- Email: [email protected]
Application of artificial neural networks to predict chemical desulfurization of Tabas coal
Available online at www.sciencedirect.com
Fuel 87 (2008) 2727–2734 www.fuelfirst.com
Application of artificial neural networks to predict chemical desulfurization of Tabas coal E. Jorjani *, S. Chehreh Chelgani, Sh. Mesroghli Department of Mining Engineering, Research and Science Campus, Islamic Azad University, Poonak, Hesarak, Tehran, Iran Received 26 May 2007; received in revised form 25 January 2008; accepted 29 January 2008 Available online 27 February 2008
Abstract This paper presents a neural network model to predict the effects of operational parameters on the organic and inorganic sulfur removal from coal by sodium butoxide. The coal particle size, leaching temperature and time, sodium butoxide concentration and pre oxidation time by peroxyacetic acid (PAA) were used as inputs to the network. The outputs of the models were organic and inorganic sulfur reduction. Feed-forward artificial neural network with 5-7-10-1 arrangement, were capable to estimate organic and inorganic sulfur reduction, respectively. Simulated values obtained with neural network correspond closely to the experimental results. It was achieved quite satisfactory correlations of R2 = 1 and 0.96 in training and testing stages for pyritic sulfur and R2 = 1 and 0.97 in training and testing stages, respectively, for organic sulfur reduction prediction. The proposed neural network model accurately reproduces all the effects of operational variables and can be used in the simulation of Tabas coal desulfurization plant. Ó 2008 Elsevier Ltd. All rights reserved. Keywords: Artificial neural networks; Coal; Chemical desulfurization
1. Introduction Artificial neural network (ANN) is an empirical modeling tool, which is analogous to the behavior of biological neural structures [1]. Neural networks are powerful tools that have the ability to identify underlying highly complex relationships from input–output data only [2]. Over the last 10 years, artificial neural networks (ANNs), and particularly feed-forward artificial neural networks (FANNs), have been extensively studied to present process models, and their use in industry has been rapidly growing [3]. The use of such networks can now be found for number prediction such as modelling the greenhouse effect [4], simulation N2O emissions from a temperate grassland ecosystem [5], modelling of rare earth solvent extraction [6], bioleaching of metals [7] and coal microbial desulfurization [8].
*
Corresponding author. Tel.: +98 912 1776737; fax: +98 21 44817194. E-mail address: [email protected] (E. Jorjani).
0016-2361/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.fuel.2008.01.029
The use of neural networks to predict coal final sulfur content, after desulfurization with Acidithiobacillus bacteria, was reported by Acharya et al. [8]. Type of coal, initial pH, pulp density, particle size, residence time, media composition and initial sulfur content of coal were fed as inputs to the network and output of the model was final sulfur content (about 0.2–6.6 (%) for the tested samples) after desulfurization. The aim of the present work is the assessment of Tabas coal with reference to the organic and inorganic sulfur removal by sodium butoxide, as leaching agent, and possible variations with change of coal particle sizes, leaching temperature and time, sodium butoxide concentration and duration time of peroxyacetic acid (PAA) pretreatment of coal (to enhancing of coal desulfurization by sodium butoxide), using experimental data obtained at a laboratory level and simulated data by means of a neural network, MATLAB software package. To our knowledge, this is the first time that ANNs have been used to predict both of organic and inorganic sulfur removal from coal in a chemical desulfurization process.
2728
E. Jorjani et al. / Fuel 87 (2008) 2727–2734
2. Area description 2.1. Desulfurization of coal with sodium butoxide The use of high-sulfur coals for energy production requires a cleaning stage to meet environmental regulations. Sulfur in coal occurs in organic and inorganic forms. The organic form is about 50% of the total sulfur that cannot be removed by physical coal cleaning methods [9]. Removal of organic sulfur requires chemical desulfurization techniques that can remove inorganic sulfur and ash-forming minerals too. Generally, cleaning is carried out over the flue gases, but chemical desulfurization has also received attention. Physical and microbial methods were also used. Although chemical desulfurization of coal is not profitable in the current economic conditions, it may become economical in the future, as SO2 emission regulations are tightened and low sulfur coal reserves are depleted. Well known desulfurization processes include: caustic treatment [10], pyrolysis [11,12], oxidation [13–15], IGT hydrodesulfurization [9], magnex process [16] and chemical comminution [17]. Oxidation is the most often applied chemical method of coal desulfurization [18]. Hydrogen peroxide, sodium hypochlorite, nitric acid, potassium permanganate, air, and many other oxidants are used in this method. Oxidation of coal is a very complex process, dependent on a number of parameters such as temperature, pressure, time, reagent concentration, type of coal and its grain size distribution. Oxidation can change coal characteristics such as calorific value, free swelling index, sinter ability and extractability [19,20]. The use of PAA as oxidative reagent for the desulfurization of coal has been reported by Palmer et al. [21,22], Sonmez et al. [23] and Aelst et al. [24]. PAA is believed to produce hydroxyl cations, which are strong electrophiles and react with sulfur atoms because they are considerably more nucleophilic than carbon atoms. It was also found that selective oxidation of coal with PAA was a very effective pretreatment for enhancing desulfurization of coal with various hydroxide and carbonate bases. The structural properties of coal, such as porosity, surface area and pore size, play an important role in chemical desulfurization. Coal contains moisture in its pores and this moisture can be removed by heating at 100 °C. Moisture removal is accompanied by a substantial increase in porosity [25]. Borah et al. [26] have shown that coal oxidation by heating, as a pretreatment process for coal desulfurization by the process of electron transfer, decomposes the large organic sulfur molecules to low molecular weight products. These smaller molecules are much more prone to attack by the leaching solution in subsequent stages for the rupture of C–S as well as S–S bounds. They also found that low temperature oxidation converts coal organic sulfur to compounds containing S@O and –SO2 [26]. In these aerial oxidized samples, besides aliphatic sulfur,
aromatic disulfide compounds can also be leached out [25]. Pietrzak and Wachowska [27] have studied the effect of oxidants such as PAA and air on the oxidation of the coal organic structure by FTIR. They found that during oxidation with atmospheric oxygen at 125 °C for 7 days, hydrocarbon groups are more susceptible to oxidation than sulfur groups; whilst PAA can convert organically bound sulfur to oxidized forms such as sulphones (–SO2) and sulphoxides (S@O). The other chemical method of coal desulfurization is leaching with straight-chain aliphatic or aromatic organometallic compounds [28,29]. The mechanism of pyritic sulfur removal from coal is not clear but the reactions for organic sulfurs are: R–ONa þ R1 –S–S–R2 ! Na2 S þ ROR1 þ ROR2
ð1Þ
R–ONa þ R1 –SH ! Na2 S þ ROR1
ð2Þ
R can be benzyl, methyl and n-butyl. During previous studies the reduction of sulfate, pyritic, organic and total sulfur for sodium benzoxide was found to be about 91, 68, 33 and 45.9%, respectively, and 75, 60, 11 and 28% for sodium butoxide [29]. In this work the optical microscopy investigation shows that pyrite is present in forms of discrete grains, fracture and cavity filling, regular and irregular framboidal with particle size ranging from 1 to 40 lm. Scanning electron microscopy studies show that some of pyrite particles are distributed on finer than 1 lm. Fine grinding is needed for the removal of this fine distributed sulfur from coal and is impossible using the conventional methods in practice. Therefore it was applied the chemical desulfurization method. PAA + air oxidation was used as suitable pretreatment processes, for the subsequent desulfurization of coal with sodium butoxide that was reported on previously published work [30]. The effects of other parameters such as sodium butoxide concentration, leaching temperature and time, and particle size, on the extent of different forms of sulfur removal from Tabas coal were investigated. 3. Materials and methods 3.1. Coal sample The bulk sample 600 kg in mass was collected from all operating mine workings of C1 seam the Tabas coal deposit in Iran. The sampling methods similar of Jones riffles, coning, and coning-and-quartering were used. Proximate and ultimate analyses were performed according to standard techniques. In all samples, the content of total, pyritic, and sulfate sulfur were determined by the methods ISO 334 and 157 in replication [31,32]. For pyritic sulfur, the iron concentration in nitric acid solution was determined by colorimetry. The difference yielded the amount of organic sulfur. The mineralogical composition of the sample was also established (Table 1).
E. Jorjani et al. / Fuel 87 (2008) 2727–2734
2729
Table 1 Characterization of Tabas coal representative sample Proximate analysis (wt% as received) Moisture Ash Volatile matter Fixed carbon
0.75 32.3 20.12 46.83
Ultimate analysis (wt% daf) C H N S Odiff
86.25 4.31 2.45 0.67 6.32
Forms of sulfur (wt% db) Total Pyritic Sulfate Organic
1.44 0.77 0.0 0.67
Mineralogical composition Illite, quartz, kaolinite, goethite, feldspar, calcite, pyrite, hematite
3.2. Oxidation of coal with PAA + Air PAA oxidation was carried out by dispersing 8 g of coal in 240 ml of glacial acetic acid and warming it to the desired temperature, followed by adding 80 ml of H2O2 solution (30% w/v) [33]. The experiments were carried out in a 750 ml Pyrex reactor equipped with a thermometric tube and stirrer. Reaction times of 10, 20, 30 and 40 min were used, and the reaction temperature was maintained at 40 °C. After oxidation, the reactor was cooled and filtered to recover the oxidized coal. The filtrate was washed with hot water and dried at room temperature for 24 h. The samples which oxidized using PAA (dried at room temperature), were used for further oxidation with air in an oven, at 100 °C for 1 h. It also helps to evaporate liquids from coal pores and increasing the accessibility of sodium butoxide to the oxidized sulfurs. 3.3. Preparation of sodium butoxide Sodium butoxide was prepared by adding the requisite amount of sodium to 1-butanol (0.8 kg/l) in a flask fitted with a water condenser, and then placed in a cooling bath to decrease the heat of the reaction. A 17% solution was prepared by adding 42.84 g of sodium to 1000 ml of 1-butanol. This solution was then diluted with 1-butanol to prepare the other concentrations of sodium butoxide that were needed. 3.4. Desulfurization procedure The desulfurization experiments were carried out in an 80 ml microreactor (Fig. 1) heated in an oven. When the oven reached the reaction temperature, the reactor, containing 40 ml of sodium butoxide and 7 g of oxidized coal, was placed in it. The experiments were carried out using sodium
Fig.1. Reactor assembly.
butoxide concentrations of 5 and 10%, reaction times of 30, 60, 90 and 120 min, particle sizes of 850–1400, 300–850 and <300 lm, and temperatures of 90, 120, 150 and 190 °C. Following the reaction, the reactor was cooled in cool water and filtered to recover the leached coal. The filtrate was washed with hot water, dried in a vacuum oven at 60 °C, and analyzed for forms of sulfur. Percentage changes in the sulfur in comparison to their original values were calculated as described in the literature [10]. The results for the optimization of sodium butoxide concentration, leaching temperature and time, particle size and PAA oxidation pretreatment time are shown in Table 2. 3.5. Artificial neural networks Since the 1940s, artificial neural networks (ANNs) have been used in various applications in engineering and science [34–41]. ANNs are generally the software systems that imitate the neural networks of the human brain [42]. It is also possible to accept the ANNs as a parallel distributed data process system. The ANNs can be applied successfully in learning, relating, classification, generalization, characterization and optimization functions. Because ANNs have the ability to work with incomplete data, posses error tolerance, and show graceful degradation, they can easily form models for complex problems. Especially in the development of solutions for semi-structural or non-structural problems, artificial neural network (ANN) models can have very successful results. Moreover, they can be cheaper, faster and more adaptable than traditional methods [43]. The main advantages of the ANN models are: (1) no particular knowledge is needed about the system being modeled, unknown effects could be involved through a proper design of the input–output patterns; (2) relative simplicity of neural network architecture [44]; (3) ANNs are also very powerful to effectively represent complex non-linear systems [45]. These application processes of an ANN model design include the steps below [42]: 1. 2. 3. 4.
Collecting the whole data in one place. Determining the train and test sets. Converting the data into the ANN inputs. Determining, training and testing the network topology.
2730
E. Jorjani et al. / Fuel 87 (2008) 2727–2734
Table 2 Results of experiments for sulfur removal by sodium butoxide in different operational conditions Test number
PAA oxidation pretreatment time (min)
Leaching temperature (°C)
Leaching time (min)
Sodium butoxide concentration (%)
Particle size (lm) ((upper limit + lower limit)/2)
Organic sulfur reduction (%)
Inorganic sulfur reduction (%)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
0 10 20 30 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40
120 120 120 120 120 90 90 90 90 120 120 120 150 150 150 150 190 190 190 190 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150
90 90 90 90 90 30 60 90 120 30 60 120 30 60 90 120 30 60 90 120 30 60 90 120 30 60 90 120 30 60 90 120 30 60 90 120
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 10 10 10 10 5 5 5 5 5 5 5 5 5 5 5 5
700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 1125 1125 1125 1125 575 575 575 575 150 150 150 150
13.21 20.31 23.54 27.89 33.58 8.49 18.4 24.59 30.21 14.59 24.89 37.2 20.48 28.94 38.79 41.6 24.5 31.7 41.59 44.6 17.79 32.97 40.78 45.7 17.45 27.26 35.3 39.29 22.12 29.13 40.12 42.13 25.18 33.12 45.17 49.17
52.17 64.27 65.89 66.91 67.41 28.45 45 56 62.8 38.64 57.3 71.6 44.27 60.6 71.1 76 47.56 64 74.56 80.7 46.19 55.76 72.01 77.55 28.45 44.04 54.25 61.36 40.12 58.16 71.77 78 45.31 61.84 78.45 84.36
5. Repeating the 1st, 2nd, 3rd and the 4th steps as long as it is required to determine the optimal model. 6. Application of the optimal ANN model. An ANN can be considered as a black box (hidden layers) consisting of a series of complicated equations for the calculation of outputs based on a given series of input values [44]. It is able to develop a model relating the network’s output to existing actual data used as inputs. After determining the number of input variables by statistical means, the most appropriate architecture for the network should be determined. In this stage, several networks should be created, trained and tested. The number of layers, the optimum number of neurons per layer and the transfer function(s) in the hidden layer(s), obtain by trial and error. Care was taken to avoid overtraining. Therefore it was ensured not to include more weights and biases in both networks than the number of data in the training set [44].
A parameter wij (known as weight) is associated with each connection between two cells. Thus each cell in the upper layer receives weighted inputs from each node in the layer below and then processes these collective inputs before the unit sends a signal to other layers [46]. One of the major advantages of ANN is efficient handling of highly non-linear relationships in data, even when the exact nature of such relationship is unknown [47]. The most popular ANN is the feed-forward multi-layer ANN which uses back-propagation learning algorithm. This type of network consists of three layers of nodes namely an input layer, hidden layers and an output layer. Feed-forward neural network usually has one or more hidden layers, which enable the network to model non-linear and complex functions. Scaled data are introduced into the input layer of the network and then is propagated from input layer to hidden layer and finally to the output layer [48]. Each node in hidden or output layer firstly acts as a summing junction which combines and modifies the
E. Jorjani et al. / Fuel 87 (2008) 2727–2734
inputs from the previous layer using the following equation: yi ¼
i X
xi wij þ bj
ð3Þ
j¼1
where yi is the net input to node j in hidden or output layer, xi are the inputs to node j (or outputs of previous layer), wij are the weights representing the strength of the connection between the ith node and jth node, i is the number of nodes and bj is the bias associated with node j. Each neuron consists of a transfer function expressing internal activation level. Output from a neuron is determined by transforming its input using a suitable transfer function. Generally, the transfer functions for function approximation (regression) are sigmoidal function, hyperbolic tangent and linear function, of which the most widely used for non-linear relationship is the sigmoidal function. The general form of this function is as follows [49]: zj ¼ 1=ð1 þ ey y Þ
ð4Þ
zj, the output of node j, is also an element of the inputs to the nodes in the next layer. The values of the interconnection weights are determined by a neural network training or learning procedure using a set of data. The objective is to find the value of the weight that minimizes differences between the actual output and the predicted output in the output layer in order to minimize the mean square errors (MSE), the average squared error between the network predicted outputs and the target outputs [1]. In the learning process, there are several variables that have an effect on the ANN training. These variables are the number of iterations, learning
2731
rate, the momentum coefficient, number of hidden layers and the number of hidden neurons. To find the best set of these variables and parameters, all of those must be varied and the best combination chosen [47]. In this work, the ANN model have been developed by considering two hidden layer in MLP architecture and with training using the EBP algorithm. The 5-7-10-1 ANN model (Fig. 2), which adequately recognized the effects of different operational conditions on the coal desulfurization, can predict sulfur reduction for the inorganic and organic forms. 4. Results and discussion Neural network training can be made more efficient by certain pre-processing steps [45]. In the present work all inputs (before feeding to the network) and output data in training phase, were used for pre-processing by normalizing the inputs and targets so that they have means of zero and standard deviations of 1: N p ¼ ðAp meanAps Þ=stdAp
ð5Þ
where Ap is actual parameter, meanAps is mean of actual parameters, stdAp is standard deviation of actual parameter and Np is normalized parameter (input). The mean and standard deviation for pre-processing of input variables are given in Table 3. A total of 36 sets of data were used in the predictions by ANN; 25 data sets were used for training and 11 sets for testing the network, for both organic and inorganic sulfur reduction prediction. The training process was stopped after 10 epochs (Figs. 3 and 4). In each epoch, the entire training set is presented to the network, case by case; errors are calculated and used to adjust the weights in the network using sigmoid transfer function. This method is based on the BP error algorithm, which is an iterative supervisedlearning technique. A set of training examples is considered, and for each the desired output of the MLP is known. The network learns the trends contained in the data set and correlates the inputs and the outputs by finding the optimum set of weights that minimizes the differences between the predicted and actual output values. For each iterations, an error between the predicted value and the actual value is propagated backward from the output layer towards the input through the hidden layers until the error is within an acceptable limit [50]. The correlation coefficient (R2) values in training stages were 1 (Figs. 5 and 6).
Table 3 Pre-processing parameters for ANN
Fig. 2. FANN architecture with two hidden layers and 5-7-10-1 arrangement.
Variables
Mean
Standard deviation
PAA oxidation time (min) Leaching temperature (°C) Leaching time (min) Particle size (lm) Sodium butoxide con. (%)
36 146.8 80.4 675 5.8
10.41 25.12 34.34 247.07 1.87
2732
E. Jorjani et al. / Fuel 87 (2008) 2727–2734
Fig. 3. Parity plot for epoch and mean square error for training sets (organic sulfur).
Fig. 4. Parity plot for epoch and mean square error for training sets (inorganic sulfur).
Fig. 6. Inorganic sulfur removal predicted by neural network in training process versus actual measured in laboratory.
Fig. 7. Organic sulfur removal predicted by neural network in testing process in comparison to actual measured in laboratory.
Fig. 5. Organic sulfur removal predicted by neural network in training process versus actual measured in laboratory.
Fig. 8. Inorganic sulfur removal predicted by neural network in testing process in comparison to actual measured in laboratory.
The testing set which actually tests how good the model is, shows that the models could estimate the sulfur reduction quite satisfactorily. The correlation coefficient (R2)
values for testing sets were 0.97 and 0.96 in organic and pyritic sulfur reduction predictions, respectively (Figs. 7 and 8). It was observed that organic and inorganic sulfur
E. Jorjani et al. / Fuel 87 (2008) 2727–2734
reduction from coal using ANN model could be predicted satisfactory.
5. Conclusions
The effects of time of oxidation with PAA, as pretreatment process, particle size, leaching agent concentration and leaching temperature and time on coal desulfurization with an organo metallic compound, sodium butoxide, were investigated in laboratory. In the experiments, the pretreatment time of 40 min on the temperature of 45 °C by PAA, leaching temperature and time of 150 °C and 120 min, sodium butoxide concentration of 5%, and particle size <300 lm (with mean particle size of 150 lm) were determined as the optimum operational conditions for removal of organic (49%) and inorganic sulfur (84%). The produced data, on laboratory optimization process, were used to the simulation by means of artificial neural network. A feed-forward artificial neural network with 5-7-10-1 arrangement was capable to estimate both of organic and inorganic sulfur reduction. In testing process the used model could estimate the sulfur reductions quite satisfactorily. The correlation coefficient (R2) values for testing sets were 0.97 and 0.96 in organic and pyritic sulfur removal predictions, respectively. These studies on chemical removal of organic and inorganic sulfur from coal constitute new unexamined conditions that where ANN have never been used to predict the amount of organic and inorganic sulfur removal from coal. The used method and its related results can further be used as an expert system in Tabas coal desulfurization plant, in order to optimize the process parameters and to evaluate the parameters interactions, for the expected sulfur removal without having to conduct the new experiments in laboratory.
References [1] Yao HM, Vuthaluru HB, Tade MO, Djukanovic D. Artificial neural network-based prediction of hydrogen content of coal in power station boilers. Fuel 2005;84:1535–42. [2] Haykin S. Neural Networks, a comprehensive foundation, USA. 2nd ed. USA: Prentice Hall; 1999. [3] Ungar LH, Hartman EJ, Keeler JD, Martin GD. Process modelling and control using neural networks. Am Inst Chem Eng Symp Ser 1996;92:57–66. [4] Seginer I, Boulard T, Bailey BJ. Neural network models of the greenhouse climate. J Agric Eng Res 1994;59:203–16. [5] Ryan M, Muller C, Keith HJD, Cameron KC. The use of artificial neural networks (ANNs) to simulate N2O emissions from a temperate grassland ecosystem. Ecol Model 2004;175:189–94. [6] Giles AE, Aldrich C, Van JSJ. Modelling of rare earth solvent extraction with artificial neural nets. Hydrometallurgy 1996;43(1–3): 241–55.
2733
[7] Laberge C, Cluis D, Mercier G. Metal bioleaching prediction in continuous processing of sewage with thiobacillus ferrooxidans using neural networks. Water Res 2000;34(41):1145–56. [8] Acharya C, Mohanty S, Sukla LB, Misra VN. Prediction of sulphur removal with Acidithiobacillus sp. using artificial neural networks. Ecol Model 2006;190:223–30. [9] Morrison GF. Chemical desulfurization of coal. IEA Coal Research, London; 1981. [10] Ratanakandilok S, Ngamprasertsith S, Prasassarakich P. Coal desulfurization with methanol/water and methanol/KOH. Fuel 2001;80:1937–42. [11] Gryglewicz G. Effectiveness of high temperature pyrolysis in 475 sulfur removal from coal. Fuel Process Technol 1996;46(3):217–26. [12] Ibarra JV, Palacios JM, Moliner R. Evidence of reciprocal organic matter–pyrite interaction affecting sulfur removal during coal pyrolysis. Fuel 1994;73(7):1046–50. [13] Meyers RA. Coal desulfurization. New York, NY: Marcel Dekker; 1977. p. 254. [14] Meyers RA. Personal communication; 1996. [15] Fan CW, Dong GW, Markuszewski R, Wheelock TD. Coal desulfurization by leaching with ferric or cupric salt solutions, processing and utilization of high-sulfur coals. Elsevier; 1987. p. 172–82. [16] Kindig JK, Goens DN. The dry removal of pyrite and ash from coal by magnex process, coal properties and process variables, proceeding of the symposium on coal cleaning to achieve energy and environmental goals, Hollywood, FL, September 1978, PB 299 384, Washington, DC: US Environmental protection agency; 1979. p. 1165–96. [17] Contos GW, Frankel IF, Mccandless LC. Assessment of coal cleaning technology: an evaluation of chemical coal cleaning processing, PB 289 493, Washington, DC: US Environmental protection agency; 1978. p. 299. [18] Palmer SR, Hippo EJ, Dorai XA. Fuel 1994;73:161. [19] Kubica K, Stompel Z, Jastrzebski J. Koks Smola Gaz 1991:163. [20] Yurum Y, Altuntas N. Fuel 1998;77:1809. [21] Palmer SR, Hippo EJ, Kruge MA, Crelling JC. Coal Prep J 1992; 10:93. [22] Palmer SR, Hippo EJ, Dorai XA. Selective oxidation pretreatment for enhanced desulfurization of coal. Fuel 1995;74(2):193–200. [23] Sonmez O, Giray ES. The influence of process parameters on desulfurization of two Turkis lignites by selective oxidation. Fuel Process Technol 2001;70:159–69. [24] Aelst JV, Yperman J, Franco DV, Mullens J, Van Poucke LC, Palmer SR. Sulfur distribution in Illinois no. 6 coal subjected to different oxidation pre-treatments. Fuel 1997;76(14–15):1377–81. [25] Borah D, Baruah MK. Electron transfer process Part 2. Desulfurization of organic sulfur from feed and mercury-treated coals oxidized in air at 50, 100 and 150 °C. Fuel 2000;79:1785–96. [26] Borah D, Baruah MK, Haque I. Oxidation of high-sulfur coal Part 1. Desulfurization and evidence of the formation of oxidized organic sulfur species. Fuel 2001;80:501–7. [27] Pietrzak R, Wachowska H. Low temperature oxidation of coals of different rank and different sulfur content. Fuel 2003;82:705–13. [28] Mazumder B, Saikia C, Sain B, Buruah BP, Bordoloi CS. Fuel 1989;68:610. [29] Prasassarakich P, Thaweesri T. Kinetics of coal desulfurization with sodium benzoxide. Fuel 1996;75(7):816–20. [30] Jorjani E, Rezai B, Vossoughi M, Osanloo M, Abdollahi M. Oxidation pretreatment for enhancing desulfurization of coal with sodium butoxide. Miner Eng 2004;17:545–52. [31] International Standard, ISO 334. Solid mineral fuels – determination of total sulfur-Eschka method; 1992. p. 1–5. [32] International Standard, ISO 157. Coal – determination of forms of sulfur; 1992. p. 1–15. [33] Boron DJ, Taylor SR. Processing and utilization of high-sulfur coals. New York: Elsevier; 1985. p. 337. [34] Kalra R, Deo MC, Kumar R, Agarwal VK. Artificial neural network to translate offshore satellite wave data to coastal locations. Ocean Eng 2005;32:1917–32.
2734
E. Jorjani et al. / Fuel 87 (2008) 2727–2734
[35] Sozen A, Arcaklioglu E. Effect of relative humidity on solar potential. Appl Energ 2005;82:345–67. [36] Abbassi A, Bahar L. Application of neural network for the modeling and control of evaporative condenser cooling load. Appl Therm Eng 2005;25:3176–86. [37] Yang J, Rivard H, Zmeureanu R. On-line building energy prediction using adaptive artificial neural networks. Energy Build 2005;37: 1250–9. [38] Peisheng L, Youhui X, Dunxi Y, Xuexin S. Prediction of grindability with multivariable regression and neural network in Chinese coal. Fuel 2005;84:2384–8. [39] Yagci O, Mercan DE, Cigizoglu HK, Kabdasli MS. Artificial intelligence methods in breakwater damage ratio estimation. Ocean Eng 2005;32:2088–106. [40] Rezzi S, Axelson DE, Heberger K, Reniero F, Mariani C, Guillou C. Classification of olive oils using high throughput flow HNMR fingerprinting with principal component analysis, linear discriminant analysis and probabilistic neural networks. Anal Chim Acta 2005;552: 13–24. [41] Madan A. Vibration control of building structures using selforganizing and self-learning neural networks. J Sound Vibr 2005; 287:759–84. [42] Trippi RR, Turban E. Neural networks in finance and investing-using artificial intelligence to improve real-world performance. Revised ed. New York: McGraw-Hill; 1996.
[43] Celik AE, Karatepe Y. Evaluating and forecasting banking crises through neural network models: an application for Turkish banking sector. Expert Syst Appl 2007;33:809–15. [44] Ahadian S, Moradian S, Sharif F, Tehran MA, Mohseni M. Application of artificial neural network (ANN) in order to predict the surface free energy of powders using the capillary rise method. Colloids Surface A: Physicochem Eng Aspects 2007;302:280–5. [45] Demuth H, Beale M. Neural network toolbox for use with MATLAB. Handbook 2002. [46] Li YY, Bridgwater J. Prediction of extrusion pressure using an artificial neural network. Powder Technol 2000;108:65–73. [47] Rad ER, Ghanbarzadeh B, Mousavi SM, Emam Djomeh Z, Khazaei J. Prediction of rheological properties of Iranian bread dough from chemical composition of wheat flour by using artificial neural networks. J Food Eng 2007;81:728–34. [48] Hussain MA, Shafiur RM. Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network. J Food Eng 2002;51(3):239–48. [49] Razavi MA, Mortazavi A, Mousavi M. Dynamic modeling of milk ultrafiltration by artificial neural network. J Membrane Sci 2003;220: 47–58. [50] Statsoft. Statistica Neural Network, Statsoft, Tulsa, OK; 1998.
Fuel 87 (2008) 2727–2734 www.fuelfirst.com
Application of artificial neural networks to predict chemical desulfurization of Tabas coal E. Jorjani *, S. Chehreh Chelgani, Sh. Mesroghli Department of Mining Engineering, Research and Science Campus, Islamic Azad University, Poonak, Hesarak, Tehran, Iran Received 26 May 2007; received in revised form 25 January 2008; accepted 29 January 2008 Available online 27 February 2008
Abstract This paper presents a neural network model to predict the effects of operational parameters on the organic and inorganic sulfur removal from coal by sodium butoxide. The coal particle size, leaching temperature and time, sodium butoxide concentration and pre oxidation time by peroxyacetic acid (PAA) were used as inputs to the network. The outputs of the models were organic and inorganic sulfur reduction. Feed-forward artificial neural network with 5-7-10-1 arrangement, were capable to estimate organic and inorganic sulfur reduction, respectively. Simulated values obtained with neural network correspond closely to the experimental results. It was achieved quite satisfactory correlations of R2 = 1 and 0.96 in training and testing stages for pyritic sulfur and R2 = 1 and 0.97 in training and testing stages, respectively, for organic sulfur reduction prediction. The proposed neural network model accurately reproduces all the effects of operational variables and can be used in the simulation of Tabas coal desulfurization plant. Ó 2008 Elsevier Ltd. All rights reserved. Keywords: Artificial neural networks; Coal; Chemical desulfurization
1. Introduction Artificial neural network (ANN) is an empirical modeling tool, which is analogous to the behavior of biological neural structures [1]. Neural networks are powerful tools that have the ability to identify underlying highly complex relationships from input–output data only [2]. Over the last 10 years, artificial neural networks (ANNs), and particularly feed-forward artificial neural networks (FANNs), have been extensively studied to present process models, and their use in industry has been rapidly growing [3]. The use of such networks can now be found for number prediction such as modelling the greenhouse effect [4], simulation N2O emissions from a temperate grassland ecosystem [5], modelling of rare earth solvent extraction [6], bioleaching of metals [7] and coal microbial desulfurization [8].
*
Corresponding author. Tel.: +98 912 1776737; fax: +98 21 44817194. E-mail address: [email protected] (E. Jorjani).
0016-2361/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.fuel.2008.01.029
The use of neural networks to predict coal final sulfur content, after desulfurization with Acidithiobacillus bacteria, was reported by Acharya et al. [8]. Type of coal, initial pH, pulp density, particle size, residence time, media composition and initial sulfur content of coal were fed as inputs to the network and output of the model was final sulfur content (about 0.2–6.6 (%) for the tested samples) after desulfurization. The aim of the present work is the assessment of Tabas coal with reference to the organic and inorganic sulfur removal by sodium butoxide, as leaching agent, and possible variations with change of coal particle sizes, leaching temperature and time, sodium butoxide concentration and duration time of peroxyacetic acid (PAA) pretreatment of coal (to enhancing of coal desulfurization by sodium butoxide), using experimental data obtained at a laboratory level and simulated data by means of a neural network, MATLAB software package. To our knowledge, this is the first time that ANNs have been used to predict both of organic and inorganic sulfur removal from coal in a chemical desulfurization process.
2728
E. Jorjani et al. / Fuel 87 (2008) 2727–2734
2. Area description 2.1. Desulfurization of coal with sodium butoxide The use of high-sulfur coals for energy production requires a cleaning stage to meet environmental regulations. Sulfur in coal occurs in organic and inorganic forms. The organic form is about 50% of the total sulfur that cannot be removed by physical coal cleaning methods [9]. Removal of organic sulfur requires chemical desulfurization techniques that can remove inorganic sulfur and ash-forming minerals too. Generally, cleaning is carried out over the flue gases, but chemical desulfurization has also received attention. Physical and microbial methods were also used. Although chemical desulfurization of coal is not profitable in the current economic conditions, it may become economical in the future, as SO2 emission regulations are tightened and low sulfur coal reserves are depleted. Well known desulfurization processes include: caustic treatment [10], pyrolysis [11,12], oxidation [13–15], IGT hydrodesulfurization [9], magnex process [16] and chemical comminution [17]. Oxidation is the most often applied chemical method of coal desulfurization [18]. Hydrogen peroxide, sodium hypochlorite, nitric acid, potassium permanganate, air, and many other oxidants are used in this method. Oxidation of coal is a very complex process, dependent on a number of parameters such as temperature, pressure, time, reagent concentration, type of coal and its grain size distribution. Oxidation can change coal characteristics such as calorific value, free swelling index, sinter ability and extractability [19,20]. The use of PAA as oxidative reagent for the desulfurization of coal has been reported by Palmer et al. [21,22], Sonmez et al. [23] and Aelst et al. [24]. PAA is believed to produce hydroxyl cations, which are strong electrophiles and react with sulfur atoms because they are considerably more nucleophilic than carbon atoms. It was also found that selective oxidation of coal with PAA was a very effective pretreatment for enhancing desulfurization of coal with various hydroxide and carbonate bases. The structural properties of coal, such as porosity, surface area and pore size, play an important role in chemical desulfurization. Coal contains moisture in its pores and this moisture can be removed by heating at 100 °C. Moisture removal is accompanied by a substantial increase in porosity [25]. Borah et al. [26] have shown that coal oxidation by heating, as a pretreatment process for coal desulfurization by the process of electron transfer, decomposes the large organic sulfur molecules to low molecular weight products. These smaller molecules are much more prone to attack by the leaching solution in subsequent stages for the rupture of C–S as well as S–S bounds. They also found that low temperature oxidation converts coal organic sulfur to compounds containing S@O and –SO2 [26]. In these aerial oxidized samples, besides aliphatic sulfur,
aromatic disulfide compounds can also be leached out [25]. Pietrzak and Wachowska [27] have studied the effect of oxidants such as PAA and air on the oxidation of the coal organic structure by FTIR. They found that during oxidation with atmospheric oxygen at 125 °C for 7 days, hydrocarbon groups are more susceptible to oxidation than sulfur groups; whilst PAA can convert organically bound sulfur to oxidized forms such as sulphones (–SO2) and sulphoxides (S@O). The other chemical method of coal desulfurization is leaching with straight-chain aliphatic or aromatic organometallic compounds [28,29]. The mechanism of pyritic sulfur removal from coal is not clear but the reactions for organic sulfurs are: R–ONa þ R1 –S–S–R2 ! Na2 S þ ROR1 þ ROR2
ð1Þ
R–ONa þ R1 –SH ! Na2 S þ ROR1
ð2Þ
R can be benzyl, methyl and n-butyl. During previous studies the reduction of sulfate, pyritic, organic and total sulfur for sodium benzoxide was found to be about 91, 68, 33 and 45.9%, respectively, and 75, 60, 11 and 28% for sodium butoxide [29]. In this work the optical microscopy investigation shows that pyrite is present in forms of discrete grains, fracture and cavity filling, regular and irregular framboidal with particle size ranging from 1 to 40 lm. Scanning electron microscopy studies show that some of pyrite particles are distributed on finer than 1 lm. Fine grinding is needed for the removal of this fine distributed sulfur from coal and is impossible using the conventional methods in practice. Therefore it was applied the chemical desulfurization method. PAA + air oxidation was used as suitable pretreatment processes, for the subsequent desulfurization of coal with sodium butoxide that was reported on previously published work [30]. The effects of other parameters such as sodium butoxide concentration, leaching temperature and time, and particle size, on the extent of different forms of sulfur removal from Tabas coal were investigated. 3. Materials and methods 3.1. Coal sample The bulk sample 600 kg in mass was collected from all operating mine workings of C1 seam the Tabas coal deposit in Iran. The sampling methods similar of Jones riffles, coning, and coning-and-quartering were used. Proximate and ultimate analyses were performed according to standard techniques. In all samples, the content of total, pyritic, and sulfate sulfur were determined by the methods ISO 334 and 157 in replication [31,32]. For pyritic sulfur, the iron concentration in nitric acid solution was determined by colorimetry. The difference yielded the amount of organic sulfur. The mineralogical composition of the sample was also established (Table 1).
E. Jorjani et al. / Fuel 87 (2008) 2727–2734
2729
Table 1 Characterization of Tabas coal representative sample Proximate analysis (wt% as received) Moisture Ash Volatile matter Fixed carbon
0.75 32.3 20.12 46.83
Ultimate analysis (wt% daf) C H N S Odiff
86.25 4.31 2.45 0.67 6.32
Forms of sulfur (wt% db) Total Pyritic Sulfate Organic
1.44 0.77 0.0 0.67
Mineralogical composition Illite, quartz, kaolinite, goethite, feldspar, calcite, pyrite, hematite
3.2. Oxidation of coal with PAA + Air PAA oxidation was carried out by dispersing 8 g of coal in 240 ml of glacial acetic acid and warming it to the desired temperature, followed by adding 80 ml of H2O2 solution (30% w/v) [33]. The experiments were carried out in a 750 ml Pyrex reactor equipped with a thermometric tube and stirrer. Reaction times of 10, 20, 30 and 40 min were used, and the reaction temperature was maintained at 40 °C. After oxidation, the reactor was cooled and filtered to recover the oxidized coal. The filtrate was washed with hot water and dried at room temperature for 24 h. The samples which oxidized using PAA (dried at room temperature), were used for further oxidation with air in an oven, at 100 °C for 1 h. It also helps to evaporate liquids from coal pores and increasing the accessibility of sodium butoxide to the oxidized sulfurs. 3.3. Preparation of sodium butoxide Sodium butoxide was prepared by adding the requisite amount of sodium to 1-butanol (0.8 kg/l) in a flask fitted with a water condenser, and then placed in a cooling bath to decrease the heat of the reaction. A 17% solution was prepared by adding 42.84 g of sodium to 1000 ml of 1-butanol. This solution was then diluted with 1-butanol to prepare the other concentrations of sodium butoxide that were needed. 3.4. Desulfurization procedure The desulfurization experiments were carried out in an 80 ml microreactor (Fig. 1) heated in an oven. When the oven reached the reaction temperature, the reactor, containing 40 ml of sodium butoxide and 7 g of oxidized coal, was placed in it. The experiments were carried out using sodium
Fig.1. Reactor assembly.
butoxide concentrations of 5 and 10%, reaction times of 30, 60, 90 and 120 min, particle sizes of 850–1400, 300–850 and <300 lm, and temperatures of 90, 120, 150 and 190 °C. Following the reaction, the reactor was cooled in cool water and filtered to recover the leached coal. The filtrate was washed with hot water, dried in a vacuum oven at 60 °C, and analyzed for forms of sulfur. Percentage changes in the sulfur in comparison to their original values were calculated as described in the literature [10]. The results for the optimization of sodium butoxide concentration, leaching temperature and time, particle size and PAA oxidation pretreatment time are shown in Table 2. 3.5. Artificial neural networks Since the 1940s, artificial neural networks (ANNs) have been used in various applications in engineering and science [34–41]. ANNs are generally the software systems that imitate the neural networks of the human brain [42]. It is also possible to accept the ANNs as a parallel distributed data process system. The ANNs can be applied successfully in learning, relating, classification, generalization, characterization and optimization functions. Because ANNs have the ability to work with incomplete data, posses error tolerance, and show graceful degradation, they can easily form models for complex problems. Especially in the development of solutions for semi-structural or non-structural problems, artificial neural network (ANN) models can have very successful results. Moreover, they can be cheaper, faster and more adaptable than traditional methods [43]. The main advantages of the ANN models are: (1) no particular knowledge is needed about the system being modeled, unknown effects could be involved through a proper design of the input–output patterns; (2) relative simplicity of neural network architecture [44]; (3) ANNs are also very powerful to effectively represent complex non-linear systems [45]. These application processes of an ANN model design include the steps below [42]: 1. 2. 3. 4.
Collecting the whole data in one place. Determining the train and test sets. Converting the data into the ANN inputs. Determining, training and testing the network topology.
2730
E. Jorjani et al. / Fuel 87 (2008) 2727–2734
Table 2 Results of experiments for sulfur removal by sodium butoxide in different operational conditions Test number
PAA oxidation pretreatment time (min)
Leaching temperature (°C)
Leaching time (min)
Sodium butoxide concentration (%)
Particle size (lm) ((upper limit + lower limit)/2)
Organic sulfur reduction (%)
Inorganic sulfur reduction (%)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
0 10 20 30 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40
120 120 120 120 120 90 90 90 90 120 120 120 150 150 150 150 190 190 190 190 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150 150
90 90 90 90 90 30 60 90 120 30 60 120 30 60 90 120 30 60 90 120 30 60 90 120 30 60 90 120 30 60 90 120 30 60 90 120
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 10 10 10 10 5 5 5 5 5 5 5 5 5 5 5 5
700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 1125 1125 1125 1125 575 575 575 575 150 150 150 150
13.21 20.31 23.54 27.89 33.58 8.49 18.4 24.59 30.21 14.59 24.89 37.2 20.48 28.94 38.79 41.6 24.5 31.7 41.59 44.6 17.79 32.97 40.78 45.7 17.45 27.26 35.3 39.29 22.12 29.13 40.12 42.13 25.18 33.12 45.17 49.17
52.17 64.27 65.89 66.91 67.41 28.45 45 56 62.8 38.64 57.3 71.6 44.27 60.6 71.1 76 47.56 64 74.56 80.7 46.19 55.76 72.01 77.55 28.45 44.04 54.25 61.36 40.12 58.16 71.77 78 45.31 61.84 78.45 84.36
5. Repeating the 1st, 2nd, 3rd and the 4th steps as long as it is required to determine the optimal model. 6. Application of the optimal ANN model. An ANN can be considered as a black box (hidden layers) consisting of a series of complicated equations for the calculation of outputs based on a given series of input values [44]. It is able to develop a model relating the network’s output to existing actual data used as inputs. After determining the number of input variables by statistical means, the most appropriate architecture for the network should be determined. In this stage, several networks should be created, trained and tested. The number of layers, the optimum number of neurons per layer and the transfer function(s) in the hidden layer(s), obtain by trial and error. Care was taken to avoid overtraining. Therefore it was ensured not to include more weights and biases in both networks than the number of data in the training set [44].
A parameter wij (known as weight) is associated with each connection between two cells. Thus each cell in the upper layer receives weighted inputs from each node in the layer below and then processes these collective inputs before the unit sends a signal to other layers [46]. One of the major advantages of ANN is efficient handling of highly non-linear relationships in data, even when the exact nature of such relationship is unknown [47]. The most popular ANN is the feed-forward multi-layer ANN which uses back-propagation learning algorithm. This type of network consists of three layers of nodes namely an input layer, hidden layers and an output layer. Feed-forward neural network usually has one or more hidden layers, which enable the network to model non-linear and complex functions. Scaled data are introduced into the input layer of the network and then is propagated from input layer to hidden layer and finally to the output layer [48]. Each node in hidden or output layer firstly acts as a summing junction which combines and modifies the
E. Jorjani et al. / Fuel 87 (2008) 2727–2734
inputs from the previous layer using the following equation: yi ¼
i X
xi wij þ bj
ð3Þ
j¼1
where yi is the net input to node j in hidden or output layer, xi are the inputs to node j (or outputs of previous layer), wij are the weights representing the strength of the connection between the ith node and jth node, i is the number of nodes and bj is the bias associated with node j. Each neuron consists of a transfer function expressing internal activation level. Output from a neuron is determined by transforming its input using a suitable transfer function. Generally, the transfer functions for function approximation (regression) are sigmoidal function, hyperbolic tangent and linear function, of which the most widely used for non-linear relationship is the sigmoidal function. The general form of this function is as follows [49]: zj ¼ 1=ð1 þ ey y Þ
ð4Þ
zj, the output of node j, is also an element of the inputs to the nodes in the next layer. The values of the interconnection weights are determined by a neural network training or learning procedure using a set of data. The objective is to find the value of the weight that minimizes differences between the actual output and the predicted output in the output layer in order to minimize the mean square errors (MSE), the average squared error between the network predicted outputs and the target outputs [1]. In the learning process, there are several variables that have an effect on the ANN training. These variables are the number of iterations, learning
2731
rate, the momentum coefficient, number of hidden layers and the number of hidden neurons. To find the best set of these variables and parameters, all of those must be varied and the best combination chosen [47]. In this work, the ANN model have been developed by considering two hidden layer in MLP architecture and with training using the EBP algorithm. The 5-7-10-1 ANN model (Fig. 2), which adequately recognized the effects of different operational conditions on the coal desulfurization, can predict sulfur reduction for the inorganic and organic forms. 4. Results and discussion Neural network training can be made more efficient by certain pre-processing steps [45]. In the present work all inputs (before feeding to the network) and output data in training phase, were used for pre-processing by normalizing the inputs and targets so that they have means of zero and standard deviations of 1: N p ¼ ðAp meanAps Þ=stdAp
ð5Þ
where Ap is actual parameter, meanAps is mean of actual parameters, stdAp is standard deviation of actual parameter and Np is normalized parameter (input). The mean and standard deviation for pre-processing of input variables are given in Table 3. A total of 36 sets of data were used in the predictions by ANN; 25 data sets were used for training and 11 sets for testing the network, for both organic and inorganic sulfur reduction prediction. The training process was stopped after 10 epochs (Figs. 3 and 4). In each epoch, the entire training set is presented to the network, case by case; errors are calculated and used to adjust the weights in the network using sigmoid transfer function. This method is based on the BP error algorithm, which is an iterative supervisedlearning technique. A set of training examples is considered, and for each the desired output of the MLP is known. The network learns the trends contained in the data set and correlates the inputs and the outputs by finding the optimum set of weights that minimizes the differences between the predicted and actual output values. For each iterations, an error between the predicted value and the actual value is propagated backward from the output layer towards the input through the hidden layers until the error is within an acceptable limit [50]. The correlation coefficient (R2) values in training stages were 1 (Figs. 5 and 6).
Table 3 Pre-processing parameters for ANN
Fig. 2. FANN architecture with two hidden layers and 5-7-10-1 arrangement.
Variables
Mean
Standard deviation
PAA oxidation time (min) Leaching temperature (°C) Leaching time (min) Particle size (lm) Sodium butoxide con. (%)
36 146.8 80.4 675 5.8
10.41 25.12 34.34 247.07 1.87
2732
E. Jorjani et al. / Fuel 87 (2008) 2727–2734
Fig. 3. Parity plot for epoch and mean square error for training sets (organic sulfur).
Fig. 4. Parity plot for epoch and mean square error for training sets (inorganic sulfur).
Fig. 6. Inorganic sulfur removal predicted by neural network in training process versus actual measured in laboratory.
Fig. 7. Organic sulfur removal predicted by neural network in testing process in comparison to actual measured in laboratory.
Fig. 5. Organic sulfur removal predicted by neural network in training process versus actual measured in laboratory.
Fig. 8. Inorganic sulfur removal predicted by neural network in testing process in comparison to actual measured in laboratory.
The testing set which actually tests how good the model is, shows that the models could estimate the sulfur reduction quite satisfactorily. The correlation coefficient (R2)
values for testing sets were 0.97 and 0.96 in organic and pyritic sulfur reduction predictions, respectively (Figs. 7 and 8). It was observed that organic and inorganic sulfur
E. Jorjani et al. / Fuel 87 (2008) 2727–2734
reduction from coal using ANN model could be predicted satisfactory.
5. Conclusions
The effects of time of oxidation with PAA, as pretreatment process, particle size, leaching agent concentration and leaching temperature and time on coal desulfurization with an organo metallic compound, sodium butoxide, were investigated in laboratory. In the experiments, the pretreatment time of 40 min on the temperature of 45 °C by PAA, leaching temperature and time of 150 °C and 120 min, sodium butoxide concentration of 5%, and particle size <300 lm (with mean particle size of 150 lm) were determined as the optimum operational conditions for removal of organic (49%) and inorganic sulfur (84%). The produced data, on laboratory optimization process, were used to the simulation by means of artificial neural network. A feed-forward artificial neural network with 5-7-10-1 arrangement was capable to estimate both of organic and inorganic sulfur reduction. In testing process the used model could estimate the sulfur reductions quite satisfactorily. The correlation coefficient (R2) values for testing sets were 0.97 and 0.96 in organic and pyritic sulfur removal predictions, respectively. These studies on chemical removal of organic and inorganic sulfur from coal constitute new unexamined conditions that where ANN have never been used to predict the amount of organic and inorganic sulfur removal from coal. The used method and its related results can further be used as an expert system in Tabas coal desulfurization plant, in order to optimize the process parameters and to evaluate the parameters interactions, for the expected sulfur removal without having to conduct the new experiments in laboratory.
References [1] Yao HM, Vuthaluru HB, Tade MO, Djukanovic D. Artificial neural network-based prediction of hydrogen content of coal in power station boilers. Fuel 2005;84:1535–42. [2] Haykin S. Neural Networks, a comprehensive foundation, USA. 2nd ed. USA: Prentice Hall; 1999. [3] Ungar LH, Hartman EJ, Keeler JD, Martin GD. Process modelling and control using neural networks. Am Inst Chem Eng Symp Ser 1996;92:57–66. [4] Seginer I, Boulard T, Bailey BJ. Neural network models of the greenhouse climate. J Agric Eng Res 1994;59:203–16. [5] Ryan M, Muller C, Keith HJD, Cameron KC. The use of artificial neural networks (ANNs) to simulate N2O emissions from a temperate grassland ecosystem. Ecol Model 2004;175:189–94. [6] Giles AE, Aldrich C, Van JSJ. Modelling of rare earth solvent extraction with artificial neural nets. Hydrometallurgy 1996;43(1–3): 241–55.
2733
[7] Laberge C, Cluis D, Mercier G. Metal bioleaching prediction in continuous processing of sewage with thiobacillus ferrooxidans using neural networks. Water Res 2000;34(41):1145–56. [8] Acharya C, Mohanty S, Sukla LB, Misra VN. Prediction of sulphur removal with Acidithiobacillus sp. using artificial neural networks. Ecol Model 2006;190:223–30. [9] Morrison GF. Chemical desulfurization of coal. IEA Coal Research, London; 1981. [10] Ratanakandilok S, Ngamprasertsith S, Prasassarakich P. Coal desulfurization with methanol/water and methanol/KOH. Fuel 2001;80:1937–42. [11] Gryglewicz G. Effectiveness of high temperature pyrolysis in 475 sulfur removal from coal. Fuel Process Technol 1996;46(3):217–26. [12] Ibarra JV, Palacios JM, Moliner R. Evidence of reciprocal organic matter–pyrite interaction affecting sulfur removal during coal pyrolysis. Fuel 1994;73(7):1046–50. [13] Meyers RA. Coal desulfurization. New York, NY: Marcel Dekker; 1977. p. 254. [14] Meyers RA. Personal communication; 1996. [15] Fan CW, Dong GW, Markuszewski R, Wheelock TD. Coal desulfurization by leaching with ferric or cupric salt solutions, processing and utilization of high-sulfur coals. Elsevier; 1987. p. 172–82. [16] Kindig JK, Goens DN. The dry removal of pyrite and ash from coal by magnex process, coal properties and process variables, proceeding of the symposium on coal cleaning to achieve energy and environmental goals, Hollywood, FL, September 1978, PB 299 384, Washington, DC: US Environmental protection agency; 1979. p. 1165–96. [17] Contos GW, Frankel IF, Mccandless LC. Assessment of coal cleaning technology: an evaluation of chemical coal cleaning processing, PB 289 493, Washington, DC: US Environmental protection agency; 1978. p. 299. [18] Palmer SR, Hippo EJ, Dorai XA. Fuel 1994;73:161. [19] Kubica K, Stompel Z, Jastrzebski J. Koks Smola Gaz 1991:163. [20] Yurum Y, Altuntas N. Fuel 1998;77:1809. [21] Palmer SR, Hippo EJ, Kruge MA, Crelling JC. Coal Prep J 1992; 10:93. [22] Palmer SR, Hippo EJ, Dorai XA. Selective oxidation pretreatment for enhanced desulfurization of coal. Fuel 1995;74(2):193–200. [23] Sonmez O, Giray ES. The influence of process parameters on desulfurization of two Turkis lignites by selective oxidation. Fuel Process Technol 2001;70:159–69. [24] Aelst JV, Yperman J, Franco DV, Mullens J, Van Poucke LC, Palmer SR. Sulfur distribution in Illinois no. 6 coal subjected to different oxidation pre-treatments. Fuel 1997;76(14–15):1377–81. [25] Borah D, Baruah MK. Electron transfer process Part 2. Desulfurization of organic sulfur from feed and mercury-treated coals oxidized in air at 50, 100 and 150 °C. Fuel 2000;79:1785–96. [26] Borah D, Baruah MK, Haque I. Oxidation of high-sulfur coal Part 1. Desulfurization and evidence of the formation of oxidized organic sulfur species. Fuel 2001;80:501–7. [27] Pietrzak R, Wachowska H. Low temperature oxidation of coals of different rank and different sulfur content. Fuel 2003;82:705–13. [28] Mazumder B, Saikia C, Sain B, Buruah BP, Bordoloi CS. Fuel 1989;68:610. [29] Prasassarakich P, Thaweesri T. Kinetics of coal desulfurization with sodium benzoxide. Fuel 1996;75(7):816–20. [30] Jorjani E, Rezai B, Vossoughi M, Osanloo M, Abdollahi M. Oxidation pretreatment for enhancing desulfurization of coal with sodium butoxide. Miner Eng 2004;17:545–52. [31] International Standard, ISO 334. Solid mineral fuels – determination of total sulfur-Eschka method; 1992. p. 1–5. [32] International Standard, ISO 157. Coal – determination of forms of sulfur; 1992. p. 1–15. [33] Boron DJ, Taylor SR. Processing and utilization of high-sulfur coals. New York: Elsevier; 1985. p. 337. [34] Kalra R, Deo MC, Kumar R, Agarwal VK. Artificial neural network to translate offshore satellite wave data to coastal locations. Ocean Eng 2005;32:1917–32.
2734
E. Jorjani et al. / Fuel 87 (2008) 2727–2734
[35] Sozen A, Arcaklioglu E. Effect of relative humidity on solar potential. Appl Energ 2005;82:345–67. [36] Abbassi A, Bahar L. Application of neural network for the modeling and control of evaporative condenser cooling load. Appl Therm Eng 2005;25:3176–86. [37] Yang J, Rivard H, Zmeureanu R. On-line building energy prediction using adaptive artificial neural networks. Energy Build 2005;37: 1250–9. [38] Peisheng L, Youhui X, Dunxi Y, Xuexin S. Prediction of grindability with multivariable regression and neural network in Chinese coal. Fuel 2005;84:2384–8. [39] Yagci O, Mercan DE, Cigizoglu HK, Kabdasli MS. Artificial intelligence methods in breakwater damage ratio estimation. Ocean Eng 2005;32:2088–106. [40] Rezzi S, Axelson DE, Heberger K, Reniero F, Mariani C, Guillou C. Classification of olive oils using high throughput flow HNMR fingerprinting with principal component analysis, linear discriminant analysis and probabilistic neural networks. Anal Chim Acta 2005;552: 13–24. [41] Madan A. Vibration control of building structures using selforganizing and self-learning neural networks. J Sound Vibr 2005; 287:759–84. [42] Trippi RR, Turban E. Neural networks in finance and investing-using artificial intelligence to improve real-world performance. Revised ed. New York: McGraw-Hill; 1996.
[43] Celik AE, Karatepe Y. Evaluating and forecasting banking crises through neural network models: an application for Turkish banking sector. Expert Syst Appl 2007;33:809–15. [44] Ahadian S, Moradian S, Sharif F, Tehran MA, Mohseni M. Application of artificial neural network (ANN) in order to predict the surface free energy of powders using the capillary rise method. Colloids Surface A: Physicochem Eng Aspects 2007;302:280–5. [45] Demuth H, Beale M. Neural network toolbox for use with MATLAB. Handbook 2002. [46] Li YY, Bridgwater J. Prediction of extrusion pressure using an artificial neural network. Powder Technol 2000;108:65–73. [47] Rad ER, Ghanbarzadeh B, Mousavi SM, Emam Djomeh Z, Khazaei J. Prediction of rheological properties of Iranian bread dough from chemical composition of wheat flour by using artificial neural networks. J Food Eng 2007;81:728–34. [48] Hussain MA, Shafiur RM. Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network. J Food Eng 2002;51(3):239–48. [49] Razavi MA, Mortazavi A, Mousavi M. Dynamic modeling of milk ultrafiltration by artificial neural network. J Membrane Sci 2003;220: 47–58. [50] Statsoft. Statistica Neural Network, Statsoft, Tulsa, OK; 1998.