Controls of the temperature, pressure and flowing quantity are important for the stable operation of the product quality in the distillation tower. The usual measuring way of product quality estimation is made by the off-line analysis. In this paper, online estimation method of product quality is studied for improving the product quality. The estimation method based on stochastic analysis was developed for online estimation. In this paper, the
data of temperature, pressure and flow volum in the distillation tower are treated.
As the estimation models, RNN (Recurrent Neural Net Work) and PLS (Partial Least Square Regression Method) were adopted. The actual plant data were used in the analysis.
Both PLS and RNN models could compensate each other to improve the accuracy in estimation

Imai, Jun

Konishi, Masami

Nishi, Tatsushi

Nukina, Takashi

Okayama University Scientific Achievement Repository

Takashi Nukina ∗Dept. of Electrical and Electronic EngineeringOkayama University3-1-1, Tsushima-Naka, Okayama, 700-8530　　　　Masami KonishiDept. of Electrical and Electronic EngineeringDivision of Industrial Innovation SciencesThe Graduate School ofNatural Science and TechnologyOkayama University3-1-1, Tsushima-Naka, Okayama, 700-8530Tatsushi NishiDept. of Electrical and Electronic EngineeringDivision of Industrial Innovation SciencesThe Graduate School ofNatural Science and TechnologyOkayama University3-1-1, Tsushima-Naka, Okayama, 700-8530Jun ImaiDept. of Electrical and Electronic EngineeringDivision of Industrial Innovation SciencesThe Graduate School ofNatural Science and TechnologyOkayama University3-1-1, Tsushima-Naka, Okayama, 700-8530Controls of the temperature, pressure and flowing quantity are important for the stableoperation of the product quality in the distillation tower. The usual measuring way ofproduct quality estimation is made by the off-line analysis. In this paper, online estimationmethod of product quality is studied for improving the product quality. The estimationmethod based on stochastic analysis was developed for online estimation. In this paper, thedata of temperature, pressure and flow volum in the distillation tower are treated.As the estimation models, RNN (Recurrent Neural Net Work) and PLS (Partial LeastSquare Regression Method) were adopted. The actual plant data were used in the analy-sis. Both PLS and RNN models could compensate each other to improve the accuracy inestimation.1 IntroductionControls of temperature, pressure, and flow volumeare inevitable for stable operation and assurance of theproduct quality in the distillation tower. The usualmeasuring way of product quality was the off-line anal-ysis of the product. To improve the product quality, it∗nukina@cntr.elec.okayama-u.ac.jpis necessary develop a new method to estimate it foronline use. In the present time, an expensive high tech-nological quality sensor is used for online use. How-ever, to adjust the sensor, expert technology is needed.To solve the above problem, acculate estimation tech-nology for product qualities is needed. To attain thepurpose, signal processing method is studied using thedata of temperature, pressure and flow volume in theStatistical Estimation Model for Product Quality of PetroleumMemoirs of the Faculty of Engineering, Okayama University, Vol.40, pp.9-15, January, 2006(Received January 27, 2006)This work is subjected to copyright.All rights are reserved by this author/authors. 9distillation tower.The methods of estimating data are the methods ofusing a physical model and a statistical model. How-ever, the achievement of the physical model is difficultbecause the influence of disturbance deteriorates in ac-curacy. So, it is difficult to estimate accurate qualitiesonly by a physical model. PLS analysis and RNN anal-ysis are adopted because the multivariate analysis thatis a statistical analytical method is suitable for the dataanalysis in the distillation tower. But the deteriorationin the estimation accuracy is a problem for online useof the method for the distillation tower.In this research, sequential update method was adoptedfor a method of achieving high estimation accuracy bya statistical method. On the while, the estimation ac-curacy changes by the period of estimation or the vol-ume of used data. In this research, there are a numberof training data, a number of latent number and a thenumber of hiddenlayers as the estimation of a statisti-cal model. The method to modelate these parameterswas developed to utilize it. The effectiveness of the pro-posed method was confirmed by the application test ofthe estimation model using actual plant data.Next, the analysis method using good points of RNNand PLS model was proposed. This estimation methodproposed here, consists of a basic characteristic modelby PLS and correction model by RNN. The effective-ness of this method was confirmed by the applicationtest of estimation using the actual plant data.2 Distillation tower and its dataThe distillation tower is a plant that separates thecrude oil to diesel oil，heavy oil，and coal oil，etc. byusing the difference of the boiling point. The condi-tions of the product quality in the distillation towerare forecast by using a variety of analysis methods. Inthe following, the data used for the quality estimationin the distillation tower are explained.2.1 Distillation towerA view of the distillation tower is shown in Fig.1.Thedistillation tower separates from crude oil to various el-ements by using the difference of the boiling point. Itis not possible to separate to each element completelyin one distillation tower. So, product purity is raisedby separating through plural distillation towers. Thereare a lot of devices that measure temperatures, pres-Fig. 1. View of distillation towersures and follow volums in the distillation tower. Inthe estimation, a lot of data measured in the distil-lation tower by many measuring devices are adoptedas input data. And the temperature of the productin the distillation tower is adopted as output data. Astatistical model is made from these input and outputdatas, and the temperature of the product is estimated.After that, the way how the product qualities changeby disturbance can be estimated based on a statisticalmodel.2.2 Process data of distillation towerFig. 2. Schemaic model of distillation towerA schemaic model of the distillation tower is shownin Fig.1. As shown in Fig.2 there are 28 measuringdevices set at each places in the distillation tower andTakashi NUKINA et al. MEM.FAC.ENG.OKA.UNI. Vol.4010tower datas are measured by these devices in every 15minutes simultaneously. The data measured by a high-tech sensor is set as output data and the data of othermeasuring devices at 27 places of the distillation towerare set as input data.There are 1266 sampled data for one output and 27inputs measured in every 15 minutes.3 Description of analysis methodIn this chapter, analysis method by PLS, RNN andthe evaluation index will be explained.3.1 PLS analysisConstruction of PLS analysis is shown in Fig.3.Fig. 3. PLS analysisThere is a MRA(multiple regression analysis) in mul-tivariate analysis techniques. MRA is a least squaremethod ofN dimensions. And MRA is able to estimateprocess characteristics in good accuracy. But MRAhas a problem of multicollinearity. Multicollinearityis linear relation between variables. If there is linearrelation between variables, the estimation accuracy de-teriorates because unnecessary variables enlarges esti-mation error. The PLS analysis can solve a problemof multicollinearity of MRA. PLS analysis generatesnew variables made from original variables avoiding aproblem of multicollinearity. In addition, PLS analysismakes new variables that have a strong correlation toan output variable. So, PLS can estimate better accu-racy than MRA. The PLS analysis is adopted becausethere are correlation between the datas measured byeach sensor in the distillation tower.Here, the mathematical description of PLS analysiswill be introduced. There are P kinds of input variablesX that have each N samples, and there is one outputvariable Y that haveN samples. Input datas(measuredby each sensor) are measured by sensor. PLS makesthe first latent variable that inner product becomes themaximum from input variables. If coupling coefficientof latent variable is w1, the value of tn1 of the firstlatent variable z1 corresponded nth samples xn is asfollows.tn1 =p∑p=1wp1xnp = xnw1 (1)The value tn1 of this first latent variable z1 in matrixform is as follows.t1 =w11x11 + w21x12 + · · ·+ wp1x1pw11x21 + w21x22 + · · ·+ wp1x2p...w11xN1 + w21xN2 + · · ·+ wp1xNp (2)= Xω1 (3)So, inner product φ1 of the output variable y andthe latent variable z1 is given as follows.φ1 = yTXω1 =N∑n=1wp1yntn1 (4)To derive the latent variable z1, the coupling coeffi-cient ω1 that makes inner product φ1, maximum is tobe derived. Consequently, coupling coefficient ω1 is asfollows.ω1 =1φ1XT y (5)Thus, the coupling coefficient ω1 can be obtained.If the change of output variable can not be estimatedenough by the latent variable z1, the number of latentvariables are increased until the change of output vari-able can be estimated. The second latent variable ismade according to the same process as the first latentvariable. X(1) and y(1) that could not be expressed bythe first latent variable z1 are assumed to X and y,and the the second latent variable z2 is made by theJanuary 2006 Statistical Estimation Model for Product Quality of Petroleum11same process as the first latent variable. Also the thirdlatent variable z3 is made by the same process.The PLS analysis improves a problem of multicollinear-ity of MRA like above. So, PLS analysis is a betteranalysis than MRA because it can make new variablesthat have a strong correlation with the output. Newvariables made from original variables can solve a prob-lem of multicollinearity.3.2 RNN analysisThe method of RNN analysis is shown in Fig.4.Fig. 4. RNN analysisUsually, NN(neural network) analysis is not used forthe quality estimation of the distillation tower. NNanalysis is an excellent analysis method for a nonlin-ear element. Further, RNN(recurrent newral network)analysis can estimate an excellent estimation accuracyin the plant such as the distillation towers which issuffered by frequent turbulances. Moreover, qualitiesof the distillation tower are affected by past influences.The neural network used in this research is a type of hi-erarchical neural network with three layers, and it hasthe feedback of the estimation of output. So the neu-ral net work used in this research is RNN. The learningway of RNN is back propagation method.3.3 Evaluation IndexEach variable is shown as follows.　　　　　 yi is actual data　　　　　 y¯ is mean of actual data　　　　　 yˆi is value of estimation　　　　　 ei = yˆi − y¯　　　　　 e¯ = yi − y¯Two methods are used for the evaluation index show-ing estimation accuracy.• RMSE(Root Mean Square Error)　　　　 RMSE =√∑e2iN(6)This evaluation index shows the square mean ofthe error with the actual value.• INDEX(Index of mean square error)　　　　 INDEX = 100×∑e2i(yi − y¯)2(7)This evaluation index showing difference betweenestimation valuesand actual data.4 Analytical MethodIn this chapter, the cause of residual error by PLSanalysis and RNN analysis is tried to be solved. Inthe following, sequential and update functions of thesemodels will be explained. And, these effects are checkedbased on actual data.4.1 Sequential ModelingThe sequential modeling is a method of reconstrac-tion of the model when new data is obtained. Thesequential modeling is shown in Fig.5.Fig. 5. Sequential ModelingTakashi NUKINA et al. MEM.FAC.ENG.OKA.UNI. Vol.40124.2 Model Update FunctionAs show in Fig.6, it is necessary to decide three pa-rameters, the number of the latent variables, the num-ber of training datas and delay time when a statisticalmodel is applied. In this reseach, parameters of PLSmodel is analyzed by past data. Appropriate valuesof these three variables are decided. This proceduredefined as Model Update Function.Fig. 6. Model Update FunctionTo examine the effectiveness of the model updatefunction, output datas are analyzed by two kinds ofPLS models. One is analyzed by PLS without modelupdate function and the other is analyzed by PLS withmodel update function.Fig. 7. Seq.PLS without model update functionThe result in Fif.8 by the model update functioncan be confirmed that the estimation accuracy has im-proved more than the result in Fig.7. Moreover, it ispossible to operate the plant for a long time stably byuse of the model update function.As shown in Fig.9, it can be confirmed that the es-timation accuracy has improved by using the modelFig. 8. Seq.PLS with model update functionFig. 9. Effectiveness of Model Update Function for along timeupdate function for a long time test.4.3 Model with delay timeThere exists delay times between measure devices inthe distillation tower. For instance, when it rains, itseffects difference in the height position of distillationtower. That is, the temperature of the tower bottompart is not changing, but the temperature of the toppart of the distillation tower is changing. There areplaces of measure devices that detects a change at firstafter a certain delay time. For the above reasons, thereare delay times between all input and output data.As for reflection of delay time to estimation model,Akaike’s method is adopted.5 Proposed methodWhile the chemical plant is being operated, qualitiesof the chemical plant change gradually. PLS analysisis used because the correlations exist among variables.But, because the disturbance induces serious influence,it is necessary to consider the nonlinear element for therobustness of the model.In this chapter, qualities of the distillation tower areJanuary 2006 Statistical Estimation Model for Product Quality of Petroleum13analyzed by using PLS analysis, and the error of theestimation value by disturbance etc. is corrected byRNN. This is proposed method. Moreover, the modelevaluation Ij are added as the input to RNN. Config-uration of proposed model is shown in Fig.10.Fig. 10. Configuration of Proposed Model6 Numerical experimentsIn this chapter, the sequential PLS analysis withmodel update function named proposed method andthe simple sequential RNN analysis named conventionalmethod are compared. Numerical experiments of con-ventional method and proposed method were made. Innumerical experiment, 27 kinds of input data and onekind of output data are used. Each operation data ofdistillation tower has 1200 samples. The conditions ofthe sequence PLS analysis with model update functionare 36 samples for training data, delay time of 5 sam-ples, and 5 latent variables. And the conditions of thesequence RNN analysis are 30 samples for training dataand 10 inner layers. The conditions of the fixed PLSanalysis of proposed method are 200 samples for train-ing data, delay time of 5 samples and 5 latent variables.The conditions of the sequence RNN analysis of pro-posed method are 3 samples for training data, and 10inner layers. 501-600 samples are estimated by usingthese three method of two conventional methods and aproposed method.As shown in Fig.11,13 and Table1, PLS+RNN ofproposed method is better than sequential PLS bothin RMSE and INDEX indicies. As shown in Fig.12,13and Table1, PLS+RNN of proposed method is betterthan sequential RNN both in RMSE and INDEX in-dicies. Next as shown in Fig.13 and 14, the result ofthe fixed PLS is corrected by the sequence RNN. Inaddition, the results of applying the proposed methodto 601-1000 samples is shown in Fig.15 revealing followup ability of proposed estimation model to new outputdata.7 ConclusionIn this paper, the method of estimation for productquality in the distillation tower was studied. In thispaper, the distillation tower is analyzed by combiningtwo statistical models, PLS analysis and RNN analysis.Further, the idea of sequential model, model updatefunction, and the delay time are applied to PLS andRNN analysis. Then the effectiveness of these methodswas examined. For the actual use, a new estimationmethod of product qualities in the distillation tower isproposed in consideration of the feature of PLS andRNN respectively. The proposed method estimates anphysical phenomena in the distillation tower by PLSanalysis, and securing the effect of disturbance by RNNanalysis. High accuracy in the estimation by the se-quential PLS analysis with model update function byRNN analysis is demonstrated.Bibliography[1] G. E. P. Box and G. M. Genkins: TIME SERIESANALYASIS forecasting and Control, HOLDEN-DAY (1976)[2] I. M. Mujitaba and M. A. Hussain: Applicationof Neural Networks and Other Learning Tech-nologies in Process Engineering, Imperial CollegePress (2001)[3] S. Joe Qin: Recursive PLS Algorithms for Adap-tive Data-modeling，Computers, chem．．Engng，Vol．22 (1998)[4] P. Vijaysi,R. D. Gudi and S. Lakshminarayanan:A New Recursive PLS Structure for Rapid Iden-tification on Demand, PRE-PRINTS of 6th IFACSYMPOSIUM ON DYNAMICS AND CONTROLOF PROCESS SYSTEMS (2001)Takashi NUKINA et al. MEM.FAC.ENG.OKA.UNI. Vol.4014Table 1 Numerical ResultModel ConditionMethod Training Time latent Estimation RMSE INDEXdata Model delay number RangeSeq.PLS 36 Seq 5 5 1.9419 109.2709Seq.RNN 30 Seq 5 5 501-600 1.74621 88.3607PLS 200 Fix 5 5 1.63638 77.5945+RNN 3 Seq 0 0 601-1000 2.1647 59.4820Seq.PLS:Sequential PLS with Model Update FunctionSeq.RNN:Sequential RNNPLS+RNN:PLS Corrected by RNNFig. 11. Results of Sequential PLS Fig. 12. Results of sequential RNNFig. 13. Results of PLS+RNN Fig. 14. Errors by PLS and PLS+RNNFig. 15. Results of PLS+RNN(601-1000 samples)January 2006 Statistical Estimation Model for Product Quality of Petroleum15

Statistical Estimation Model for Product Quality of Petroleum

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