- Email: [email protected]
Artificial neural network based modeling of biomass gasification in fixed bed downdraft gasifiers
Biomass and Bioenergy 98 (2017) 264e271
Contents lists available at ScienceDirect
Biomass and Bioenergy journal homepage: http://www.elsevier.com/locate/biombioe
Research paper
Artificial neural network based modeling of biomass gasification in fixed bed downdraft gasifiers Dipal Baruah a, b, *, D.C. Baruah a, M.K. Hazarika c a
Department of Energy, Tezpur University, Tezpur, Assam, India Department of Mechanical Engineering, Girijananda Chowdhury Institute of Management and Technology, Tezpur, Assam, India c Department of Food Engineering and Technology, Tezpur University, Tezpur, Assam, India b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 July 2016 Received in revised form 11 January 2017 Accepted 30 January 2017
The study attempts at developing an artificial neural network (ANN) based model of biomass gasification in fixed bed downdraft gasifiers. The study is a novel attempt in developing an ANN based model of biomass gasification in fixed bed downdraft gasifiers as there are very few reported studies of ANN based modeling of biomass gasification in general and even fewer in the field of fixed bed downdraft gasifiers. In fact, downdraft gasifiers are one of the most widely used type of gasifiers for small scale operation. The ANN based models were formulated to predict the product gas composition in terms of concentration of four major gas species viz. CH4%, CO%, CO2% and H2%. The input parameters used in the models were C, H, O content, ash content, moisture content, and reduction zone temperature. The architecture of the models consisted of one input, one hidden and one output layer. Reported experimental data were used to train the ANNs. The output of the ANN models were found to be in agreement with experimental data with an absolute fraction of variance (R2) higher than 0.99 in the cases of CH4 and CO models and higher than 0.98 in the case of CO2 and H2 model. The results show the possibility of utilization of the model to predict the percentage composition of four major product gas species (CH4, CO, CO2 and H2). The relative importance of the input variables was also analysed using the Garson's equation. © 2017 Elsevier Ltd. All rights reserved.
Keywords: Biomass Gasification Fixed bed Downdraft Artificial neural network Model
1. Introduction One of the major challenges faced by the developing world is energy crisis. In addition, due to the increasing use of fossil fuels to supplement the energy demand, there has been an alarming increase in climate change and global warming. Utilization of alternative sources of energy is being considered to supplement the energy demand and also to help in mitigating the environmental problems. However, research issues related to targeted use of alternative sources of energy still needs to be addressed. One of the advantage of such alternative sources of energy over the conventional sources of energy is their availability over wide geographical areas. This is very significant because energy dependency of a particular region, which otherwise is primarily dependent upon fossil fuel based resources generally imported from other regions, is greatly reduced. Many technologies based on alternative sources of
* Corresponding author. Department of Mechanical Engineering, Girijananda Chowdhury Institute of Management and Technology, Tezpur, Assam, India. E-mail addresses: [email protected], [email protected] (D. Baruah). http://dx.doi.org/10.1016/j.biombioe.2017.01.029 0961-9534/© 2017 Elsevier Ltd. All rights reserved.
energy have come up. One such technology that is gaining prominence is biomass gasification. Biomass gasification involves the thermochemical conversion (partial oxidation) of biomass, within a closed reactor, into gaseous fuel that is burned to release energy or used for production of value-added chemicals [1]. Production of producer gas via gasification involves the rearrangement of the molecular structure of the feedstock. This requires the use of a gasifying agent, viz. air, oxygen or steam. Accordingly, the gasifiers are classified as air blown, oxygen or steam gasifiers. Depending upon the direction of movement of the gas and the feedstock, gasifiers are categorised as (a) fixed bed (also known as moving bed), (b) fluidised bed and (c) entrained flow. Reactors are classified as atmospheric or pressurised reactor depending upon the pressure used. Also, if the reactors are heated by an external source then they are known as allothermal or indirectly heated reactors and if the heat is provided by the partial combustion of feedstock they are known as autothermal or directly heated reactors. The overall gasification process is divided into four stages viz., drying, pyrolysis, oxidation (combustion) and reduction (char gasification). All the processes are temperature dependent with drying occurring below 150 C,
D. Baruah et al. / Biomass and Bioenergy 98 (2017) 264e271
pyrolysis occurring in the temperature range of 150e700 C, oxidation occurring in the range of 700e1500 C and reduction in the range of 800e1100 C [2]. The four major reduction reactions viz. (a) Wateregas reaction (equation (1)), (b) Boudouard reaction (equation (2)), (c) Shift conversion (equation (3)) and (d) Methanation (equation (4)) result in the production of a mixture of combustible gases primarily containing hydrogen, carbon monoxide, carbon dioxide and methane [3].
C þ H2 O4CO þ H2 þ 131 kJ=mol
(1)
C þ CO2 42CO þ 172 kJ=mol
(2)
CO þ H2 O 4CO2 þ H2 41:2 kJ=mol
(3)
C þ 2H2 4CH4 74:8 kJ=mol
(4)
Flow arrangement of the downdraft gasifiers enables the pyrolysis gases to pass through a bed of hot char resulting in the cracking of most of the tars into non-condensable gases and water. The gas produced in downdraft gasifiers has been found to be suitable in gas burners, internal combustion engines, gas turbines and/or to transport the product gas in pipelines. However, in most cases the gas should be cleaned before being used. Other added advantages of high char conversion, lower ash and tar carry over, quick response to load change and simple construction makes downdraft gasifiers one of the most widely used type of gasifiers for small scale application [4]. However, downdraft gasifiers have higher gas outlet temperatures, are difficult of scale up, faces problem of ash fusion at high grate temperatures and has fuel moisture limitations [5]. Performance evaluation of biomass gasifier systems has been the central theme of most of the research. The feedstock characteristics, the reactor design and the operating parameters are the influencing variables in the gasification process which effects the -vis end-gas composition of a given gasifier [6]. performance vis-a The influencing feedstock characteristics are moisture content (MC), volatile matter (VM), ash content, char, thermal conductivity, organic constituents and inorganic constituents [6]. The composition and thermo-chemical properties of different feedstock exhibit inherent heterogeneity. Further, it is also observed that there are very complex thermo-chemical interactions taking place within the gasifier. As a result, experimentation to find the optimum conditions for a given gasifier design utilizing a particular feedstock becomes time consuming and expensive. In this regard, mathematical modeling has been found to serve as an important tool to study the gasifier behaviour in order to optimize its design and operation avoiding physical experimentation. Mathematical models become useful tool in representing the real life situation with the help of mathematical equations. The representation of the gasification process in the form of a mathematical model helps in gaining an insight about the significance of the variables affecting the gasifier performance. Different kinds of models have been implemented for studying the gasification process [7]. A more recent approach for simulation of gasifier is the neural network analysis in which the neural network learns by itself from sample experimental data mimicking the working of the human brain in terms of mathematical functions. ANN based modeling does not require mathematical description of phenomena associated with the system. Thus, ANNs become handy in the case of outcome prediction when important interactions of complex nonlinearities exist in a data set, like in biomass gasification, because they can approximate nonlinear functions [8]. Also, ANN models are capable of providing better prediction with the same or lesser number of input variables in comparison to other modeling
265
techniques such as equilibrium modeling. ANNs are used to represent complex processes including nonlinear and discrete systems with the help of a dense distribution of simple processing elements. An introduction to the fundamentals of ANNs can be found elsewhere [9,10]. Various architectures of ANNs are being used of which Multilayer Perceptron (MLP) is the most popular one [11]. MLP consists of an input layer, a hidden layer and an output layer of neurons. While the neurons in the input layer are typically linear, the neurons in the hidden layer have nonlinear activation functions. The neurons in the output layer may be linear or nonlinear. The input layer consists of input neurons with associated weight. The hidden layer receives the signals from the input layer. A threshold parameter known as bias is associated with each neuron in the hidden and output layers. ANNs are used to represent complex processes including nonlinear and discrete systems with the help of a dense distribution of simple processing elements. ANNs are being extensively used in many studies as a standard modeling tool. Although neural network analysis finds extensive application in the fields of pattern recognition, signal processing, function approximation and process simulation, it has been seldom used in biomass gasification modeling. In the reported studies of ANN based modeling of biomass gasification, the objective was to predict the end gas composition for different types of gasifiers [8e16]. Puig-Arnavat et al. [8] developed ANN models for circulating fluidised bed gasifier and bubbling fluidised bed gasifier whereas Guo et al. [12] developed ANN model for atmospheric pressure steam fluidised bed gasifier. The models were able to predict the gasification process parameters and showed good agreement with experimental data. Adequate amount of experimental data is required for (i) formulation, (ii) calibration and (iii) evaluation of model constants. Data inadequacy results in poor prediction. Another crucial step in the development of the model is the selection of appropriate set of variables. The performance of the model is heavily dependent on the input variables [8]. A generalised feedstock independent ANN model was found better than feedstock specific model [12]. In another approach, combined use of an equilibrium model and artificial neural network regressions were investigated for modeling biomass gasification. The benefits of the approach were to improve the accuracy of the equilibrium calculations, and to prevent the neural network model from learning mass and energy balances, thereby minimizing experimental data requirements [13]. Stoichiometric equilibrium model was used to compute the reaction temperature difference within various zones of the reactor. ANN models were then developed to relate these temperature differences with fuel composition and gasifier operating conditions. This resulted in the reduction of experimental data requirement for development of the ANN models. The outputs of the ANN models were then used to modify the equilibrium model. Further investigation and improvement of this combined equilibrium and ANN model was carried out by the same authors [14]. A complete stoichiometry was formulated, and corresponding reaction zone temperature difference parameters were computed under the constraint of the non-equilibrium distribution of gasification products determined by mass balance data reconciliation. The results showed very good conformity with experimental data. Kumar et al. [15] developed an NN model for both air and steam gasification to predict the calorific value of producer gas and composition of producer gas for the given input conditions namely air flow rate, fuel flow rate, steam flow rate, oxidation temperature, composition of the fuel etc. The developed model exhibited good prediction when it was specifically built for a particular gasifier and a particular gasification agent. Extremely high association rates between predictive and observed data were obtained. In another reported work, Mikulandric' et al. [16] developed an
266
D. Baruah et al. / Biomass and Bioenergy 98 (2017) 264e271
artificial neural network modeling approach for a fixed bed biomass gasifiers. Neural network models showed good capability to predict biomass gasification process parameters with reasonable accuracy and speed for the gasifier under study but operating on a specific feedstock. Although ANN based modeling of biomass gasification has been carried out by a few authors, there are very few reported studies utilizing ANN to predict the performance of fixed bed downdraft gasifiers. In the present study an attempt has been made to develop a MLP based ANN model of the biomass gasification process in a fixed bed downdraft gasifier. The objective of the study is to develop ANN model and utilize it in predicting the product gas composition in terms of percentage composition of the product gas species for variations in the operating parameters. Further, it is attempted to determine the relative influence of some specific operating parameters on output gas.
temperature, TR ( C) with one hidden layer and one output. In the models, the activation function used in the hidden layers was a hyperbolic tangent sigmoid function (TANSIG). This transfer function results in an output that lies in the range of (1.0 to 1.0) and generally used for back propagation networks. The input-output relationship for this transfer function is given by equation (5).
Uk ¼
eaðVk Þ eaðVk Þ eaðVk Þ þ eaðVk Þ
Where, Vk and Uk are the input and output of the kth neuron and ‘a’ represents the coefficient of transfer function. It generates zero output, corresponding to its input u ¼ 0.0. The linear transfer function (PURELIN) was used in the output layer. The output of this transfer function is made equal to its input and it lies in the range of (1.0 to 1.0). The input-output relationship of this transfer function is expressed by equation (6).
Uk ¼ Vk
2. Methods 2.1. Topology of the ANNs An ANN based model for fixed bed downdraft type of gasifiers is developed to study gasification behaviour in terms of product gas composition. An ANN is an architecture consisting of a large number of neurons organized in different layers and the neurons of one layer are connected to those of another layer by means of weights and it can be trained to perform a particular task by making proper adjustment of its connecting weights, bias and architecture [17]. In this study, ANN models were developed in the MATLAB® environment using the Neural Network Toolbox (nntool). Fig. 1 represents the architecture of the ANN models developed for each output (CH4%, CO%, CO2% and H2%). Each ANN has one input layer with six variables viz. C (wt% dry basis), H (wt% dry basis), O (wt% dry basis), ash (wt% dry basis), MC (%), and reduction zone
(5)
(6)
There is no well-defined rule to determine the number of hidden layers and the number of neurons in the hidden layer. Therefore, trial and error method was applied to find the number of hidden layers and the number of neurons in the hidden layer. In order to find out the most appropriate or best solution, a large number of different ANN models were developed with different number of hidden layers and different number of neurons for each hidden layer. The optimum solution was selected by minimizing the Mean Square Error (MSE). The best results were considered as one hidden layer with five neurons in the case of CH4 and CO, one hidden layer with four neurons in the case of CO2 and one hidden layer with three neurons in the case of H2. 2.2. Data selection for the model An extensive literature review was conducted to obtain
Fig. 1. ANN architecture to predict the four main producer gas components for fixed bed downdraft gasifiers.
D. Baruah et al. / Biomass and Bioenergy 98 (2017) 264e271
267
Table 1 Experimental data used in formulation of the models. Sl. No.
Author(s)
Feedstock used
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Antonopoulos et al. [18] Dogru [19] Erlich and Fransson [20] Gai and Dong [21] Janajreh and Al Shrah [22] Jaojaruek et al. [23] Jordan and Akay [24] Kallis et al. [25] Lapuerta et al. [26] Lv et al. [27] Melgar et al. [28] Sharma [6,29] Sheth and Babu [30] Tinaut et al. [31] Van de steene et al. [32] Varunkumar et al. [33] Warnecke [34] Wei et al. [35]
Olive wood, miscanthus and cardoon Hazelnut shells Empty fruit brunch, Bagasse, Wood Corn straw pelletes Wood chips Eucalyptus wood Fuel Cane Bagasse Pellet Miscanthus and bioethanol waste pellets Wood Chips Pine wood blocks Rubber Wood Kiker wood, Douglas Fir Bark Sesame or rose wood Pine Bark Wood chips Wood Hazelnut shells Hardwood chips blended with crude glycerol
experimental data for biomass gasification under atmospheric pressure in downdraft fixed bed gasifiers. In order to maintain homogeneity in the different input variables considered for the development of the ANN based model, only experimental data pertaining to woody biomass and pellet gasification were considered. As results from large scale gasifiers cannot be compared with results from small scale gasifiers, experimental data from small scale gasifiers only were incorporated in the development of the model. Table 1 lists out the experimental works utilised in development of the model. The data set for the downdraft fixed bed gasifiers contain data from 63 different experimental runs [18e35] for various woody feedstock. Feedstock characteristics viz. moisture (MC), ash, C, H and O content along with reduction temperature (TR) were considered to be the input variables in the formulation of the ANN Table 2 Characteristics of input and output variables in the ANN based model for downdraft gasifiers. Input variables to the ANN's
Range
C (wt% dry basis) H (wt% dry basis) O (wt% dry basis) Ash (wt% dry basis) Moisture Content (%) Reduction Temperature ( C) Output variables for the ANN's CO content (%) CH4 content (%) H2 content (%) CO2 content (%)
43.83e53.40 5.42e7.18 37.24e45.83 4.25e9.48 4.20e14.70 600e1206 10.83e24.00 2.00e6.91 9.30e19.00 10.02e23.93
models. Fixed carbon (FC) and volatile matter (VM) as stated by Brown et al. [36] based on the previous works of van Krevelen [37] and Jenkins et al. [38] are considered as dependent variables because the FC ratio is proportional to both the H/C and O/C ratios. Thus, FC and VM were excluded from the input variables. Nitrogen and sulphur content were also not considered as they are assumed to have little influence in the production of gas species viz. CO, CO2, H2 and CH4. Table 2 lists the characteristics of the input and output variables obtained from the experimental data published in different standard literature.
2.3. Training, validation and prediction ability of the models In order to check the robustness, validation and to prediction ability of the models, the database was divided into two parts as training (70%) and validation-testing (30%) sub-sets. The training function used in the models were based on the TRAINLM function which updates the weight and bias values according to LevenbergMarquardt optimization. This function is often the fastest backpropagation algorithm. Also, the Gradient descent with momentum weight and bias learning function (LEARNGDM) was used to minimise the errors. This function calculates the weight change for a given neuron from the neuron's input and error, the weight (or bias), learning rate, and momentum constant, according to gradient descent with momentum backpropagation. Training and validation-test subsets were randomly selected from the available database. Details of the ANN models are shown Table 3. The prediction ability of the ANNs were statistically appraised by root mean square error (RMSE) and absolute fraction of variance
Table 3 Details of the ANN models. Sl. No.
Particulars
Specifications
1 2 3 4 5 6 7 8 9 10 11
Network type Training function or Training algorithm Adaption learning function Performance function Transfer function Data division Number of input layer unit Number of output layer unit Number of hidden layer Number of hidden layer neuron Number of epoch (Learning cycle)
Feed Forward Backpropagation Levenberg-Marquardt backpropagation (TRAINLM) Gradient Descent with Momentum Weight and Bias (LEARNGDM) Mean Square Error (MSE) Hyperbolic Tangent Sigmoid (TRANSIG) Random (Dividerand) 6 1 1 5 each for CH4 and CO, 4 for CO2 & 3 for H2 1000 iterations
268
Table 4 Weights and biases of the ANN models for predicting the end gas components (CO, CH4, H2 and CO2) for downdraft fixed bed gasifiers. CH4 Neuron number (j)
CO2 IWj,i (Weight to jth neuron of hidden layer from ith input variable)
Neuron number (j)
IWj,i (Weight to jth neuron of hidden layer from ith input variable)
Input variable
1 2 3 4 5
Input variable
C
H
O
Ash
MC
TR
17.6027 7.3800 2.1373 5.1071 13.0804
17.2542 6.0896 3.2973 8.3481 5.2580
2.3069 2.7296 2.3466 2.0617 4.1597
3.1062 9.0988 4.3181 2.9964 14.8558
6.4648 0.1463 11.0760 3.6625 2.3186
4.1790 5.8396 11.1414 25.5160 5.2698
1 2 3 4
C
H
O
Ash
MC
TR
2.1725 0.3007 2.0216 4.2785
2.6782 0.0159 0.8526 1.2942
3.5350 0.2462 1.8596 0.2411
1.6459 0.5012 1.2226 0.8927
0.5401 0.7507 2.5651 2.1661
4.0581 1.9872 1.8108 1.8877
LWi,j (Weight to output layer from jth neuron of hidden layer) 0.99 0.55832 0.45439 2.7099
b1j (Bias to jth neuron of hidden layer) 14.9758 5.4333 7.3736 2.0769 3.9161
b1j (Bias to jth neuron of hidden layer) 3.4931 3.2483 0.77562 4.3819 2.3498
b2 (Bias to output layer) 3.0962 CO Neuron number (j)
H2 IWj,i (Weight to jth neuron of hidden layer from ith input variable)
Neuron number (j)
Input variable
1 2 3 4 5
IWj,i (Weight to jth neuron of hidden layer from ith input variable) Input variable
C
H
O
Ash
MC
TR
0.4364 1.6237 0.5719 2.7569 0.9000
2.6874 0.1854 1.5379 1.7178 0.5645
1.2367 1.1010 0.8304 0.4123 0.0592
0.2085 1.0626 0.5450 0.0756 2.1174
0.0112 0.7254 0.0289 0.9075 1.5968
2.7939 0.3726 3.1442 1.2324 2.3453
1 2 3
C
H
O
Ash
MC
TR
0.59987 2.5778 0.037595
1.2142 0.32723 1.6371
1.8458 0.44952 3.6629
0.50212 2.3254 0.5319
0.22205 0.85187 2.8321
2.5718 0.51569 0.58103
LWi,j (Weight to output layer from jth neuron of hidden layer) 2.1202 0.7772 1.8259 2.1282 2.0266
LWi,j (Weight to output layer from jth neuron of hidden layer) 2.1085 0.11924 0.58747
b1j (Bias to jth neuron of hidden layer) 1.9201 0.14213 0.4524
b1j (Bias to jth neuron of hidden layer) 3.6986 1.409 2.1341
1.4228 1.5034
b2 (Bias to output layer) 1.5765
b2 (Bias to output layer) 0.78426
D. Baruah et al. / Biomass and Bioenergy 98 (2017) 264e271
LWi,j (Weight to output layer from jth neuron of hidden layer) 0.6780 2.7211 2.3511 0.7188 3.1098
D. Baruah et al. / Biomass and Bioenergy 98 (2017) 264e271
269
Fig. 2. Comparison between experimental data and data predicted by the ANN models for downdraft fixed bed gasifiers.
2.4. Relative influence of input variables on model outputs It is desirable to have an understanding of the influence of different input variables on the outputs of the model. Such knowledge would help in specifying optimum values of input variables for optimizing the performance the gasifier. Influence of the input variables on the outputs was evaluated with the help of Garson equation which is based on neural net weight matrix [17]. Garson [17] proposed an equation that is based on the partitioning of connection weights. In the equation the numerator describes the sums of absolute products of weights for each input and the denominator represents the sum of all weights feeding into hidden unit, taking the absolute values. The Garson equation, adapted to the present ANN topology is given in equation (9).
Pj¼n
Fig. 3. Comparison between modelled data and experimental data.
j¼1
(R2) which were calculated with the experimental values and networks predictions using equations (7) and (8), respectively.
11 X 2 2 1 @ T O A RMSE ¼ j j p 0
(7)
j
0P 2 1 j Tj Oj A @ R ¼1 P 2 j ðOjÞ 2
(8)
where, p is the number of samples, Tj is the target (actual) value and Oj is the output (predicted) value.
Ii ¼
( Pi¼6 Pj¼n i¼1
j¼1
! ! jIW j Pi¼6 j;i $LWj;i IWj;i i¼1 !) ! jIW j Pi¼6 j;i $LWj;i IWj;i i¼1
(9)
where, i is the input variables, j is the hidden layer neurons, Ii is the relative influence of the ith input variable on the output variable, IWj,i is the weight to jth neuron of hidden layer from ith input variable, LWj,i is the weight to output layer from jth neuron of hidden layer and n is the number of neurons (5 for CH4 and CO, 4 for CO2 and 3 for H2). After computing the relative influence of the input variables on the output of each of the models, the input variables were ranked in descending order of magnitude. A comparative study of the variable ranking was then carried out.
270
D. Baruah et al. / Biomass and Bioenergy 98 (2017) 264e271
Fig. 4. Influence (relative %) of input variables on the different outputs of the ANN models.
3. Results and discussion Four neural networks with six inputs, five neurons for CH4 and CO, four neurons for CO2 and three neurons for H2 in the hidden layer and one output each, were found to be efficient in predicting producer gas composition. The parameters (IWj,i, LW1,j, b1j, b2) of the best fit for each of the four ANN developed are shown in Table 4. The simulated and experimental values of each output CO, CO2, H2 and CH4 were compared satisfactorily through a linear regression model as shown in Fig. 2. It can be observed that R2 values are higher than 0.99 in the cases of CH4 and CO models and higher than 0.98 in the case of CO2 and H2 model. Further, the RMSE was found to be 0.0688 in the case of CO whereas it was 0.0523, 0.0915 and 0.0873 in the case of CH4, H2 and CO2 respectively. After computing the limits for the statistical test of intercept and slope [39], the ANNs passed the test with 99.2% of confidence level. This test guarantees that whole ANN model, containing four ANNs, has a satisfactory level of confidence. Similar results were also reported by Puig-Arnavat et al. [8] for ANN models of fluidised bed gasifiers. The developed models were also compared with experimental results [21] outside the data set. The experimental results however had input values within the range of the input variables used in the development of the models. A comparative plot of the predicted and experimental result in shown in Fig. 3. Model results show good conformity with the experimental results with an average relative error of 2.65%. The influence of the variables on the output prediction was
calculated using the Garson's equation. Fig. 4 depicts the influence of input variables on each of the output for the developed ANNs. It is observed that variables accounting for biomass composition (C, H and O) represent between 8% and 29% of the influence on the end gas composition. Further, TR was the most important variable in CO and H2 prediction while it was the second most important variable in CH4 and CO2 prediction. Reduction zone temperature was found to be the most influential variable in conformation to the fact that increase in temperature leads to a noticeable increase in gas yield [40]. However, reactor temperature is significantly affected by MC of biomass due to heat required for evaporation of moisture [41]. MC was found to have a comparatively similar influence (9.24e11.09%) in the case of CO, CH4 and H2 while it had a relatively higher influence (17.08%) in the case of CO2. H content was found to be the 2nd most influential variable in case of CO while it ranked 3rd in the case of H2 and 4th in the case of CH4 and CO2 prediction. Ash content and C content were the most influential variable in the case of CH4 and CO2 prediction respectively. Ash content, however, showed relatively lower influence in the case of CO, H2 and CO2 where it ranked 4th, 6th and 5th respectively. Although higher ash content can lead to serious agglomeration, fouling, and corrosion in gasifiers, it is capable of influencing the gasification process as is evident from the results. However, the exact influencing phenomenon of ash on the gasification process may be a subject of further research. It may, however, be commented that each of the variables have a strong influence on the outputs with variations in the range of
D. Baruah et al. / Biomass and Bioenergy 98 (2017) 264e271
8%e31%. The results show how the percentage composition of the major four gas species in producer gas can be successfully predicted by applying a neural network with one hidden neurons in the hidden layer and using backpropagation algorithm. The models are applicable for a wide variety of woody feedstock. However, expansion of the database with data from experimental runs for other varieties of woody feedstock becomes desirable to further increase the applicability of the models. The results also depict the relative importance of different operating parameters on the composition of the product gas species. The models may find practical application in screening potential feedstock for biomass energy extension programmes based on gasification technology. 4. Conclusions There are very few references of ANN based modeling of biomass gasification. An ANN based model could be successfully developed for downdraft fixed bed type of gasifier. Prediction of end gas composition using neural network with one hidden layer and five hidden neurons in the case of CH4 and CO, four hidden neurons in the case of CO2 and three hidden neurons in the case of H2 and using backpropagation algorithm has been possible. ANNs show agreement with experimental data with absolute fraction of variance (R2) greater than 0.99 in the cases of CH4 and CO model and higher than 0.98 in the case of CO2 and H2 model along with small RMSE. All of the variables have a strong effect on the outputs for each of the ANNs. The variables accounting for biomass composition (C, H and O) represent between 8% and 29% of the importance on the end gas composition. Reduction temperature was the most important variable in CO and H2 prediction while it was the second most important variable in CH4 and CO2 prediction. All the variables were found to have strong influence on the outputs with variations in the range of 8%e31%. However, addition of more experimental data pertaining to downdraft gasifiers will help in expanding the database to further improve the spectrum of applicability of the models. The proposed ANN based model can serve as a useful tool to optimize and control the process of biomass gasification in downdraft fixed bed gasifiers. References [1] P. Basu, Biomass Gasification and Pyrolysis: Practical Design and Theory, Elsevier, MA, U.S, 2010. [2] P. Basu, Combustion and Gasification in Fluidized Beds, Taylor and Francis Group/CRC Press, London, 2006. [3] G. Duman, A. Uddin Md, J. Yanik, The effect of char properties on gasification reactivity, Fuel Process Technol. 118 (2014) 75e81. [4] J.D. Martínez, K. Mahkamov, R.V. Andrade, E.E.S. Lora, Syngas production in downdraft biomass gasifiers and its application using internal combustion engines, Renew. Energ 38 (2012) 1e9. [5] M. Dogru, C.R. Howarth, G. Akay, B. Keskinler, A.A. Malik, Gasification of hazelnut shells in a downdraft gasifier, Energy 27 (5) (2002) 415e427. [6] A.K. Sharma, Modeling and simulation of a downdraft biomass gasifier 1. Model development and validation, Energ. Convers. Manage 52 (2011) 1386e1396. [7] D. Baruah, D.C. Baruah, Modeling of biomass gasification: a review, Renew. Sust. Energ. Rev. 39 (2014) 806e815. ndez, J.C. Bruno, A. Coronas, Artificial neural [8] M. Puig-Arnavat, J.A. Herna network models for biomass gasification in fluidized bed gasifiers, Biomass Bioenerg. 49 (2013) 279e289. [9] S. Haykin, Neural Networks: a Comprehensive Foundation, second ed., Prentice-Hall, New Jersey, 1999. [10] J. Leonard, M.A. Kramer, Improvement of the backpropagation algorithm for training neural networks, Comput. Chem. Eng. 14 (1990) 337e341. [11] M.B. de Souza Jr., AG. Barreto Jr., C.P.B. Quitete, L.C. Nemer, Neural network based modeling and operational optimization of biomass gasification processes, in: Y. Yongseung (Ed.), InTech. Gasification for Practical Applications, 2012, pp. 297e312. [12] B. Guo, D. Li, C. Cheng, Z. Lu, Y. Shen, Simulation of biomass gasification with a hybrid neural network model, Bioresour. Technol. 76 (2001) 77e83.
271
[13] D. Brown, T. Fuchino, F. Marechal, Solid fuel decomposition modelling for the design of biomass gasification systems, in: Proceedings of the 16th European Symposium on Computer Aided Process Engineering and 9th International Symposium on Process Systems Engineering, July 9e13, 2006, pp. 1661e1666. Garmisch- Partenkirchen, Germany. [14] D. Brown, T. Fuchino, F. Marechal, Stoichiometric equilibrium modelling of biomass gasification: validation of artificial neural network temperature difference parameter regressions, J. Chem. Eng. Jpn. 40 (2007) 244e254. [15] S.S. Kumar, P. Sathyabalan, S. Ragunathan, Simulation of biomass downdraft gasifier based on neural networks, Asian J. Res. Soc. Sci. Hum. 6 (6) (2016) 1548e1560. € hning, R. Bo €hme, M. Beckmann, Artificial [16] R. Mikulandri c, D. Lon car, D. Bo neural network modelling approach for a biomass gasification process in fixed bed gasifiers, Energ. Convers. Manage 87 (2014) 1210e1223. [17] G.D. Garson, Interpreting neural-network connection weights, AI Expert 6 (1991) 47e51. [18] I.S. Antonopoulos, A. Karagiannidis, A. Gkouletsos, G. Perkoulidis, Modelling of a downdraft gasifier fed by agricultural residues, Waste Manage 32 (2012) 710e718. [19] M. Dogru, Experimental results of olive pits gasification in a fixed bed downdraft gasifier system, Int. J. Green Energy 10 (2013) 348e361. [20] C. Erlich, T.H. Fransson, Downdraft gasification of pellets made of wood, palmoil residues respective bagasse: experimental study, Appl. Energ 88 (2011) 899e908. [21] C. Gai, Y. Dong, Experimental study on non-woody biomass gasification in a downdraft gasifier, Int. J. hydrogen energy 37 (6) (2012) 4935e4944. [22] I. Janajreh, M. Al Shrah, Numerical and experimental investigation of downdraft gasification of wood chips, Energ. Convers. Manage 65 (2013) 783e792. [23] K. Jaojaruek, S. Jarungthammachote, M.K.B. Gratuito, H. Wongsuwan, S. Homhual, Experimental study of wood downdraft gasification for an improved producer gas quality through an innovative two-stage air and premixed air/gas supply approach, Bioresour. Technol. 102 (2011) 4834e4840. [24] C.A. Jordan, G. Akay, Effect of CaO on tar production and dew point depression during gasification of fuel cane bagasse in a novel downdraft gasifier, Fuel Process. Technol. 106 (2013) 654e660. [25] K.X. Kallis, A.P. Susini, J.E. Oakey, A comparison between Miscanthus and bioethanol waste pellets and their performance in a downdraft gasifier, Appl. Energ 101 (2013) 333e340. [26] M. Lapuerta, A. Hernandez, J.L. Pazo, Gasification and co-gasification of biomass wastes: effect of the biomass origin and the gasifier operating conditions, Fuel Process. Technol. 89 (2008) 828e837. [27] P. Lv, Z. Yuan, L. Ma, C. Wu, Y. Chen, J. Zhu, Hydrogen-rich gas production from biomass air and oxygen/steam gasification in a downdraft gasifier, Renew. Energ 32 (13) (2007) 2173e2185. [28] A. Melgar, J.F. Perez, H. Laget, A. Horillo, Thermochemical equilibrium modelling of a gasifying process, Energ. Convers. Manage 48 (1) (2007) 59e67. [29] A.K. Sharma, Experimental investigations on a 20 kWe, solid biomass gasification system, Biomass Bioenerg. 35 (2011) 421e428. [30] P.N. Sheth, B.V. Babu, Experimental studies on producer gas generation from wood waste in a downdraft biomass gasifier, Bioresour. Technol. 100 (2009) 3127e3133. [31] F.V. Tinaut, A. Melgar, J.F. Perez, A. Horrillo, Effect of biomass particle size and air superficial velocity on the gasification process in a downdraft fixed bed gasifier. An experimental and modelling study, Fuel Process. Technol. 89 (11) (2008) 1076e1089. [32] L. Van de Steene, J.P. Tagutchou, F.J. Escudero Sanz, S. Salvador, Gasification of woodchip particles: experimental and numerical study of char-H2O, char-CO2, and char-O2 reactions, Chem. Eng. Sci. 66 (2011) 4499e4509. [33] S. Varunkumar, N.K.S. Rajan, H.S. Mukunda, Experimental and computational studies on a gasifier based stove, Energ. Convers. Manage 53 (2012) 135e141. [34] R. Warnecke, Gasification of biomass: comparison of fixed bed and fluidized bed gasifier, Biomass Bioenerg 18 (2000) 489e497. [35] L. Wei, S. Xu, J. Liu, C. Lu, S. Liu, C. Liu, A novel process of biomass gasification for hydrogen-rich gas with solid heat carrier: preliminary experimental results, Energ. Fuel 20 (2006) 2266e2273. chal, Solid fuel decomposition modelling for the [36] D. Brown, T. Fuchino, F. Mare design of biomass gasification systems, Comput. Aided Chem. Eng. 21 (2006) 1661e1666. [37] D.W. van Krevelen, Graphical statistical method for the study of structure and reaction processes of coal, Fuel 29 (1950) 269e284. [38] B. Jenkins, L.L. Baxter, T.R. Miles, Combustion properties of biomass, Fuel Process. Technol. 54 (1) (1998) 17e46. [39] B.G. Tabachnick, L.S. Fidell, S.J. Osterlind, Using Multivariate Statistics, Pearson, Boston, 2001. [40] G. Chen, J. Andries, Z. Luo, H. Spliethoff, Biomass pyrolysis/gasification for product gas production: the overall investigation of parametric effects, Energ. Convers. Manage 44 (2003) 1875e1884. [41] H. Ghassemi, R. Shahsavan-Markadeh, Effects of various operational parameters on biomass gasification process; A modified equilibrium model, Energ. Convers. Manage 79 (2014) 18e24.
Contents lists available at ScienceDirect
Biomass and Bioenergy journal homepage: http://www.elsevier.com/locate/biombioe
Research paper
Artificial neural network based modeling of biomass gasification in fixed bed downdraft gasifiers Dipal Baruah a, b, *, D.C. Baruah a, M.K. Hazarika c a
Department of Energy, Tezpur University, Tezpur, Assam, India Department of Mechanical Engineering, Girijananda Chowdhury Institute of Management and Technology, Tezpur, Assam, India c Department of Food Engineering and Technology, Tezpur University, Tezpur, Assam, India b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 July 2016 Received in revised form 11 January 2017 Accepted 30 January 2017
The study attempts at developing an artificial neural network (ANN) based model of biomass gasification in fixed bed downdraft gasifiers. The study is a novel attempt in developing an ANN based model of biomass gasification in fixed bed downdraft gasifiers as there are very few reported studies of ANN based modeling of biomass gasification in general and even fewer in the field of fixed bed downdraft gasifiers. In fact, downdraft gasifiers are one of the most widely used type of gasifiers for small scale operation. The ANN based models were formulated to predict the product gas composition in terms of concentration of four major gas species viz. CH4%, CO%, CO2% and H2%. The input parameters used in the models were C, H, O content, ash content, moisture content, and reduction zone temperature. The architecture of the models consisted of one input, one hidden and one output layer. Reported experimental data were used to train the ANNs. The output of the ANN models were found to be in agreement with experimental data with an absolute fraction of variance (R2) higher than 0.99 in the cases of CH4 and CO models and higher than 0.98 in the case of CO2 and H2 model. The results show the possibility of utilization of the model to predict the percentage composition of four major product gas species (CH4, CO, CO2 and H2). The relative importance of the input variables was also analysed using the Garson's equation. © 2017 Elsevier Ltd. All rights reserved.
Keywords: Biomass Gasification Fixed bed Downdraft Artificial neural network Model
1. Introduction One of the major challenges faced by the developing world is energy crisis. In addition, due to the increasing use of fossil fuels to supplement the energy demand, there has been an alarming increase in climate change and global warming. Utilization of alternative sources of energy is being considered to supplement the energy demand and also to help in mitigating the environmental problems. However, research issues related to targeted use of alternative sources of energy still needs to be addressed. One of the advantage of such alternative sources of energy over the conventional sources of energy is their availability over wide geographical areas. This is very significant because energy dependency of a particular region, which otherwise is primarily dependent upon fossil fuel based resources generally imported from other regions, is greatly reduced. Many technologies based on alternative sources of
* Corresponding author. Department of Mechanical Engineering, Girijananda Chowdhury Institute of Management and Technology, Tezpur, Assam, India. E-mail addresses: [email protected], [email protected] (D. Baruah). http://dx.doi.org/10.1016/j.biombioe.2017.01.029 0961-9534/© 2017 Elsevier Ltd. All rights reserved.
energy have come up. One such technology that is gaining prominence is biomass gasification. Biomass gasification involves the thermochemical conversion (partial oxidation) of biomass, within a closed reactor, into gaseous fuel that is burned to release energy or used for production of value-added chemicals [1]. Production of producer gas via gasification involves the rearrangement of the molecular structure of the feedstock. This requires the use of a gasifying agent, viz. air, oxygen or steam. Accordingly, the gasifiers are classified as air blown, oxygen or steam gasifiers. Depending upon the direction of movement of the gas and the feedstock, gasifiers are categorised as (a) fixed bed (also known as moving bed), (b) fluidised bed and (c) entrained flow. Reactors are classified as atmospheric or pressurised reactor depending upon the pressure used. Also, if the reactors are heated by an external source then they are known as allothermal or indirectly heated reactors and if the heat is provided by the partial combustion of feedstock they are known as autothermal or directly heated reactors. The overall gasification process is divided into four stages viz., drying, pyrolysis, oxidation (combustion) and reduction (char gasification). All the processes are temperature dependent with drying occurring below 150 C,
D. Baruah et al. / Biomass and Bioenergy 98 (2017) 264e271
pyrolysis occurring in the temperature range of 150e700 C, oxidation occurring in the range of 700e1500 C and reduction in the range of 800e1100 C [2]. The four major reduction reactions viz. (a) Wateregas reaction (equation (1)), (b) Boudouard reaction (equation (2)), (c) Shift conversion (equation (3)) and (d) Methanation (equation (4)) result in the production of a mixture of combustible gases primarily containing hydrogen, carbon monoxide, carbon dioxide and methane [3].
C þ H2 O4CO þ H2 þ 131 kJ=mol
(1)
C þ CO2 42CO þ 172 kJ=mol
(2)
CO þ H2 O 4CO2 þ H2 41:2 kJ=mol
(3)
C þ 2H2 4CH4 74:8 kJ=mol
(4)
Flow arrangement of the downdraft gasifiers enables the pyrolysis gases to pass through a bed of hot char resulting in the cracking of most of the tars into non-condensable gases and water. The gas produced in downdraft gasifiers has been found to be suitable in gas burners, internal combustion engines, gas turbines and/or to transport the product gas in pipelines. However, in most cases the gas should be cleaned before being used. Other added advantages of high char conversion, lower ash and tar carry over, quick response to load change and simple construction makes downdraft gasifiers one of the most widely used type of gasifiers for small scale application [4]. However, downdraft gasifiers have higher gas outlet temperatures, are difficult of scale up, faces problem of ash fusion at high grate temperatures and has fuel moisture limitations [5]. Performance evaluation of biomass gasifier systems has been the central theme of most of the research. The feedstock characteristics, the reactor design and the operating parameters are the influencing variables in the gasification process which effects the -vis end-gas composition of a given gasifier [6]. performance vis-a The influencing feedstock characteristics are moisture content (MC), volatile matter (VM), ash content, char, thermal conductivity, organic constituents and inorganic constituents [6]. The composition and thermo-chemical properties of different feedstock exhibit inherent heterogeneity. Further, it is also observed that there are very complex thermo-chemical interactions taking place within the gasifier. As a result, experimentation to find the optimum conditions for a given gasifier design utilizing a particular feedstock becomes time consuming and expensive. In this regard, mathematical modeling has been found to serve as an important tool to study the gasifier behaviour in order to optimize its design and operation avoiding physical experimentation. Mathematical models become useful tool in representing the real life situation with the help of mathematical equations. The representation of the gasification process in the form of a mathematical model helps in gaining an insight about the significance of the variables affecting the gasifier performance. Different kinds of models have been implemented for studying the gasification process [7]. A more recent approach for simulation of gasifier is the neural network analysis in which the neural network learns by itself from sample experimental data mimicking the working of the human brain in terms of mathematical functions. ANN based modeling does not require mathematical description of phenomena associated with the system. Thus, ANNs become handy in the case of outcome prediction when important interactions of complex nonlinearities exist in a data set, like in biomass gasification, because they can approximate nonlinear functions [8]. Also, ANN models are capable of providing better prediction with the same or lesser number of input variables in comparison to other modeling
265
techniques such as equilibrium modeling. ANNs are used to represent complex processes including nonlinear and discrete systems with the help of a dense distribution of simple processing elements. An introduction to the fundamentals of ANNs can be found elsewhere [9,10]. Various architectures of ANNs are being used of which Multilayer Perceptron (MLP) is the most popular one [11]. MLP consists of an input layer, a hidden layer and an output layer of neurons. While the neurons in the input layer are typically linear, the neurons in the hidden layer have nonlinear activation functions. The neurons in the output layer may be linear or nonlinear. The input layer consists of input neurons with associated weight. The hidden layer receives the signals from the input layer. A threshold parameter known as bias is associated with each neuron in the hidden and output layers. ANNs are used to represent complex processes including nonlinear and discrete systems with the help of a dense distribution of simple processing elements. ANNs are being extensively used in many studies as a standard modeling tool. Although neural network analysis finds extensive application in the fields of pattern recognition, signal processing, function approximation and process simulation, it has been seldom used in biomass gasification modeling. In the reported studies of ANN based modeling of biomass gasification, the objective was to predict the end gas composition for different types of gasifiers [8e16]. Puig-Arnavat et al. [8] developed ANN models for circulating fluidised bed gasifier and bubbling fluidised bed gasifier whereas Guo et al. [12] developed ANN model for atmospheric pressure steam fluidised bed gasifier. The models were able to predict the gasification process parameters and showed good agreement with experimental data. Adequate amount of experimental data is required for (i) formulation, (ii) calibration and (iii) evaluation of model constants. Data inadequacy results in poor prediction. Another crucial step in the development of the model is the selection of appropriate set of variables. The performance of the model is heavily dependent on the input variables [8]. A generalised feedstock independent ANN model was found better than feedstock specific model [12]. In another approach, combined use of an equilibrium model and artificial neural network regressions were investigated for modeling biomass gasification. The benefits of the approach were to improve the accuracy of the equilibrium calculations, and to prevent the neural network model from learning mass and energy balances, thereby minimizing experimental data requirements [13]. Stoichiometric equilibrium model was used to compute the reaction temperature difference within various zones of the reactor. ANN models were then developed to relate these temperature differences with fuel composition and gasifier operating conditions. This resulted in the reduction of experimental data requirement for development of the ANN models. The outputs of the ANN models were then used to modify the equilibrium model. Further investigation and improvement of this combined equilibrium and ANN model was carried out by the same authors [14]. A complete stoichiometry was formulated, and corresponding reaction zone temperature difference parameters were computed under the constraint of the non-equilibrium distribution of gasification products determined by mass balance data reconciliation. The results showed very good conformity with experimental data. Kumar et al. [15] developed an NN model for both air and steam gasification to predict the calorific value of producer gas and composition of producer gas for the given input conditions namely air flow rate, fuel flow rate, steam flow rate, oxidation temperature, composition of the fuel etc. The developed model exhibited good prediction when it was specifically built for a particular gasifier and a particular gasification agent. Extremely high association rates between predictive and observed data were obtained. In another reported work, Mikulandric' et al. [16] developed an
266
D. Baruah et al. / Biomass and Bioenergy 98 (2017) 264e271
artificial neural network modeling approach for a fixed bed biomass gasifiers. Neural network models showed good capability to predict biomass gasification process parameters with reasonable accuracy and speed for the gasifier under study but operating on a specific feedstock. Although ANN based modeling of biomass gasification has been carried out by a few authors, there are very few reported studies utilizing ANN to predict the performance of fixed bed downdraft gasifiers. In the present study an attempt has been made to develop a MLP based ANN model of the biomass gasification process in a fixed bed downdraft gasifier. The objective of the study is to develop ANN model and utilize it in predicting the product gas composition in terms of percentage composition of the product gas species for variations in the operating parameters. Further, it is attempted to determine the relative influence of some specific operating parameters on output gas.
temperature, TR ( C) with one hidden layer and one output. In the models, the activation function used in the hidden layers was a hyperbolic tangent sigmoid function (TANSIG). This transfer function results in an output that lies in the range of (1.0 to 1.0) and generally used for back propagation networks. The input-output relationship for this transfer function is given by equation (5).
Uk ¼
eaðVk Þ eaðVk Þ eaðVk Þ þ eaðVk Þ
Where, Vk and Uk are the input and output of the kth neuron and ‘a’ represents the coefficient of transfer function. It generates zero output, corresponding to its input u ¼ 0.0. The linear transfer function (PURELIN) was used in the output layer. The output of this transfer function is made equal to its input and it lies in the range of (1.0 to 1.0). The input-output relationship of this transfer function is expressed by equation (6).
Uk ¼ Vk
2. Methods 2.1. Topology of the ANNs An ANN based model for fixed bed downdraft type of gasifiers is developed to study gasification behaviour in terms of product gas composition. An ANN is an architecture consisting of a large number of neurons organized in different layers and the neurons of one layer are connected to those of another layer by means of weights and it can be trained to perform a particular task by making proper adjustment of its connecting weights, bias and architecture [17]. In this study, ANN models were developed in the MATLAB® environment using the Neural Network Toolbox (nntool). Fig. 1 represents the architecture of the ANN models developed for each output (CH4%, CO%, CO2% and H2%). Each ANN has one input layer with six variables viz. C (wt% dry basis), H (wt% dry basis), O (wt% dry basis), ash (wt% dry basis), MC (%), and reduction zone
(5)
(6)
There is no well-defined rule to determine the number of hidden layers and the number of neurons in the hidden layer. Therefore, trial and error method was applied to find the number of hidden layers and the number of neurons in the hidden layer. In order to find out the most appropriate or best solution, a large number of different ANN models were developed with different number of hidden layers and different number of neurons for each hidden layer. The optimum solution was selected by minimizing the Mean Square Error (MSE). The best results were considered as one hidden layer with five neurons in the case of CH4 and CO, one hidden layer with four neurons in the case of CO2 and one hidden layer with three neurons in the case of H2. 2.2. Data selection for the model An extensive literature review was conducted to obtain
Fig. 1. ANN architecture to predict the four main producer gas components for fixed bed downdraft gasifiers.
D. Baruah et al. / Biomass and Bioenergy 98 (2017) 264e271
267
Table 1 Experimental data used in formulation of the models. Sl. No.
Author(s)
Feedstock used
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Antonopoulos et al. [18] Dogru [19] Erlich and Fransson [20] Gai and Dong [21] Janajreh and Al Shrah [22] Jaojaruek et al. [23] Jordan and Akay [24] Kallis et al. [25] Lapuerta et al. [26] Lv et al. [27] Melgar et al. [28] Sharma [6,29] Sheth and Babu [30] Tinaut et al. [31] Van de steene et al. [32] Varunkumar et al. [33] Warnecke [34] Wei et al. [35]
Olive wood, miscanthus and cardoon Hazelnut shells Empty fruit brunch, Bagasse, Wood Corn straw pelletes Wood chips Eucalyptus wood Fuel Cane Bagasse Pellet Miscanthus and bioethanol waste pellets Wood Chips Pine wood blocks Rubber Wood Kiker wood, Douglas Fir Bark Sesame or rose wood Pine Bark Wood chips Wood Hazelnut shells Hardwood chips blended with crude glycerol
experimental data for biomass gasification under atmospheric pressure in downdraft fixed bed gasifiers. In order to maintain homogeneity in the different input variables considered for the development of the ANN based model, only experimental data pertaining to woody biomass and pellet gasification were considered. As results from large scale gasifiers cannot be compared with results from small scale gasifiers, experimental data from small scale gasifiers only were incorporated in the development of the model. Table 1 lists out the experimental works utilised in development of the model. The data set for the downdraft fixed bed gasifiers contain data from 63 different experimental runs [18e35] for various woody feedstock. Feedstock characteristics viz. moisture (MC), ash, C, H and O content along with reduction temperature (TR) were considered to be the input variables in the formulation of the ANN Table 2 Characteristics of input and output variables in the ANN based model for downdraft gasifiers. Input variables to the ANN's
Range
C (wt% dry basis) H (wt% dry basis) O (wt% dry basis) Ash (wt% dry basis) Moisture Content (%) Reduction Temperature ( C) Output variables for the ANN's CO content (%) CH4 content (%) H2 content (%) CO2 content (%)
43.83e53.40 5.42e7.18 37.24e45.83 4.25e9.48 4.20e14.70 600e1206 10.83e24.00 2.00e6.91 9.30e19.00 10.02e23.93
models. Fixed carbon (FC) and volatile matter (VM) as stated by Brown et al. [36] based on the previous works of van Krevelen [37] and Jenkins et al. [38] are considered as dependent variables because the FC ratio is proportional to both the H/C and O/C ratios. Thus, FC and VM were excluded from the input variables. Nitrogen and sulphur content were also not considered as they are assumed to have little influence in the production of gas species viz. CO, CO2, H2 and CH4. Table 2 lists the characteristics of the input and output variables obtained from the experimental data published in different standard literature.
2.3. Training, validation and prediction ability of the models In order to check the robustness, validation and to prediction ability of the models, the database was divided into two parts as training (70%) and validation-testing (30%) sub-sets. The training function used in the models were based on the TRAINLM function which updates the weight and bias values according to LevenbergMarquardt optimization. This function is often the fastest backpropagation algorithm. Also, the Gradient descent with momentum weight and bias learning function (LEARNGDM) was used to minimise the errors. This function calculates the weight change for a given neuron from the neuron's input and error, the weight (or bias), learning rate, and momentum constant, according to gradient descent with momentum backpropagation. Training and validation-test subsets were randomly selected from the available database. Details of the ANN models are shown Table 3. The prediction ability of the ANNs were statistically appraised by root mean square error (RMSE) and absolute fraction of variance
Table 3 Details of the ANN models. Sl. No.
Particulars
Specifications
1 2 3 4 5 6 7 8 9 10 11
Network type Training function or Training algorithm Adaption learning function Performance function Transfer function Data division Number of input layer unit Number of output layer unit Number of hidden layer Number of hidden layer neuron Number of epoch (Learning cycle)
Feed Forward Backpropagation Levenberg-Marquardt backpropagation (TRAINLM) Gradient Descent with Momentum Weight and Bias (LEARNGDM) Mean Square Error (MSE) Hyperbolic Tangent Sigmoid (TRANSIG) Random (Dividerand) 6 1 1 5 each for CH4 and CO, 4 for CO2 & 3 for H2 1000 iterations
268
Table 4 Weights and biases of the ANN models for predicting the end gas components (CO, CH4, H2 and CO2) for downdraft fixed bed gasifiers. CH4 Neuron number (j)
CO2 IWj,i (Weight to jth neuron of hidden layer from ith input variable)
Neuron number (j)
IWj,i (Weight to jth neuron of hidden layer from ith input variable)
Input variable
1 2 3 4 5
Input variable
C
H
O
Ash
MC
TR
17.6027 7.3800 2.1373 5.1071 13.0804
17.2542 6.0896 3.2973 8.3481 5.2580
2.3069 2.7296 2.3466 2.0617 4.1597
3.1062 9.0988 4.3181 2.9964 14.8558
6.4648 0.1463 11.0760 3.6625 2.3186
4.1790 5.8396 11.1414 25.5160 5.2698
1 2 3 4
C
H
O
Ash
MC
TR
2.1725 0.3007 2.0216 4.2785
2.6782 0.0159 0.8526 1.2942
3.5350 0.2462 1.8596 0.2411
1.6459 0.5012 1.2226 0.8927
0.5401 0.7507 2.5651 2.1661
4.0581 1.9872 1.8108 1.8877
LWi,j (Weight to output layer from jth neuron of hidden layer) 0.99 0.55832 0.45439 2.7099
b1j (Bias to jth neuron of hidden layer) 14.9758 5.4333 7.3736 2.0769 3.9161
b1j (Bias to jth neuron of hidden layer) 3.4931 3.2483 0.77562 4.3819 2.3498
b2 (Bias to output layer) 3.0962 CO Neuron number (j)
H2 IWj,i (Weight to jth neuron of hidden layer from ith input variable)
Neuron number (j)
Input variable
1 2 3 4 5
IWj,i (Weight to jth neuron of hidden layer from ith input variable) Input variable
C
H
O
Ash
MC
TR
0.4364 1.6237 0.5719 2.7569 0.9000
2.6874 0.1854 1.5379 1.7178 0.5645
1.2367 1.1010 0.8304 0.4123 0.0592
0.2085 1.0626 0.5450 0.0756 2.1174
0.0112 0.7254 0.0289 0.9075 1.5968
2.7939 0.3726 3.1442 1.2324 2.3453
1 2 3
C
H
O
Ash
MC
TR
0.59987 2.5778 0.037595
1.2142 0.32723 1.6371
1.8458 0.44952 3.6629
0.50212 2.3254 0.5319
0.22205 0.85187 2.8321
2.5718 0.51569 0.58103
LWi,j (Weight to output layer from jth neuron of hidden layer) 2.1202 0.7772 1.8259 2.1282 2.0266
LWi,j (Weight to output layer from jth neuron of hidden layer) 2.1085 0.11924 0.58747
b1j (Bias to jth neuron of hidden layer) 1.9201 0.14213 0.4524
b1j (Bias to jth neuron of hidden layer) 3.6986 1.409 2.1341
1.4228 1.5034
b2 (Bias to output layer) 1.5765
b2 (Bias to output layer) 0.78426
D. Baruah et al. / Biomass and Bioenergy 98 (2017) 264e271
LWi,j (Weight to output layer from jth neuron of hidden layer) 0.6780 2.7211 2.3511 0.7188 3.1098
D. Baruah et al. / Biomass and Bioenergy 98 (2017) 264e271
269
Fig. 2. Comparison between experimental data and data predicted by the ANN models for downdraft fixed bed gasifiers.
2.4. Relative influence of input variables on model outputs It is desirable to have an understanding of the influence of different input variables on the outputs of the model. Such knowledge would help in specifying optimum values of input variables for optimizing the performance the gasifier. Influence of the input variables on the outputs was evaluated with the help of Garson equation which is based on neural net weight matrix [17]. Garson [17] proposed an equation that is based on the partitioning of connection weights. In the equation the numerator describes the sums of absolute products of weights for each input and the denominator represents the sum of all weights feeding into hidden unit, taking the absolute values. The Garson equation, adapted to the present ANN topology is given in equation (9).
Pj¼n
Fig. 3. Comparison between modelled data and experimental data.
j¼1
(R2) which were calculated with the experimental values and networks predictions using equations (7) and (8), respectively.
11 X 2 2 1 @ T O A RMSE ¼ j j p 0
(7)
j
0P 2 1 j Tj Oj A @ R ¼1 P 2 j ðOjÞ 2
(8)
where, p is the number of samples, Tj is the target (actual) value and Oj is the output (predicted) value.
Ii ¼
( Pi¼6 Pj¼n i¼1
j¼1
! ! jIW j Pi¼6 j;i $LWj;i IWj;i i¼1 !) ! jIW j Pi¼6 j;i $LWj;i IWj;i i¼1
(9)
where, i is the input variables, j is the hidden layer neurons, Ii is the relative influence of the ith input variable on the output variable, IWj,i is the weight to jth neuron of hidden layer from ith input variable, LWj,i is the weight to output layer from jth neuron of hidden layer and n is the number of neurons (5 for CH4 and CO, 4 for CO2 and 3 for H2). After computing the relative influence of the input variables on the output of each of the models, the input variables were ranked in descending order of magnitude. A comparative study of the variable ranking was then carried out.
270
D. Baruah et al. / Biomass and Bioenergy 98 (2017) 264e271
Fig. 4. Influence (relative %) of input variables on the different outputs of the ANN models.
3. Results and discussion Four neural networks with six inputs, five neurons for CH4 and CO, four neurons for CO2 and three neurons for H2 in the hidden layer and one output each, were found to be efficient in predicting producer gas composition. The parameters (IWj,i, LW1,j, b1j, b2) of the best fit for each of the four ANN developed are shown in Table 4. The simulated and experimental values of each output CO, CO2, H2 and CH4 were compared satisfactorily through a linear regression model as shown in Fig. 2. It can be observed that R2 values are higher than 0.99 in the cases of CH4 and CO models and higher than 0.98 in the case of CO2 and H2 model. Further, the RMSE was found to be 0.0688 in the case of CO whereas it was 0.0523, 0.0915 and 0.0873 in the case of CH4, H2 and CO2 respectively. After computing the limits for the statistical test of intercept and slope [39], the ANNs passed the test with 99.2% of confidence level. This test guarantees that whole ANN model, containing four ANNs, has a satisfactory level of confidence. Similar results were also reported by Puig-Arnavat et al. [8] for ANN models of fluidised bed gasifiers. The developed models were also compared with experimental results [21] outside the data set. The experimental results however had input values within the range of the input variables used in the development of the models. A comparative plot of the predicted and experimental result in shown in Fig. 3. Model results show good conformity with the experimental results with an average relative error of 2.65%. The influence of the variables on the output prediction was
calculated using the Garson's equation. Fig. 4 depicts the influence of input variables on each of the output for the developed ANNs. It is observed that variables accounting for biomass composition (C, H and O) represent between 8% and 29% of the influence on the end gas composition. Further, TR was the most important variable in CO and H2 prediction while it was the second most important variable in CH4 and CO2 prediction. Reduction zone temperature was found to be the most influential variable in conformation to the fact that increase in temperature leads to a noticeable increase in gas yield [40]. However, reactor temperature is significantly affected by MC of biomass due to heat required for evaporation of moisture [41]. MC was found to have a comparatively similar influence (9.24e11.09%) in the case of CO, CH4 and H2 while it had a relatively higher influence (17.08%) in the case of CO2. H content was found to be the 2nd most influential variable in case of CO while it ranked 3rd in the case of H2 and 4th in the case of CH4 and CO2 prediction. Ash content and C content were the most influential variable in the case of CH4 and CO2 prediction respectively. Ash content, however, showed relatively lower influence in the case of CO, H2 and CO2 where it ranked 4th, 6th and 5th respectively. Although higher ash content can lead to serious agglomeration, fouling, and corrosion in gasifiers, it is capable of influencing the gasification process as is evident from the results. However, the exact influencing phenomenon of ash on the gasification process may be a subject of further research. It may, however, be commented that each of the variables have a strong influence on the outputs with variations in the range of
D. Baruah et al. / Biomass and Bioenergy 98 (2017) 264e271
8%e31%. The results show how the percentage composition of the major four gas species in producer gas can be successfully predicted by applying a neural network with one hidden neurons in the hidden layer and using backpropagation algorithm. The models are applicable for a wide variety of woody feedstock. However, expansion of the database with data from experimental runs for other varieties of woody feedstock becomes desirable to further increase the applicability of the models. The results also depict the relative importance of different operating parameters on the composition of the product gas species. The models may find practical application in screening potential feedstock for biomass energy extension programmes based on gasification technology. 4. Conclusions There are very few references of ANN based modeling of biomass gasification. An ANN based model could be successfully developed for downdraft fixed bed type of gasifier. Prediction of end gas composition using neural network with one hidden layer and five hidden neurons in the case of CH4 and CO, four hidden neurons in the case of CO2 and three hidden neurons in the case of H2 and using backpropagation algorithm has been possible. ANNs show agreement with experimental data with absolute fraction of variance (R2) greater than 0.99 in the cases of CH4 and CO model and higher than 0.98 in the case of CO2 and H2 model along with small RMSE. All of the variables have a strong effect on the outputs for each of the ANNs. The variables accounting for biomass composition (C, H and O) represent between 8% and 29% of the importance on the end gas composition. Reduction temperature was the most important variable in CO and H2 prediction while it was the second most important variable in CH4 and CO2 prediction. All the variables were found to have strong influence on the outputs with variations in the range of 8%e31%. However, addition of more experimental data pertaining to downdraft gasifiers will help in expanding the database to further improve the spectrum of applicability of the models. The proposed ANN based model can serve as a useful tool to optimize and control the process of biomass gasification in downdraft fixed bed gasifiers. References [1] P. Basu, Biomass Gasification and Pyrolysis: Practical Design and Theory, Elsevier, MA, U.S, 2010. [2] P. Basu, Combustion and Gasification in Fluidized Beds, Taylor and Francis Group/CRC Press, London, 2006. [3] G. Duman, A. Uddin Md, J. Yanik, The effect of char properties on gasification reactivity, Fuel Process Technol. 118 (2014) 75e81. [4] J.D. Martínez, K. Mahkamov, R.V. Andrade, E.E.S. Lora, Syngas production in downdraft biomass gasifiers and its application using internal combustion engines, Renew. Energ 38 (2012) 1e9. [5] M. Dogru, C.R. Howarth, G. Akay, B. Keskinler, A.A. Malik, Gasification of hazelnut shells in a downdraft gasifier, Energy 27 (5) (2002) 415e427. [6] A.K. Sharma, Modeling and simulation of a downdraft biomass gasifier 1. Model development and validation, Energ. Convers. Manage 52 (2011) 1386e1396. [7] D. Baruah, D.C. Baruah, Modeling of biomass gasification: a review, Renew. Sust. Energ. Rev. 39 (2014) 806e815. ndez, J.C. Bruno, A. Coronas, Artificial neural [8] M. Puig-Arnavat, J.A. Herna network models for biomass gasification in fluidized bed gasifiers, Biomass Bioenerg. 49 (2013) 279e289. [9] S. Haykin, Neural Networks: a Comprehensive Foundation, second ed., Prentice-Hall, New Jersey, 1999. [10] J. Leonard, M.A. Kramer, Improvement of the backpropagation algorithm for training neural networks, Comput. Chem. Eng. 14 (1990) 337e341. [11] M.B. de Souza Jr., AG. Barreto Jr., C.P.B. Quitete, L.C. Nemer, Neural network based modeling and operational optimization of biomass gasification processes, in: Y. Yongseung (Ed.), InTech. Gasification for Practical Applications, 2012, pp. 297e312. [12] B. Guo, D. Li, C. Cheng, Z. Lu, Y. Shen, Simulation of biomass gasification with a hybrid neural network model, Bioresour. Technol. 76 (2001) 77e83.
271
[13] D. Brown, T. Fuchino, F. Marechal, Solid fuel decomposition modelling for the design of biomass gasification systems, in: Proceedings of the 16th European Symposium on Computer Aided Process Engineering and 9th International Symposium on Process Systems Engineering, July 9e13, 2006, pp. 1661e1666. Garmisch- Partenkirchen, Germany. [14] D. Brown, T. Fuchino, F. Marechal, Stoichiometric equilibrium modelling of biomass gasification: validation of artificial neural network temperature difference parameter regressions, J. Chem. Eng. Jpn. 40 (2007) 244e254. [15] S.S. Kumar, P. Sathyabalan, S. Ragunathan, Simulation of biomass downdraft gasifier based on neural networks, Asian J. Res. Soc. Sci. Hum. 6 (6) (2016) 1548e1560. € hning, R. Bo €hme, M. Beckmann, Artificial [16] R. Mikulandri c, D. Lon car, D. Bo neural network modelling approach for a biomass gasification process in fixed bed gasifiers, Energ. Convers. Manage 87 (2014) 1210e1223. [17] G.D. Garson, Interpreting neural-network connection weights, AI Expert 6 (1991) 47e51. [18] I.S. Antonopoulos, A. Karagiannidis, A. Gkouletsos, G. Perkoulidis, Modelling of a downdraft gasifier fed by agricultural residues, Waste Manage 32 (2012) 710e718. [19] M. Dogru, Experimental results of olive pits gasification in a fixed bed downdraft gasifier system, Int. J. Green Energy 10 (2013) 348e361. [20] C. Erlich, T.H. Fransson, Downdraft gasification of pellets made of wood, palmoil residues respective bagasse: experimental study, Appl. Energ 88 (2011) 899e908. [21] C. Gai, Y. Dong, Experimental study on non-woody biomass gasification in a downdraft gasifier, Int. J. hydrogen energy 37 (6) (2012) 4935e4944. [22] I. Janajreh, M. Al Shrah, Numerical and experimental investigation of downdraft gasification of wood chips, Energ. Convers. Manage 65 (2013) 783e792. [23] K. Jaojaruek, S. Jarungthammachote, M.K.B. Gratuito, H. Wongsuwan, S. Homhual, Experimental study of wood downdraft gasification for an improved producer gas quality through an innovative two-stage air and premixed air/gas supply approach, Bioresour. Technol. 102 (2011) 4834e4840. [24] C.A. Jordan, G. Akay, Effect of CaO on tar production and dew point depression during gasification of fuel cane bagasse in a novel downdraft gasifier, Fuel Process. Technol. 106 (2013) 654e660. [25] K.X. Kallis, A.P. Susini, J.E. Oakey, A comparison between Miscanthus and bioethanol waste pellets and their performance in a downdraft gasifier, Appl. Energ 101 (2013) 333e340. [26] M. Lapuerta, A. Hernandez, J.L. Pazo, Gasification and co-gasification of biomass wastes: effect of the biomass origin and the gasifier operating conditions, Fuel Process. Technol. 89 (2008) 828e837. [27] P. Lv, Z. Yuan, L. Ma, C. Wu, Y. Chen, J. Zhu, Hydrogen-rich gas production from biomass air and oxygen/steam gasification in a downdraft gasifier, Renew. Energ 32 (13) (2007) 2173e2185. [28] A. Melgar, J.F. Perez, H. Laget, A. Horillo, Thermochemical equilibrium modelling of a gasifying process, Energ. Convers. Manage 48 (1) (2007) 59e67. [29] A.K. Sharma, Experimental investigations on a 20 kWe, solid biomass gasification system, Biomass Bioenerg. 35 (2011) 421e428. [30] P.N. Sheth, B.V. Babu, Experimental studies on producer gas generation from wood waste in a downdraft biomass gasifier, Bioresour. Technol. 100 (2009) 3127e3133. [31] F.V. Tinaut, A. Melgar, J.F. Perez, A. Horrillo, Effect of biomass particle size and air superficial velocity on the gasification process in a downdraft fixed bed gasifier. An experimental and modelling study, Fuel Process. Technol. 89 (11) (2008) 1076e1089. [32] L. Van de Steene, J.P. Tagutchou, F.J. Escudero Sanz, S. Salvador, Gasification of woodchip particles: experimental and numerical study of char-H2O, char-CO2, and char-O2 reactions, Chem. Eng. Sci. 66 (2011) 4499e4509. [33] S. Varunkumar, N.K.S. Rajan, H.S. Mukunda, Experimental and computational studies on a gasifier based stove, Energ. Convers. Manage 53 (2012) 135e141. [34] R. Warnecke, Gasification of biomass: comparison of fixed bed and fluidized bed gasifier, Biomass Bioenerg 18 (2000) 489e497. [35] L. Wei, S. Xu, J. Liu, C. Lu, S. Liu, C. Liu, A novel process of biomass gasification for hydrogen-rich gas with solid heat carrier: preliminary experimental results, Energ. Fuel 20 (2006) 2266e2273. chal, Solid fuel decomposition modelling for the [36] D. Brown, T. Fuchino, F. Mare design of biomass gasification systems, Comput. Aided Chem. Eng. 21 (2006) 1661e1666. [37] D.W. van Krevelen, Graphical statistical method for the study of structure and reaction processes of coal, Fuel 29 (1950) 269e284. [38] B. Jenkins, L.L. Baxter, T.R. Miles, Combustion properties of biomass, Fuel Process. Technol. 54 (1) (1998) 17e46. [39] B.G. Tabachnick, L.S. Fidell, S.J. Osterlind, Using Multivariate Statistics, Pearson, Boston, 2001. [40] G. Chen, J. Andries, Z. Luo, H. Spliethoff, Biomass pyrolysis/gasification for product gas production: the overall investigation of parametric effects, Energ. Convers. Manage 44 (2003) 1875e1884. [41] H. Ghassemi, R. Shahsavan-Markadeh, Effects of various operational parameters on biomass gasification process; A modified equilibrium model, Energ. Convers. Manage 79 (2014) 18e24.