clean and sober processes. Each equipment should be controlled. Knowledge about what they reject is necessary, as well as about how to reduce these rejections. The same remark can be made with water consumption and saving. The simulation of these parameters and relevant knowledge should be very usefull to the winemaker for decision aiding. The more detailled the simulation, the more the winemaker will be interested in using the software instead of rough estimations, provided the ergonomics be good enough.

Filtration adjuvants are an important source of pollution (Rochard, 1992). Clean filters dry the adjuvants after use by means of a gas and they are recuperated by centrifugation. But the top solution for pollution reduction is the use of tangential filters which do not need adjuvants. Chemical tartar removal can be done by recovery of both soda and tartrate. Mains recover water of bottles washing. This water is neutralized and stored for settling clarification (Mayer, 1993). Bottles can be sterilized by ultraviolet exrosure without any chemical (Geiser, 1994). Rochard (1994) has emphasized the state of floors in water consumption. Concrete floors are difficult to clean, while epoxy are easy and carrelages even better considering their resistance to shocks and chemicals.

The dynamic simulator presented by Niviere et al (1994), here called by its Imeca name P3 TM, can be a good tool for aiding the winemaker to fmd appropriate solutions of lowering the depollution cost. It has been modified and tested at the winery Caves du Sieur d'Arques at Limoux (France).

Reduction of wastes rejection and water consumption can be really significant. In the dairy industry, 66

Massette (1989) has observed that with the 50 most important dairy factories of the region AdourGaronne in France, pollution was reduced by 13,4 tons of organic matter per day between 1977 and 1987 for a total rejection of 38 tons per day in 1987, while the volume of milk processed passed from 6,3 to 10,3 millions of liters in the same period. Weise and Trantolo (1994) proposed the following solutions for the dairy industry: fully emptying of tanks and pipes before washing, fractionning of fIrst washing waters, automatic taps at end of each hose, recycling solid matters, linkage and dripping protection, byproducts recovery.

methods, should be characterized for insertion in the model of the simulator.

In a fIrst step, the winemaking equipments and processes implemented in the wineries should be characterized. In the case of white winemaking, the pressing area is the most polluting zone of the winery during grape-picking and contains operations difficult to characterize as far as washing is concerned. It should therefore be emphasized. A wahings model for this area of the winery should be calibrated and tested. In a second step, it will be necessary to study the variability of wineries or food plants and to suggest a methodology for adapting easily the washings model from in-situ measurements and a general data bank.

Alosi (1990) has conducted a similar approach at the Angouleme plant of SanofI-Bio-Industrie for production of gelatine. A fIrst step was to recycle refrigeration water, separate rain from washing waters, recycling washing water. A second step was to eliminate suspended solids by grids, fIltration of muds and drying, settling, fIltration and chemicals for grease removal and neutralization. A third step was the building of a biological depollution unit. The overall methodology has permitted to reduce the Oxygen Chemical Demand (OCD) from 5900 to 270 kg per day.

The work reported in this paper presents preliminary results of the fIrst step.

4. MATERIAL AND METHODS The winery Caves du Sieur d' Arques has been chosen as an ex.gerimental site for the application of the software P3 M to water and effluents study. This winery processes in average 90000 hi per year and makes essentially sparkling wines with Cremant as the best selection. All operations are important for fInal quality, but pressing has a special importance. Juice selection starts with pressing: the best juices make the cuvee, then the fITst taille and the second taille. For each 17 tons of grapes press, 85 hi of cuvee juices are obtained, 27 hi of fITst taille and 9 hi of second taille.

In the distilled beverages industry, pretreatment of distillery rejections has been achieved by ceramics membrane before aerobic treatment, with a cut down of 50 % in OCD and nearly 100 % of suspended solids. Some distilleries could valorize the rejections by means of concentration until 70 % solids content, followed by precipitation and potassium removal (Leroy, 1993). In the brewery industry, collecting separately residual beer permits to cut down by 50 % the rejections. A good choice of labels and glue associated to separative techniques gave interesting results with a very signifIcant drop of pollution (Schumann, 1986). In Obernai, the Kronenbourg plant separates clean refrigeration waters going to the river from charged used waters to be treated (Leroy, 1993).

The difficult point of this work is the fItting of calibration curves for water and rejections prediction. Racault (1992) has suggested a methodology permitting to estimate the daily pollution charge of a winery. The basic assumption is that the winery is made of a succession of elementary operations which specific pollution charge can be measured. The main operations are: reception and handling of grapes, pressing, temporary storage, settling preclarifying, settling clarifying, centrifugation, fermentation, ... For each operation, the rejected volume of water is estimated and the average concentration in OCD of the sample is measured. The pollution charge per unit operation is thus estimated, yielding specific charges: g of MES (suspended solids) and g of OCD. This method is not very precise because of size effect of the tanks and the variability of musts and washings. But it gives orders of magnitude.

Reverse osmosis is used in Dieppe where the Coffee plant of Soprad Nestle concentrates effluents from marc juice. Concentrated effluents are incinerated (Leroy, 1993).

3. RATIONALE Present european regulation encourages wineries to equip for reducing the volume of used waters and the pollution charge. The technologies actually used by the winemakers, as well as novel technologies or

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5.2. Modelling of water consumption and organic matters rejection

Water consumption is easy to estimate through installation of counters. Concerning used waters, global measurements at the exit of the winery were made during grape-picking and the results were compared to the pollution calculated on a daily basis. Global rejections were measured by automatic uptakes of samples and flowrates measurements in canals exits.

The configuration of the winery in the software P3 TM describes equipments and technologies. The washing model of the pressing area has been established with the following assumptions: automatic washings took place at end of each cycle as soon as they were empty and they consumed 450 liters for 170 hi of grapes (131 hi of must) containing after use 80 g of QCD per hi of must. It was assumed that temporary storage tanks of extracted musts were cleant with 8 I of water per hi of must for the cuvees and 12 I for the tailles, with respective charges of 60 g and 40 g of QCD per hi of must (Table I).

In a first campaign (1993), two 48 hour periods of observations were achieved with instrumentation sets at the three main exits of polluted waters in the winery. In a second campaign (1994), the global consumption of water was measured at two levels: the whole winery with general counters, and the pressing area with specific counters. The pollution charge has been measured only at the pressing area, in connection with the fractionning of used waters: inspection hol!! inside of the area collecting part of the used waters, inspection hole outside the area collecting all waters minus the most charged separated by a fractionning equipment, and a tank collecting the most charged waters. The tank of charged waters was studied through average daily uptake after mixing by an immerged pump. In the two mentionned inspection holes, automatic on-line sample uptake and flowrate measurement was set up.

Washings of the equipments for last juices extraction (rebeches, sold to fruit juice industry), belt conveyors, floors, and transportation tools, as well as the manual rinsing of presses and all circuits, were considered in a first approximation as being made on the morning from 8 to 13:00. To each hour of this period, the quantity QL corresponded to the overall washing volume in the pressing area divided by 6 hours, minus automatic washing volume of presses and minus manual inside washing of temporary storage tanks. To this quantity QL was associated QOCD L which was the pollution charge transported by these washing waters.

5. RESULTS

Quantities MI and M2 were defined as the respective amounts of washing water and QCD calculated by P3 TM on the basis of the data of Table 1 for automatic washing of presses and manual inside washing of temporary storage tanks . For the period of time delimited by 9/19/94 at 12:00 to 9/21 at 12:00, MI = 33733 I and M2 = 425060 g.

5.1. Estimation of data for the knowledge base of the simulator The unit operations considered are presented at Table 1. According to the methodology described, ratios of consumed water to processed volumes of must were determined as well as rejected organic matter per hi of processed must. The units are respectively liters of water per hectoliter of must and gramms of QCD per hectoliter of must (Racault and Vedrenne, 1994).

Using these two days for calibrating the model, the following equations are set: QWIlCf = XI' J + YI . (J-l) - MI

Table 1. Estimations of water consumption and matter rejection per hi of processed must for some unit operations at the Caves du Sieur d' ArQues in Limoux in 1993

QOCD =

X2'

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M2

or~anic

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water (l/hl) 3 8 12

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QCD (glhl) 80 60 40

XI = 8,884 and YI = 19,621 x 2 = 17,23 and Y2 = 300,17

with J and (J-l) the input grapes of the two dates corresponding to each 24 hour period from noon to noon, QwalCf and QOCD the estimations of Table 2.

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Table 2. Consumed water and rejected Q[~anic matter at the pressin~ area for 24 hour periods from noon to noon endin~ on September 20. 2 I. 22 and 23 at the Winery Cave des Sieurs d' ATQues in 1994 Water (m' ) 129 105 124 101

Date Sept 20 Sept 21 Sept 22 Se pt 23

6. CONCLUSIONS In spite of all variability factors of washings and organic matters rejections per unit operation and per hI of processed must, it is reasonnable to think that correct balances of water consumption and organic rejections are feasible through the methodology developed. Results are quite encouraging, as the punctual errors are rather systematic and as global differences are around 20 % and lower when considering long periods. Improvement of the washings model is possible, and this should lead to a good accuracy for a given situation defmed by a winery and its washings processes.

OCD(Kg) 1632 975 1557 967

5.3. Prediction The consumed water and and pollution discharge for the pressing area in 1994 have been predicted for the whole period of grapepicking by steps of 1 hour. The graphic can be read with a cursor. For a better lisibility, the dynamic balances have been transposed to Excel™. Figure 1 compares the prediction of consumed water in the pressing area to extrapolated measurements. The general trend is well respected. However, morning washings have been overestimated to the expense of washing made during the rest of the day, with significant differences. These differences are quite systematic, which encourages to think that the model could be improved. Moreover, when cumulating the predicted water consumptions from 9/13 at 11 :00 am to 9/23 at 18 :00, the relative error is only of 4.5 %.

In a near future, the use of a software for dynamic simulation of resources in the winery should help the enologist to use its equipments in a cleaner and more sober way. He will be able to visualize the impact of different processes and equipments on water consumption and pollution, to know the exact level of pollution for calculation of environmental taxes, to reason about how to reduce this pollution before treatment, and to size depollution units.

ACKNOWLEDGEMENTS We thank Mrs Gayda and Leclercq, Heads of the technical service at the winery Caves du Sieur d' Arques in Limoux, for their welcome during grapepicking, and the Regional Centers for Industrial Transfers of Technology (CRIII) Trial and Verseau in Languedoc-Roussillon, France.

Getting into more detail with the period from 9/ 19 to 9/23 , Table 3 compares predictions of consumed water and OCD rejections to field measurements. It appears that for this four-day period, the predicted cumulated consumed waters were 21 % lower than measured consumptions (Figure 2) and predicted rejected OCD were 10 % above measured rejections (Figure 3). Taking into account that the measurements of OCD are corrected values which are upper limits, the error might be close to 20 %.

REFERENCES Adams K. , 1986. Techniques favorables a I'environnement, utilisables en cave vinicole. Der Deutsche Weinbau, 15, 725-732. Alosi A., 1990. Sanofi-BioIndustrie, vingt ans de depollution a I'usine d' Angouleme. Magazine Adour-Garonne, 46. Daubas 8., Pingaud H. , Koehret 8. Developpement d'un simulateur de procedes discontinus, semicontinus et continus: ProSim. Recents Progres en Genie des Procedes, Ed GFGP (France), 5, 307-311 (1991). Geiser A., 1994. Une nouvelle technique de sterilisation des laveuses de bouteille dans les entreprises des boissons. Documents Rehman Process lndustrie. Grenier P., Sablayrolles JM, Chabas J, Barre P., 1992. Refrigeration des mouts en fermentation : progres recents . Revue generale du froid, 5, 31-35 .

Table 3 Comparison between measured and predicted amounts of consumed water and rejected Q[~anic matter at the pressin~ area for 24 hour periods from noon to noon endin~ on September 20. 21. 22 and 23 at the Winery Cave des Sieurs d' Amues in 1994. Date 9/20 9/21 9/22 9/23 Total Error

Predicted water (I) 113638 74912 107302 68739 364591 20,7%

Measured water 129240 105160 124500 101000 459900

Predicted OCD (g) 1735347 1178051 1638259 1091023 5642680 10%

Measured OCD 1632190 975120 1556690 967280 5131280

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Hitzmann B., LUbbert A., ShUgerl K., 1991. An expert system approach for the control of a bioprocess. I: Knowledge representation and processing. Biotechnology and Bioengineering, 39,33-43 . Konstantinov K. B. and Yoshida T, 1992. Mini Review. Knowledge-based control of fermentation processes. Biotechnology and Bioengineering, 39, 479-486. Leroy C., 1993. Deux solutions pour les effluents: separer et valoriser. R.l.A., 50S, 78-80. Lucas P., Isambert A., Depeyre D., Cuille P., Rossiny P., 1988. Dynamic simulation and control strategy of a continuous separation system with unsteady inputs. Chemdata Congress, June 13IS, Goteborg (Suede). Massette M. , 1989. Industrie laitiere: dix ans de depollution . Adour-Garonne, 41. Mayer A., 1992. Indices de calcul et simulation des flux de matiere en brasserie. Brauwelt, 46, 2427-2432. Mayer A., 1993. Nettoyage ecologique et ingenieux des bouteilles. Brauindustrie, 4. Mozetic Igor, 1990. Hierarchical model-based diagnosis. International Journal of ManMachines Studies, 35, 329-362. Ministere Delegue a I'Environnement et a la Prevention des Risques Technologiques et Naturels Majeurs, 1991 . Pollution des eaux industrielles en France. Rapport annuel.

Nivii:re V., Grenier P., Roger lM., Sevila F., Oussalah M., 1994. Intelligent simulation of plant operation in the wine industry. Journal of Food Control , S, 2, 91-95 . Oussalah M., 1988. Modi:les hierarchises multi-vues pour le support de raisonnement dans les domaines techniques. These de l'Universite d'Aix Marseille. Racault Y., 1992. Les effluents des caves vin icoles, evaluation de la pollution, caracteristiques des rejets. Informations techniques du Cemagref, 92, 4, 1-8. Racault Y., Vedrenne J., 1994. Etude de la charge polluante des Caves du Sieur d' Arques a Limoux. Rapport Cemagref. Rochard J., 1992. Reduction de la charge polluante et du volume des rejets dans les caves vinicoles. Revue Fran9aise d'Oenologie, 134. Rochard l , 1994. Oenologie propre, reduction de la charge polluante et des volumes de rejets. Actes du Congres International sur le traitement des effluents vinicoles, Ed. Cemagref, 261-266. Schumann G., 1986. Minimisation des rejets de papier d'etiquette et de biere residuelle. Brauwelt, 49, 2440-2443 . Weise D.L. , Trantolo DJ., 1994. Reduction de la pollution dand I'industrie laitiere. Procedes industriels pour le contr61e de la pollution et la minimisation des rejets, 705-713 .

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