model based predictive control of an olive oil mill

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JFOE 4915 No. of Pages 11, Model 5+

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www.elsevier.com/locate/jfoodeng

Journal of Food Engineering xxx (2007) xxx–xxx

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Model based predictive control of an olive oil mill

Carlos Bordons *, Amparo Nunez-Reyes

Departamento de Ingenieria de Sistemas y Automatica, Escuela Superior de Ingenieros, Universidad de Sevilla,

Camino de los descubrimientos s/n, 41092 Seville, Spain

Received 10 October 2006; received in revised form 9 March 2007; accepted 8 April 2007

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Abstract

This paper presents the application of model based predictive techniques to an olive oil mill. The work presents a solution to the inte-grated control of the mill, where a predictive strategy has been used to optimize oil yield while keeping quality standards. The workincludes multivariable identification as well as the implementation of a predictive controller on the real plant, taking into account oper-ating constraints that appear in this process. The work also shows the application problems that arise when implementing advanced con-trollers in an industrial control system with low computational capabilities. The application of the proposed control strategy to an actualolive oil plant has shown that great benefits can be obtained both in oil yield and extraction performance.� 2007 Published by Elsevier Ltd.

Keywords: Olive oil; Process control; Predictive control; Food Engineering
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36373839404142434445464748495051525354

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EC1. Introduction

The automatic control of the extraction of oil out ofolives is still an open field, since there are still many instal-lations operated in manual mode. As olive oil mills arebecoming bigger the chances for automation are increas-ing, therefore it is important to acquire the necessaryknowledge of the process behavior in order to design theappropriate control strategies.

The objective of this paper is to propose a control strat-egy that facilitates the maximization of the oil yield whilekeeping the quality of the final product. The proposed con-trol strategy is based on model predictive control (MPC)(Camacho & Bordons, 2004; Maciejowski, 2002; Rossiter,2003), which is considered as the most popular advancedcontrol technique in industry, due to its ability to operatethe process in such a way that multiple and changing oper-ational criteria (economical, safety, environmental or qual-ity) can be fulfilled in the presence of changes in process

55565758

0260-8774/$ - see front matter � 2007 Published by Elsevier Ltd.

doi:10.1016/j.jfoodeng.2007.04.011

* Corresponding author. Tel.: +34 954 487348; fax: +34 954 487340.E-mail addresses: [email protected] (C. Bordons), amparo@cartu-

ja.us.es (A. Nunez-Reyes).

Please cite this article in press as: Bordons, C., & Nunez-Reyes, A., MEngineering (2007), doi:10.1016/j.jfoodeng.2007.04.011

characteristics. Model predictive control has been used tocontrol several industrial processes (Qin & Badgwell,2003) and its basic idea is to calculate a sequence of futurecontrol signals in such a way that it minimises a multistagecost function defined over a control horizon. The index tobe optimized is normally the expectation of a function mea-suring the distance between the predicted system outputand some predicted reference sequence over the controlhorizon plus a function measuring the control effort overthe same horizon. A model of the plant is used to predictthe future outputs based on past and current values ofthe input and the output of the plant.

The number of olive oils mills in a country such as Spain(biggest producer worldwide) was around 1800 in 2006(Aguilera & Ortega, 2005; Ministerio de Agricultura,2007), and many of them have a net production of around20 hundred tons of oil per day. In spite of this, there are noreports about the penetration of automation technologiesin this sector, although the current state depicted in a sur-vey of automation practices in the food industry (Ilyukhin,Haley, & Singh, 2001) can be extrapolated to the olive oilsector. There are some references related to the automationof one of the plants that appear in this industry: a rotary

odel based predictive control of an olive oil mill, Journal of Food

mailto:[email protected]



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2 C. Bordons, A. Nunez-Reyes / Journal of Food Engineering xxx (2007) xxx–xxx



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dryer. Modeling of an olive cake thin-layer drying processcan be found in Akgun and Ibrahim (2005), while (Arjona,Ollero, & Vidal, 2005; Perez-Correa, Cubillos, Zavala,Shene, & Alvarez, 1998) present two proposals for theautomatic control of the dryer.

In many olive oil mills the process is controlled manu-ally or with single control loops that maintain some flowsand temperatures at constant values, since there are manyfactors that affect production. There are many objectivesto be fulfilled and operators must use their experience tohave the process under control.

The objective of this work is to develop a control schemebased on model based predictive control, where a predictivestrategy is used to optimize oil yield while keeping qualitystandards. Notice that quality of the oil highly depends onagronomic parameters (Del Caro, Vacca, Poiana, Fenu, &Piga, 2006) and that a good control strategy must operatethe plant in such a way that the quality is not lost duringthe elaboration process, at the same time that oil yield ismaximized. The work presented here tries to show thatadvanced control techniques can be successfully applied inthe olive oil industry providing great benefits in oil yieldincrease as well as in extraction performance improvement,without the need of a great investment in new machinery.

2. Process description

The elaboration of olive oil is achieved by extracting oilout of olives purely by mechanical means, without chemicalreactions. All the operations that are performed are aimedat extracting the maximum quantity of juice of the rawmaterial without losing quality. In order to do that, the pro-cess is composed of several operations: reception of rawmaterial (olives), washing, preparation, extraction, andstorage of the produced oil. Fig. 1 shows the most importantphases of the process, whose description can be found inCivantos (1999), Furferi, Carfagni, and Daoub (2007) orPiacquadio, De Stefano, and Sciancalepore (1998).

The preparation phase consists of two subprocesses. Thefirst one is olive crushing by an special mill, whose objec-

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Clean olives

Mill Thermomixer

Fig. 1. Olive oil mill –


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tive is to destroy the olive cells where oil is stored. The sec-ond one aims at homogenizing the paste by revolving itwhile its temperature is kept constant at a specified value(around 35 �C). This is performed in a machine called ther-momixer, which homogenizes the three phases of the paste(oil, water and by-product (alpeorujo)) while exchangingenergy with surrounding pipes of hot water. This is donein order to facilitate oil extraction in the mechanical sepa-rator. The operation conditions in the thermomixer arereally important since they can dramatically affect the qual-ity and quantity of the final product. As a good homogeni-zation is needed, the paste is heated in order to facilitatemixing, since the paste turns more fluent when temperaturerises. But there is an upper temperature limit behind whicholive oil loses quality (flavour, fragrance, etc.) due to theoxidation process and the loss of volatile components.Therefore, keeping low values of temperature will be ahigh-priority objective. The next stage is based on the sep-aration of the product phases by means of a centrifuge.This is a continuous process which separates the differentcomponents that constitute the paste by means of centrifu-gal force. This separation is made in the horizontal centri-fuge or decanter, that separates olive oil from by-product.In order to perform a good separation, the paste that entersthe decanter must be accommodated. Its flow must be con-trolled to a set-point that depends on operating conditionsand some water must be added depending on the propertiesof the raw material. Finally, the last stage of the systemconsists of the storage and the conservation of the obtainedoil. The final product quality and the industrial yield areinfluenced by different process variables. For instance: tem-perature in the thermomixer, residence time, paste consis-tency, paste flow to decanter and water flow to decanter.

The experiments have been carried out in a medium-sizeolive oil mill located at the village of Rus, in the province ofJaen (Spain).

The main variable to be controlled is olive oil flow. Theobjective is to obtain the maximum yield but withoutaffecting product quality. It implies that some operationalconstraints have to be fulfilled.

Addition water

Paste pump

OliveOil

By-product

Heating water

Decanter

process description.


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3. Automation in the olive oil industry

In many olive oil mills the process is controlled almostmanually, using operator’s expertise to keep the processunder control (Aguilera & Ortega, 2005). Automationcan solve operating problems in three crucial zones of themill: in the raw material receiving and inspection area(called patio), in the extraction line and in the warehousearea (bodega). In the first and third areas, the improvementcan be achieved by adequate strategies of information man-agement, inventory and quality control as done in otherprocesses of the food industry (Ilyukhin et al., 2001).

Some promising results have been obtained by the use ofanalytical methods (mainly Near Infra Red spectroscopy,NIR) for control, as shown in the report by Jimenez,Molina, and Pascual (2005), which presents on-line qualitycontrol and characterization of virgin olive oil. Real-timesoftware for the estimation of acidity level and of peroxidesnumber is presented in Furferi et al. (2007). Although theseon-line analytical methods are usually employed for qualityanalysis and detection of potential adulterants, they can behelpful for feedback control.

The place where a major benefit can be obtained by theuse of a control system is the extraction line, which is theproduction unit inside the mill. But up to now only super-vision and basic control is usually done. In the majority ofmills, several variables are measured and sent to an indus-trial controller, usually a Programmable Logic Controller(PLC) which processes them and present useful informa-tion to the operator, who decides which are the better deci-sions to make at each moment. In some mills, the PLC isalso in charge of the automatic control of basic loops suchas flows and temperatures. In this situation, the operator

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Hammer Mill

Crushed Olives

Heatingwater

Additionwater

Paste pump

Thermomixer

Fig. 2. Process diagram of the plant


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selects the desired set-point for a certain variable (ther-momixer temperature, for instance) and a ProportionalIntegral Derivative (PID) controller drives this variableto the selected value, trying to reject external disturbances.

These basic control loops are crucial for the optimiza-tion of the extraction process, since they constitute thelow-level layer that can be used by the optimization proce-dure, as shown below. The loops that are usually controlledare: paste temperature in the thermomixer, flow of additionwater, temperature of addition water, paste flow, pastemoisture and mixing (residence) time. In this work, an opti-mization module is added as a high-level layer that com-putes the optimal set-points for the PIDs minimizing acost function, as will be shown bellow. This is done by amultivariable predictive controller that is described in nextsection.

4. Control system

4.1. Control scheme

Several variables take part in the process of oil extrac-tion out of olives. The final product quality and the indus-trial yield are influenced by different process variables,being the most important the following ones, which aremarked in the P&I scheme (Fig. 2):

� Temperature in the thermomixer. The heating of thepaste has to be constant and gradual since abruptchanges affect negatively the quality of the final product.Two main difficulties appear: the first one is the exis-tence of large delays due to the thermal nature of theprocess and the second one is caused by the on-off mech-

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Filtering& washing

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showing the main instruments.


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PID2

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Olive Oil MillOil Flow

Thermomixervalve

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Fig. 3. Control strategy.




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anism of feeding the paste. These difficulties can deteri-orate the performance of the local temperature control-ler, which is usually a PID. Other advanced controlalgorithms can be used to overcome this problem (Bor-dons & Cueli, 2004);� Residence time. Another important fact to be considered

is the mixing time (residence time) inside the thermom-ixer. A short time drives to incomplete mixing and along one can give rise to emulsions, which interfere withthe extraction process;� Paste flow to decanter. The paste addition to decanter

and the water/mass ratio determine the maximum indus-trial yield. The mass flow is adjustable according to theolive type;� Water flow to decanter. This also determines the extrac-

tion effectiveness. The amount of water that is introducedin the decanter must be constant; that is, the sum of thevegetation water of the olive plus the added water mustbe constant. The raw material does not contain a homo-geneous moisture (Furferi et al., 2007), which forces thatthe water flow must be continually adjusted in order toobtain the maximum oil in the decanter. The temperatureof this water also influences extraction.

The product yield (olive oil flow) is the controlled variable,which is a noisy measurement which has to be pre-pro-cessed before being used for feedback.

In many olive oil mills the process is controlled manu-ally, since there are many factors that affect production.There are many objectives to be fulfilled and operatorsmust use their experience to have the process under con-trol. This situation justifies the use of a multivariable pre-dictive controller that is able to manipulate severalactuators in order to obtain the desired performance. Thecontrol algorithm is described in next section.

The control system can manipulate the followingactuators:

� Heating water valve in the thermomixer. This 3-way valveallows temperature control inside this unit by letting hotwater circulate around the jacket;� Paste pump. It manages the flow of paste that is sent to

the decanter;� Water pump. It controls the quantity of additional water

that is mixed with the paste at the decanter input.

The control strategy that is proposed in this work to con-trol the olive oil mill can be seen as two control levels ina cascade structure (see Fig. 3). A multivariable con-strained model predictive control was implemented to trackthe oil flow to a desired reference, modifying the manipu-lated variables that are the setpoints to the basic controlloops mentioned in Section 3, that operate with classicalmonovariable PID controllers. The industrial implementa-tion of MPC has shown the importance of including eco-nomical and control objectives in the oil productionsystem (Scheffer-Dutra, Nunez Reyes, & Bordons, 2002).


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FThe controlled variable is the oil flow, that will be calledy(t) and the manipulated variables are the set-points to thebasic control loops:

� u1(t): thermomixer temperature set-point;� u2(t): paste flow to decanter set-point;� u3(t): addition water flow set-point.

4.2. Control system hardware

An integrated platform for the control and automationof the oil mill production has been used, that consists of aPLC connected to a PLC where the supervisory and opti-mizing control is executed. The PID controllers that consti-tute the low-level layer of the control system have beenimplemented in the PLC. A PC with an SCADA (Supervi-sory Control and Data Acquisition) serves as a man–machine interface, where the operator can visualize theevolution of the different variables and can act on the pro-cess. Most of the process information is provided to theSCADA by means of a local network that connects thePC to the PLC. The PLC is also responsible for the startingup and shutting down procedures, and for the managementof alarms and emergency shutdown. The optimizing con-trol (high-level layer) that computes the setpoints of thebasic loops is programmed in a high-level programminglanguage (C++) and runs on a separate PC connectedthrough a local network.

The control of the olive oil mill is a complex problemwith a significant number of variables whose operationcharacteristics depend on olive properties. The multivari-able process has three inputs (paste temperature, paste flowand water flow) and one output (olive oil flow). The systemis characterized by long dead-times, constraints and distur-bances. The main disturbances are related to incomingolives, whose properties (moisture and oil content) can bemeasured in some situations and can therefore be consid-ered as measurable disturbances.

4.3. Model predictive control

Model predictive control (MPC) is a good candidate forthe high-level layer of the control strategy. This techniquehas been successfully used in many applications in the pro-cess industry and it has also been proposed in other food


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processes. The survey (Qin & Badgwell, 2003) enumeratessome MPC applications in the food industry and it includesdiscussion of model predictive control in this sector.Didriksen (2002) reports simulation results of the applica-tion of this technique to a sugar beet rotary dryer. An inter-esting application of MPC to other edible oils processingindustries can be found in Wills and Heath (2005) wheremultivariable predictive control is used to reduce variationsin the flow and back-pressure associated with two indus-trial separator units forming part of an edible oil refiningline. That process is similar to the one presented here,although the controlled and manipulated variables arenot the same.

Model predictive control designates a control methodwhich makes explicit use of a model of the process toobtain the control signal by minimising an objective func-tion. The basic concepts of MPC are:

� explicit use of a model to predict the process output atfuture time instants (horizon);� calculation of a control sequence minimising an objec-

tive function; and� receding strategy, so that at each instant the horizon is

displaced towards the future, which involves the applica-tion of the first control signal of the sequence calculatedat each step.

Other control techniques could also have considered ascandidates to tackle the problem, but MPC is a good solu-tion since it integrates multivariable control and on-lineoptimization.

The predictive strategy that is used in this work includesmeasurable disturbances, constraints on the amplitude andspeed of the manipulated variables and amplitude limits inthe controlled variable. There exist operating limitationssince temperature must be kept inside a range, out of whichthe quality of the product is drastically reduced. Therefore,the controller must compute the control actions (u1, u2 andu3 as described in Section 4.1) in order to get the maximumoil flow (ymax) taking into account the operational con-straints described above. The algorithm consists of apply-ing a control sequence that minimizes a multistage costfunction of the form

J ¼XN2

j¼N1

½yðt þ jjtÞ � ymaxðt þ jÞ�2

þXNu

j¼1

½uðt þ j� 1Þ�T K½uðt þ j� 1Þ� ð1Þ

where yðt þ jjtÞ is an optimum j step ahead prediction ofthe system output on data up to time t, N1 and N2 arethe minimum and maximum costing horizons, Nu is thecontrol horizon, K is the control weighting matrix and t

is the current time instant. The objective of predictive con-trol is to compute the future control sequence u(t),u(t + 1), . . . , u(t + Nu � 1) in such a way that the future


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plant output y(t + j) is driven close to ymax(t + j). Noticethat u(t) is a vector that has three components: u1(t), u2(t)and u3(t), since this process is multivariable. For a detailedexplanation of the MPC algorithm, see for instance (Cam-acho & Bordons, 2004). The objective is accomplished byminimising J of Eq. (1) and then the problem can be for-mulated as

min J

subject to : Umin1 6 u1ðt þ jÞ 6 U max

1 ; j ¼ 0; 1; . . . ;N u

U min2 6 u2ðt þ jÞ 6 U max

2 ; j ¼ 0; 1; . . . ;N u

U min3 6 u3ðt þ jÞ 6 U max

3 ; j ¼ 0; 1; . . . ;N u

being U mini and U max

i the minimum and maximum ampli-tude values, respectively, of the manipulated variables.For this plant, giving the design and operating conditions,these limits are given by

U min ¼25

3200

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480

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The introduction of constraints in the design phase allowsto keep the thermomixer temperature as near as possible tothe optimum value to guarantee the best oil characteristics,to keep the paste flow as close as possible to the operatorreference (reduce the necessary flow) as well as to reducethe water flow necessary in the production. Notice thatthe use of constraints is crucial, since it allows to keepthe quality of the produced oil.

The choice of the tuning parameters is based on thecommon rules used in practice, see for instance (Qin &Badgwell, 2003). The minimum prediction horizon N1 isset to the smallest dead time of the plant, which is this caseis N1 = 4. The value of the horizon N2 is a basic tuningparameter and is generally set long enough to capture thesteady-state effects of all computed future control moves;in this case it has been set to N2 = 40. Notice that thisparameter is also related to closed loop stability in thesense that long horizons improve stability (Maciejowski,2002). The control horizon is set to a smaller value(Nu = 10) in order to facilitate implementation, since itsvalue sets the number of decision variables to be solvedin the on-line optimization problem.

The control weighting matrix has been set to

K ¼170 0 0

0 1:5 0

0 0 15

264

375

Their values are chosen so that the terms in the objectivefunction (1) corresponding to the three manipulated vari-ables have similar values, in spite of their different ranges(seen in Eq. (2)). Notice that there are no off-diagonal ele-ments since, in this application, the crossed weights haveno physical meaning.


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Since the cost function is quadratic and constraints arelinear, this is a Quadratic Programming (QP) problem, thatmust be solved at every sampling instant. Notice that well-tested and efficient algorithms to solve this are available inthe market.

4.4. Model identification

The optimization strategy is based on the dynamicmodel of the process, therefore the identification of theplant model is needed before the optimization can be done.The process model plays, in consequence, a decisive role inthe controller. The chosen model must be able to capturethe process dynamics to precisely predict the future outputsand be simple to implement and understand. Most pro-cesses in industry when considering small changes aroundan operating point can be described by a linear model of,normally, very high order. These models would be difficulttoo use for control purpose but, fortunately, it is possibleto approximate the behavior of such high-order processesby a system with one time constant and a dead time (Bor-dons & Camacho, 1998). This is the type of model that isused for identification.

In order to identify the parameters of the transfer func-tions that relate process inputs and output, input variableshave been excited with different steps. The parameters ofthe system model are determined by recursive least squaresestimation (Ljung, 1999). This model has been validatedusing real data obtained from the actual olive oil mill. Data

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Fig. 4. Identification of th


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was obtained from a series of tests performed in the plantduring one year. These data have been treated (filtered,sampled, normalized) suitably to reach an acceptablemodel.

A model with the following discrete time linear transferfunction (or transfer operator, see e.g. Ljung (1999)) is used

yðtÞ ¼ Gðz�1ÞuðtÞ þ Gdðz�1ÞdðtÞwhere z�1 denotes the backward shift operator, i.e.y(t)z�1 = y(t � 1) and t stands for the current discrete timeinstant. The controlled variable y(t) is the oil flow, u(t) isthe vector of manipulated variables u1(t), u2(t) and u3(t)which are, respectively, the temperature in the thermomixerset-point, the paste flow set-point and the water flow set-point. The measurable disturbance d(t) is a measurementof oil content in the paste, which is available in some oliveoil mills. This measurable disturbance has a great influencein the performance, because it represents the properties ofthe olive inlet at the thermomixer.

The process matrix fraction description is given by

yðtÞ ¼ G1ðz�1ÞG2ðz�1ÞG3ðz�1Þ� � u1ðtÞ

u2ðtÞu3ðtÞ

264

375þ Gdðz�1Þ

� �dðtÞ

Each matrix Gi corresponds to a first order system withdead time of the form

Giðz�1Þ ¼ biz�1

1� aiz�1z�di ; i ¼ 1; 2; 3

e multivariable model.


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being ai the pole, bi the zero and di the discretized deadtime.

The accuracy of the model fitness to real data can beseen in Fig. 4. Better results could be obtained withhigher-order models, but paying the cost of losing simplic-ity. To get more information about the plant model identi-fication, see Scheffer-Dutra et al. (2002).

5. Experimental results

Several experiments have been performed on the realplant to show the behavior of the proposed controller.Fig. 5 shows the behavior of the oil flow, y(t), when con-trolled by the proposed strategy, starting at instant 2850.It can be seen that the flow is kept around a constant value(dashed line), that is the maximum that can be obtainedtaking into account the characteristics of the incomingolives. It is possible to observe how the output signalreaches the desired set-point. Constraints and disturbancehave been satisfied and compensated respectively by thecontrol action.

This behavior is achieved by manipulating the set-pointsof the low-level control layer. Fig. 6 shows the set-pointsthat are computed by the MPC at each sampling time(u1, u2 and u3).

Notice that these values are computed solving the QPproblem at every sampling time. This optimization prob-lem takes operational constraints into account, such asthermomixer temperature, which has a great influence in

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product quality. The upper graph of Fig. 6 clearly showsthat this value is always below the maximum temperatureconstraint limit (dashed line).

Fig. 7 presents the PIDs performance in the same exper-iment (dashed lines are the set-points and solid lines thePIDs controlled variables). The water flow and the pasteflow show good behavior in set-point tracking while thethermomixer temperature may have en error of around1 �C around the reference. This is due to the big dead timeof the variable and the changes in the level of the thermom-ixer. The control can be improved by the use of anotherpredictive controller instead of the PID in this loop, ashas been done in Bordons and Cueli (2004). That papershows how a careful identification process followed by apredictive strategy that takes measurable disturbances intoaccount can achieve a good regulatory control for the ther-momixer temperature loop.

The process behavior has been improved with respect tothe usual way of operating this plant, in which the operatorused to make decisions based on his experience, acting onthe set-points of the low-level control loops, that is, with-out the MPC. Fig. 8 shows the evolution of olive oil yieldunder existing control, showing worse behavior that underthe proposed control strategy (Fig. 5) in terms of signalvariance. In fact, the graphs correspond to two differentdays and the operating conditions (disturbances, olives,etc.) may not be exactly the same, but the benefit of theproposed control is evident. This will be quantified in nextsection.

200 3300 3400 3500

y

ults. Olive oil flow.

00 3100 3200 3300

00 3100 3200 3300

00 3100 3200 3300ples

sp_u1

sp_u2

sp_u3

ts to the low-level control loops.


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Wat

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ow (l

/h)

PID2

PID1

PID3

Fig. 7. Experimental results. PIDs performance.

500 1000 1500 2000 2500

350

400

450

500

550

600

650

700

Oliv

e O

il Fl

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Fig. 8. Olive oil flow under existing control.




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This section presents the main operating resultsobtained when the proposed control scheme was appliedduring one season in the olive oil mill. Four key indicatorsare analyzed:

� Daily production: tons of olives that enter the mill per24 h. It is a clear indicator of the plant capacity;� Extraction performance: defined as the ratio of oil pro-

duced divided by the quantity of incoming olives. It isthe indicator of good operating practices in the plant.It must be as high as possible;� Byproduct oil content: is the quantity of oil that is not

extracted in the mill, expressed as percentage of oil inthe dry byproduct (called alperujo). Although this oilcan be recovered later by chemical means, the benefitis lower since it is a low-quality oil. It must be as lowas possible;


� Olive oil yield: tons of oil produced every day. It is aclear indicator of the plant production.

The results come from analysis performed by independentlaboratories: Laboratorio particular de Analisis Agrarios,located at Ubeda, Atres, Calidad y Medio Ambiente,S.C.A., located at Torredonjimeno and Ecologia del olivar

at Menjibar, all of them in Jaen, Spain.Table 1 compares these key indicators for the 2004–2005

and 2005–2006 seasons. Notice that the difference betweenthese seasons also depend on other factors (weather, timeof harvest, state of maturity) that depend on the own fruitand agronomic practice and not on the process, and conse-quently can vary year on year.

During the 2004–2005 season a basic control systemalready existed, with basic control loops whose set-pointswere set by the operator. The proposed control strategyincluding the high-level control layer was implemented inthe 2005–2006 season. Therefore Table 1 compares the


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Table 1Comparison of manual and automatic control operation

Indicator 2004–2005 2005–2006

Daily production (tons per 24 h) 75.2 84.5Extraction performance (%) 21.15 21.62Byproduct oil content (%) 2.65 2.01Olive oil yield (tons per 24 h) 14.8 18.3




results of an improvement of the control system (mainlysoftware) over a plant with an existing basic automation,that is, not completely manual.

The second row of the table shows that the daily pro-duction was substantially incremented (from 75.2 to 84.5tons per day). This is great benefit taking into account thatthe new control scheme has almost no additional cost sincethe main investment on the control system (hardware) wasalready done.

The third row shows the improvement achieved onextraction performance. The difference is not so big sincethis oil mill had a good performance on the 2004–2005 sea-son, similar to the average of the zone, which was 21.1%(de Agricultura, 2004). Anyway, this value is very highcompared to those obtained in other countries where auto-mation practices are not widely spread; for instance, inArgentina the average in 2004 was 14% (Ministerio de

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0 10 20 314

16

18

20

22

24

26

Daily values from 3 De

Extra

ctio

n Pe

rform

ance

(%)

Fig. 9. Evolution of the extraction

0 10 20 31.4

1.6

1.8

2

2.2

2.4

2.6

2.8

Daily values from 3 De

Bypr

oduc

t oil

cont

ent (

%)

Fig. 10. Evolution of the byproduc


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Economia, 2004). The use of the proposed controller hasraised this value in order of 0.5, producing a big economi-cal profit to the mill.

The reduction achieved in the byproduct oil content isnear the technological limit. The value obtained is verylow, indicating that the maximum oil yield (objective tobe met by the optimization) has been fulfilled. Notice thatthe limit depends on the characteristics of the incomingolives. This value is lower at the beginning of the seasonthan at the end.

The following figures show daily values of this key indi-cators during 61 days. Fig. 9 shows the evolution of theextraction performance during the season that, with anaverage value of 21.62 can reach values as good as 23.5some days.

The daily evolution of the byproduct oil content isdepicted in Fig. 10. Note that during the first three weeksthis variable is kept under 2%, which is an excellent value.

The daily evolution of the quantity of crushed olives isdrawn in Fig. 11. Notice that good automation practicesreduce the wasted time associated to shut-down andstart-up. However, some of the shut-downs cannot beavoided since they are due to the lack of incoming olives.An average value of 84.5 is obtained for this mill that, withthe same machinery, could crush only 75.2 tons the previ-ous season (see Fig. 12).
E
0 40 50 60c 2005 to 31 Jan 2006

performance during the season.

0 40 50 60c 2005 to 31 Jan 2006

t oil content during the season


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F579580581582583584585586587588589590

591

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603604605606607608609610611612613614

615

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621

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0 10 20 30 40 50 600

2

4

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8

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12 x104

Daily values from 3 Dec 2005 to 31Jan 2006

Dai

ly p

rodu

ctio

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of o

lives

)

Fig. 11. Evolution of the quantity of crushed olives during the season.

0 10 20 30 40 50 600

0.5

1

1.5

2

2.5 x104

Daily values from 3 Dec 2005 to 31 Jan 2006

Oliv

e oi

l yie

ld (k

g)

Fig. 12. Evolution of the oil yield during the season.




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ECThe last figure shows the quantity of oil produced in the

mill, which gives the main profit. Note that this quantity issmall at the beginning of the season, when small amount ofolives come to the mill and increases along the year. Theincrement of oil yield as a consequence of the proposedcontrol scheme is not only due to the increment in theextraction performance, but also to the capability of thecontrol system to keep the mill operating in stable condi-tions for a longer time.

The previous figures have shown that all the key indica-tors have been improved, especially olive oil yield, whichresults in an economical profit.

7. Conclusions

This work has presented an application of model predic-tive control to an olive oil mill. Thanks to this control strat-egy, the yield of oil can be optimized without losingproduct quality. This is achieved by means of a two-levelcontrol strategy that takes operation constraints intoaccount. Information from the incoming raw material isincluded as a measurable disturbance allowing the processto rapidly adapt to changes in operating conditions.

The results show that this advanced control strategy canbe implemented in simple industrial control systems byusing a cascade structure where the basic control is done

Please cite this article in press as: Bordons, C., & Nunez-Reyes, A. (2of Food Engineering (2007), doi:10.1016/j.jfoodeng.2007.04.011

by PIDs running on the PLC and the high-level controlcomputes the optimal setpoints to be transferred to thePIDs. This allows a safe operation even in case that the cas-cade structure is broken.

The application of the proposed control strategy to anactual olive oil plant has shown that great benefits can beobtained both in oil yield and extraction performance.Notice that this improvement from the previous season isdone with the same machinery, which implies that no addi-tional investment is needed. The optimization algorithmalso reduces energy and water consumption, since flowsand temperatures are constrained.

Acknowledgements

The authors acknowledge the Spanish Ministry of Sci-ence and Technology for funding the work under GrantDPI2004-07444-C04-01 and Francisco Carta and JuanHermida for their help in the application of the techniqueto the actual oil mill.

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653654655656657658659660661662663664665666667668669670671672673674675676677678679680




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