Resources, Conservation & Recycling 133 (2018) 169–178
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Full length article
Application of ANFIS, ANN, and logistic methods in estimating biogas production from spent mushroom compost (SMC) ⁎
Bahman Najafi , Sina Faizollahzadeh Ardabili
T
⁎
Department of Biosystem Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
A R T I C L E I N F O
A B S T R A C T
Keywords: ANFIS Biogas Energy crisis Logistic model MLP
In this study, a small-scale production of biogas was undertaken using spent mushroom compost (SMC). The carbon to nitrogen (C/N) ratio, temperature of the reactor (T), and retention time (RT) were the independent variables of the study. Maximum production of biogas is related to the C/N ratio of 20 at temperature of 35 °C (cumulative values of 40.5183 ml/g of VS) and C/N ratio of 30 at temperature of 55 °C (cumulative value of 44.1001 ml/g of VS). Logistic, ANFIS, and ANN models were employed in modeling the production process of biogas. Comparing the values of the results indicate that the total values of RMSE and r in case of mesophilic temperature (35 (°C)) for ANFIS network are 0.1940 and 0.9998, for MLP network are 0.780 and 0.9981, and for logistic model are 0.5111 and 0.9992, respectively. And also at the thermophilic temperature (55 (°C)), the total values of RMSE and R were calculated as 0.3033 and 0.9997 for ANFIS network, 0.3430 and 0.9992 for MLP network, and 0.5506 and 0.9991 for the logistic model, respectively. Therefore, it can be reported that the ANFIS network accurately predicted the output values of the both thermophilic and mesophilic situations.
1. Introduction Globally, a heavy reliance on fossil energy sources has resulted in the energy crisis. On the one hand, the continuation of current trends will result in the depletion of fossil resources (which is an economic threat to exporter countries such as Iran), and on the other hand, future generations will face with a serious problem of energy supply (Najafi et al., 2007a; Najafi et al., 2007b). Although, Iran is blessed with the benefits of rich oil and gas resources, it must not be forgotten that resources are limited, besides being a national capital that has to be preserved for future generations. In different countries, various solutions have been proposed to solve this serious crisis of which one of the most efficient is the use of biogas production technology (BPT). BPT, in addition to solve many environmental problems, will provide a part of the energy requirement as a clean energy source. Unfortunately, in Iran, despite the high potential, ease, and advantages of BPT, there has not been much attention paid to the development of this technology (Esmaeil pour and Najafi, 2015). Biogas is obtained from anaerobic digestion (AD) or fermentation of organic matter (such as agricultural waste, animal waste, sewage sludge, and municipal solid waste). AD is defined as a process to convert the organic material into methane (CH4) and carbon dioxide (CO2) in the absence of oxygen (Klass, 2004). This process occurs in four stages: 1) hydrolysis, 2) acidogenesis, 3) acetogenesis, and 4)
⁎
methanogenesis. In the hydrolysis stage, complex molecules such as lipids, proteins, and carbohydrates are converted into simpler organic materials such as sugar, fatty acids with long hydrocarbon chains, and amino acids. Then, in the acidogenesis stage these materials are converted into carbon dioxide, hydrogen, and volatile fatty acids (VFAs). The VFAs are converted to hydrogen, carbon dioxide, and acetic acids in the acetogenesis stage and finally in the methanogenesis stage, hydrogen, carbon dioxide, and acetic acid undergo decomposition to produce methane (Abatzoglou and Boivin, 2009, Demirbas and Balat, 2009; Kao et al., 2012; Ramaraj and Dussadee, 2015; Salminen and Rintala, 2002). Fig. 1 demonstrates the stages in methane production as an anaerobic fermentation process (Ramaraj and Dussadee, 2015). Fig. 1 The anaerobic fermentation process is performed by mesospheric bacteria at a temperature of 30–35 °C and the digestion by thermophile bacteria is possible at a temperature of 55 °C. However, the sustainability of the production process is decreased at temperatures above 55 °C (De la Rubia et al., 2002). Research shows that for the optimal production of biogas occurs in the carbon to nitrogen ratio (C/ N) of 25–30 (Al-Juhaimi et al., 2014; Dioha et al., 2013; Okonkwo et al., 2016). The desired C/N ratio may not be achieved from organic matter if it does not biodegrade properly (El-Hinnawi and Biswas, 1981). Organic wastes have about 13.8% total solid (TS), which about 80% is volatile solids (VS), making it possible to convert only 50% of
Corresponding authors at: Department of Biosystem Engineering, University of Mohaghegh Ardabili, Ardabil, Iran. E-mail addresses: Najafi
[email protected] (B. Najafi),
[email protected] (S. Faizollahzadeh Ardabili).
https://doi.org/10.1016/j.resconrec.2018.02.025 Received 6 November 2017; Received in revised form 19 February 2018; Accepted 19 February 2018
0921-3449/ © 2018 Elsevier B.V. All rights reserved.
Resources, Conservation & Recycling 133 (2018) 169–178
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Nomenclature AD TS VS mTS m0 V1 V2 V3 MLP ANFIS
ANN Y t A μm λ Y* RMSE r R2 P* P
Anaerobic digestion Total solid (%) Total volatile solids (%) The weight of TS (kg) The weight of primary biomass (kg) The weight of plate (kg) The weight of TS + plate (kg) The weight of ash + plate (kg) Multilayer perceptron Adaptive neuro fuzzy inference system
Artificial neural network Cumulative production of biogas (m3 kg/VS) Time (day) Maximum potential of biogas production (m3 kg/VS) Maximum rate of cumulative production (m3 kg/VS) Delay time of biogas production (day) Predicted values by developed model (m3 kg/VS) Root mean square error Correlation coefficient Coefficient of determination The target value The predicted value by models
disadvantages of ANN and FIS (Ghiasi et al., 2016). There are a lot of studies that have used the ANNs and ANFIS as a prediction process in the biogas field. (Beltramo et al., 2016) have developed neural network models to predict the biogas flow rate by simulation of the anaerobic digestion process for agricultural substrates. (Nair et al., 2016) have developed a model based on the ANN to study methane production from an anaerobic bioreactor in laboratory-scale. The pH, total VS, and moisture content (MC) were independent factors of study and the CH4 ratio was a dependent factor. In another study, (Kana et al., 2012) reported the modeling of biogas production using ANN and optimized the developed model by coupling with a genetic algorithm (GA). The study substrate was a mixture of paper waste, cow dung, saw dust, rice bran, and banana stem. (Akbaş et al., 2015) developed a model to predict the biogas production using an ANN method. They optimized the developed model by using particle swarm optimization techniques with three objectives: the maximization of biogas production, the maximization of methane production, and the maximization of biogas quality. In a study by (Qdais et al., 2010) biogas production process from the digester of Russaifah biogas plant in Jordan was modeled using ANN and GA by employing temperature (T), total solids (TS), total volatile solids (TVS), and pH as independent variables and biogas yield as the only dependent variable. Based on results, ANN model estimated the methane production with correlation coefficient of 0.87. (Waewsak et al., 2010) used the ANN method for prediction of the production situation from the viewpoint of pH, alkalinity, and total volatile acids. Then, they applied a control system for treatment and production of biogas in an anaerobic hybrid digester based on the neuro-fuzzy method. The aim of this study was to investigate the factors (such as carbon to nitrogen ratio, temperature, and residence time) that affect the production of biogas from biomass derived from spent mushroom compost through the application of biogas production and production modeling using ANNs and ANFIS. To the best of our knowledge, this is a pioneering study reporting the use of soft computing methods for modeling the biogas production from SMC. This study has four stages. The first stage discussed the production of biogas from mushroom compost waste. The second stage used the prediction methods to predict the production conditions, using inputs and outputs, the third stage discussed the details of the obtained results, and then lastly, a comparison of the methods was undertaken, noting the factors that affected the production of biogas.
the biodegradable solids to methane and carbon dioxide (Mattocks et al., 2000). There are several studies in case of biogas production from different resources. (Agustini et al., 2018) reported the fermentation process of the mixture of leather shavings and sludge of the tanning. The results indicated the methane potential of 4.1–11.3 ml/g. (Paul and Dutta, 2018) developed a review study on anaerobic digestion of lignocellulosic biomass. Based on results, hydrothermal pre-treatment increases the biogas production potential. On the other hand a higher biomass to water ratio in hydrothermal pre-treatment process reduces the input energy. (dos Santos et al., 2018) studied on the potential of biogas produced from multiple organic wastes as the main organic waste in Brazil. Based on results, the use of this potential for supplying the energy can reduce CO2 emissions by 4.93% per year in Brazil. So far, several models have been presented based on the population growth of bacteria in an anaerobic environment inside the reactor for predicting the anaerobic digestion process (López et al., 2004, Smith, 2007; Yano et al., 1998; Zwietering et al., 1990). These models are too complex and need a lot of input information, however the use of artificial intelligence base models can be a fast, accurate, and efficient solution. These models can do complex tasks without needing to know the mathematical model of the system (Faizollahzadeh_Ardabili et al., 2016a). In recent years, intelligent and soft computing methods were used in all fields of science (Faizollahzadeh_Ardabili et al., 2016b). ANNs are one of the most efficient modeling methods of all the analytical and statistical techniques. These methods have been developed to be used in prediction, estimation, modeling or decision making (Naderloo et al., 2012) and are used in a wide range of sciences such as mathematics, engineering, medicine, economics, agriculture etc. ANN based systems do not require any assumptions or mathematical equations with regard to the studied system’s nature and this is the main advantage of artificial neural network based systems over other computational systems. They are trained on experimental data to find the relationship, hence becoming very popular as estimation tools, known to be efficient in the modeling of complex systems (Pahlavan et al., 2012). The neural network based methods are prediction methods with a learning ability to estimate any nonlinear relationship (Faizollahzadeh_Ardabili et al., 2016b). Artificial neural networks have different types. One of the most commonly used artificial neural networks is the multilayer perceptron (MLP), which produces the backpropagation algorithm for training neural networks. Additionally, there is an adaptive network-based fuzzy inference system (ANFIS) that combines the artificial neural network (ANN) and fuzzy inference system (FIS). This system was introduced to overcome the
Fig. 1. The stages of anaerobic (Methane production process).
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fermentation
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2. Material and methods
reproduce more quickly. Data collection was done at a certain time every day for 12 days. To evaluate the reproducibility of testing, the tests were performed with three replications. To determine the effect of the factors affecting the production of biogas from experimental design, the completely randomized design (CRD) was used with three replications. The selection of the factors influencing the biogas production were as follows:
2.1. Production and measurement of biogas In this study, the main materials used were spent mushroom compost (SMC) and wheat straw. The required SMC was collected from a mushroom growing hall after mushroom production. It was mixed with wheat straw by the use of a mixer. In order to measure the moisture content of the SMC biomass, 100 g was placed in an oven at a temperature of 110 °C for 24 h, after drying, its weight was measured again. The difference in weights (before and after drying) helps determine the initial moisture of the biomass (Esmaeil pour and Najafi, 2015). The total solids (TS) is defined as the total remaining solid content after evaporation of the sample’s moisture content. After determination of the moisture content, TS is calculated as shown in Eq. (1) and is based on the APHA standard (American Public Health Association (APHA), 2011):
%TS =
mTS × 100 m0
- C/N ratio (A factor) in four levels - Temperature of digester (B factor) in two levels - Retention time (C factor) in fourteen levels 2.2. Modeling of microbial growth The modified logistic equation used in order to predict the trend of microbial growth in an anaerobic digester is as shown in Eq. (3) (Zwietering et al., 1990):
1 + exp
Increasing or decreasing the moisture content of the substrate in the digester has a significant effect on gas production. Since bacteria have to absorb the organic matter, it needs to be in a diluted solution form. Increasing the concentration of the substances increases the adhesion and inhibits the growth of bacteria, and reducing the concentration makes a layered solution which requires continuous mixing. The recommended percentage for TS as per different references ranges from 7 to 9%. In this study, TS was considered as 8% based on a study by (Omrani, 1996). The volatile solids (VS) are the material lost after TS becomes ash, in fact vs is the part of TS that becomes biogas after the fermentation process. The percentage of vs was determined by burning 10 g of TS at a temperature of 550 °C for 2 h in an electric furnace based on the APHA standard (American Public Health Association (APHA), 2011) as shown in Eq. (2):
V − V1 ⎞ ⎞ VS % = ⎜⎛100 − ⎛ 3 ⎟ V 2 − V1 ⎠ ⎠ ⎝ ⎝ ⎜
A
Y=
(1)
(
4μm A
)
(λ − t ) + 2
(3)
The present model predicts the cumulative production of biogas (Y) at time t in m3kg/VS. The experimental data was used to determine the maximum production potential of biogas (A), the maximum rate of biogas production (μm), and phase delay time (or RT) (λ). 2.3. Development of predictive models MATLAB 2012a was used for implementing the predictive models. The developed models of prediction were the ANN method based on multilayer perceptron (MLP) algorithm and adaptive neuro-fuzzy inference system (ANFIS). To develop predictive models using the aforementioned systems, the ratio of carbon to nitrogen (C/N), temperature of the reactor (T), and retention time (RT) were used as input parameters, and the volume of produced biogas (VB) was used as the only output parameter. The most important variables on biogas production are%VS, %TS, temperature (T), C/N and retention time (RT). Moisture content is directly related to%TS. In present study and based on the performed study in lab in order to reduce the costs and time of tests and to study the effect of three parameters (temperature, C/N and retention time), it was considered a constant value of moisture as is mentioned in text (92%) therefore the%TS be also constant (8%) and on the other hand the biogas production results are based on “ml/g.VS” which makes variables to be independent from%VS. therefore the variables of T, C/N and RT were selected to be as the independent variables. This study can present a different approach compared to similar studies for studying the biogas production based on these three parameters. The next discussion explains how to implement each of the models.
⎟
(2)
The amount of nitrogen and the organic carbon level in the biomass of SMC were measured by APHA standard using kjeldahl method and chemical burns, respectively. Table 1 indicates the experimental results. Table 1 In this study, the potential of biogas production was evaluated in a laboratory-scale batch type anaerobic (LSBTA) digester. Due to its simplicity and easy setup, it is convenient to use a batch type reactor for monitoring and evaluation (Kratky et al., 2012). 2.5-l plastic bottles were used as digesters (Fig. 2). The European standards were the basis of the design and construction of the digesters (VDI4630, 2006). Fig. 2 The digesters were placed in two separate hot water baths at two different temperatures (35 °C and 55 °C). The bath temperatures were measured and controlled by digital thermostats with an accuracy of ± 0.1 °C. The produced biogas from the digesters were transferred to other plastic bottles and their volumes were measured by a water displacement method (VDI4630, 2006). The produced biogas was passed into a NaOH solution and after absorbing CO2 and H2S, methane was extracted. The percentage of methane was calculated using a displacement method with accuracy of ± 5 ml (Ware and Power, 2016). According to Table 1, the values of TS, VS, and C/N for SMC were 19.1, 64.2, and 12.1, respectively, and for wheat straw, were 86.7, 81.7, and 126, respectively. The moisture in the SMC and wheat straw were 80.9 and 13.3, respectively. In order to expedite the digestion process, a 10 g enriched microbial solution was added to each digester. This enriched solution was prepared by mixing 300 g of bovine rumen content with 300 ml of water. The prepared solution was placed in an oven at a temperature of 37 °C for five days to help the bacteria grow and
2.3.1. MLP modeling Not only are the MLP networks used in most scientific fields, but they are also the most popular models of neural networks (Venkatesan and Anitha, 2006). Fig. 3 shows the structure of a developed MLP model. As shown in Fig. 3, in general, a MLP network has three main Table 1 Characteristics of SMC and wheat strew
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parameter
unit
SMC
wheat strew
Total Solids (TS) Volatile Solid (VS) Moisture C/N value Nitrogen percent Organic carbon
W% W% W% % %
19.1 64.2 80.9 12.1 2.4 29
86.7 81.7 13.3 80.7 0.78 63
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Fig. 2. The setup of Production and measuring of biogas.
is determined by comparing the model's output and the desired output and this error is returned to the hidden and input layers for the following training processes. The network training operation ends when the error decreases to the user-specified value (Faizollahzadeh_Ardabili et al., 2017). In this study, at first, the allocated percentage of data for training, testing, and validating were selected as 70, 15, and 15, respectively. The Levenberg–Marquardt backpropagation algorithm training was employed for the training task and the sigmoid transfer function was selected as the transfer function because it enables the network to predict the non-linear behaviour of the process. The LevenbergMarquardt algorithm (using trainlm rule) is used to solve non-linear least squares problems and for solving generic curve-fitting problems (Hines et al., 1997). One of the main abilities of Levenberg-Marquardt algorithm is its good characteristic for solving fitting problems (Kipli et al., 2012). Fortunately, it has the speed advantage of and more stability (Grosan and Abraham, 2011) and also it is more robust than other algorithms. The output layer includes the (linear) purelin transfer function. Purelin transfer function is a linear transfer function which calculates a layer’s output from its net input (Faizollahzadeh_Ardabili, 2014). To determine the number of hidden layer neurons (HLN) and accordingely determine the best network for reaching the optimum condition, the training process was conducted by different number of HLN and the performance of the network was obtained by a performance parameter (Table 4). Each time the network was trained, the weights and bias were modified to reduce the slope of the performance function. Finally, an architecture with 16 neurons on the hidden layer was selected as the best network with the highest performance.
Fig. 3. The structure of developed MLP network.
layers including input layer, hidden layer and output layer which the hidden layer can have one or more layers. In present study the ANN structure have two layer in hidden layer. Each layer has a number of processing units and is connected to subsequent layer. The first layer is an input layer that consists of input vectors. In this study, the input parameters are C/N, T, and RT. The second layer is a hidden layer that consists of neurons layers, wherein the number of neurons and layers in a hidden layer can be varied in order to get the best model structure and to improve its prediction ability. The last layer is the output layer with output neurons. The only output of this study or the output layer is VB. Fig. 3 This network uses a back-propagation algorithm for the training process. In training of the back-propagation method, the error
Fig. 4. The structure of ANFIS method.
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different treatments and test conditions, seems logical.
2.3.2. ANFIS modeling ANFIS combines the fuzzy logic and neural network approaches to create a hybrid intelligent model with advantages of both methods (Wali et al., 2012). Fig. 4 depicts the structure of a ANFIS model. ANFIS consists of five layers (Mostafaei et al., 2016; Wali et al., 2012; Yilmaz and Yuksek, 2009). The first layer acquires the inputs parameters and introduces them to the ANFIS model. This layer is considered as the input of the fuzzy system. The output of the first layer becomes the input of the second layer carrying prior values of MFs that are allocated based on the input values. The nodes on the second layer decide the fuzzy rules and send them to the third layer with a related degree of activity. The third layer normalizes the degree of activity for all rules. The fourth layer adopts the nodes and function and provides the first model by derived parameters (Keshavarzi et al., 2017) sending them to the output layer. The ANFIS model was developed by the ANFIS toolbox on MATLAB. The input parameters were C/N, T, and RT and the only output parameter was VB. The network was trained with three membership functions and the membership function type selected was the Gaussian membership function. In order to select the type of membership function, training of the network was done with triangular-shaped, generalized bell-shaped, trapezoidal-shaped, and Gaussian membership functions, following which the Gaussian was selected based on the performance parameter that was calculated for each type (Table 5). The output membership function type selected was the linear type because of its ability to further reduce errors. The training of FIS was done with a hybrid optimum method and an error tolerance value of zero.
3.2. The rate of biogas production The results of this study indicate that biogas production in all the samples was started without any delay time, reaching their maximum values after 5–7 days. Figs. 5 and 6 indicate the daily rate of biogas production at the temperatures of 35 °C and 55 °C, respectively. The maximum daily biogas production (production rate), is related to a C/N ratio of 20 at a temperature of 35 °C (cumulative production of 40.5183 ml/g as VS) and a C/N ratio of 30 at a temperature of 55 °C (cumulative production of 44.1001 ml/g as VS). The biogas production increased on the first and second days due to a mutation trend resulting from the microbial enriched injection. But after two days, the daily rate of biogas production decreased due to the thermal shock and reduction of the digester’s microbial population. Microbial activity is mainly sensitve to temperature variations, and the optimal temperature for this in the digester is 37 °C. The biogas production rate was at a maximum increase in the first 5–7 days thanks to the digester microbial activity being at its maximum value during this period, after which the continuing activity of the digester microbes adopted a downward trend resulting in the decreased rate of biogas production, which finally ceased after 21 days. 3.3. The results of microbial growth modeling The results of the estimation of logistic model parameter and correlation coefficient for eight biogas reactors (four cases of C/N ratio of 12.2, 20, 30, and 40 at temperatures of 35 °C and 55 °C) are tabulated in Table 3. By the overall examination and evaluation of the figures, it can be concluded that the goodness of fit varies according to the temperature variation. Table 3 shows the estimated parameters using SPSS software for coefficient of determination for the logistic model, where A is the cumulative production potential, μm is the maximum daily rate of biogas production, and λ is the delay time. For each condition (C/N and T) there are different values for A and μm which are presented in Table 3. These values forms the logistic equation (Eq. (3)) and RT be directly used instead of λ. This variables cooperate to estimate Y*. As seen in Table 3, the values of A as calculated for 14 days are close to Y*. Fig. 7 indicates the target values (actual values) of production against the output values of the logistic model. Accordingly, the vertical axis denotes the target values and the horizental axis represents the output values. This diagram shows the linearity and accuracy of the network in predicting the actual values. Fig. 7 indicates the linearity of the model outputs with target values. The coefficient of determination (R2) is the indicator that shows the linearity. In the logistic model, the output values have 99.84% of linearity with target values. The total result of the logistic model is tabulated in Table 6, and shows the r and RMSE values between the output and target values of the logistic model.
2.4. Evaluation of the developed models The comparative performance of the MLP and ANFIS models was measured using statistical parameters, namely correlation coefficient (r), the root mean square error (RMSE) and the determination coefficient (R2) as follows. 1/2
n
⎛ ⎛ ∑ (P *−P )2 ⎞ ⎞ ⎜ ⎟⎟ ⎜ R = ⎜1 − ⎜ i = 1 n ⎟⎟ ⎜⎜ ⎜ ∑ P * ⎟ ⎟⎟ ⎠⎠ ⎝ i=1 ⎝ RMSE =
1 N
(4)
N
∑
(P *−P )2
i=1
(5)
n
R2
⎛ ∑ (P *−P )2 ⎞ ⎟ ⎜ = 1 − ⎜ i=1 n ⎟ 2 ⎜ ∑ P *i ⎟ ⎠ ⎝ i=1
(6)
where P* is the target value and P is the predicted value by the models (Faizollahzadeh_Ardabili et al., 2017; Yilmaz and Yuksek, 2009). The root mean square error (RMSE) measures the difference between the predicted and target values and Pearson correlation (r) and the determination coefficient (R2) measure the linear correlation between the predicted and target values showing the degree of linearity for the predicted and target values (Faizollahzadeh_Ardabili et al., 2016b).
Table 2 The results of variance analysys of the produced biogas. parameters Rt T CN Rt*T Rt*CN T* CN Rt*T*CN
3. Results and discussion 3.1. Factors affecting the production of biogas The results of the variance analysis for the parameters that affect amount of biogas produced are tabulated in Table 2 using the SPSS statistical software. The coefficient of variation (CV) value was calculated as 16.24% and the distribution rate, based on the number of
Degree of freedom 1 4 4 4 4 16 16
sum of squares
Mean Square
F
246.909 62.049 10.853 12.477 1.308 8.135 8.73
344.744 6.339 7.264 21.060 22.512 1.123 15.609
748.9147** 47.512** 8.23** 9.4613** 0.9915ns 1.5421ns 1.6551ns
ns: non-significant **: significant at 1% level. MR: moisture of Reactor T: Temperature of Reactor. RT: Retention Time.
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Fig. 7. The results of logistic model.
from the developed networks.
Fig. 5. Daily rate of biogas production at temperature of 35 °C.
3.5. Evaluation of MLP network 3.5.1. Network training The training of the neural network with multilayer perceptron method was performed to generate a network with an ability to predict the dependent variables (RT, C/N, and T) based on the independent variable (VB). The goal of generating this network is to create a prediction network with the aim of predicting the VB variable. The most important process of creating a network is the training step. An important step in training a network is the determination of the optimized number of neurons on a hidden layer. For this purpose, training the network started with neurons on the hidden layer and in the next stage of training, four neurons were added to the number of neurons. The MSE value was recorded as a performance factor to make the best decision, as seen in Table 4. According to Table 4, the performance factor of order 4 with value 0.3637 has the minimum performance value, while the performance factor of order 1 with the value of 5.3936 has the maximum performance value, which based on the definition of MSE is a factor of difference between estimator and what is estimated (Faizollahzadeh_Ardabili et al., 2016b), hence a small amount of MSE indicates the accuracy of the prediction network. Here, the order 4 with a small value of MSE (0.3637) and 16 number of neurons on the hidden layer (8 neurons in 1th layer and 2 neurons in 2th layer) was selected to train the network.
Fig. 6. Daily rate of biogas production at temperature of 55 °C.
3.4. The results of ANN and ANFIS modeling The modeling of the prediction network has been done by MATLAB software using two methods, MLP of neural network and ANFIS. The modeling was performed under such conditions that did not need preprocessing or normalization operations for the data. So the actual data was directly imported to the modeling process. The following part focuses on the review and analysis of the obtained experimental results
3.5.2. Network testing After training the network, it was the time to test the developed network. The testing network is a conventional way to obtain the output (output value) and compare it with actual values (target values). Fig. 8 indicates the scatter plot of the obtained results. The vertical axis
Table 3 Comparing of estimated parameters of biogas production by logistic model. Temperature (oC)
C/N ratio
35
12 20 30 40 12 20 30 40
45
Y* (m3.kg/VS)
A (mL/grVS )
μm (mL/grVS )
λ (day)
R2
30.536 40.518 36.413 30.926 32.297 39.768 44.100 37.805
30.747 41.096 36.740 30.752 31.381 40.002 44.032 39.439
3.728 5.269 4.974 4.427 3.905 4.656 5.407 4.347
0.817 1.779 1.649 1.762 1.160 2.055 2.597 2.921
0.994 0.997 0.997 0.996 0.994 0.997 0.997 0.998
Y*: cumulative biogas production at tiome of t that was measured experimentally.
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Table 4 MSE value based on Neuron number on hidden layer. Order
1 2 3 4
Table 6 The total results of developed models.
VB
Number of neurons
MSE
1th layer
2th layer
2 4 6 8
2 2 2 2
5.3936 2.9221 0.434 0.3637
Temperature
35
Total 55
Total
C/N
12 20 30 40 12 20 30 40
MLP
ANFIS
Logistic
RMSE
r
RMSE
r
RMSE
r
0.0364 1.3064 0.7756 0.3606 0.780 0.5584 0.2004 0.2733 0.2093 0.3430
1.0 0.9971 0.9983 0.9996 0.9981 0.9992 0.9999 0.9999 0.9999 0.9992
0.2346 0.2202 0.1475 0.1593 0.1940 0.2860 0.4233 0.2512 0.2098 0.3033
0.9997 0.9999 0.9999 0.9999 0.9998 0.9996 0.9995 0.9999 0.9999 0.9997
0.4094 0.5 0.5327 0.5865 0.5111 0.5691 0.5501 0.5771 0.5035 0.5506
0.9993 0.9995 0.9992 0.9987 0.9992 0.9986 0.9992 0.9993 0.9993 0.9991
3.6. Evaluation of ANFIS network A data share of 70% was used to train the model, and the other 30% was used to test the developed model. The initial ANFIS network was developed by three different types of MFs, Gbell, Gauss, and Trap types of membership functions. The results of each membership function is tabulated in Table 5 by using the MSE value as a factor of network performance. Based on the results of Table 5, the Gaussian membership function with minimum MSE value (0.2547) has the minimum error between output and target values and can export more accurate results compared to the other two types of membership functions. Therefore, it was selected as the fuzzification membership function on the training process. After the training process, the ANFIS models were tested using an independent data set. The outputs of network were extracted and the performance of network was calculated. As an initial result, Fig. 9 indicates the results of ANFIS on the modeling dataset. Fig. 9 Based on the results of Fig. 9 and according to the description mentioned for the coefficient of determination in the testing MLP network section, it can be said that the output values have 96.57% of linearship with target values. The total result of ANFIS network is tabulated in Table 6 and shows the r and RMSE values between output and target values of the ANFIS network.
Fig. 8. The results of MLP network.
Table 5 MSE value based on type of membership function for fuzzification of input values on ANFIS. Order
VB MSE
Type of membership function
1 2 3 4
0.4142 0.3372 0.3852 0.2547
Triangular-shaped Generalized bell-shaped Trapezidal-shaped Gaussian-shaped
3.7. The comparison of ANFIS and MLP networks and logistic model Based on the results of Figs. 7–9 and comparing the coefficient values of the three models, it can be said that the MLP network (with correlation value 0.998) has minimum linearity between target and output values, the ANFIS network (with R2 value 0.9996) has maximum linearity between target and output values, the logistic model is midrange in terms of total data values since these figures are plotted based on total data series to compare the overall prediction capability of the models. Fig. 10 indicates the predicted and actual values for the cumulative production of biogas based on the temperature and ratio of C/N, separately. Based on Fig. 10, the “a” column is for temperature 35 (°C) and “b” column is for temperature 55 (°C). At first sight and as the initial result, it can be said that prediction lines almost fit on the target data. If we divide the horizontal axis of the figures to the first five days and second nine days, we will see that the biogas production in the first five days has an almost constant trend such that this constant trend on a temperature of 35 °C (Fig. 10a) is higher than at the temperature of 55 °C (Fig. 10b). As seen in Fig. 10, in the second nine days, (Fig. 10a for temperature of 35 (°C)), the variation trends of CN 40 (C/N ratio of 40) and CN 12 (C/N ratio of 12) are similar and almost have the minimum amount of biogas production, while CN 20 (C/N ratio of 20) has the maximum production value.
Fig. 9. The results of ANFIS network.
specifies the target values and the horizontal axis, the output values. Fig. 8 indicates the linearity of the network outputs with target values. The coefficient of determination (R2) is the indicator that shows the linearity. In the MLP results, the output values have 99.8% of linearity with target values. The total result of the MLP network is tabulated in Table 6. This results show r and RMSE values between the output and target values of the MLP network.
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Fig. 10. The predicted and actual values as cumulative production of biogas.
production amount and the blue line with CN 30 has the maximum production amount. Comparing the values of Table 6 indicates that the total values of RMSE and R in case of mesophilic temperature (35 (°C)) for ANFIS
As seen in Fig. 10b (for temperature of 55 (°C)), CN 12 (C/N ratio of 12) with red color, the first five days shows the maximum production amount but after that it reduces. In the second nine days, the production trend changed such that the red line with CN 12 has the minimum 176
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prediction performance and in case of thermophilic temperature, the logistic output (yellow line) has the maximum deviation from target value therefore it has the lowest prediction performance. Therefore, by this study, it can be clearly said that the ANFIS network has high ability to predict the biogas volume at the thermophilic and mesophilic temperatures using RT, C/N, and temperature values as independent parameters. 4. Conclusion The present study was developed with the aim of investigating the factors affecting the production of biogas from biomass derived from SMC, including C/N ratio, temperature, and residence time as the independent variables in the amount of biogas production and production modeling using logistics, ANN, and ANFIS methods. The APHA standard was used as the main pattern of production and measuring the biogas. The results of the variance analysis of the said parameters on the amount of biogas produced show that the independent variables of study have significant effects on biogas production. Based on the results, the logistic model had 99.84% of linearity with target values. Accordingly, the ANN model with 16 neurons in the hidden layer had a small value of MSE (0.3637) as the performance factor in training and had 99.8% of linearity in the testing stage. Furthermore, in developing of the ANFIS method, the Gaussian membership function by obtaining a MSE value of 0.2547 was selected as the best function of the ANFIS method with 96.57% of linearity. By comparing the result values, it was found that the total values for RMSE at the mesophilic temperature are 0.1940, 0.780, and 0.5111 for ANFIS, MLP, and logistic models, respectively and this value in case of the thermophilic temperature were calculated as 0.3033, 0.3430, and 0.5506 for the ANFIS, MLP and logistic models, respectively. Therefore, by this study, it was concluded that the ANFIS network accurately predicted the output values in the thermophilic and mesophilic situations. Finally, the logistic model is considered to be in mid-range with close prediction capability in case of mesophilic temperature. Moreover, since the accuracy of intelligent methods such as ANN and ANFIS is related to the training skill of the operator, a logistic model with comfortable accessbility can be a more effective method in the prediction process, however in the case of requiring high accuracy, intelligent methods should be employed.
Fig. 11. The deviation from actual value for ANFIS, MLP and logistic models for mesophilic and thermophilic temperatures.
network are 0.1940 and 0.9998, for MLP network are 0.780 and 0.9981, and for logistic model are 0.5111 and 0.9992, respectively. It is clear that at mesophilic temperature, the ANFIS network with a higher r value (0.9998) and lower RMSE value (0.0376) has the maximum prediction performance and the MLP with lower r value (0.9981) and higher RMSE value (0.609) has minimum prediction performance in this study. And also at the thermophilic temperature (55 (°C)), we reach to similar results. The total values of RMSE and R were reported as 0.3033 and 0.9997 for the ANFIS network, 0.3430 and 0.9992 for the MLP network, and 0.5506 and 0.9991 for the logistic model, respectively. This result shows that at the thermophilic temperature, ANFIS with the higher total value of r (0.9997) and lower total value of RMSE (0.3033) has the maximum prediction performance and logistic method with lower total r value (0.9991) and higher total RMSE value (0.5506) has minimum prediction performance. Therefore, from this study, it can be concluded that the ANFIS accurately predicted the output values of the both mesophilic and thermophilic situations. This claim is also visible in Fig. 11. In a study by (Beltramo et al., 2016), the correlation value and RMSE for the best predictor of biogas production yield from agricultural substrates were 0.95 and 5.34, respectively using an ANN-ant colony algorithm. In another study by (Nair et al., 2016), the biogas production from an organic fraction of municipal solid waste was modeled using the MLP network wherein the correlation value obtained was 0.956. (Akbaş et al., 2015) modeled the biogas production from waste water using MLP-PSO methodology. Based on their study, the best predictor resulted in a correlation value of 0.9135. Accordingly, in comparison to results of similar studies, the present study provided high accuracy and performance. Therefore, the prediction condition and the employed methods of the present study can help improve the estimation process. Fig. 11 shows the deviation of each model from the target value. The zero value of deviation is related to the target value. The black line indicates the deviations related to the MLP output, the red line indicates the deviations related to the ANFIS output, and the yellow line indicates the deviations related to the logistic model. This figure clearly shows that in both mesophilic and thermophilic temperatures, the ANFIS output (red line) has the least deviation from target value among the outputs of other models, hence it will have the highest prediction performance. Whereas in case of mesophilic temperature the MLP network with the highest deviation (black line) will have the lowest
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