Prediction of operational parameters effect on coal flotation using artificial neural network

Journal of University of Science and Technology BelingVolume 15, Number 5, October 2008, Page 528MineralELSEVIERPrediction of operational parameters effect on coal flotation usingartificial neural networkE. Jorjani, Sh. Mesroghli, and S. Chehreh ChelganiDepartment of Mining Engineering, Science and Research Branch, Islamic Azad University, Poonak, Hesarak, Tehran, Iran〔 Received20080l-11)Abstract: Artificial neural network procedures were used to predict the combustible value(i.e. 100-Ash)and combustible recoveryof coal flotation concentrate in different operational conditions. The pulp density, pH, rotation rate, coal particle size, dosage of col-lector, frother and conditioner were used as inputs to the network Feed-forward artificial neural networks with 5-30-2-1 and 7-10-3-1arrangements were capable to estimate the combustible value and combustible recovery of coal flotation concentrate respectively asthe outputs. quite satisfactory correlations of l and 0.91 in training and testing stages for combustible value and of I and 0.95 intraining and testing stages for combustible recovery prediction were achieved. The proposed neural network models can be used todetermine the most advantageous operational conditions for the expected concentrate assay and recovery in the coal flotation processo 2008 University of Science and Technology Beijing. All rights reserved.Key words: coal flotation; operational parameters; artificial neural networks; combustible recovery1 IntroductionThe use of neural networks to predict locked cycleflotation test results was reported by Cilek [18]. It wArtificial intelligence techniques have been widely found that the neural network model could be used tostudied and used in engineering problems. Expert sys- simulate various flotation circuit types with an error ofless than 4%, instead of the simulation method. alsotechnique in the field of artificial intelligence [1-6]. it was found that the neural network model, as an al-These are used in different fields as process control intermative to the simulation method, can be used to dethe iron and steel industry [7-9). An expert system that termine the effects of changes in certain flotationis designed to emulate the expertise of experts anvariables on the number of cleaner and scavengeveteran operators in performing control activities is stages in a flotation circuit[18]. Labidi et al. [19]usedcalled an expert control system. Such a system uses neural networks to predict the effect of operationalempirical knowledge to solve the control proparameters on the efficiency of ink removing frois a powerful technique for controlling a complex paper by flotation. They found that the neural networkprocess with nonlinearities and uncertainties [10-13]. model accurately reproduces all the effects of operational variables and could be used in a simulation of aNeural networks are effectively used for the modeling, deinking plant to determine the optimal operationalidentification, and control of complex systems, and alarge number of neural network algorithms have beenconditions [19].developed [14-17]The aim of the present study is the assessment ofTabas coal with reference to the ash removal by flota-Flotation is one of these processes for whichtion and possible variations with the change of pH,cial neural networks(ANNs) can be utilized withsize of coal particles, pulp density, mixing rate,benefits. The multiplicity of the factors to be taken amount of collector, frother and conditioner using exinto consideration in the flotation processes compli- percates any modeling using classical statistical techYH中国煤化工 boratory level.andral network, MATniques.LABCNMHGCorrespondingauthorEJorjani,E-mail:esjorjani@yahoo.comAlso avallable online atwwysdencedirect.come 2008 University of Science and Technology Beijing. All rights reserved.E Jorjanief al, Predicton of operational parameters effect on coal flotation using2. Backgroundble material for different reagent conditions; however,Coal is the altered remains of prehistoric vegetation procedure is tedious s are time-consuming and thethe statistical methodthat originally accumulated in swamps and peat bogs.Coal has several important uses worldwide. The mostTo develop an optimum circuit configuration for asignificant uses are in electricity generation, stecoal washery plant, several batch flotation tests haveproduction, cement manufacturing and other industrial to be conducted to predict the metallurgical perform-processes, and as a liquid fuel. It plays a vital role inance of circuit under different operation conditionspower generation and this role is set to continue. CoalArtificial neural network simulation method used tocurrently fuels 40% of the world's electricity and this predict locked cycle flotation test results from data ofproportion is expected to remain at similar levels overindividual batch tests [18] and also to predict the ef-the next 30 years. It is also essential for iron and steelfect of operational parameters on the efficiency of inkproduction; about 64% of steel production worldwide removing from paper[19]is available in the literaturecomes from iron made in blast furnace that uses coal. In this work, artificial neural network was used toThe world crude steel production was 965 million predict the combustible value and recovery of coaltones in 2003, using around 543 Mt of coal [20 Ash flotation concentrate based on the laboratory experi-has a highly adverse effect on the productivity of blast ments. The results can be used to develop an optimumfurnace and on the consumption of coke in the blastcircuit configuration for a coal washery plant, alsofurnace [21]. An increase in the ash content of a con-after commissioning of plant, to optimize the processcentrate by 1% over a critical limit, results in anparameters(pulp density, pH, rotation rate(rcrease in coke consumption by about 4%-5%and a coal particle size, amount of collector, frother anddecrease in blast fumace productivity by about conditioner)and to evaluate their interactions,for the3%-6%.Thus, the pressure is always on coal prepara- expected ash removal and concentrate recovery,tion plants to supply coal with very low ash [21]without having to conduct the new experiments inConventionally, coarse coal is processed throughravity separation systems and fine coal through flota- 3. Materials and methodsingly finding their applications. Froth flotation is the 3. 1. Coal samplewell-established process for fine coal cleaning. FlotaThe bulk sample of 600 kg in mass was collectedtion is based on exploiting differences in the surface from all operating mine workings of CI seam of theproperties of the organic material in coal and the min- Tabas coal deposit in Iran. The sampling method iseral matter. The organic matter is hydrophobic, par- similar to Jones riffles, coning and coning-and-quarticularly in bituminous coals, and is not readily wetted tering were used and the -850 um size fraction ofmineral surfaces. In a flotation devicerepresentative sample was used for flotation studies.cleaner-coal particles with little or no mineral matter Proximate and ultimate analyses were performed acthrough the cell, and hence rise to the top. p&, ising cording to the standard methods. The content of total,pyrite, and sulfate sulfur were determined by thewith a high mineral content exposed on their surface methods Is0 334 and 157 in replication[24-25].Thetend to be fully wetted, sink, and rejected.mineralogical composition of the sample was alsoThe performance of a continuous operation circuit tablished, as shown in Table 1is influenced by the flotation variables. Therefore, the3. 2. Flotation studieseffects of these variables should be fully determined toobtain acceptable metallurgical performance from the The experiments were performed in a denver labo-coal washery plant. Some researchers used statistical ratory flotation cell. In these experiments, the processmodels for the optimization and prediction of combus. parameters were studied for ash reduction in the flotatible value and recovery of coal flotation concentrate tion process, and the parameters are as follows: PH 5,[22-23]. Naik et al. [23] assessed the effect of three 6, 7, 8 and 9; concentration of collector(diesel oil)(g/t)most important reagents for coal flotation: sodium 500,中国煤化工 ration of frothersilicate, collector (kerosene)and frother (MIBC), us- (pineconcentration ofing 23 full factorial designs. The regression models depreCNMHwere developed using factorial experiment data to 1200; pulp density( %)7, 10, 13 and 15; and particlequantify the effect of sodium silicate, collector and size, dioo (um):-180,-350,-500 and-850.Beforefrother and to predict grade and recovery of combusti- optimizing, a comparison between gas oil and kero-530J Univ Sct TechnoL Beiing, VoL 15, Na. 5, Oct 2008sene, as collector, and pine oil and mibC, as frother, lyzed ( for ash to calculate the combustible value andwere conducted and gas oil and pine oil were selected recovery. In the calculations, the three achieved con-as the collector and frother, respectively. Three con centrates were combined and considered as the finalcentrates were prepared in a stage of 1.5 min(Cl, C2, concentrate. The results are shown in Table 2.and C3), and the final tail was filtered, dried, and ana-Table 1. Characterization of-850 um size fraction of Tabas coal sampleProximate analysis(as received wt% Ultimate analysis(dry ash free ywt% Sulfur forms(dry )wt% Mineralogical compositionMoisture84.67TotalIlliteFeldspar23.8H5.25Pyritic 0. 65QuartzCalciteVolatile matter21.6Fixed carbon53.7047Organic0.47HematiteTable 2. Experimental results for ash removal in different operational conditionsConditionerParticleTest No. pH rate/ (gas oily (pine oily (sodium silidensityCombustible Recoveryrmin(g-t(g-t cate/(gtum510001000<5005449610009237100092.18484512091.1958.94910005700919846.2790695399889891.3052998100092.1637.5312886.7340.35909214810008350898383.62168100084.268927178100085.6487588100084.671981000<18089.7873.752081000<3508497920021100120<850853584.4422810001084.85922487200<5008507862933. Artificial neural network procedureprising of I,J, K, and L number of processing nodes,respectively. Each layer of neurons receives its inpNeural networks are well-suited for many applica- from the previous layer or from the network input.tions such as control problems, diagnostics, mapping, The output of each neuron feeds the next layer or thesion and pattern recognition [26]. An artificial neural output of the network [28]. The network stores the in-network consists of interconnected layers of ticnon-linear processing elements, which are commonly pro中国煤化工 The ability to ap.n and informationreferred to as neurons, as they resemble biological prooCNMH Completely dependneurons [27]. A neural network in its basic form is on the weight of the link between the neurons. Sincecomposed of several layers of neurons, an input layer, these weights cannot be pre-determined for ane or more hidden layers, and an output layer com- large-scale neural network, the learning ability is nec-E. Jorjaniet aL, Prediction of operational parameters effect on coal notation using.531essary for a neural network to adjust the weights fromthe training pattern [27]The most popular and successful learming algorithmased to train multi-layer networks is theback-propagation(BP)scheme, and some modifica-3tions, such as the momentum strategy and the adaptiveleaming rate coefficient method, have been used toimprove the performance of the original version of theHidden layer-2BP algorithm [29]. The widely used back-propagation(BP)neural network is a feed-forward, multi-layernetwork, which can be considered as non-linear map-Fig. 1. Multilayer perceptron neural network modelping of the input pattem to the output pattern [27](7-10-3-1Training of such a network involves using a data- 4. Results and discussionbase of examples for the input and output of the network.An input is any data that is used by the expert to In this study, feed-forward artificial neural networkarrive at a solution, prediction, or decision. An output ( FANN) was used to estimate the combustible valueis the solution, prediction, or decision that the neural and recovery of coal flotation concentrate, using thenetwork will learn to produce. The neural network experimental data shown in Table 2, with 5-30-2-1tries to find the relationship between the inputs and and 7-10-3-1 arrangements, respectively. The detailsthe outputs by calculating their relative importance of the two ANN models are shown in Table 3. The(weights). It calculates and compares the results with number of neurons in the hidden layers was obtainedthe actual answer in the data. The network will lean by the trial and error method so that the error betweenby adjusting the weights to minimize the error of the the desired and estimated outputs was minimized. Theoutputs[28]. As an example feed-forward network input and the hidden layers also have a bias, which haswith one input layer, two hidden layers, and one out- a constant value of 1. The interaction and correlationput layer, which were used for recovery prediction, is between the predictors and outputs of the ANN mod-shown in Fig. 1els are shown in Table 4Table 3. Details of ANN modelsStructureModelTraining,幅6camn,b2Ⅱh, rotation rate, collector, frotherRecovery 7 1002Table 4. Correlation coefficients between predicted values and outputsPH RotationCollector Frother Conditioner Particle Pulp den-CombustisIze sItyble valueRotation rate 0.08 1.00CollectorFrother -0.03 0.030.041,00Conditioner -0.06 0.060.02Particle size -0.01 0.010010.01Pulp density 0.10 -0.10Combustible0.350.170.250.130.30Recovery 0.25 -0.150.110.19中国煤化工087100In this study, pre-processing step was used, which by neCNMHGwas so that they havecan make the neural network training more efficient. the mean of zero and the standard deviation of l usingPre-processing of the network training set was done the following equation:J Univ Sci TechnoL Beiing, voL15, No5, Oct 2008Np( performance of the ann models are shown in Table 6Performance is7.08337×106,g0lisl×10where, Aa is the actual parameter, Aap the mean ofthe actual parameter, d dap the standard deviation of 10[30]. The mean and standard deviation forpre-processing of input and output variables are givenin table 510Table 5. Pre-processing parameters for Ann which wereused in normalizing process101172.22117.85Fig 3. Parity plot between epoch and mean square error145.52for the training sets of recovery.Frother/(gt)1169425.04Particle size/um8726Best linear fit: A=0.765T+21.8772089Rotation rate/rmin10166778.59Solid/%0.71R0.909Combustible value/%88.133.72Recovery/%20.69Data pointsBest linear fita total of 24 sets of data were used in the presenttudy, out of which 18 sets were used for training thenetwork and 6 sets for testing. The training processwas stopped after 9 and 70 epochs for the combustiblevalue and recovery, respectively(Figs. 2 and 3). The Fig. 4. Comparlson of experimental combustible valuescorrelation coefficients(R)for the training set on both with those estimated by ANn in the test process(the testof dependent variables(combustible value and recov- numbers 3, 10, 15, 19, 21 and 23 in the Table 2 were usedery) were equal to 1for testing and the others for training of the network).Performance is 6.*10- goal is I*1tBest linear fit: A= 1. 157-6.94104R095350o Data pomtsEpochActual recovery, TFig. 2. Parity plot between epoch and mean square error Fig. 5. Comparison of experimental recovery with thosefor the training sets of combustible valueestimated by ann in the test process(the test numbers 411,12, 21, 23 and 24 in the Table 2 were used for testingThe test set that actually determines how good the and the others for training of the network)model is shows that the models can estimate the outputs quite satisfactorily. The R values for the testing Table 6. Statistical analysis of combustible value and resets were 0.91 and 0.95 in combustible value and re- covery predictions and generalization performance of ANNcovery predictions(Figs. 4 and 5). It was observed中国煤化工that the combustible value and recovery of coal flota-tion concentrate could be predicted using the ANNCNMH Quare Correlationmodel satisfactorily. The statistical analysis of the Combustible value 1.00 6.05x10 0.909combustible value and recovery predictions and the1.00708×106E Jorjaniet al, Prediction of operational parameters effect on coal flotation using..5. Conclusions[13] Z.X. Cai,Y N. Wang, and J F. Cai, A real-time expertcontrol system, Artif Intell. Eng, 10(1996), P.317.(1)The optimum conditions for maximum removal [14] D.E. Rumelhart, G.E. Hinton, and R. Williams, Lean-of ash(combustible value: 91.19%)and a corre-ing intemal representations by error propagation, [in]DE.sponding reasonable recovery of 59% were as followsRumelhart and J. L. McClelland, Parallel Data ProcesspH8, particle size,( d oo)500 um, pulp density 7%o, [15 K.S. Narendra and K. Parthasarathy, Identification androtation rate 1000 r/min, gas oil 1200 g/t, pine oil 120control of dynamic system using neural networks, IEEEg/t and sodium silicate 1000 g/tTrans. Neural Networks, I(1990), p 4(2)In the testing process, the proposed ANN mod- [16] M. Piovoso, K.Kosanovich, V.Rokhlenko, and AGuez,els can estimate theof three nocorrelation coefficients (R) for testing sets are 0.91to a non adiabatic first-order exothermic reaction in aCSTR, [in] Proceedings of American Control Conference,and 0.95 in combustible value and recovery pretions, respectively.[17 M.T. Hagan, H B Demuth, and M H Beale, Neural Net-(3)The used method and its related results can furwork Design, PWS Publishing, Boston, 1996.ther be used as an expert system in coal washerylocked cycle flotation test results, Minerals Eng, 15(2002),plants to optimize the process parameters(pH, coalP.1095particle size, pulp density, rotation rate, and reagent [19] J Labidi, M.A. Pelach, X. Turon, and P Mute, Predictdosages) and to evaluate their interactions, for the exing flotation efficiency using neural networks, Chem. Engpected ash removal without having to conduct the newProcess,46(2007),p314experiments in laboratory[20] World Coal Institute, The Coal Resource, a Comprehenive Overview of Coal, 2007, p 1References[21]S H. Dey and K.K. Bhattacharyya, Split and collector lesslotation to medium coking coal fines for multi-product[] F. Hayes-Roth, D A. Waterman, and D B. Lenat, Buildingzero waste concept, Fuel Process. Technol, 88(2007),Expert Systems, Addison-Wesley, London, 1983, p 507.[2] P. Jackson, Introduction to Expert Systems, Addi- [22]R Sripriya, P V T. Rao, and B R Choudhury, Optimiza-son-Wesley, Wokingham, London, 1986on of operating variables of fine coal flotation using a[]J. Liebowitz, Introduction to Expert Systems, Mitchellcombination of modified flotation parameters and statisti-Publishing McGraw Hill, Watsonville, 1988cal techniques, Int.J. Miner. Process, 71(2003), No 22,[4]J. Liebowitz and D.A. DeSalvo, Structuring Expert Sys-p.l31tems: Domain, Design, and Development, Prentice-Hall, [23] P K. Naik, P.S.R. Reddy, and V N. Misra, InterpretationEnglewood Cliffs, New Jersey, 1989of interaction effects and optimization of reagent dosages[]R. Mockler, D. Dologite, Introduction to Expert Systems,for fine coal flotation, Int. J. Miner. Process, 75(2005)Macmillan, New York, 1992No1-2p.83.[6] J. Liebowitz, Expert systems: a short introduction, Eng. [24] International Standard, ISO 334, Solid Mineral Fu-fract. Mech., 50(1995), P601els-Determination of Total Sulfur-Eschka Method, 1992[7 I. Tsushima, T. Tashiro, N. Komoda, K. Baba, and S. Ta- [25] International Standard, ISo 157, Coal-Determination ofkakura, Application of rule based control to flow line con-Forms of Sulfur, 1992.trol: billet conditioning line control, Trans. Soc. Instrum. [26] R.R. Jensen, S. Karki, and H. Salehfar, Artificial neuralControl Eng, 21(1985), No 10, p. 1113.network-based estimation of mercury speciation in com-[8] K Yui, H. Nishikawa, S. Watanabe, T. Seki, and Ybustion flue gases, Fuel Process. TechnoL., 85(2004)Kawamura, Application of knowledge system forfumace operation, J. Soc. Insmum. Control Eng[27] Z Hao and X.P. Qian, K.F. Cen, J.R. Fan, Optimizing(1987),No8,p712pulverized coal combustion performance based on ANN9]M. Ishiduka and s. Kobayashi, Introduction to Expertand ga, Fuel Process. Technol., 85(2003), p 113Systems, Addison-Wesley, Wokingham, 1991[28]Q. Zhu, J.M. Jones, A. williams, and K M. Thomas, The[10]KJ. Astrom, J.J. Anton, and K E. Arzen, Expert controL.Automatica, 22(1986), No 3, P 277.neural network approach, Fuel, 78(1999), p 1755.[11] J. Efstathiou, Expert Systems in Process Control, Long- [29]C. Yin, Z. Luo, M. Ni, and K. Cen, Predicting coal ashman, Essex, 1989fusion temperature with a back-propagation neural net-[12]K M. Passion and A D. Lunardhi, Qualitative analysis ofwork model, Fuel, 77(1998), p 1777.expert control systems, [in]M.M. Gupta and N K Sinha, [30]Intelligent Control Systems, Theed applications中国煤化工 etwork Toolbar forIEEE Press, New York, 1996, p. 404CNMHG