- Email: [email protected]
Determination of optimal reformer temperature in a reformed methanol fuel cell system using ANFIS models and numerical optimization methods
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/he
Determination of optimal reformer temperature in a reformed methanol fuel cell system using ANFIS models and numerical optimization methods Kristian Kjær Justesen*, Søren Juhl Andreasen Department of Energy Technology, Aalborg University, Pontoppidanstræde 101, 9220 Aalborg East, Denmark
article info
abstract
Article history:
In this work a method for choosing the optimal reformer temperature for a reformed
Received 1 April 2015
methanol fuel cell system is presented based on a case study of a H3 350 module produced
Received in revised form
by Serenergy A/S. The method is based on ANFIS models of the dependence of the reformer
13 May 2015
output gas composition on the reformer temperature and fuel flow, and the dependence of
Accepted 17 May 2015
the fuel cell voltage on the fuel cell temperature, current and anode supply gas CO content.
Available online 10 June 2015
These models are combined to give a matrix of system efficiencies at different fuel cell currents and reformer temperatures. This matrix is then used to find the reformer tem-
Keywords:
perature which gives the highest efficiency for each fuel cell current. The average of this
Reformed methanol fuel cell
optimal efficiency curve is 32.11% and the average efficiency achieved using the standard
Fuel cell modeling
constant temperature is 30.64% an increase of 1.47 percentage points. The gain in efficiency
Operating point optimization
is 4 percentage points, from 23 % to 27 %, at full power where the gain is largest. The
System efficiency optimization
constant reformer temperature which gives the highest average efficiency is found to be
HTPEM fuel cells
252 C at which temperature it is 32.08%, only 0.03 percentage points lower than the
ANFIS modeling
maximum efficiency curve. Copyright © 2015, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
Introduction Polymer Electrolyte Membrane (PEM) fuel cells are gaining popularity in both stationary and mobile applications due to their potential ability to provide energy with little or no harmful on-site emissions at a high efficiency [1]. They do, however, have challenges with impractical and energy consuming fuel storage if a fuel cell system is operated on pure hydrogen (H2 ) [2]. This can be under high pressure in tanks, in liquid form at temperatures under e 253 ½ C or in
metal hydrides. All these methods are energy consuming and the storage capacity can not be expanded easily. This problem is limited, if the H2 for the fuel cell system is produced online as needed by reforming a liquid fuel such as methanol. Such systems are becoming increasingly common and examples are described in [3], [4], and [5]. These systems consist of more components than traditional fuel cell systems and more factors affect their efficiency. The two main factors are the efficiency with which the reformer turns the fuel into hydrogen, i.e. how much of the fuel pumped into the system is turned into hydrogen, and the efficiency of the fuel cell. These
* Corresponding author. Fax: þ45 9815 1411. E-mail addresses: [email protected] (K.K. Justesen), [email protected] (S.J. Andreasen). http://dx.doi.org/10.1016/j.ijhydene.2015.05.085 0360-3199/Copyright © 2015, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
9506
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
CO þ H2 O/H2 þ CO2
Nomenclature maxð:Þ minð:Þ x a b c Oi wi oi
pi
ri Tr m_ fuel m_ H2 need lH2 NCell MH2 F IFC
calculates the maximum of the inputs calculates the minimum of the inputs crisp input for ANFIS model adaptive premise parameter that determines the shape of a membership function adaptive premise parameter that determines the shape of a membership function adaptive premise parameter that determines the shape of a membership function contribution of the ith rule to the output of an ANFIS function normalized firing level of the ith rule adaptive consequent parameter that determines the influence of the first input on the output of the ith rule adaptive consequent parameter that determines the influence of the second input on the output of the ith rule adaptive consequent parameter which determines the constant part of the ith rule reformer temperature reformer fuel flow necessary hydrogen mass flow hydrogen stoichiometry at the anode number of cells in the fuel cell molar mass of hydrogen Faraday's constant fuel cell current
two efficiencies and their effect on each other is therefore of great interest and the focus of this work. The composition of the output gas of a fuel reformer is dependent on the type of reforming which is used. The most widely used varieties are: Steam reforming, which has the following reaction: CH3 OH þ H2 O/3H2 þ CO2
(1)
The water gas shift reaction removes some of the CO in the gas but not all of it. This means that both with steam reforming and partial oxidation reformers, higher temperatures lead to an increased CO content in the output gas of the reformer and therefore a lower fuel cell efficiency. Lower reformer temperatures do, however, mean that more of the fuel pumped into the system goes through unreformed. This means that a higher fuel flow rate is needed to sustain a certain fuel cell anode stoichiometry, leading to a lower system efficiency. This effect is further reinforced at higher fuel flows as observed in [8] and [9]. There is therefore an optimal reformer temperature set point, which is a compromise between low CO content and high reforming efficiency and this work presents a method for finding this optimal temperature at different fuel cell current set points. In this work a H3 350 Reformed Methanol Fuel Cell (RMFC) system produced by Serenergy A/S will be used as a case study. It is a 350 W off-grid battery charger which uses an air cooled high temperature PEM fuel cell and a steam reformer to produce electricity from a methanol/water mixture. The authors of this paper have previously published work on modeling of similar systems in [10], [11], and [12]. Fig. 1 shows a picture of a H3 350 system. The module consists of a fuel pump which pumps the methanol/water mixture into an evaporator, which heats and evaporates the fuel using the excess heat from the fuel cell. From there the evaporated fuel is fed into the reformer where the steam reforming process takes place. The now H2 rich reformed gas is fed to the fuel cell where most of the H2 in the gas is used. A H2 over stoichiometry of 1.35 is used on the fuel cell anode, on the recommendation of the manufacturer, to prevent anode starvation in the fuel cell. The remaining H2 is fed to a catalytic burner, which supplies the reformer with the necessary process heat. Fig. 2 shows a diagram of the H3 350 module. The performance of the reformer system will be evaluated on a test setup as will the performance of the fuel cell, and the following Adaptive Neuro-Fuzzy Inference System (ANFIS) models will be produced:
partial oxidation: 1 CH3 OH þ O2 /2H2 þ CO2 2
(2)
and methanol decomposition: CH3 OH/2H2 þ CO
(3)
[6] The first two are most widely used in fuel cell applications, as the carbon monoxide (CO) created by the methanol decomposition reaction is harmful to PEM fuel cells. Both in reformers that utilize steam reforming and partial oxidation reactions, the methanol decomposition reaction will also occur to some degree, leading to an amount of CO in the output gas of the reformer. This reaction is more prevalent at higher temperatures, which in general means that higher reformer temperatures lead to increased CO content as observed in [7] and [8]. In a steam reformer the water gas shift reaction can also take place:
(4)
Fig. 1 e Picture of a Serenus H3 350 RMFC module from Serenergy [13].
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
Fig. 2 e Concept drawing of a H3 350 module from Serenergy. In the diagram green signifies the methanol/ water mixture, blue signifies atmospheric air and exhaust gas and magenta signifies a hydrogen rich gas. Solid lines signify flow in pipes or hoses and dotted lines signify flow inside a component. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Model of the CO concentration in the reformer output gas. Model of the H2 mass flow in the reformer output gas. Model of the dependence of the fuel cell voltage on fuel cell current, temperature and anode supply gas CO concentration. These models will be used to analyze the influence of the reformer temperature on the system efficiency. In the following sections these models will be described. The influence of operating parameters on the system efficiency has been investigated before for other kinds of system. One such system is the solid oxide fuel cell system described in [14] where optimal operating conditions are found based on physical system models. Another is the low temperature (LT) PEM system powered by natural gas through a fuel reformer system with CO cleanup described in [15]. Here a multi-level optimization approach is used to optimize the efficiency of the system while insuring safe operation. In addition [16] describes the optimization of operating parameters such as temperature, pressure ratios and reactant stoichiometries for a LTPEM fuel cell system. A parametric optimization of a system like the H3 350 has, however, not been presented before.
9507
The models have the additional advantage that they are fast evaluating and can be evaluated online in a control system, or in a larger model offline for evaluation purposes. The ANFIS structure used in this work is the one originally suggested in [17] and illustrated in Fig. 3 for a system with two inputs and two membership functions. As the figure shows, the structure consists of four different layers. The first layer is the Fuzzification layer where the crisp inputs, in this case the reformer temperature Tr and the fuel flow m_ fuel , are converted to fuzzy variables, which are numbers between zero and one. This is done by evaluating the degree of membership of a certain group for the specified inputs. For the system in Fig. 3 these groups are high and low numbers. The degree of membership is calculated using membership functions. These can have different forms, for example triangular, trapezoidal, sigmoidal and bell-shaped. In this work triangular membership functions are used, because it is the least computationally heavy and experiments showed that no additional performance was gained by using the more complex models. The following equation shows a generalized triangular membership function. xa cx ; ;0 A ¼ max min ba cb
(5)
Here the parameters a, b and c are adaptive premise parameters which mark the corners of the triangle and are subject to optimization during the training process. It is worth noticing that in the functions at the edge of the input range one of the functions in the minð:; :Þ is replaced by a 1. In the second layer the structure is split up into rules and the firing level of each rule is calculated. The structure in Fig. 3 has four rules marked by different colors. The firing level of a rule is in this work calculated using the fuzzy AND and can be interpreted as the extent to which the rule is active. In the first rule, which is marked in red, this means to which extent both Tr and m_ fuel are low. In the third layer the firing levels are normalized to give a sum of 1. In the fourth layer the contribution of each rule to the output is calculated using the following equation:
ANFIS modeling Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are, as the name suggests, a neuro-fuzzy model structure which can be adapted to behave like a physical system. This is especially useful when modeling nonlinear systems on the basis of experimental data or when the process variable needed for physical modeling is not available but a derived or related variable is. This is, for example, the case for the reformer system where the temperature of the reformer casing is available but not the temperature of the reformer bed itself.
Fig. 3 e The ANFIS modeling structure employed in this work. The different colors signify the different rules. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
9508 Oi ¼ wi $ oi $Tr þ pi $m_ fuel þ ri
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
(6)
where i denotes the rule number, wi is the normalized firing level and oi , pi and ri are adaptive consequent parameters which are subject to optimization during the training process.Finally the contributions of each rule are summed in the fifth layer to give the output of the ANFIS model. The training process works by initializing the membership functions with starting values and evaluating the model up until the last layer. The consequent parameters are then optimized using least squares regression. The premise parameters are then updated using gradient decent methods. In the second iteration of the training process, the model is evaluated until the last layer using the membership functions found in the first iteration, and the premise parameters are updated again using least squares regression. The consequent parameters are updated again using gradient descent methods. This process is then repeated for a set number of iterations or until the error of the model is below a certain threshold.
Reforming process modeling Modeling the output gas composition using ANFIS models has been chosen because of the complexity of the integrated system and the lack of detailed information about the temperature distribution and available catalyst surface. The method of using ANFIS models to predict the content of the output gas of a methanol steam reformer was first presented in [11], where an identification experiment spanning the operating range of the reformer was performed and the output gas composition measured. The entire data set was then used for training. In this work an identification experiment spanning the operating range is also performed. The data is then divided into blocks corresponding to each operating point, and an average value is calculated for each point giving a performance matrix which is then saved. The ANFIS models are then trained on the basis of this data. This means that the training process is performed on fewer data points. Calculating the average for each operating point also has the benefit of making it easier to visualize the measurements, when they are arranged into matrices and plotted in contour plots. For the reformer identification experiments a H3 350 module, where the fuel cell is replaced by a gas analyzer, is used. The hot cathode air of the fuel cell is replaced by a mass flow controller and an electric heater, the anode waste gas for the burner is replaced by a mass flow controller which supplies pure H2 and the burner blower is replaced by a mass flow controller. Fig. 4 shows a diagram of the experimental setup. On the basis of the initial experiments with the reformer and the datasheet of the catalyst [18], the identification experiments have been run with reformer temperatures between 235 and 290 C in 5 C increments. At each temperature the performance is evaluated at 5 different fuel flows corresponding to fuel cell currents of 5, 8.75, 11.5, 14.75 and 18 ½A with a stoichiometry of 1.35 at assumed full reformation. The maximum rated fuel cell current is 16 ½A but 18 ½A is used because it gives a buffer which allows the
Fig. 4 e Concept drawing of the experimental setup used for the reformer identification experiment.
fuel flow to be increased to correct the stoichiometry during the optimization process. Fig. 5a shows a contour plot of the CO content measurements from the experiment and the output of the CO ANFIS model when subjected to the same conditions. Experiments showed that the best performance was obtained with three membership functions and as the figure shows, the model and the measurements fit well and the Mean Absolute Error (MAE) of this model is 0.323%. The figure also shows that, as expected, high reformer temperatures generally yield a higher CO content. The effect of increased flow is less definitive with increased flow yielding lower concentration at low temperatures and a local maximum appearing at medium flows at higher temperatures. Fig. 5b shows a contour plot of the H2 mass flow measured in the experiment, the output of the H2 ANFIS model when subjected to the same conditions and the fuel flow if full reforming is assumed. As the figure shows, the difference between full reforming and the measured H2 flow is small at low flows and constant at all temperatures. However, as the fuel flow is increased, the difference to full reforming is increased more at low temperatures than at high temperatures. This is in accordance with the tendencies observed in [8] and [9]. The figure also shows that the model fits the measurements well and the MAE is 0.074%. The tendencies observed in the reformer output gas composition are concluded to be those that were expected based on the literature and that the ANFIS model is able to predict the desired system states with sufficient precision. It is therefore relevant to proceed with the modeling of the fuel cell and the operating point optimization.
Fuel cell model In this work a model which can predict the fuel cell voltage at different fuel cell temperatures, currents and anode CO concentrations is needed. For this purpose the model presented in [19] is suitable, as it presents an ANFIS model of this relationship for a similar Serenergy fuel cell, albeit a version with a different cell area but the same membrane type. The input of
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
9509
Fig. 5 e (a) Contour plot of the measured and modeled CO content in the reformed gas at different fuel flows and reformer temperatures. (b) Contour plot of the measured and modeled H2 mass flow in the reformed gas at different fuel flows and reformer temperatures and the H2 mass flow at full reforming.
the model is the CO concentration in %, the fuel cell temperature in ½ C and the fuel cell current density in ½A=cm2 . Other factors, such as the water and CO2 content of the supply gas also affect the performance of the fuel cell. But during the training of the model they were kept at values which are consistent with typical measurements on the reformed gas in this work and they can therefore be left out. The output of the model is the cell voltage in ½V. The MAE of this model is 0.87% and Fig. 6 shows a plot of the training data and the fit of the ANFIS model at a fuel cell temperature of 170 ½ C. The fuel cell stack in the H3 350 module has 45 cells and a cell area of 45.16 ½cm2 . The fuel cell current is therefore divided by 45.16 before it is fed to the model and the model's output is multiplied by 45 to give the stack voltage.
The initial optimization is performed with a fuel cell temperature of 170 ½ C as this is within the operating range of the fuel cell. Another choice of fuel cell temperature will yield a different optimal reformer temperature, as the CO tolerance of the fuel cell is higher at higher temperatures. The optimization process will therefore be repeated for fuel cell temperatures of 165 and 160 ½ C and a summary of the results will be given.
Optimization The first step in the optimization process is to construct a fuel flow matrix which gives the fuel flow that is necessary to keep the anode stoichiometry constant at a given fuel cell current and reformer temperature. Here keeping the stoichiometry at 1.35 has been chosen because this is what the manufacturer recommends. This stoichiometry is also used in the training of the fuel cell model. The flow matrix is constructed by calculating the necessary H2 mass flow at different fuel cell currents and using the H2 ANFIS model to find the fuel flow which is necessary to give this H2 flow. The necessary H2 mass flow is calculated using the following equation: m_ H2
Fig. 6 e Plot of the training data and fit of the ANFIS model at TFC ¼ 170 ½ C from [19].
need
¼
lH2 $Ncell $MH2 $IFC 2$F
(7)
where lH2 is the H2 stoichiometry of the anode, Ncell is the number of cells, MH2 is the molar mass of hydrogen, F is Faradays constant and IFC is the fuel cell current. It is not possible to make an inverse of the ANFIS model that calculates the hydrogen production from the fuel flow and the reformer temperature. Instead an optimization algorithm which uses the ANFIS model to adjust the fuel flow to fit the hydrogen requirement is used. This algorithm
9510
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
Fig. 7 e Contour plot of the fuel flow which is necessary to keep the stoichiometry constant at 1.35 at different fuel cell currents and reformer temperatures.
works by adding small increments to the fuel flow until the output of the ANFIS model matches the needed H2 flow, taking the flow assuming full reforming as a starting point. Fig. 7 shows a contour plot of the resulting matrix of necessary fuel flows at different fuel cell currents and reformer temperatures. As the figure shows, the necessary fuel flow is the same for all reformer temperatures at low fuel cell currents. But as the fuel cell current is increased, the necessary fuel flow is higher at low reformer temperatures. This can also be seen in Fig. 8 where a matrix of the input power at different reformer temperatures and fuel cell currents is plotted using the lower heating value of the methanol in the fuel. This matrix will be used to calculate an efficiency matrix later in this work. The fuel flow matrix plotted in Fig. 7 is used to construct a CO content matrix. The entries in this matrix are then used in
Fig. 8 e Contour plot of the necessary input power at different fuel cell currents and reformer temperatures with stoichiometry corrected fuel flow using lower heating value.
the fuel cell ANFIS model to construct a fuel cell voltage matrix, which is plotted in Fig. 9a. As the figure shows, the influence of the increased CO content is relatively low at low fuel cell currents. But as the fuel cell current is increased, the influence of the CO in the reformed gas at higher reformer temperatures on the fuel cell voltage is increased. By multiplying the fuel cell current and voltage, the output power of the fuel cell can be calculated. Fig. 9b shows the resulting output power matrix in a contour plot. As the figure shows, the output power is higher at lower reformer temperatures due to the increased fuel cell voltage at these operating points. However, at these temperatures a larger input power is also necessary as can be seen in Fig. 8. The fuel cell power matrix and the input power matrix are
Fig. 9 e (a) Contour plot of the fuel cell voltage at different currents and reformer temperatures with stoichiometry corrected fuel flow. (b) Contour plot of the output power at different fuel cell currents and reformer temperatures with stoichiometry corrected fuel flow.
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
9511
Fig. 10 e (a) Contour plot of the calculated efficiencies at different fuel cell currents and reformer temperatures with a line marking the reformer temperature which yields the highest efficiency at each fuel cell current. (b) Contour plot of the calculated efficiencies at different fuel cell currents and reformer temperatures including lines for the developed reformer temperature control strategies.
therefore combined to give the system efficiency matrix using the following equation: hsystem ¼
PFC out Pfuel in
(8)
The resulting efficiency matrix is plotted in Fig. 10a. Here a line representing the highest efficiency at each fuel cell current is also plotted. The resulting reformer temperature control strategy will in the following be called Optimal Tr . The figure shows, that there is an optimal reformer temperature which changes with the fuel cell current. At low currents, the efficiency is effected relatively little by the reformer temperature and there is a local maximum at low temperatures and a global maximum at high temperatures. At higher fuel cell currents, the optimum becomes more unequivocal and there is a clear optimal temperature running between 245 and 255 ½ C.
The plotted optimal temperature strategy will, however, yield some practical problems. Namely that large changes in the reformer temperature set point can lead to thermal instability throughout the RMFC system. Other strategies are therefore investigated as well. First a strategy called Lower optimal Tr is investigated where the local maximum at lower temperatures is used. Then a strategy called Tr ¼ 290 where the reformer temperature is kept constant at 290 ½ C is investigated because this corresponds to the strategy which is currently used in the module. The average efficiency at each reformer temperature is also calculated and the constant temperature which yields the highest efficiency is found to be 252 ½ C. A strategy called Tr ¼ 252 is therefore also investigated. Fig. 10b shows a plot of the efficiency matrix with all of these strategies included. To illustrate the difference in performance between the four strategies, the efficiency for each strategy is plotted in Fig. 11a as a function of the fuel cell current.
Fig. 11 e (a) Plot of the efficiency achieved using different Tr strategies at different fuel cell currents. (b) Plot of the deficit in efficiency achieved using different Tr strategies at different fuel cell currents when compared to Optimal Tr .
9512
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
Fig. 12 e (a) Contour plot of the calculated efficiencies at different fuel cell currents and reformer temperatures with TFC ¼ 165 ½ C including lines for the developed reformer temperature control strategies. (b) Contour plot of the calculated efficiencies at different fuel cell currents and reformer temperatures with TFC ¼ 160 ½ C including lines for the developed reformer temperature control strategies.
As the figure illustrates, there is little difference between the strategies Optimal Tr , Lower optimal Tr and Tr ¼ 252, but the strategy Tr ¼ 290 has a much lower performance at higher currents. To illustrate the difference better the performance deficit of the last three strategies to Optimal Tr is plotted in Fig. 11b. As this plot illustrates, the difference between the first three strategies is indeed small, with a maximum deficit of 0.2 percentage points for Tr ¼ 252. For the Tr ¼ 290 strategy the deficit is up to 4 percentage points at a fuel cell current of 16 ½A. The average efficiency of each strategy appears from Table 1. The numbers illustrate that the difference between Optimal Tr and Lower optimal Tr is only 0.01 percentage points and the deficit of Tr ¼ 252 is 0.03 percentage points. The deficit of Tr ¼ 290 is, however, 1.47 percentage points. To investigate the effect of changes in the fuel cell temperature, the optimization is repeated with fuel cell temperatures of 165 and 160 ½ C. The resulting efficiency plots with optimal strategy lines can be seen in Fig. 12a and b. The tendencies for these optimizations are the same as for TFC ¼ 170 ½ C, but none of them have the maximum at higher reformer temperatures observed earlier. Instead there is a continuous optimal temperature. For TFC ¼ 160 ½ C there is a sudden change in the optimal operating point at IFC ¼ 16 ½A. This is caused by the fuel cell model which is out of range at this operating point. This point is therefore not considered. For TFC ¼ 165 ½ C the optimal constant temperature is 248 ½ C and at TFC ¼ 160 ½ C it is 243 ½ C. Table 2 shows the average
efficiencies obtained with different reformer temperature strategies at a fuel cell temperature of 165 ½ C. The average system efficiencies are generally lower as would be expected. The efficiency is only 0.02 percentage points lower using Tr ¼ 248 compared to Optimal Tr , but the efficiency drops 2.39 percentage points if Tr ¼ 290 is used. If the optimal constant temperature of Tr ¼ 252 ½ C calculated at TFC ¼ 170 ½ C is used, the efficiency drops 0.06 percentage points compared to Optimal Tr . Table 3 shows the results obtained with a fuel cell temperature of 160 ½ C. The average efficiencies are reduced further and again the deficit of best constant temperature to Optimal Tr is 0.02 percentage points. The deficit using Tr ¼ 290 is 4.25 percentage points, but the deficit using Tr ¼ 252 is only 0.24 percentage points. It is therefore concluded, that the optimal constant reformer temperature is not very sensitive to the choice of fuel cell temperature.
Table 1 e Average efficiency with different Tr control strategies at a fuel cell temperature of 170 ½ C and fuel cell currents between 5 and 16 ½A.
Table 2 e Average efficiency with different Tr control strategies at TFC ¼ 165 ½ C and fuel cell currents between 5 and 16 ½A.
Discussion As the graphs in Fig. 11a and b and the values in Table 1 illustrate, a significant gain in efficiency can be achieved by choosing the right reformer temperature. This work also illustrates that the gain from making the reformer temperature dependent on the fuel cell current is marginal in the tested H3 350 RMFC module. It might, however, be a beneficial on other
Strategy
h%
Strategy
h%
Optimal Tr Lower optimal Tr Tr ¼ 252 Tr ¼ 290
32.11 32.10 32.08 30.64
Optimal Tr Tr ¼ 248 Tr ¼ 252 Tr ¼ 290
31.49 31.47 31.43 29.10
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
Table 3 e Average efficiency with different Tr control strategies at TFC ¼ 160 ½ C and fuel cell currents between 5 and 16 ½A. Strategy
h%
Optimal Tr Tr ¼ 243 Tr ¼ 252 Tr ¼ 290
31.16 31.14 30.92 26.91
systems where the reformer temperature has a higher effect on the CO concentration and the reforming efficiency. Changing the fuel cell temperature was found to have an effect on the optimal constant reformer temperature, but the effect was found to be small. It is worth noticing that the models presented in this work do not include any degradation mechanisms. This means that the conclusions drawn on the basis of them are only strictly valid at the state of degradation where the models where developed. However, the fuel cell voltage will still decrease when the CO content in the anode gas is increased and the CO content in the output gas of the reformer will always increase with increased reformer temperature. It is therefore reasonable to assume that there will always be an optimum operating point and the one suggested in this work is a good suggestion to go for.
Conclusion In this work, a method for determining the optimal reformer temperature for a reformed methanol fuel cell (RMFC) system has been developed. The method uses ANFIS models of the reformers output gas composition and the voltage of the fuel cell trained on the basis of experimental data to predict the system efficiency at different reformer temperatures and fuel cell currents. The reformer output gas models include a model of the hydrogen mass flow out of the reformer, which has an MAE of 0.0760% and a model of the CO concentration, which has an MAE of 0.3245%. The fuel cell ANFIS model was adapted from the one in [19] and calculates the expected fuel cell voltage on the basis of the fuel cell current and temperature and the CO concentration in the anode gas. The reformer temperature which gives the best efficiency at each fuel cell current is then found and this is compared with the efficiency which is obtained using the present control strategy, which is equivalent to a constant reformer temperature of 290 ½ C. The average gain in efficiency across fuel cell currents is 1.47 percentage points and the maximum gain is found at a fuel cell current of 16 ½A where the gain is 4 percentage points. These results where achieved using a fuel cell temperature of 170 ½ C. Constantly changing reformer temperature in an RMFC system can lead to thermal instability and the optimal constant reformer temperature is therefore found to be 252 ½ C. With this constant temperature the average efficiency across fuel cell currents is 0.03 percentage points lower than the optimal strategy and the maximum deficit is 0.2 percentage points at low fuel cell currents. This strategy is therefore recommended in the H3 350 RMFC module.
9513
Future work As stated earlier in this paper, the models developed do not include degradation. If a data set which includes the degradation of the reformer and fuel cell over time is made, the models can be updated with this extra variable. Efficiency matrices could then be made for different states of degradation and a degradation-dependent optimal reformer temperature could be recommended.
Acknowledgment We gratefully acknowledge the financial support of the EUDP (The Journal number of the project is: 64011-0370) program and the cooperation of Serenergy A/S.
references
[1] Curtin S, Gangi J. 2013 fuel cell technologies market report. Tech. rep. Washington, D.C: Breakthrough Technologies Institute, Inc.; 2014. [2] Satyapal S, Petrovic J, Read C, Thomas G, Ordaz G. The U.S. Department of energys national hydrogen storage project: progress towards meeting hydrogen-powered vehicle requirements. Catal Today 2007;120. 246256, http://dx.doi. org/10.1016/j.cattod.2006.09.022. [3] Chrenko D, Gao F, Blunier B, Bouquain D, Miraoui A. Methanol fuel processor and PEM fuel cell modeling for mobile application. Int J Hydrogen Energy 2010;35:6863e71. http://dx.doi.org/10.1016/j.ijhydene.2010.04.022. [4] Kolb G, Keller S, Tiemann D, Schelhaas K-P, Schrer J, Wiborg O. Design and operation of a compact microchannel 5 kw el,net methanol steam reformer with novel pt/in2o3 catalyst for fuel cell applications. Chem Eng J 2012;207(0):388e402. 22nd International Symposium on Chemical Reaction Engineering (ISCRE 22), http://dx.doi.org/ 10.1016/j.cej.2012.06.141. [5] Hulteberg P, Porter B, Silversand F, Woods R. A versatile, steam reforming based small-scale hydrogen production process. In: 16th World Hydrogen Energy Conference 2006, WHEC; 2006. [6] Yong S, Ooi C, Chai S, Wu X. Review of methanol reformingcu-based catalysts, surface reaction mechanisms, and reaction schemes. Int J Hydrogen Energy 2013;38(22):9541e52. http://dx.doi.org/10.1016/j.ijhydene. 2013.03.023. [7] Hammoud D, Gennequin C, Aboukais A, Aad EA. Steam reforming of methanol over xPreparation by adopting memory effect of hydrotalcite and behavior evaluation. Int J Hydrogen Energy 2015;40(2):1283e97. http://dx.doi.org/10. 1016/j.ijhydene.2014.09.080. [8] Dagle R, Platon A, Palo D, Datye A, Vohs J, Wang Y. Pdznal catalysts for the reactions of water-gas-shift, methanol steam reforming, and reverse-water-gas-shift. Appl Catal A General 2008;342(1e2):63e8. http://dx.doi.org/10.1016/j. apcata.2008.03.005. [9] Wang G, Wang F, Li L, Zhang G. A study of methanol steam reforming on distributed catalyst bed. Int J Hydrogen Energy 2013;38(25):10788e94. http://dx.doi.org/10.1016/j.ijhydene. 2013.02.061. [10] Justesen KK, Andreasen SJ, Shaker HR. Dynamic modeling of a reformed methanol fuel cell system using empirical data
9514
[11]
[12]
[13] [14]
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
and adaptive neuro-fuzzy inference system models. J Fuel Cell Sci Technol 2014;11. http://dx.doi.org/10.1115/1.4025934. Justesen KK, Andreasen SJ, Shaker HR, Ehmsen MP, Andersen J. Gas composition modeling in a reformed methanol fuel cell system using adaptive neuro-fuzzy inference systems. Int J Hydrogen Energy 2013;38:10577e84. http://dx.doi.org/10.1016/j.ijhydene.2013.06.013. Andreasen SJ, Kr SK, Sahlin S. Control and experimental characterization of a methanol reformer for a 350w high temperature polymer electrolyte membrane fuel cell system. Int J Hydrogen Energy 2013;38(3):1676e84. 2011 Zing International Hydrogen and Fuel Cells Conference: from Nanomaterials to Demonstrators, http://dx.doi.org/10.1016/j. ijhydene.2012.09.032. Serenergy a/s home page. 2014. http://www.serenergy.com/. Cao H, Li X, Deng Z, Li J, Qin Y. Thermal management oriented steady state analysis and optimization of a kw scale solid oxide fuel cell stand-alone system for maximum system efficiency. Int J Hydrogen Energy 2013;38(28):12404e17. http://dx.doi.org/10.1016/j.ijhydene. 2013.07.052.
[15] Kim K, von Spakovsky MR, Wang M, Nelson DJ. A hybrid multi-level optimization approach for the dynamic synthesis/design and operation/control under uncertainty of a fuel cell system. Energy 2011;36(6):3933e43 {ECOS} 2009, http://dx.doi.org/10.1016/j.energy.2010.08.024. [16] Wishart J, Dong Z, Secanell M. Optimization of a {PEM} fuel cell system based on empirical data and a generalized electrochemical semi-empirical model. J Power Sources 2006;161(2):1041e55. http://dx.doi.org/10.1016/j.jpowsour. 2006.05.056. [17] Jang J-SR. Anfis: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 1993;23:665e85. [18] BASF. Basf catalyst selectra® rp-60-technical information. March 2008. [19] Justesen KK, Andreasen SJ, Sahlin SL. Modeling of a HTPEM fuel cell using adaptive neuro-fuzzy inference systems. In: Poster at 4th Carisma Conference 2014 in cape Town South Africa; 2014. http://vbn.aau.dk/da/publications/modeling-of-a-htpemfuel-cell-using-adaptive-neurofuzzy-inference-systems (bbd2292ee8e3b-472a-8b1a-9ceda2785e5a).html (12 2014).
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/he
Determination of optimal reformer temperature in a reformed methanol fuel cell system using ANFIS models and numerical optimization methods Kristian Kjær Justesen*, Søren Juhl Andreasen Department of Energy Technology, Aalborg University, Pontoppidanstræde 101, 9220 Aalborg East, Denmark
article info
abstract
Article history:
In this work a method for choosing the optimal reformer temperature for a reformed
Received 1 April 2015
methanol fuel cell system is presented based on a case study of a H3 350 module produced
Received in revised form
by Serenergy A/S. The method is based on ANFIS models of the dependence of the reformer
13 May 2015
output gas composition on the reformer temperature and fuel flow, and the dependence of
Accepted 17 May 2015
the fuel cell voltage on the fuel cell temperature, current and anode supply gas CO content.
Available online 10 June 2015
These models are combined to give a matrix of system efficiencies at different fuel cell currents and reformer temperatures. This matrix is then used to find the reformer tem-
Keywords:
perature which gives the highest efficiency for each fuel cell current. The average of this
Reformed methanol fuel cell
optimal efficiency curve is 32.11% and the average efficiency achieved using the standard
Fuel cell modeling
constant temperature is 30.64% an increase of 1.47 percentage points. The gain in efficiency
Operating point optimization
is 4 percentage points, from 23 % to 27 %, at full power where the gain is largest. The
System efficiency optimization
constant reformer temperature which gives the highest average efficiency is found to be
HTPEM fuel cells
252 C at which temperature it is 32.08%, only 0.03 percentage points lower than the
ANFIS modeling
maximum efficiency curve. Copyright © 2015, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
Introduction Polymer Electrolyte Membrane (PEM) fuel cells are gaining popularity in both stationary and mobile applications due to their potential ability to provide energy with little or no harmful on-site emissions at a high efficiency [1]. They do, however, have challenges with impractical and energy consuming fuel storage if a fuel cell system is operated on pure hydrogen (H2 ) [2]. This can be under high pressure in tanks, in liquid form at temperatures under e 253 ½ C or in
metal hydrides. All these methods are energy consuming and the storage capacity can not be expanded easily. This problem is limited, if the H2 for the fuel cell system is produced online as needed by reforming a liquid fuel such as methanol. Such systems are becoming increasingly common and examples are described in [3], [4], and [5]. These systems consist of more components than traditional fuel cell systems and more factors affect their efficiency. The two main factors are the efficiency with which the reformer turns the fuel into hydrogen, i.e. how much of the fuel pumped into the system is turned into hydrogen, and the efficiency of the fuel cell. These
* Corresponding author. Fax: þ45 9815 1411. E-mail addresses: [email protected] (K.K. Justesen), [email protected] (S.J. Andreasen). http://dx.doi.org/10.1016/j.ijhydene.2015.05.085 0360-3199/Copyright © 2015, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
9506
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
CO þ H2 O/H2 þ CO2
Nomenclature maxð:Þ minð:Þ x a b c Oi wi oi
pi
ri Tr m_ fuel m_ H2 need lH2 NCell MH2 F IFC
calculates the maximum of the inputs calculates the minimum of the inputs crisp input for ANFIS model adaptive premise parameter that determines the shape of a membership function adaptive premise parameter that determines the shape of a membership function adaptive premise parameter that determines the shape of a membership function contribution of the ith rule to the output of an ANFIS function normalized firing level of the ith rule adaptive consequent parameter that determines the influence of the first input on the output of the ith rule adaptive consequent parameter that determines the influence of the second input on the output of the ith rule adaptive consequent parameter which determines the constant part of the ith rule reformer temperature reformer fuel flow necessary hydrogen mass flow hydrogen stoichiometry at the anode number of cells in the fuel cell molar mass of hydrogen Faraday's constant fuel cell current
two efficiencies and their effect on each other is therefore of great interest and the focus of this work. The composition of the output gas of a fuel reformer is dependent on the type of reforming which is used. The most widely used varieties are: Steam reforming, which has the following reaction: CH3 OH þ H2 O/3H2 þ CO2
(1)
The water gas shift reaction removes some of the CO in the gas but not all of it. This means that both with steam reforming and partial oxidation reformers, higher temperatures lead to an increased CO content in the output gas of the reformer and therefore a lower fuel cell efficiency. Lower reformer temperatures do, however, mean that more of the fuel pumped into the system goes through unreformed. This means that a higher fuel flow rate is needed to sustain a certain fuel cell anode stoichiometry, leading to a lower system efficiency. This effect is further reinforced at higher fuel flows as observed in [8] and [9]. There is therefore an optimal reformer temperature set point, which is a compromise between low CO content and high reforming efficiency and this work presents a method for finding this optimal temperature at different fuel cell current set points. In this work a H3 350 Reformed Methanol Fuel Cell (RMFC) system produced by Serenergy A/S will be used as a case study. It is a 350 W off-grid battery charger which uses an air cooled high temperature PEM fuel cell and a steam reformer to produce electricity from a methanol/water mixture. The authors of this paper have previously published work on modeling of similar systems in [10], [11], and [12]. Fig. 1 shows a picture of a H3 350 system. The module consists of a fuel pump which pumps the methanol/water mixture into an evaporator, which heats and evaporates the fuel using the excess heat from the fuel cell. From there the evaporated fuel is fed into the reformer where the steam reforming process takes place. The now H2 rich reformed gas is fed to the fuel cell where most of the H2 in the gas is used. A H2 over stoichiometry of 1.35 is used on the fuel cell anode, on the recommendation of the manufacturer, to prevent anode starvation in the fuel cell. The remaining H2 is fed to a catalytic burner, which supplies the reformer with the necessary process heat. Fig. 2 shows a diagram of the H3 350 module. The performance of the reformer system will be evaluated on a test setup as will the performance of the fuel cell, and the following Adaptive Neuro-Fuzzy Inference System (ANFIS) models will be produced:
partial oxidation: 1 CH3 OH þ O2 /2H2 þ CO2 2
(2)
and methanol decomposition: CH3 OH/2H2 þ CO
(3)
[6] The first two are most widely used in fuel cell applications, as the carbon monoxide (CO) created by the methanol decomposition reaction is harmful to PEM fuel cells. Both in reformers that utilize steam reforming and partial oxidation reactions, the methanol decomposition reaction will also occur to some degree, leading to an amount of CO in the output gas of the reformer. This reaction is more prevalent at higher temperatures, which in general means that higher reformer temperatures lead to increased CO content as observed in [7] and [8]. In a steam reformer the water gas shift reaction can also take place:
(4)
Fig. 1 e Picture of a Serenus H3 350 RMFC module from Serenergy [13].
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
Fig. 2 e Concept drawing of a H3 350 module from Serenergy. In the diagram green signifies the methanol/ water mixture, blue signifies atmospheric air and exhaust gas and magenta signifies a hydrogen rich gas. Solid lines signify flow in pipes or hoses and dotted lines signify flow inside a component. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Model of the CO concentration in the reformer output gas. Model of the H2 mass flow in the reformer output gas. Model of the dependence of the fuel cell voltage on fuel cell current, temperature and anode supply gas CO concentration. These models will be used to analyze the influence of the reformer temperature on the system efficiency. In the following sections these models will be described. The influence of operating parameters on the system efficiency has been investigated before for other kinds of system. One such system is the solid oxide fuel cell system described in [14] where optimal operating conditions are found based on physical system models. Another is the low temperature (LT) PEM system powered by natural gas through a fuel reformer system with CO cleanup described in [15]. Here a multi-level optimization approach is used to optimize the efficiency of the system while insuring safe operation. In addition [16] describes the optimization of operating parameters such as temperature, pressure ratios and reactant stoichiometries for a LTPEM fuel cell system. A parametric optimization of a system like the H3 350 has, however, not been presented before.
9507
The models have the additional advantage that they are fast evaluating and can be evaluated online in a control system, or in a larger model offline for evaluation purposes. The ANFIS structure used in this work is the one originally suggested in [17] and illustrated in Fig. 3 for a system with two inputs and two membership functions. As the figure shows, the structure consists of four different layers. The first layer is the Fuzzification layer where the crisp inputs, in this case the reformer temperature Tr and the fuel flow m_ fuel , are converted to fuzzy variables, which are numbers between zero and one. This is done by evaluating the degree of membership of a certain group for the specified inputs. For the system in Fig. 3 these groups are high and low numbers. The degree of membership is calculated using membership functions. These can have different forms, for example triangular, trapezoidal, sigmoidal and bell-shaped. In this work triangular membership functions are used, because it is the least computationally heavy and experiments showed that no additional performance was gained by using the more complex models. The following equation shows a generalized triangular membership function. xa cx ; ;0 A ¼ max min ba cb
(5)
Here the parameters a, b and c are adaptive premise parameters which mark the corners of the triangle and are subject to optimization during the training process. It is worth noticing that in the functions at the edge of the input range one of the functions in the minð:; :Þ is replaced by a 1. In the second layer the structure is split up into rules and the firing level of each rule is calculated. The structure in Fig. 3 has four rules marked by different colors. The firing level of a rule is in this work calculated using the fuzzy AND and can be interpreted as the extent to which the rule is active. In the first rule, which is marked in red, this means to which extent both Tr and m_ fuel are low. In the third layer the firing levels are normalized to give a sum of 1. In the fourth layer the contribution of each rule to the output is calculated using the following equation:
ANFIS modeling Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are, as the name suggests, a neuro-fuzzy model structure which can be adapted to behave like a physical system. This is especially useful when modeling nonlinear systems on the basis of experimental data or when the process variable needed for physical modeling is not available but a derived or related variable is. This is, for example, the case for the reformer system where the temperature of the reformer casing is available but not the temperature of the reformer bed itself.
Fig. 3 e The ANFIS modeling structure employed in this work. The different colors signify the different rules. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
9508 Oi ¼ wi $ oi $Tr þ pi $m_ fuel þ ri
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
(6)
where i denotes the rule number, wi is the normalized firing level and oi , pi and ri are adaptive consequent parameters which are subject to optimization during the training process.Finally the contributions of each rule are summed in the fifth layer to give the output of the ANFIS model. The training process works by initializing the membership functions with starting values and evaluating the model up until the last layer. The consequent parameters are then optimized using least squares regression. The premise parameters are then updated using gradient decent methods. In the second iteration of the training process, the model is evaluated until the last layer using the membership functions found in the first iteration, and the premise parameters are updated again using least squares regression. The consequent parameters are updated again using gradient descent methods. This process is then repeated for a set number of iterations or until the error of the model is below a certain threshold.
Reforming process modeling Modeling the output gas composition using ANFIS models has been chosen because of the complexity of the integrated system and the lack of detailed information about the temperature distribution and available catalyst surface. The method of using ANFIS models to predict the content of the output gas of a methanol steam reformer was first presented in [11], where an identification experiment spanning the operating range of the reformer was performed and the output gas composition measured. The entire data set was then used for training. In this work an identification experiment spanning the operating range is also performed. The data is then divided into blocks corresponding to each operating point, and an average value is calculated for each point giving a performance matrix which is then saved. The ANFIS models are then trained on the basis of this data. This means that the training process is performed on fewer data points. Calculating the average for each operating point also has the benefit of making it easier to visualize the measurements, when they are arranged into matrices and plotted in contour plots. For the reformer identification experiments a H3 350 module, where the fuel cell is replaced by a gas analyzer, is used. The hot cathode air of the fuel cell is replaced by a mass flow controller and an electric heater, the anode waste gas for the burner is replaced by a mass flow controller which supplies pure H2 and the burner blower is replaced by a mass flow controller. Fig. 4 shows a diagram of the experimental setup. On the basis of the initial experiments with the reformer and the datasheet of the catalyst [18], the identification experiments have been run with reformer temperatures between 235 and 290 C in 5 C increments. At each temperature the performance is evaluated at 5 different fuel flows corresponding to fuel cell currents of 5, 8.75, 11.5, 14.75 and 18 ½A with a stoichiometry of 1.35 at assumed full reformation. The maximum rated fuel cell current is 16 ½A but 18 ½A is used because it gives a buffer which allows the
Fig. 4 e Concept drawing of the experimental setup used for the reformer identification experiment.
fuel flow to be increased to correct the stoichiometry during the optimization process. Fig. 5a shows a contour plot of the CO content measurements from the experiment and the output of the CO ANFIS model when subjected to the same conditions. Experiments showed that the best performance was obtained with three membership functions and as the figure shows, the model and the measurements fit well and the Mean Absolute Error (MAE) of this model is 0.323%. The figure also shows that, as expected, high reformer temperatures generally yield a higher CO content. The effect of increased flow is less definitive with increased flow yielding lower concentration at low temperatures and a local maximum appearing at medium flows at higher temperatures. Fig. 5b shows a contour plot of the H2 mass flow measured in the experiment, the output of the H2 ANFIS model when subjected to the same conditions and the fuel flow if full reforming is assumed. As the figure shows, the difference between full reforming and the measured H2 flow is small at low flows and constant at all temperatures. However, as the fuel flow is increased, the difference to full reforming is increased more at low temperatures than at high temperatures. This is in accordance with the tendencies observed in [8] and [9]. The figure also shows that the model fits the measurements well and the MAE is 0.074%. The tendencies observed in the reformer output gas composition are concluded to be those that were expected based on the literature and that the ANFIS model is able to predict the desired system states with sufficient precision. It is therefore relevant to proceed with the modeling of the fuel cell and the operating point optimization.
Fuel cell model In this work a model which can predict the fuel cell voltage at different fuel cell temperatures, currents and anode CO concentrations is needed. For this purpose the model presented in [19] is suitable, as it presents an ANFIS model of this relationship for a similar Serenergy fuel cell, albeit a version with a different cell area but the same membrane type. The input of
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
9509
Fig. 5 e (a) Contour plot of the measured and modeled CO content in the reformed gas at different fuel flows and reformer temperatures. (b) Contour plot of the measured and modeled H2 mass flow in the reformed gas at different fuel flows and reformer temperatures and the H2 mass flow at full reforming.
the model is the CO concentration in %, the fuel cell temperature in ½ C and the fuel cell current density in ½A=cm2 . Other factors, such as the water and CO2 content of the supply gas also affect the performance of the fuel cell. But during the training of the model they were kept at values which are consistent with typical measurements on the reformed gas in this work and they can therefore be left out. The output of the model is the cell voltage in ½V. The MAE of this model is 0.87% and Fig. 6 shows a plot of the training data and the fit of the ANFIS model at a fuel cell temperature of 170 ½ C. The fuel cell stack in the H3 350 module has 45 cells and a cell area of 45.16 ½cm2 . The fuel cell current is therefore divided by 45.16 before it is fed to the model and the model's output is multiplied by 45 to give the stack voltage.
The initial optimization is performed with a fuel cell temperature of 170 ½ C as this is within the operating range of the fuel cell. Another choice of fuel cell temperature will yield a different optimal reformer temperature, as the CO tolerance of the fuel cell is higher at higher temperatures. The optimization process will therefore be repeated for fuel cell temperatures of 165 and 160 ½ C and a summary of the results will be given.
Optimization The first step in the optimization process is to construct a fuel flow matrix which gives the fuel flow that is necessary to keep the anode stoichiometry constant at a given fuel cell current and reformer temperature. Here keeping the stoichiometry at 1.35 has been chosen because this is what the manufacturer recommends. This stoichiometry is also used in the training of the fuel cell model. The flow matrix is constructed by calculating the necessary H2 mass flow at different fuel cell currents and using the H2 ANFIS model to find the fuel flow which is necessary to give this H2 flow. The necessary H2 mass flow is calculated using the following equation: m_ H2
Fig. 6 e Plot of the training data and fit of the ANFIS model at TFC ¼ 170 ½ C from [19].
need
¼
lH2 $Ncell $MH2 $IFC 2$F
(7)
where lH2 is the H2 stoichiometry of the anode, Ncell is the number of cells, MH2 is the molar mass of hydrogen, F is Faradays constant and IFC is the fuel cell current. It is not possible to make an inverse of the ANFIS model that calculates the hydrogen production from the fuel flow and the reformer temperature. Instead an optimization algorithm which uses the ANFIS model to adjust the fuel flow to fit the hydrogen requirement is used. This algorithm
9510
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
Fig. 7 e Contour plot of the fuel flow which is necessary to keep the stoichiometry constant at 1.35 at different fuel cell currents and reformer temperatures.
works by adding small increments to the fuel flow until the output of the ANFIS model matches the needed H2 flow, taking the flow assuming full reforming as a starting point. Fig. 7 shows a contour plot of the resulting matrix of necessary fuel flows at different fuel cell currents and reformer temperatures. As the figure shows, the necessary fuel flow is the same for all reformer temperatures at low fuel cell currents. But as the fuel cell current is increased, the necessary fuel flow is higher at low reformer temperatures. This can also be seen in Fig. 8 where a matrix of the input power at different reformer temperatures and fuel cell currents is plotted using the lower heating value of the methanol in the fuel. This matrix will be used to calculate an efficiency matrix later in this work. The fuel flow matrix plotted in Fig. 7 is used to construct a CO content matrix. The entries in this matrix are then used in
Fig. 8 e Contour plot of the necessary input power at different fuel cell currents and reformer temperatures with stoichiometry corrected fuel flow using lower heating value.
the fuel cell ANFIS model to construct a fuel cell voltage matrix, which is plotted in Fig. 9a. As the figure shows, the influence of the increased CO content is relatively low at low fuel cell currents. But as the fuel cell current is increased, the influence of the CO in the reformed gas at higher reformer temperatures on the fuel cell voltage is increased. By multiplying the fuel cell current and voltage, the output power of the fuel cell can be calculated. Fig. 9b shows the resulting output power matrix in a contour plot. As the figure shows, the output power is higher at lower reformer temperatures due to the increased fuel cell voltage at these operating points. However, at these temperatures a larger input power is also necessary as can be seen in Fig. 8. The fuel cell power matrix and the input power matrix are
Fig. 9 e (a) Contour plot of the fuel cell voltage at different currents and reformer temperatures with stoichiometry corrected fuel flow. (b) Contour plot of the output power at different fuel cell currents and reformer temperatures with stoichiometry corrected fuel flow.
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
9511
Fig. 10 e (a) Contour plot of the calculated efficiencies at different fuel cell currents and reformer temperatures with a line marking the reformer temperature which yields the highest efficiency at each fuel cell current. (b) Contour plot of the calculated efficiencies at different fuel cell currents and reformer temperatures including lines for the developed reformer temperature control strategies.
therefore combined to give the system efficiency matrix using the following equation: hsystem ¼
PFC out Pfuel in
(8)
The resulting efficiency matrix is plotted in Fig. 10a. Here a line representing the highest efficiency at each fuel cell current is also plotted. The resulting reformer temperature control strategy will in the following be called Optimal Tr . The figure shows, that there is an optimal reformer temperature which changes with the fuel cell current. At low currents, the efficiency is effected relatively little by the reformer temperature and there is a local maximum at low temperatures and a global maximum at high temperatures. At higher fuel cell currents, the optimum becomes more unequivocal and there is a clear optimal temperature running between 245 and 255 ½ C.
The plotted optimal temperature strategy will, however, yield some practical problems. Namely that large changes in the reformer temperature set point can lead to thermal instability throughout the RMFC system. Other strategies are therefore investigated as well. First a strategy called Lower optimal Tr is investigated where the local maximum at lower temperatures is used. Then a strategy called Tr ¼ 290 where the reformer temperature is kept constant at 290 ½ C is investigated because this corresponds to the strategy which is currently used in the module. The average efficiency at each reformer temperature is also calculated and the constant temperature which yields the highest efficiency is found to be 252 ½ C. A strategy called Tr ¼ 252 is therefore also investigated. Fig. 10b shows a plot of the efficiency matrix with all of these strategies included. To illustrate the difference in performance between the four strategies, the efficiency for each strategy is plotted in Fig. 11a as a function of the fuel cell current.
Fig. 11 e (a) Plot of the efficiency achieved using different Tr strategies at different fuel cell currents. (b) Plot of the deficit in efficiency achieved using different Tr strategies at different fuel cell currents when compared to Optimal Tr .
9512
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
Fig. 12 e (a) Contour plot of the calculated efficiencies at different fuel cell currents and reformer temperatures with TFC ¼ 165 ½ C including lines for the developed reformer temperature control strategies. (b) Contour plot of the calculated efficiencies at different fuel cell currents and reformer temperatures with TFC ¼ 160 ½ C including lines for the developed reformer temperature control strategies.
As the figure illustrates, there is little difference between the strategies Optimal Tr , Lower optimal Tr and Tr ¼ 252, but the strategy Tr ¼ 290 has a much lower performance at higher currents. To illustrate the difference better the performance deficit of the last three strategies to Optimal Tr is plotted in Fig. 11b. As this plot illustrates, the difference between the first three strategies is indeed small, with a maximum deficit of 0.2 percentage points for Tr ¼ 252. For the Tr ¼ 290 strategy the deficit is up to 4 percentage points at a fuel cell current of 16 ½A. The average efficiency of each strategy appears from Table 1. The numbers illustrate that the difference between Optimal Tr and Lower optimal Tr is only 0.01 percentage points and the deficit of Tr ¼ 252 is 0.03 percentage points. The deficit of Tr ¼ 290 is, however, 1.47 percentage points. To investigate the effect of changes in the fuel cell temperature, the optimization is repeated with fuel cell temperatures of 165 and 160 ½ C. The resulting efficiency plots with optimal strategy lines can be seen in Fig. 12a and b. The tendencies for these optimizations are the same as for TFC ¼ 170 ½ C, but none of them have the maximum at higher reformer temperatures observed earlier. Instead there is a continuous optimal temperature. For TFC ¼ 160 ½ C there is a sudden change in the optimal operating point at IFC ¼ 16 ½A. This is caused by the fuel cell model which is out of range at this operating point. This point is therefore not considered. For TFC ¼ 165 ½ C the optimal constant temperature is 248 ½ C and at TFC ¼ 160 ½ C it is 243 ½ C. Table 2 shows the average
efficiencies obtained with different reformer temperature strategies at a fuel cell temperature of 165 ½ C. The average system efficiencies are generally lower as would be expected. The efficiency is only 0.02 percentage points lower using Tr ¼ 248 compared to Optimal Tr , but the efficiency drops 2.39 percentage points if Tr ¼ 290 is used. If the optimal constant temperature of Tr ¼ 252 ½ C calculated at TFC ¼ 170 ½ C is used, the efficiency drops 0.06 percentage points compared to Optimal Tr . Table 3 shows the results obtained with a fuel cell temperature of 160 ½ C. The average efficiencies are reduced further and again the deficit of best constant temperature to Optimal Tr is 0.02 percentage points. The deficit using Tr ¼ 290 is 4.25 percentage points, but the deficit using Tr ¼ 252 is only 0.24 percentage points. It is therefore concluded, that the optimal constant reformer temperature is not very sensitive to the choice of fuel cell temperature.
Table 1 e Average efficiency with different Tr control strategies at a fuel cell temperature of 170 ½ C and fuel cell currents between 5 and 16 ½A.
Table 2 e Average efficiency with different Tr control strategies at TFC ¼ 165 ½ C and fuel cell currents between 5 and 16 ½A.
Discussion As the graphs in Fig. 11a and b and the values in Table 1 illustrate, a significant gain in efficiency can be achieved by choosing the right reformer temperature. This work also illustrates that the gain from making the reformer temperature dependent on the fuel cell current is marginal in the tested H3 350 RMFC module. It might, however, be a beneficial on other
Strategy
h%
Strategy
h%
Optimal Tr Lower optimal Tr Tr ¼ 252 Tr ¼ 290
32.11 32.10 32.08 30.64
Optimal Tr Tr ¼ 248 Tr ¼ 252 Tr ¼ 290
31.49 31.47 31.43 29.10
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
Table 3 e Average efficiency with different Tr control strategies at TFC ¼ 160 ½ C and fuel cell currents between 5 and 16 ½A. Strategy
h%
Optimal Tr Tr ¼ 243 Tr ¼ 252 Tr ¼ 290
31.16 31.14 30.92 26.91
systems where the reformer temperature has a higher effect on the CO concentration and the reforming efficiency. Changing the fuel cell temperature was found to have an effect on the optimal constant reformer temperature, but the effect was found to be small. It is worth noticing that the models presented in this work do not include any degradation mechanisms. This means that the conclusions drawn on the basis of them are only strictly valid at the state of degradation where the models where developed. However, the fuel cell voltage will still decrease when the CO content in the anode gas is increased and the CO content in the output gas of the reformer will always increase with increased reformer temperature. It is therefore reasonable to assume that there will always be an optimum operating point and the one suggested in this work is a good suggestion to go for.
Conclusion In this work, a method for determining the optimal reformer temperature for a reformed methanol fuel cell (RMFC) system has been developed. The method uses ANFIS models of the reformers output gas composition and the voltage of the fuel cell trained on the basis of experimental data to predict the system efficiency at different reformer temperatures and fuel cell currents. The reformer output gas models include a model of the hydrogen mass flow out of the reformer, which has an MAE of 0.0760% and a model of the CO concentration, which has an MAE of 0.3245%. The fuel cell ANFIS model was adapted from the one in [19] and calculates the expected fuel cell voltage on the basis of the fuel cell current and temperature and the CO concentration in the anode gas. The reformer temperature which gives the best efficiency at each fuel cell current is then found and this is compared with the efficiency which is obtained using the present control strategy, which is equivalent to a constant reformer temperature of 290 ½ C. The average gain in efficiency across fuel cell currents is 1.47 percentage points and the maximum gain is found at a fuel cell current of 16 ½A where the gain is 4 percentage points. These results where achieved using a fuel cell temperature of 170 ½ C. Constantly changing reformer temperature in an RMFC system can lead to thermal instability and the optimal constant reformer temperature is therefore found to be 252 ½ C. With this constant temperature the average efficiency across fuel cell currents is 0.03 percentage points lower than the optimal strategy and the maximum deficit is 0.2 percentage points at low fuel cell currents. This strategy is therefore recommended in the H3 350 RMFC module.
9513
Future work As stated earlier in this paper, the models developed do not include degradation. If a data set which includes the degradation of the reformer and fuel cell over time is made, the models can be updated with this extra variable. Efficiency matrices could then be made for different states of degradation and a degradation-dependent optimal reformer temperature could be recommended.
Acknowledgment We gratefully acknowledge the financial support of the EUDP (The Journal number of the project is: 64011-0370) program and the cooperation of Serenergy A/S.
references
[1] Curtin S, Gangi J. 2013 fuel cell technologies market report. Tech. rep. Washington, D.C: Breakthrough Technologies Institute, Inc.; 2014. [2] Satyapal S, Petrovic J, Read C, Thomas G, Ordaz G. The U.S. Department of energys national hydrogen storage project: progress towards meeting hydrogen-powered vehicle requirements. Catal Today 2007;120. 246256, http://dx.doi. org/10.1016/j.cattod.2006.09.022. [3] Chrenko D, Gao F, Blunier B, Bouquain D, Miraoui A. Methanol fuel processor and PEM fuel cell modeling for mobile application. Int J Hydrogen Energy 2010;35:6863e71. http://dx.doi.org/10.1016/j.ijhydene.2010.04.022. [4] Kolb G, Keller S, Tiemann D, Schelhaas K-P, Schrer J, Wiborg O. Design and operation of a compact microchannel 5 kw el,net methanol steam reformer with novel pt/in2o3 catalyst for fuel cell applications. Chem Eng J 2012;207(0):388e402. 22nd International Symposium on Chemical Reaction Engineering (ISCRE 22), http://dx.doi.org/ 10.1016/j.cej.2012.06.141. [5] Hulteberg P, Porter B, Silversand F, Woods R. A versatile, steam reforming based small-scale hydrogen production process. In: 16th World Hydrogen Energy Conference 2006, WHEC; 2006. [6] Yong S, Ooi C, Chai S, Wu X. Review of methanol reformingcu-based catalysts, surface reaction mechanisms, and reaction schemes. Int J Hydrogen Energy 2013;38(22):9541e52. http://dx.doi.org/10.1016/j.ijhydene. 2013.03.023. [7] Hammoud D, Gennequin C, Aboukais A, Aad EA. Steam reforming of methanol over xPreparation by adopting memory effect of hydrotalcite and behavior evaluation. Int J Hydrogen Energy 2015;40(2):1283e97. http://dx.doi.org/10. 1016/j.ijhydene.2014.09.080. [8] Dagle R, Platon A, Palo D, Datye A, Vohs J, Wang Y. Pdznal catalysts for the reactions of water-gas-shift, methanol steam reforming, and reverse-water-gas-shift. Appl Catal A General 2008;342(1e2):63e8. http://dx.doi.org/10.1016/j. apcata.2008.03.005. [9] Wang G, Wang F, Li L, Zhang G. A study of methanol steam reforming on distributed catalyst bed. Int J Hydrogen Energy 2013;38(25):10788e94. http://dx.doi.org/10.1016/j.ijhydene. 2013.02.061. [10] Justesen KK, Andreasen SJ, Shaker HR. Dynamic modeling of a reformed methanol fuel cell system using empirical data
9514
[11]
[12]
[13] [14]
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 0 ( 2 0 1 5 ) 9 5 0 5 e9 5 1 4
and adaptive neuro-fuzzy inference system models. J Fuel Cell Sci Technol 2014;11. http://dx.doi.org/10.1115/1.4025934. Justesen KK, Andreasen SJ, Shaker HR, Ehmsen MP, Andersen J. Gas composition modeling in a reformed methanol fuel cell system using adaptive neuro-fuzzy inference systems. Int J Hydrogen Energy 2013;38:10577e84. http://dx.doi.org/10.1016/j.ijhydene.2013.06.013. Andreasen SJ, Kr SK, Sahlin S. Control and experimental characterization of a methanol reformer for a 350w high temperature polymer electrolyte membrane fuel cell system. Int J Hydrogen Energy 2013;38(3):1676e84. 2011 Zing International Hydrogen and Fuel Cells Conference: from Nanomaterials to Demonstrators, http://dx.doi.org/10.1016/j. ijhydene.2012.09.032. Serenergy a/s home page. 2014. http://www.serenergy.com/. Cao H, Li X, Deng Z, Li J, Qin Y. Thermal management oriented steady state analysis and optimization of a kw scale solid oxide fuel cell stand-alone system for maximum system efficiency. Int J Hydrogen Energy 2013;38(28):12404e17. http://dx.doi.org/10.1016/j.ijhydene. 2013.07.052.
[15] Kim K, von Spakovsky MR, Wang M, Nelson DJ. A hybrid multi-level optimization approach for the dynamic synthesis/design and operation/control under uncertainty of a fuel cell system. Energy 2011;36(6):3933e43 {ECOS} 2009, http://dx.doi.org/10.1016/j.energy.2010.08.024. [16] Wishart J, Dong Z, Secanell M. Optimization of a {PEM} fuel cell system based on empirical data and a generalized electrochemical semi-empirical model. J Power Sources 2006;161(2):1041e55. http://dx.doi.org/10.1016/j.jpowsour. 2006.05.056. [17] Jang J-SR. Anfis: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 1993;23:665e85. [18] BASF. Basf catalyst selectra® rp-60-technical information. March 2008. [19] Justesen KK, Andreasen SJ, Sahlin SL. Modeling of a HTPEM fuel cell using adaptive neuro-fuzzy inference systems. In: Poster at 4th Carisma Conference 2014 in cape Town South Africa; 2014. http://vbn.aau.dk/da/publications/modeling-of-a-htpemfuel-cell-using-adaptive-neurofuzzy-inference-systems (bbd2292ee8e3b-472a-8b1a-9ceda2785e5a).html (12 2014).