Effect of biochar fuel characteristics on the performance of direct carbon fuel cells:

Effect of biochar fuel characteristics on the performance of direct carbon fuel cells:

An empirical model

Nida Jafri¹, L.W. Yoon¹, W.Y. Wong^2*, Veena Doshi¹, K.H. Cheah³

1School of Engineering, Faculty of Built Environment, Technology & Design, Taylor’s University, Subang Jaya, Malaysia

2Fuel Cell Institute, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor DE, Malaysia.

3School of Engineering and Physical Sciences, Heriot-Watt University, Malaysia Campus, Putrajaya, Malaysia

*Email: waiyin.wong@ukm.edu.my

Abstract

This study investigates an empirical model based on regression analysis to predict the power density output of a Direct Carbon Solid Oxide Fuel Cell (DC- SOFC). The model is based on proximate analysis accounting for carbon, volatile matter, ash and moisture content for acid and alkali treated oil palm mesocarp fiber (MF) biochar.

The model equations are validated against experimental data for a laboratory scale fuel cell set-up over a range of different fuel cell operating temperatures. The model may be used as a starting point to better understand the relation between the biochar fuel characteristics and the power density generated by the fuel cell. Analytical and predictive techniques are performed to explore the correlation between fuel properties and DC-SOFC chemical and electrochemical mechanisms. The results showed that acid pretreatment generated carbon-rich biochars and significantly helped in the removal of ash contents (from 8.58 % to 0.07 %) from the raw biomass which in turn facilitated the electrochemical performance of the biochar as a fuel in the DC-SOFC. The empirical model showed that the most significant and strong affects seen through the model comes from the carbon and ash content values.

This implies that these two parameters are the major parameters in determining the quality of the fuel used in the DC-SOFC system.

Keywords: Direct carbon solid oxide fuel cell; oil palm; mesocarp fiber; biochar; acid pretreatment; empirical modelling

1. Introduction

Research is being conducted for the efficient and cost-effective conversion of carbonaceous solids (coal, biomass, municipal solid waste, char, etc.) directly to electric power through direct carbon fuel cells (DCFCs) [1][2]. DCFCs have a thermodynamic advantage over other fuel cells. They are capable of electrochemically converting solid carbon fuel into CO2 and/or CO. The change in entropy in the overall cell reaction is about zero (for CO2 as the reaction product) or positive (for CO as the product). Because of this, the theoretical electric efficiency (known as ∆G/∆H of the overall cell reaction) of the DCFC comes to be around 100%. Two most common types of DCFCs which have been investigated upon are the molten carbonate and solid oxide electrolyte type. The latter type referred to as the DC-SOFC, is a promising alternative owing to its easy set-up and uncomplicated maintenance. However, the current densities obtained are still relatively low [3][4]. The reason for low power in DC-SOFC arises from the limited reaction zone occurring between the direct contact of solid carbon fuel and solid oxide electrolyte.

In general, the operation of DCFCs has been restricted by low rates of anodic reaction, accumulation of ash on the electrolyte surface and logistics of fuel delivery to the cell [5]. These issues are being currently

the surface properties of the carbon which is used as the fuel source. Surface treatments, including pretreatment with acid or alkali agents, heat, laser or plasma treatments are employed to enhance the electrochemical reactivities of the carbon fuel which gives a better performance in the DCFC [6].

Researchers have used surface treatment methods in the form of acids to reduce the ash content from the biomass which in turn aids in a better performance in DCFC [7][8][9]. As the surface characteristics of the biochar are important in determining its overall electrochemical activity in the DCFC in the first place, thus it is necessary to build an empirical model which allows to correlate these properties to its performance.

Regression analysis could help to build a model, which could forecast the power density values of a biochar depending on the input values of carbon content, ash content, moisture and volatile matter contents. The resulting empirical models could be useful in not only predicting the power densities but also could be used to compare experimental data from different biochars. This could as well help in gaining an improved understanding of the importance of different factors that govern the power density outputs in DCFCs. Researchers have mostly used empirical modeling to model the durability of the fuel cell components and predict the overall fuel cell lifetime. Pucheng et al. [10] analyzed 1300 hours of fuel cell bus data and found 10% voltage loss after 1100 hours. Degradation rates for each part of the fuel cell were also examined. Zhang et al. [11] built a model based on fuel cell load demand. In another study, empirical datasets were built on degradation of proton exchange membrane (PEM) fuel cell cathode electrodes to understand fuel cell durability. The model was able to predict the voltage losses due to electrode degradation [12].

In this paper, pretreatment methods involving acid agent was chosen to generate biochars with different surface characteristics, to be utilized as a fuel source in a DC-SOFC system. An empirical model based on polynomial regression is created which gives the correlation between the properties of the biochar and their respective power density outputs in the fuel cell. In our case, the investigated variables were carbon content, moisture content, ash content and volatile matter content and the measured response was the power density output generated by the DC-SOFC. The model could help bridge the gap in understanding how the basic proximate analysis of the biochar affects the power density outputs in the DC-SOFC.

2. Experimental 2.1. Carbon fuel

Mesocarp fiber (MF) from oil palm trees were cut into smaller pieces and visible impurities were removed. The MF biomass was then washed with distilled water and sun dried before being oven dried at 105 ºC for 24 h (ASTM D2867-09 standard method). MF pretreated with HCl and pyrolyzed at 500 °C is referred to as‘MF_HCl.’

2.1.1. Fuel preparation

the pretreated biomass is washed with distilled water and dried in the oven (UN75, Memmert, USA) following the ASTM D2867-09 standard method at 105 ºC for 24 h. The pretreated MF_HCl biomass was then heated in a quartz tube (HST 12/400, Carbolite) pyrolysis reactor under an atmosphere of 99.9% N2

flowing at 1 L min^_1. The heating rate was set to 10 ºC min^-1 to a final temperature of 500 ºC where it was held for 1 h before cooling to room temperature to generate MF biochar.

2.2. Materials characterization

The proximate analysis of the carbon samples were determined by means of thermogravimetric analysis in a STA 6000, Perkin Elmer thermo-balance. Moisture, fixed carbon, ash and volatile contents were determined using the TGA analysis. These quantities are determined by measuring the mass loss that the biochar sample undergoes when heated to 900 °C under a nitrogen atmosphere and then held at 900 °C with the atmosphere switched to air.

2.3. Electrochemical performance analysis

The setup of the DCFC with commercial button cell from Ningbo SOFCMAN Energy, Technology Ltd., Co. China was set up. The cathode of the DCFC was made up of Lanthanum strontium manganite (LSM) which is an oxide ceramic having a thickness of 25 µm. The anode material was made up of Nickel-Yttria stabilized zirconia (Ni-YSZ) having a thickness of 400 µm which provided the conductive surface for effective oxygen anions transportation. The electrolyte was made up of ceramic material, Yttria-stabilized zirconia (YSZ) with a thickness of 15 µm. Silver wires were used as electrical contacts on the electrodes.

The electrochemical cell units were placed between the two ceramic cylindrical tubes. Fig. 1 shows the design and the schematic components of the DCFC. 0.1 g of MF biochar was placed on the cathode side of the button cell (cell surface area of 1.743 cm²). The vertical furnace was set to run to a temperature of 800 °C with a ramping rate of 10 °C min^-1 and the temperature was maintained at 800°C for 2 h. Nitrogen gas was supplied into the system through the anode and cathode side at 200 and 600 ml min^-1 respectively.

The gas supply at the cathode end was switched to purified air about 10 mins before the system reached 800 °C with a flow rate of 600 ml min^-1. The electrochemical testing was performed at a temperature of 800 °C after the Open Circuit Potential (OCP) reached a steady state. The anode side was purged with N2

gas (constant flow rate of 200 ml min^-1, STP) and the anode side was purged with purified air (constant flow rate of 600 ml min^-1, STP). The OCP was recorded during the experimental run using a Potentiostat (model Gamry Interface 1000). The electrochemical impedance spectroscopy (EIS) of the DCFC was calculated at a temperature of 800°C with a ramping 10 °C min^-1 in the frequency range of 100 KHz to 0.1 Hz. The current-voltage (I-V) curve of the run was evaluated through electrochemical workstation with a scan rate of 10 mV s^-1.

Fig. 1: Setup of Direct Carbon Fuel Cell. 1: flange, 2: ceramic tube, 3: furnace, 4:

anode current collector, 5: biochar placed on top of button cell, 6: cathode current collector.

2.4 Empirical modelling 2.4.1 Regression Analysis

The method of regression analysis used in the study is the polynomial regression. MATLAB R2017b version is used to perform multivariate polynomial regression. Power density is the response that we want to model in relation to four different variables namely carbon content, moisture content, ash content and volatile matter content denoted by x1, x2, x3 and x4, respectively.

The general form of a complete second-degree polynomial regression model in two variables x1, x2 may be expressed as:

The complete second-degree polynomial model includes the linear terms x1 and x2, second-degree terms x12 and x22, and the interaction term x1x2. The estimated model may be written as-

This model refers to a complete second-degree polynomial model in three (or more) variables. Similarly, the model can be expanded to higher degree polynomial models. The model parameter estimators are found by minimizing the sum of squares of errors, SSE-

In order to minimize this quadratic function SSE, partial derivatives of the function are taken with respect to each of the unknowns bj, for j=0,1,2,3,4,5. Then the partial derivatives are set to zero and the resulting system of linear equations are solved simultaneously. The unknowns are the b's.

The system of linear equations in (k+1=6) unknowns (b0,...,b5) becomes-

The system can be represented using matrix notation with three matrices A, b and c as follows:

The matrix (or vector) of unknowns b

The right-hand-side (RHS) matrix (or vector) c

The system of linear equations in matrix form is solved for the vector b by the following matrix operations:

3. Results and discussion 3.1. Chemical analysis of fuels

The results of proximate analysis of all the biochar samples are shown in Table 1. The results in Table 1 illustrate that pretreated MF biochar contains a high carbon content which makes it a potential reacting fuel in the DCFC system. The important outcome of this analysis was that acid pretreatment of the raw MF biomass appears to have been effective in reducing the inorganic content of the fuel substantially. The resulting acid pretreated biochar showed an ash content of 0.07 wt. % for MF_2.0 sample, considerably lower than the 8.58 wt. % of the starting sample. Solid fuels with a high carbon content and low contamination by inorganic elements is favorable as a fuel in DC–SOFC systems [13][14][15]. The ash content of the fuels starting from the lowest follow the order: MF_2.0 > MF_2.5 > MF_1.5 > MF_ 3.0 >

MF_4.0 > MF_0.1 > MF_0.5 > MF_1.0 samples. The MF_1.0 sample contained the highest amounts of ash content, which could negatively affect the performance in the DC-SOFC [16][17][18]. The ash removal from the biochar samples could significantly reduce the issues associated with slagging, agglomeration, deposition and corrosion of the machinery in the long run [19]. While the MF_2.0 sample is seen to have the least amount of ash content. On doing the ultimate analysis test of the MF_2.0 sample (Table 2), it was seen that the sample contained a high oxygen content, which probably contributes to a higher reactivity and therefore to a higher carbon conversion and superior electrochemical performance in the DC-SOFC [13][20]. Li et al. [21][22] performed pretreatment on coal and pointed out that the electrodes and the electrolytes were protected through removal of the mineral impurities from coal.

Table 1. Proximate analysis of the fuels used in the study

Proximate analysis Fixed carbon

(wt. %) Volatile matter

(wt. %) Moisture

(wt. %) Ash (wt. %)

Raw MF 35.12 48.69 7.53 8.58

MF_0.1 71.03 20.16 4.73 3.08

MF_0.5 71.39 20.77 4.75 3.09

MF_1.0 72.53 17.50 5.77 3.12

MF_1.5 72.23 19.80 5.85 2.09

MF_2.0 73.07 20.01 6.68 0.07

MF_2.5 70.07 22.55 6.85 0.42

MF_3.0 69.46 22.30 6.09 2.12

MF_4.0 68.86 22.20 6.68 2.34

Table 2. Ultimate analysis of MF_2.0 sample

Ultimate analysis C

(wt. %) H

(wt. %) N

(wt. %) S

(wt. %) O

(wt. %)

MF_2.0 78.99 2.02 5.14 0.21 13.63

3.3. Fuel Performance in DC-SOFC system

All samples were evaluated to investigate the performance and characteristics of electrochemical reactions at 800 °C in the DC-SOFC system. The changes of the cell voltage shown in Fig. 2, indicated that the highest open circuit voltage (OCV) of 0.85 V, was achieved for the cases when the cell was supplied with MF_2.0 biochar sample. The lowest OCV was seen for the MF_0.1 sample. Similar to the results reported and discussed by Kacprzak et al. [23], regardless of fuel type the DCFC requires some time at the beginning of its operation before a stable voltage level is achieved. The longest startup period was ascertained for MF_0.1 and MF_1.0 samples, while the best results (i.e. the shortest startup period) was determined for MF_2.0 sample followed by MF_2.5 sample.

0 0.5 1 1.5 2 2.5 3 3.5 4

Current Density (mA/cm2)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Voltage (V)

MF_4.0 MF_1.0 MF_2.0 MF_1.5 MF_0.1 MF_3.0 MF_0.5 MF_2.5

Fig. 2. Fuel cell voltage vs. current density for various fuel types (electrolyte temperature 800 °C).

In Fig. 2, the results indicate that the current densities for the case when the cell was supplied with MF_2.0 was superior as compared to the other samples. As shown in Fig. 2, for an assumed reference cell voltage of 0.8 V, the highest current density (about 7.5 mA) was achieved when the cell was supplied with MF_2.0 biochar followed by the MF_2.5 sample whose current density was recorded at 5.8 mA. The lowest current density was recorded for MF_0.1 at about 0.5 mA. The power density curves are shown in Fig. 3. The results are promising with respect to potential application of the DCFC technology for large- scale power generation since the data indicate that the DCFC may be easily supplied with pretreated MF biochar fuels. The differences in cell operation, associated with a particular fuel, are probably the effect of the reaction kinetics or are brought about by crystallographic disorders and the presence of some microelements that may support or catalyze the electrochemical reactions in the DCFC [24]. The above assumption needs, however, some further investigation. Researchers agree on the fact that a high ash content in the fuel probably acts as a limiting factor in the electrochemical reaction. The sulphur content in the ash causes significant inhibition of the anode reaction surface and pore structures by acting as poisoning species. Due to this, the reactant gas is unable to have direct contact with the carbon surface [25].

Fig. 3. Power generated by the DC-SOFC vs. current density for various fuel types (electrolyte temperature 800 °C).

3.4. Empirical model

This section summarizes the development of the empirical model and explains how to use it. Firstly, the limitations and assumptions of the model must be taken into account. The key limitation of the model is that it only applies to one specific type of fuel cell which is the DC-SOFC with the specifications as mentioned in Table 2. While the electrodes and electrolyte used in the fuel cell are state of the art, effect of changing the specifications will change the final power density values predicted by the model.

Table 3. Fuel cell specifications used in empirical modeling

Anode ^Cathode Electrolyte Biochar

loading Cell Temp.

(°C)

Nitrogen

flow rate Purified air flow rate

Ni-YSZ ^{LSM YSZ 100 mg 800 200 ml/L 600 ml/L}

The assumptions taken into account to simplify the model are as follows:

(1) Steady state conditions are assumed for each of the simulations done; hence time-dependent processes are not considered.

(2) The DC-SOFC is assumed to be working on equipotential and the current density measurements are calculated from a given voltage value.

(3) Pressure drops along channels is disregarded.

(4) The heat transfer taking place in the direction of heat flow is assumed to be convective.

(5) The carbon fuel (biochar) is sufficient and is evenly distributed in the anode compartment.

(6) The effect of different pore sizes is not taken into consideration, hence the anode and cathode layers are assumed to remain unchanged and also the length of the Triple Phase Boundary (TPB) near the electrolyte interface remains unchanged.

Multiple regression analysis is used to create the empirical model which uses the data from the experimental runs. A total of 8 samples of varying acid concentration values were used to generate the graph.

Nonlinear multiple regression provided an equation as follows:

Y = b

3

*x

3

- b

1

b

3

*x

1

*x

3

- b

2

b

4

*x

2

*x

4

+ b

4

*x

4

+ b

2

*x

2

- b

1

b

2

*x

12

*x

22

+ b

1

*x

12

where, Y is the response, which is the power density generated in the DC-SOFC system.

X1 X2 X3 are variables that are related to real variables, which are carbon content (wt. %), moisture content (wt. %), ash content (wt. %) and volatile matter content (wt. %), respectively. The polynomial function includes other terms such as b1, b2, b3 and b4 which are coefficients related to direct effects of carbon content, moisture content, ash content and volatile matter content, respectively; b2b4, b1b3, b1b2 are the coefficients related to cross-interactions between pairs of variables.

To study which factors had a major influence on the power density (P), correlations between power

carbon content (C), moisture content (M), ash content (A) and volatile content (V) that are taken into consideration. The notation is P for dependent variable and A, C, V, M for the independent variables, respectively.

MATLAB is used to perform multivariate polynomial regression based on the theory discussed in the previous section. It gives the following equation (Eq. 1):

The polynomial function includes coefficients mentioned with the variables. The coefficients are related to quadratic dependence between the variables and the response. The influence of all variables on the response is determined by the coefficient values. The value of each coefficient indicates the importance of the correlated variable. Eq. 1 depicts the variables along with the coefficient values. The individual effect of carbon and volatile matter contents appear to positively affect the power density values while ash and moisture contents makes the response (power density) decline. The combined effect of carbon and ash has an overall negative effect on the power density output, which implies that ash can drastically affect the response output even when the carbon content of the biochar is high.

Fig. 6 shows the influence of all factors on the response is displayed in a bar graph by plotting the coefficient values described by a bar with its statistical significance. Factors having bars within the boundaries of the confidence interval have no significance, while factors with bars bigger than the confidence interval are statistically significant. The value of each coefficient indicates the importance of the correlated variable.

Coefficients

-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2

Coefficients Value

b3 b1b3 b2b4 b4

b1

b1b2 b2

Fig. 6: Coefficients of the empirical obtained by multiple regression analysis. They are related to the effects of carbon, ash, moisture and volatile contents. The plot shows scaled and centered

coefficients with their corresponding statistical significance levels.

Fig. 6 shows that b1 and b4 are statistically significant. It means that carbon content and volatile matter content affects the power density output. The coefficient related to the direct effect of carbon content, b1 is high. It means that the carbon amount plays a key role in the electrochemical reactions, hence, affecting the power density outputs. The coefficient value is positive because the power density is enhanced when the current increases.The coefficient related to the direct effect of volatile matter content, b4, is positive because the electrochemical reactions are promoted with the amount of volatile matters and hence it affects the power density values. The coefficient related to the direct effect of ash content, b3, is statistically significant and negative because ash affects the power densities in a damaging way. The coefficient related to the effect of moisture, b2, is not statistically significant. The coefficient related to the interference between carbon content and ash content, b1b3, is statistically significant and negative.

The coefficient is statistically significant because there is cross-interaction between these factors. The coefficient is negative because carbon content and ash content have opposite effects: the former positively affects power density, while the latter negatively affects power density. The coefficient related to the interaction between moisture content and volatile matter content, b2b4, and the coefficient related to the interaction between carbon and moisture contents, b1b2, is not statistically significant. The predictive ability of the empirical model within its domain is confirmed by comparing the experimental values with the predicted values as shown in Fig. 7.

Fig. 7. Comparison of predicted power density values with experimental data values.

4. Conclusions

Pretreatment methods were used to produce biochars for application in a DC-SOFC system. Acid

performed to generate an empirical model which establishes the relationship between the carbon content, moisture content, ash content and volatile matter content of the biochars with respect to the power density obtained in the DC-SOFC. The model could be used as a starting point in order to understand the relationship between proximate analysis parameters and the final output (power density). The model demonstrates that carbon content and volatile matter content positively affects the power density output whereas the ash content of the biochar has an overall negative effect on the power density. The most significant and strong affects seen through the model comes from the carbon and ash content values. This implies that these two parameters are the major parameters in determining the quality of the fuel used in the DC-SOFC system. The developed empirical model is a preliminary model based on the correlation between proximate analysis parameters and power density outputs. Future work can be done to refine the model to include porosity and surface area values to determine the effect on power density measurement.

Acknowledgements

This work has been supported by the Taylor’s Research Grant Scheme (TRGS/ERFS/2/2016/SOE/003) of Taylor’s University, Malaysia and

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