Advanced Control Strategy Combined with Solar Cooling for

Article pubs.acs.org/IECR

Advanced Control Strategy Combined with Solar Cooling for Improving Ethanol Production in Fermentation Units Marcus V. Americano da Costa,*,† Manuel Pasamontes,‡ Julio E. Normey-Rico,¶ José L. Guzmán,‡ and Manuel Berenguel‡ †

Chemical Engineering Department, Polytechnic School, Federal University of Bahia - 40210-910, Salvador, BA, Brazil Informatics Department, University of Almería - Ctra de Sacramento s/n., 04120 Almería, Almería, Spain ¶ Automation and Systems Department, Federal University of Santa Catarina - 88040-900, Florianópolis, SC, Brazil ‡

ABSTRACT: This work proposes the use of solar radiation as energy source to support energy demands in the optimal control process for ethanol production. The control system proposed for the reactor has two layers: the local one composed by PID (Proportional-Integrative-Derivative) controllers for the pH, level, and temperature loops of the reactor and the master controller which uses an advanced MIMO (Multiple-Input and Multiple-Output) MPC (Model Predictive Control) strategy. The control of the solar cooling system is performed by means of a switching control scheme. Since there is no availability of a practical system which integrates the experimental solar cooling plant and a fermentation unit, the Hardware in the Loop technique has been performed on the plant of Centro de Investigación de Energiá Solar (CIESOL), located at the University of Almeriá (Spain), interconnected with a simulator that represents the ethanol fermentation in Brazilian factories. Three scenarios are analyzed in detail, in which the increments in ethanol production are shown.

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INTRODUCTION Nowadays, the idea of sustainable development and the use of renewable energy arise and are promoted as an alternative solution for decreasing dependency on conventional sources of energy that pollute. A significant reduction of greenhouse gases from the use of clean energy is estimated, and the market trend is that the costs of deployment of renewable energy become cheaper and closer to conventional energy.1 Solar energy and ethanol represent important alternatives as clean energy sources in several regions in the world. Solar energy has been successfully applied in many processes to decrease the emission of CO2 to the environment.2,3 On the other hand, ethanol, as a substitute for fossil fuels, has proven to be a concrete alternative in the fight against air pollution.4,5 Ethanol production process has several stages, from cane treatment to final product storage. The typical main steps for large scale industry include milling and refining, fermentation, distillation, and dehydration.6 The fermentation, one the most important units regarding energy consumption and productivity, takes place in the reactor whose configuration allows it to operate as a batch system. In the literature, a considerable number of papers which involve issues of biochemical and chemical engineering served as a starting point as possible ways to increase productivity in batch systems.7−9 However, with the evolution of technology and computers, the application of control and automation becomes indispensable in the efficient optimization of these processes. For this reason, many researchers have studied the configurations of bioreactor based on several structures, such as classic, adaptive, neural, and advanced control techniques.10−14 Recently, Borges15 presented a control strategy based on the solution of systems of equations, resulting from the application of the Pontryagin’s Principle and of the procedure for reducing the top indexes, to estimate and calculate the optimal feed flow. © 2014 American Chemical Society

The experiments were simulated and validated in a bench scale reactor with interesting results. Following the trend of the market in search of more modern solutions, the predictive control technique is discussed by Ochoa et al.,14 in which a model of four states simulates the continuous bioethanol fermentation. The optimization control considers the economic point of view and the obtained profits are evaluated. Moreover, a methodology to obtain the optimum process temperature for the maintenance of cell viability, reducing glycerol production and increasing efficiency, was presented by Atala et al.16 The optimum temperature has been identified as a critical variable because it requires the use of a considerable amount of energy in industry.17,18 Normally, in Brazilian ethanol plants, water from a river or a refrigeration tower are used as the coolant fluid of the heat exchanger. However, many times the temperature of the water flow is not cold enough to control the temperature of the reactor because its heat is released during the fermentation process. For that reason, solar energy can be considered as a possible energy source to face this problem, since there is a great solar energy potential which can be harnessed to cover this energy demand. As Brazil has good climatological condition in several regions to exploit this type of energy with competitive costs,19 investments in solar energy have vastly grown in the last years. The ethanol plants are implanted in locations where the annual insolation is high, varying between 2500 and 3000 sun hours a year, and the daily period has low rainfall during the crop. In this way, Americano da Costa et al.20 demonstrated by means of a Hardware in the Loop simulation the viability of the use of solar energy in Received: Revised: Accepted: Published: 11384

October 2, 2013 June 4, 2014 June 6, 2014 June 6, 2014 dx.doi.org/10.1021/ie403286m | Ind. Eng. Chem. Res. 2014, 53, 11384−11392

Industrial & Engineering Chemistry Research

Article

Figure 1. Whole process structure.

ethanol fermentation unit. However, at that time, only the development of the simulator and economic aspects were explained in the paper. Thus, this work will present in detail the complete design of the control system of the whole plant proposed by Americano da Costa et al.20 The structure uses the high solar irradiation to produce energy that assists the optimal control of the temperature in the fermentation process. The developed control system of the fermentation process has two layers: the local one composed by PID control loops for pH of the must (pH3), temperature, and level of the reactor and the master controller which uses a MIMO MPC strategy to define the optimal operation points. Due to the unavailability of an experimental coupled system composed of a solar cooling plant and a fermentation unit, a Hardware in the Loop technique was implemented to perform the evaluation. The application experiments have been performed using the solar cooling plant of the Centro de Investigación de la Energiá Solar (CIESOL), located at the University of Almeriá (UAL), Spain, and an ethanol unit model developed by means of analyzing the typical fermentation process of ethanol in the Brazilian industry.

The rest of the paper is organized as follows. In Section 2, the whole proposed plant is presented. The control strategy proposed for the fermentation unit and the design control used in the solar plant are explained in Section 3. The experimental results are presented and analyzed in Section 4. Finally, the conclusions are provided in Section 5.

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THE PROPOSED CONTROL STRUCTURE COMBINED WITH SOLAR COOLING SYSTEM The proposed structure is shown in Figure 1 and can be divided into two units: the fermentation process and the solar plant. In order to explain better each part of the structure, the operation of the systems will be described as follows. As can be seen in the figure, the reactor is operated automatically by three local control loops for the level, pH3, and temperature. An optimizer is implemented in a higher level, which calculates the set-points for these low-level control loops in a cascade configuration. Thus, during the main loop sample period these set-points are maintained constant and the local controllers manipulate the valves and pumps to drive these variables to the desired set-points. The cane juice is mixed with an acid substance to maintain the pH3 in a defined value, while 11385

dx.doi.org/10.1021/ie403286m | Ind. Eng. Chem. Res. 2014, 53, 11384−11392


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the formation of the product is associated with cell growth. The yeast cells are subjected to stresses inherent to the process, which are caused by environmental conditions and physicalchemical factors such as high temperature, salinity, pH, and high concentrations of ethanol and sugar.20,24 The fermentation model implemented in this work represents the process that is generally used in real ethanol plants in South and Central America. The behavior of ethanol fermentation is detailed in many works,8,20,25,26 and the parameters used in this model were described as a function of the temperature by Atala et al.,16 whose expressions were determined using the industrial yeast Saccharomyces cerevisiae and cane molasses as the substrate. However, the results can be extrapolated for other fermenters. Since the proposed structure integrates solar energy, an output model of the solar plant was necessary initially to design the fermentation unit, as can be observed in Figure 2.

this mixture feeds the reactor according to the level control loop. The reactor is a batch unit, and during each production period, a continuous recirculation of the wine is performed. The wine passes through a heat exchanger, which uses coolant circulation to keep the wine inside the reactor at an ideal temperature. A second heat exchanger will be used to connect the fermentation process model with the solar cooling plant. This type of connection will be necessary to decouple the two units and, therefore, to allow the operation of the absorption machine in a secure range during all the experiments (with a chilled water flow between 5 and 14 m3/h). The solar collector field is used to increase the water temperature to a desired set-point within the absorption machine inlet temperature operation range. If the tanks temperature is higher than the solar collector field outlet temperature (usually when the irradiation is low), the tanks subsystem can be included in the plant configuration to support the solar collector field. Finally, if the temperature that reaches the absorption unit is far from the desired inlet temperature, the gas heater is turned on. This hot water is used by the absorption machine, where the absorption cycle takes place to obtain chilled water. As can be observed, the chilled water, with flow rate FF(t) and temperature TF(t), refrigerates by means of the heat exchanger the water with temperature Tf10(t), whose flow ṁ f(t) manipulated by the control system is used to maintain the temperature T in the reactor at desired points. Solar Cooling Plant. Solar cooling systems can be divided in two categories: solar absorption cooling and solarmechanical systems.21 In this work, the cooling unit is an absorption machine of the Centro de Investigación de la Energiá Solar (CIESOL), located at the University of Almeriá (UAL), Spain, where the UAL and the PSA-CIEMAT ́ (Plataforma Solar de Almeria-Centro de Investigaciones Energética, Medioambientales y Tecnológicas) work together. In this center, the project Arquitectura Bioclimática y Frió Solar (ARFRISOL) takes place, whose objective is to quantify the energy saving obtained in practice by means of the applications of passive bioclimatic strategies and active renewable systems. As can be observed in Figure 1, the solar cooling plant that covers CIESOL building refrigeration demand22,23 is composed of the following: • A flat solar collector field with a total surface of 160 m2 and an operation range between −20 °C and 120 °C. • A hot water storage system composed of two 5 m3 tanks connected in series, used to accumulate hot water when there is no cooling demand or as a buffer to smooth the solar collector outlet temperature variations under disturbances. • A gas heater (conventional energy source) manufactured by ADISA, with a nominal power of 116 kW. • An absorption machine manufactured by YAZAKI, with a nominal power of 70 kW. The solar cooling scheme is composed of three subsystems. The primary one includes the collector field, tanks, gas heather, and absorption machine. The second one includes a refrigeration tower, an external component of the absorption machine. Finally, the third one includes a heat exchanger and a heat-pump used to simulate an external load. Furthermore, this third circuit connects the absorption machine evaporator output (chilled water) to the building fan-coils system when thermal load experiments are not performed. Fermentation Process Model. Ethanol production via fermentation by saccharomices cerevisiae is a process in which

Figure 2. Design for the fermentation unit model.

Considering the typical operation points of the absorption machine, its outlet temperature TF(t) can be identified as TFm(t), according to the following Hammerstein model 0.028

TFm(t ) dt

+ TFm(t ) = gk (TL(t )) ·TL(t )

in which gk is the scaled gain that depends on the input TL(t), that is the temperature of the water. One of the model validation experiments is shown in Figure 3. Note that, although the real temperature TF presents a low noise signal, the model presents an acceptable dynamic during most of the experiment.

Figure 3. Validation model. Input and output signals of the absorption machine. 11386



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• Advanced Control System: in this top layer, the optimizer computes the optimum pH3, temperature (T (°C)), and level (H (m)) in the reactor to maximize the ethanol production. This is the master controller and defines the set-point for the slave loops. • Local Control System: this layer is composed by three slave loops which work in cascade with the optimizer, as discussed above. The objective of these local controllers is to keep the fermentation process in the operating point (pH3, T, H) defined by the upper layer. As in industrial practice, simple low-order models obtained at a certain operation point are used to tune the local controllers. In this work, the tuning procedure is the same as presented by Americano da Costa et al.27 Local Control System. In the proposed approach, PID (Proportional-Integrative-Derivative) controllers are used in the pH3, temperature, and level control loops because they are the typical structures used in industry. The PID algorithm used here is27 in the Laplace domain

It is important to remember that the size of the reactor and the mass flow rates ṁ r(t) and ṁ f(t) strongly influence the energy balance of the system. Therefore, the chosen configuration must be able to control the temperature of the fermentation process. Based on this idea and using the model described above, these parameters were defined as follows. The wine from the reactor enters in the heat exchanger with a constant mass flow rate given by ṁ r = 1 × 104 kg/h and the water mass flow rate is ṁ f = 1.4 × 104·u(t) (kg/h), regulated by u(t)∈[0,1], which is used as a manipulated variable to control the temperature of the wine into the reactor, whose volumetric capacity is 80 m3. Note that, according to the complete structure shown in Figure 1, the temperature of the water Tf 0(t) is cooled down due to the temperature exchange with the chilled water (FF(t), TF(t)). The mathematical modeling of this unit is detailed by Americano da Costa and Normey-Rico,25 in which the specific growth rate μ has the following expression μ=

1.5 ⎛ X + Xd ⎞ μ′max S P ⎞⎛ e−K iS⎜1 − ⎟ ⎜1 − ⎟ Pmax ⎠⎝ X max ⎠ Λ(pH4) Ks + S ⎝

(

Kc 1 + C(s) =

(1)

where S, X, and P are substrate, biomass, and ethanol concentrations (kg/m3), respectively. μ′max = 1.2·μmax is the maximum specific growth rate (h−1), Λ is the reduction constant from pH into the reactor (pH4), Ks is the substrate saturation constant (kg/m3), Ki is the substrate inhibition coefficient (m3/kg), and Xd (kg/m3) is the dead biomass concentration into the reactor. Finally, Pmax and Xmax are, respectively, the ethanol concentration and the biomass concentration when cell growth ceases (kg/m3).

1 Tsi

+ Tds

αTds + 1

),

α ∈ [0, 1]

(2)

in which Kc is the proportional gain, Ti is the integrative time (in hours), Td is the derivative time (in hours), and α defines the low pass filter of the derivative action (in the PI case, Td = α = 0). Also, a set-point filter (F(s) = (1 + βs/1 + γs)) can be used, when necessary, to obtain smoother set-point responses. Moreover, the PID algorithm includes an antiwindup strategy. A simple PI with a reference filter (Kc = 40, Ti = 1, γ = 0.5, β = 1) was enough to control the level in the reactor, that is an integrator system modeled by GH(s) = 1/10 s. This tuning is done in order to obtain responses without overshoot, as no control action can decrease the level. Because of the high gain variability of the pH3 model, GpH3(s) = (K2(u2)/3.8 × 10−3 s + 1) (see the curve in Figure 5), the PI controller for this loop was implemented with a scaled gain:

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THE PROPOSED CONTROL STRATEGIES, ANALYSIS, AND TUNING The control strategies were made in an independent way for the two units, the fermentation model and the solar cooling plant. It is important to take into account that this is possible because the second heat exchanger used to connect the units decouples the systems, as can be noted in Figures 1 and 2. In this section, it will be explained the proposed control scheme of the fermentation unit as well as the strategy used in the solar plant. Control Design for the Fermentation Unit. The applied automation system in the fermentation unit proposes to control all the main variables (feed flow rate, temperature, and pH3) simultaneously in order to optimize the ethanol production.25 The proposed control system is illustrated in Figure 4 and is divided into two parts:

Kc =

1 |K 2(u 2)|

Ti = 3.8·10−3

Figure 5. Static curve pH3 - acid flow.

The proposed PI allows to obtain a closed-loop system with smooth response and rise time similar to the open loop one. To tune the temperature PID controller, a simple first order model is used to represent the relationship between the temperature and the cooling flow in the heat exchanger, GT(s) = (KT/2s + 1) with −10 ≤ KT ≤ −1.7. The following values were obtained for a fast response with a small overshoot: Kc = −25,

Figure 4. Control system. 11387

Ti = 0.05,

Td = 0.03,

α = 0.1



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In the case of the fermentation process, there exists cooling demand during the complete process duration, and the energy storing modes are not set during the plant operation. Hence, the tanks could only be used with two objectives during this experiment: to support the solar collector field in the unload mode when the tanks temperature is high enough and the solar collector field outlet temperature is not within the desired range or to smooth its outlet temperature transients due to disturbances or changes in the set-point. The use of the gas heater included in the solar cooling system must be avoided due to its economic and environmental cost. According to this reason, the gas heater is turned on only when the solar collector field and the accumulation tanks are not able to reach the absorption machine lower range value. Taking into consideration the interconnection among systems, the upper and lower range values of the absorption machine inlet temperature (90 and 65 °C, respectively), and the interest in smoothing the transients in its inlet, the configuration where the solar collectors and the tanks are set in unload mode will become the operation mode milestone. The set of this plant configuration where the accumulation tanks are used as a buffer allows (i) to smooth in a big degree the variations of the solar collector field and (ii) to avoid transients produced by the operation mode change between the different modes including and excluding the solar collectors field or the tanks, that could produce a considerable bump at the absorption machine inlet depending on the magnitude of the difference between their temperatures. Furthermore, the fermentation process proposed for the simulation has been dimensioned for a power equivalent to the one of the absorption machine, while the solar collector field and the accumulation tanks are dimensioned attending to the absorption machine inlets temperature at full load. Due to this reason, during the plant operation with full cooling demand, the solar collector field typically presents an outlet temperature under 90 °C, and taking into consideration that the absorption machine obtains a higher cooling capacity at higher inlet temperatures (YAZAKIdatasheet), the control problem can be simplified to keep a solar collector field outlet temperature setpoint as higher as possible attending to the environmental conditions and the flow operation ranges. In the case of the gas heater, it should be turned on whenever the tanks outlet temperature is under 65 °C in order to avoid turning off the absorption machine while there exists cooling demand. This philosophy is acceptable only due to the existence of a remarkable cooling demand during the full process. Furthermore, taking into consideration that this work is focused on the control of the fermentation process using only the solar fraction, the gas heater will not be turned on, and, therefore, it is unnecessary to design a controller for it. Hence, the switching control strategy defined by Pasamontes et al.29 to control this same solar collector field outlet temperature has been applied taking into consideration the absorption machine constraints related to the flow. The function of the switching control strategy is to adapt the control system to a new plant dynamics while rejecting the disturbances caused by the switch among controllers. This switching control strategy is composed of the following: • A local bank of PID controllers plus a local bank of feedforward controllers, designed for different operation points. • A supervisory layer to choose the current active controller. • A switching mechanism to reject the transients produced by the switch among local controllers.

As pointed out, the set-points for the Local Control System are given by the optimization process described in the following. Advanced Control System. The Advanced control system is based on a NMPC (Nonlinear Model Predictive Control) technique that uses a nonlinear model of the process in order to determine the control signal that minimizes a given cost function at each sampling time ΔQ (h) for a certain prediction horizon. The NMPC is designed to maximize the ethanol concentration by solving, at each ΔQ, the following problem in the decision variables SPH, SPT, and SPpH3:28 min J(SPH , SPT , SPpH3) = −

∫t

t + NH

P(̇ t )dt

Subject to P(̇ t ) = YP / XμX(t ) + mP X(t ) −

F3(t ) P(t ) V (t )

H(t ) ≤ SPH(t ) ≤ 8 20 ≤ SPT(t ) ≤ 40 4 ≤ SPpH3(t ) ≤ 6

(3)

in which t is the current time, YP/X (kg/kg) represents the yield factor of the ethanol based on cell growth, and mP (kg/(kg·h)) is the ethanol production associated with cell growth. NH is the prediction horizon (in hours), P(t) is the ethanol concentration, SPH is the level set-point, SPT is the temperature setpoint, and SPpH3 is the pH set-point of the must. The tuning parameters of this control strategy are NH and ΔQ (ΔQ ≤ NH). As the process has a fixed duration, the maximum value of NH is equal to this value (8 h in the case of this work). Due to the fermentation process slow dynamics, it is expected to obtain better results using bigger values of NH. The effect of ΔQ can be analyzed as follows. On the one hand, a small value of ΔQ imposes constraints to the practical implementation of the control system because of the time needed for the optimization procedure and also because of the dynamics of the local loops. On the other hand, big values of ΔQ give less degree of freedom to the optimization and a poor performance is expected. Taking these issues into consideration, and after a deep simulation study where the values of NH and ΔQ were varied in predefined intervals (NH between 0.5 and 2 and ΔQ between 1 and 7), the chosen parameters are NH = 7 and ΔQ = 2 because they give the better ethanol production. The complete model described in the previous section will be used to simulate the process behavior and to compute the predictions in the optimal controller. Moreover, it is necessary that both fermentation and solar cooling processes operates within the security constraints. Thus, a control structure which allows a secure and good performance for the solar plant must be implemented. Control Design for the Solar Cooling Plant. In order to cover the fermentation process cooling demand, the corresponding energy demand in the absortion machine must be guaranteed. Due to the serial plant components configuration, it is not possible to store hot water in the accumulation tanks at the same time that the absorption machine inlet is fed with the solar collectors outlet temperature; therefore, it is only possible to store energy in the tanks when there is no cooling demand. 11388



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Thus, in this configuration, each controller is composed of a feedback plus two feedforward controllers to reject disturbances caused by the irradiation and variations in the inlet temperature. The procedure to connect the real solar plant operation to the fermentation model and the experimental results are explained in the following section.

Table 1. Scenarios Definition

SIMULATIONS IN HARDWARE IN THE LOOP AND EXPERIMENTS OF THE FERMENTATION SOLAR COOLING PROCESS Simulations are a valuable tool widely used in order to evaluate systems characteristics, such as the performance. Hardware in the Loop is a strategy that allows to simulate a model in connection with physical components. This method improves the accuracy of experiments and saves development cost and time whenever one of the components involved in a simulation is available physically.30,31 In the case of this work, the Hardware in the Loop technique divides the proposed structure into two interconnected units: the solar cooling plant (real) and the fermentation process (model). In the simulation, a heat-pump system is used to produce the solar cooling system inlet temperature obtained by the fermentation unit model. That is, at each sampling time, the load temperature TL(t), which must enter the absorption machine of the solar cooling plant, is calculated from the fermentation model. Then, the heat-pump system generates the load temperature which is applied in the solar cooling system, and the measures available in the real plant are used to feed the fermentation model in order to update its current state. Hence, the chilled water produced by the absorption machine given by FF(t) = FL(t) with temperature TF(t) is sent back to the simulator. The fermentation model has been implemented in Matlab, and the intercommunication with the plant sensors and actuators was performed by means of LabVIEW, interconnected to the plant main computer which runs an OPC server. Several experiments and comparisons among different refrigeration alternatives of the fermentation process have been performed to analyze the benefits that could be obtained by means of using a solar cooling plant with the proposed control strategies. It is important to take into account that, although this work is focused on the use of solar energy as the main hot water source, it can be also obtained from more adequate renewable energies, such as biomass or wind power, in locations were the typical irradiation level is low. The duration of the fermentation process is set to 8 h and the maximum level in the reactor is 8 m (80 m3). The initial conditions were as follows: volume, V0 = 15 m3 (H0 = 1.5 m); biomass, X0 = 31 kg/m3; substrate, S0 = 0 kg/m3; ethanol, P0 = 33 kg/m3; dead biomass, Xd0 = 0 kg/m3; temperature, T0 = 30 °C; and pH into the reactor, pH4 = 5.01. The ethanol production is calculated using GP = VF (m3)·PF − V0 (m3)·P0, being VF (m3) and PF the final volume and concentration, respectively. Three different scenarios whose characteristics are defined in Table 1 have been compared. The first one S1 uses natural resources such as the river to cool down the fermentation process applying a nominal controller (PI), and there is not any optimization algorithm. In this experiment the river cooling temperature has a constant value equal to 28.5 °C, and its flow rate ṁ f(t) is manipulated by the nominal controller. To feed the process a ramp signal is

used, as shown in Figure 6(a). The set-points of pH for the must and of temperature into the reactor are 5.3 and 32 °C, respectively. The behavior of these variables is shown in Figures 6(b) and 6(c). As can be noted in Figures 7 and 8, in this scenario, a concentration of 76.8417 kg/m3 is obtained, and the ethanol production is equal to GP = 5653.2 kg per vat. In the

S1 S2 S3

■

cooling source

automatic control

river river solar

PI controller optimizer optimizer

Figure 6. Controlled variables of the system. 11389



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Figure 7. Performances of the system for each scenario. Figure 9. Radiation and environmental temperature curves.

it is considered one fermentation per day with the complete proposed structure (advanced control and solar plant). The conclusions of the comparative study using the three different scenarios are (i) the proposed optimization strategy allows increasing the ethanol production if compared to traditional systems and (ii) the cooling system associated with the proposed controller allows for an extra gain in the production due to the better temperature control. In the presented study, which considers a small plant with 80 m3 reactors, the estimated gain using the proposed structure, after the payback time, is approximately 111000 dollars per year, considering only one batch production per day during 200 days in 5 reactors and the current price of hydrated ethanol. It is relevant to remember that at an industrial scale, this apparently little gain produces significant results. For comparison purposes, it is possible to estimate the gain of a typical ethanol factory in Brazil that has 18 reactors of 500 m3, when the same conditions and suppositions of the plant model presented in this study are applied. If the conventional system used in scenario 1 is substituted by the proposed structure, the estimated gain is approximately 2.5 million dollars after the payback time. As the energy demand has a linear relation to the mass and, therefore, the volume, it would be necessary a surface area around 1.5 ha of solar collector field to guarantee the maximum production affordable applying a solar cooling system. Normally, in Brazil the ethanol industries used to own land with a surface area around 10000 ha, owning some of the surfaces larger than 50000 ha. Moreover, the area required to implement the system may be reduced if other types of solar collectors or double effect machine are employed. Finally, it is important to note that the experiments were made considering the ideal nominal case, that is, when the fermentation model is perfectly well-known. To analyze the impact of the modeling errors in the optimization strategy, several situations were simulated using the model of the whole process structure. Two cases were analyzed. First, it was evaluated the product concentration if absolute errors in relation to the optimal temperature and pH3 points were applied in the system. Second, modeling errors were applied in important kinetic parameters of the fermentation process. The results of the first analysis in Table 2 show the ethanol concentration for errors of ±1 °C at the temperature and ±0.5 at the pH3 of the optimal solution. Note that the system is more sensivite to the pH3 operation point. In the second part of the analysis, to study the robustness of the system, modeling errors were applied in (i) the ethanol concentration when cell growth ceases (Pmax), as shown in

Figure 8. Performances of the system for each scenario.

same figures, the evolution of the biomass, dead biomass, and substrate are also shown. The second scenario S2 uses the river as a cooling element, with the same constant temperature as the previous case, but for this time the set-points are decided by the automatic control optimization strategy presented in the section.This alternative modifies the operation points to the ones considered ideal to maximize the process production, as shown in Figures 6(a), 6(b), and 6(c). However, note that even using the maximum available water flow the system is not able to control the temperature. In this case, when the fermentation process is completed, the reactor has a concentration of 79.4918 kg/m3 and 5864.3 kg of ethanol. Thus, there is an improvement of 3.73%, if compared with the first scenario. The results obtained in this case are shown in Figures 7 and 8. Finally, the last scenario S3 replaces the river by the solar cooling plant as a refrigeration resource, and the same automatic control optimization algorithm as in the previous case is applied to control the cooling temperature of the fermentation process. To prove the viability of the proposed structure, the experiment was made using the Hardware in the Loop technique. As can be seen in Figure 6(b), with this structure, the temperature into the reactor is kept at the desired set-points and 6023.5 kg (PF = 81.4781 kg/m3) of ethanol are obtained. The behavior of the fermentation process is shown in Figures 7 and 8. As can be observed, the proposed structure results in a gain of 6.55% in relation to scenario S1 and 2.71% to scenario S2. Most of this experiment was carried out during the day. Note in Figure 9 that even with radiation (real curve) tends to zero at the end of process, the system is able to control the temperature because of its stored energy. Therefore, the initial investiment would be about US$ 49000, that represents a payback time of two years in relation to the second scenario, if 11390



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kg/m3 and 80.3595 kg/m3 for errors of +20% and −20%, respectively, applied in Pmax. The results in Table 4 are similar to the previous ones, the considered errors in S does not affect the system, which produces 81.8209 kg/m3 and 81.2418 kg/m3 of ethanol, respectively, for the +20% and −20% applied in the modeling parameter S. Finally, Table 5 illustrates critical absolute set-point errors for the pH3 in relation to the nominal case. When −7% of error is applied in pH4, the final ethanol concentration is 80.6926 kg/ m3, while the production is 77.3077 kg/m3 of ethanol for the +7% applied in the modeling error of pH4. One the one hand, theoretical and hard situations were considered with maximal variations in the interval [−20%, +20%], defined for Pmax and S in the simulations. On the other hand, an interval of [−7%, +7%] was calculated as the critical modeling error for pH4. Note that (i) appreciable variation in PF only is obtained if parameter pH4 is estimated with a great error; (ii) the first 2 h are the most sensitive stage of the process, since the control system feeds completely the reactor in this step; (iii) the system has a good robustness in relation to all kinetic parameters. However, special attention should be taken to the pH4 modeling, of which, in the worst simulated case, PF has a value which is approximately 5% lower than the optimal solution. Therefore, the previous analysis showed that even with model uncertainties, the proposed control system could improve ethanol production.

Table 2. Ethanol Production as a Function of the Absolute Errors of Temperature and pH into the Reactor ΔT

PF (kg/m3)

ΔpH3

PF (kg/m3)

+1 °C −1 °C

81.1438 81.5431

+0.5 −0.5

70.8707 80.2899

Table 3; (ii) the substrate concentration S, as shown in Table 4, and (iii) the pH4 which has a great influence in the cellular activity into the reactor, shown in Table 5. Table 3. Absolute Differences between the Optimal Points of Temperature and pH3 as a Function of the Modelling Error of Pmax (a) Ethanol Production of PF = 82.0046 kg/m3 time (in h) error Pmax +20%

0−2 h

2−4 h

4−6 h

6−8 h

−1.24 −0.46 −0.14 0.02 −0.04 0.91 1.21 1.35 (b) Ethanol Production of PF = 80.3595 kg/m3

ΔT (°C) ΔpH3

time (in h) error Pmax

0−2 h

2−4 h

4−6 h

6−8 h

−20%

0.85 −0.06

1.02 0.68

0.7 0.97

0.17 1.10

ΔT (°C) ΔpH3

Table 4. Absolute Differences between the Optimal Points of Temperature and pH3 as a Function of the Modelling Error of S (a) Ethanol Production of PF = 81.8209 kg/m

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CONCLUSIONS AND FUTURE WORK In this work, a strategy to optimize the fermentation unit production based on a cascade control structure which uses solar energy was proposed. Three slave SISO controllers deal with the principal process variables and disturbances and receive the optimal set-points from a MIMO master predictive controller which maximizes the ethanol production. For the correct operation of the control system, the interconnection of the systems has been analyzed by means of applying the fermentation unit model outlet temperature to the real solar cooling plant by using a heat-pump to feed the absorption machine. For evaluation, three different scenarios have been compared. The first scenario can be found in a considerable amount of ethanol production plants in Brazil, where the managers have avoided to change the way that the process has been performed in the past. In the second scenario, the optimizer was used to control the fermentation process, and it was noted the need of a certain extra demand of energy to set the temperature at ideal points. Thus, in the third scenario, it was demonstrated that the application of solar cooling plant allows the complete optimization and increases the final ethanol production in the fermentation process. However, it is important to consider the dependence of the proposed solution on irradiation and the fact that, in industry, fermentations are performed repeatedly during 24 h. On the other hand, excess in the hot or cold water production (at the inlet and outlet of the absorption machine, respectively) can be stored during the process in order to support the solar collectors when the environmental conditions could not allow to complete the 8 h fermentation time period. Finally, this work proposition to combine solar cooling and the fermentation process is backed by the experiment results, being an innovative solution in clean energy in Brazil by means

3

time (in h) error S

0−2 h

+20%

−0.94 −0.62 −0.62 0.19 −0.05 0.70 0.99 1.14 (b) Ethanol Production of PF = 81.2418 kg/m3

2−4 h

4−6 h

6−8 h ΔT (°C) ΔpH3

time (in h) error S

0−2 h

2−4 h

4−6 h

6−8 h

−20%

1.18 −0.05

0.60 0.67

0.41 0.96

0.01 1.10

ΔT (°C) ΔpH3

Table 5. Absolute Differences between the Optimal Points of Temperature and pH3 as a Function of the Modelling Error of pH4 (a) Ethanol Production of PF = 77.3077 kg/m3 time (in h) error pH4 +7%

0−2 h

2−4 h

4−6 h

6−8 h

−0.24 −0.04 0.01 0.01 0.38 0.46 0.76 0.89 (b) Ethanol Production of PF = 80.6926 kg/m3

ΔT (°C) ΔpH3

time (in h) error pH4

0−2 h

2−4 h

4−6 h

6−8 h

−7%

0.18 −0.44

0.05 1.38

0.02 1.69

0.00 1.85

ΔT (°C) ΔpH3

Table 3 shows the absolute errors in each sampling time (ΔQ) in relation to the optimal set-points of the temperature and pH3, calculated in the nominal case. The system presented a good robustness, since the ethanol productions were 82.0046 11391



Article

(19) Tiba, C. Atlas Solarimérico do Brasil; Ed. Universitária da UFPE: Recife, Brasil, 2000. (20) Americano da Costa, M. V.; Pasamontes, M.; Normey-Rico, J. E.; Guzmán, J. L.; Berenguel, M. Appl. Energy 2013, 102, 501−509. (21) Zhai, X.; Wang, R.; Wu, J.; Dai, Y.; Ma, Q. Appl. Energy 2008, 85, 297−311. (22) Castilla, M.; Á lvarez, J. D.; Berenguel, M.; Pérez, M.; Guzmán, J. L.; Rodríguez, F. Comfort optimization in a solar energy research center; Villamoura, Portugal, 2010; pp 29−31. (23) Castilla, M.; Á lvarez, J. D.; Berenguel, M.; Rodríguez, F.; Guzmán, J. L.; Pérez, M. Energy Build. 2011, 43, 2737−2746. (24) da Silva, F. L. H.; Rodrigues, M. I.; Maugeri, F. J. Chem. Technol. Biotechnol. 1999, 74, 176−182. (25) Americano da Costa, M. V.; Normey-Rico, J. E. Modeling, Control and Optimization of Ethanol Fermentation Process; Milan, Italy, 2011; pp 10609−10614. (26) Niu, D.; Jia, M.; Wang, F.; He, D. Ind. Eng. Chem. Res. 2013, 52, 3373−3380. (27) Americano da Costa, M. V.; Monteiro, J.; Magazoni, F. C.; Colle, S. Modeling, Simulation and Analysis of Ethanol Fermentation Process with Control Structure in Industrial Scale. 22nd Int Conf on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, 2009; Foz do Iguaçu, Brazil. (28) Logsdon, J. S.; Biegler, L. T. Ind. Eng. Chem. Res. 1989, 28, 1628−1639. (29) Pasamontes, M.; Á lvarez, J. D.; Guzmán, J. L.; Lemos, J. M.; Berenguel, M. Control Eng. Pract 2011, 19, 135−145. (30) Buford, J. A., Jr.; Jolly, A. C.; Mobley, S. B.; Sholes, W. J. Advancements in hardware in the loop simulations at the U.S. Army Aviation and Missile Command. SPIE technologies for synthetic environments: hardware in the loop testing V. 2000; pp 2−10. (31) Percival, M. W.; Dassau, E.; Zisser, H.; Jovanovic, L.; Doyle, F. J. Ind. Eng. Chem. Res. 2009, 48, 6059−6067.

control and automation. In developing the advanced control system for ethanol fermentation, there was the need for a large amount of energy to reach the optimum temperatures defined by the implemented strategy. Since the fact of Brazilian ethanol appears as an important fuel in the context of sustainable development, there was no sense in searching alternative sources considered pollutants, being, therefore, the application of solar energy a promising proposal.

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AUTHOR INFORMATION

Corresponding Author

*Phone: +55 71 32839505. Fax: +55 71 32039802. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work has been partially funded by the following projects: PSE-ARFRISOL PS-120000-2005-1, PHB2009-0008 financed by the Spanish Ministry of Education; CNPq-BRASIL; CAPESDGU 220/2010; and Spanish Ministry of Science and Innovation and EU-ERDF funds under contracts DPI201021589-C05-04 and DPI2011-27818- C02-01.

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Advanced Control Strategy Combined with Solar Cooling for

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