APPLICATION OF AN ADAPTIVE NEURO INFERENCE SYSTEM FOR CONTINUOUS MONITORING AND CONTROL OF AN EXTRACTIVE DISTILLATION PLANT

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1 Journal of Chemcal Bahman Technology ZareNezhad, and Metallurgy, Al Amnan 48,, 203, APPLICATION OF AN ADAPTIVE NEURO INFERENCE SYSTEM FOR CONTINUOUS MONITORING AND CONTROL OF AN EXTRACTIVE DISTILLATION PLANT Bahman ZareNezhad, Al Amnan Mnstry of Scence, Research and Technology, Semnan Unversty, Iran E-mal: Receved 30 July 202 Accepted 20 December 202 ABSTRACT The performance of an extractve dstllaton plant s nvestgated by usng an adaptve neuro-fuzzy nference system (ANFIS). The case of methyl cyclo hexane (MCH) extracton from toluene usng phenol solvent s employed n the present study. It s shown that the proposed ANFIS model predctons are much more accurate than those predcted by a feedforward neural network (FFNN) model. The proposed ANFIS model can be used for predctng the separaton performance of an extractve dstllaton system based on the avalable hstorcal data. Usng the proposed ANFIS estmator, the extractve dstllaton plant can be quckly controlled va a contnuous onlne montorng system over wde range of operatng condtons. Keywords: predcton, extractve dstllaton, ANFIS, control, montorng, plant. INTRODUCTION Proper operaton of extractve dstllaton column requres knowledge about the product compostons durng the operaton of the system. Although product compostons can be measured on-lne, t s well known that onlne analyzers are very costly and mantenance ntensve. They also ental sgnfcant measurement delays, whch can be detrmental from the control pont of vew []. In order to resolve these problems, a contnuous onlne method for predctng product stream compostons and consstency wth and pertnence to column operaton are needed. The use of such nferental composton estmators has long been suggested to assst the montorng and control of contnuous dstllaton columns [, 2]. The man objectve of ths work s to develop an ANFIS structure to predct the separaton performance of an extractve dstllaton column based on the avalable hstorcal data. Wth such an algorthm, the proposed estmator may lead to determne the optmal solvent flow rate over wde range of operatng condtons. PROPOSED ANFIS MODEL The purpose of an ANFIS s to establsh a set of rules along wth a set of sutable membershp functons that s capable of representng the nput-output relatonshps of a gven system [3-5]. An adaptve network refers a mult-layer feedforward type structure [6-0] wth nterconnected nodes. However, some of the nodes are adaptve, meanng that such a node s output s dependent on several parameters belongng to t. An adaptve network utlzes a supervsed learnng algorthm n order to mnmze the error of the nput-output mappng requred, by adjustng the parameters of the adaptve nodes. In ths work a frst-order Takag-Sugeno fuzzy system [] s used as follows: () 99

2 Journal of Chemcal Technology and Metallurgy, 48,, 203 ( ) f x = a + a x + a x + + a x,0,,2 2,n n where a,j are consequent parameters for the j-th nput and the -th rule and x, x 2 and x 3 are the nput varables. Y s the output from the ANFIS model and m s the frng strength of -th rule or the degree n whch the correspondng rule s fred that s defned as,.e. n μ ( x) = (2) j= 2 β x j γ j j + α j where s the parameter set referred to as antecedent parameters. The dentfyng of parameters of the ANFIS model, ncludng the premse and consequent parameters, s called tranng to make the ANFIS output match the tranng data. A hybrd-learnng algorthm was used to tran the model. A hybrd-learnng algorthm consstng of backpropagaton for the parameters assocated wth the nput membershp functons, and least squares estmaton for the parameters assocated wth the output membershp functons. The hybrd method uses one optmzaton method for the nonlnear part of the ANFIS structure and another for the lnear part. In ths work, a trangular shape membershp functon was used as the nput membershp functon. The trangular membershp functon can be wrtten as follow: The objectve functon that must be mnmzed s: (5) whch d s the desred output and Y s the output from the ANFIS model; x s the nputs and Y s the parameter vector, whch must be calculated n such a way to mnmze the objectve functon. The hybrd-learnng method composed of a forward and a backward pass. In the forward pass, the consequent parameters of the lnear output membershp functons are calculated usng least squares optmzaton method snce the values of the antecedent parameters are fxed. In the backward pass, gradent descent method was used to optmze the antecedent parameters by propagatng the error rates from the output end to the nput end whle the consequent parameters are hold fxed. RESULTS AND DISCUSSION The flowsheet of the extractve dstllaton process wth solvent recovery s shown n Fg. In ths fgure, the man control loop s desgned to control the MCH purty by adjustng the phenol flow rate. The smulaton of the ED column s carred out usng the ASPEN software. For each tray, the relatve volatlty wth respect to the component compostons are calculated from Relatve Volatlty = ( y/x) y/ x Base Component (6) (3) In ths functon, usually and the membershp grades of the nputs computed as: (4) In the ED process, t s desred to have a dstllate whch contans the solute wth hgh-purty and a bottom product, whch could be easly separate from entraned solvent n an another column wth few trays. The modeled ED column conssts of 22 staged ncludng a total condenser and a partal reboler, n whch the feed that contans a mxture of MCH and toluene enters the 4th tray and the less volatle extractve agent enters the 7th tray; the condenser pressure s. bars and the reboler pressure s.4 bars. The steady state data consdered for the ED column are Feed rate= 8.5 kmol/h, TFeed=

3 Bahman ZareNezhad, Al Amnan Solvent Feed Cold Solvent Extractve Dstllaton Cooler MCH PID Controller Recovery Recycle Solvent Toluene Fg.. Process flow dagram of MCH-toluene extractve dstllaton plant. 05 o C, the reflux rato s set to 8, and the dstllate flow rate s set to kmol/hr. Dfferent runs were carred out by varyng the feed composton, the reboler duty, and the MCH composton setpont n the dstllate. At each run, the steady state value of the phenol flow rate was recorded to acheve the correspondng value of MCH setpont. The acqured mult nput-sngle output datasets were used to construct the nonlnear model of the process. For the desgn consderatons, the modelng of the selected ED process s mportant to keep the solute purty n the dstllate at the desred value. For ths purpose, the MCH composton n the dstllate s controlled by use of a PID controller n spte of dsturbances n the column operaton. Ths control loop consders the phenol flow rate as a manpulated varable to control the purty of the MCH n the dstllate stream. For the computaton of lqud phase actvty coeffcent and vapor phase fugacty coeffcent, the UNIFAC /Soave-Redlch-Kwong equaton of state [2] s used. In ths work, the feed composton, MCH s purty setpont and the reboler duty defned as antecedent nput varables whle the phenol flow rate chosen as consequent varable. The feed composton n terms of MCH concentraton was vared between 0.3 to 0.9 mol %, the MCH s purty setpont vared from 0.98 to mol% and the reboler duty vared between 6 to 9 MMkcal/hr. The datasets were obtaned durng each run are dvded nto two sets: the tranng and testng sets. Tranng datasets used to tran the network and the testng datasets were used to analyze the generalzaton ablty of the traned network. The tranng and testng performance were carred out by usng the 70 % and 30 % of all data ponts, respectvely. Fg. 2 shows the fnal shape of MFs after tranng through the learnng process. It s found that the trangular MFs are the best choce for predctng the ED column performance because they lead to mnmum tranng and testng errors. Before performng learnng process, the ntal values of the premse parameters (α, β andγ ) must be determned. The predcted and desred values of the objectve varable for tranng and testng processes are shown n Fg. 3. As shown n ths fgure, the proposed ANFIS model provdes satsfactory predctons of the phenol flow rate regardng tranng and testng phases. The mean square error (MSE) of the traned and valdated results s about and , respectvely. The performance of the proposed ANFIS s also compared wth a three-layered FFNN. For a relable comparson between ANFIS results wth FFNN, the smlar tranng and testng datasets have been used. The Fg. 2. The membershp functons after tranng. 0

4 Journal of Chemcal Technology and Metallurgy, 48,, Fg. 3. Comparson of the proposed ANFIS Predcted phenol flow rates (vertcal axs) wth ASPEN smulaton results (horzontal axs): a) Tranng, b) Testng. predcted optmum phenol flow rate by usng the proposed ANFIS model s much more accurate than those predcted by the FFNN model durng tranng and testng phases as shown n Fg. 4. Comparson of dfferent error crterons n Table confrms that the proposed ANFIS structure s qute accurate for predcton purposes. The overall average absolute devaton percent (AAD %), mean square error (MSE) and correlaton coeffcent (R2-value) of predcted results are about 0.7 %, and 0.988, respectvely. Table. Comparson of the accuracy of the proposed ANFIS wth FFNN. Models R 2 MSE AAD % ANN % ANFIS % Fg. 4. Comparson of the ANN Predcted phenol flow rates (vertcal axs) wth ASPEN smulaton results (horzontal axs): a) Tranng, b) Testng. CONCLUSIONS In ths work the utlty and effectveness of an ntellgence technques for predctng the performance of an extractve dstllaton plant s presented. An ANFIS model s presented for accurate predcton of the process performance. The predcted optmum process varables by usng the proposed ANFIS model are much more accurate than those predcted by the FFNN model. The presented ANFIS model can be utlzed for accurate predcton of plant performance and contnuous montorng and control of extractve dstllaton processes. REFERENCES. H. Wang, S. Kwonga,Y. Jnb, W. We, K.F. Man, Mult-objectve herarchcal genetc algorthm for nterpretable fuzzy rule-based knowledge extracton, 02

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