MONITORING OF DISTILLATION COLUMN OPERATION THROUGH SELF -ORGANIZING MAPS. Y.S. Ng and R. Srinivasan*

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1 MONITORING OF DISTILLATION COLUMN OPERATION THROUGH SELF -ORGANIZING MAPS Y.S. Ng and R. Srnvasan* Laboratory for Intellgent Applcatons n Chemcal Engneerng, Department of Chemcal and Bomolecular Engneerng, Natonal Unversty of Sngapore, 10 Kent Rdge Crescent, Sngapore *Correspondng Author: Emal: chergs@nus.edu.sg Abstract: Montorng of chemcal processes s becomng ncreasngly dffcult as a result of growng complexty and larger scale of operaton. In ths paper, Kohonen self-organzng map s (SOM) s used to montor the operaton of a lab-scale dstllaton column and to dentfy process states. SOM projects hgh -dmensonal data to a lower two dmensonal grd maps whle preservng the metrc relatons of the orgnal data. The results from ths paper show that usng ths property of SOM, process montorng can be performed effectvely through observng tme seres trajectory of process operatons on SOM whle fulfllng the objectve of state dentfcaton at the same tme. Occurrence of a fault wll result n the devaton from the normal operatng trajectory. Root cause dentfcaton can also be performed through smple vsualzaton of component planes. Copyrght 2004 IFAC Keywords: montorng, self -organzng systems, fault dagnoss, Neural-networks 1. INTRODUCTION Incdents occurred n the past have shown the catastrophc consequences of mproper montorng of chemcal processes; the resultng effect s not merely the loss of captal from shar eholders but also njures and loss of precous human lves. There s an ncreasng need for montorng technque so that abnormal stuatons can be detected n the shortest amount of tme. Over the past few decades, a lot of montorng technques have been ntroduced e.g. enhanced trend analyss, prncpal-component analyss, artfcal neural-networks, etc; some have been mplemented n ndustry successfully (Venkatasubramanan, et al., 2003), but what s stll lackng n the chemcal plant s a powerful vsualzaton technque that help plant personnel to vsualze the evoluton of a process and detect abnormal stuatons effcently. These stuatons are worsenng wth the growng scale and complexty of chemcal processes; at any nstant, the number of varables that plant personnel need to track can easly range n the thousands. Due to the above mentoned reasons, montorng through conventonal control charts has become less effectve and there s a need for new and effectve methods to help plant personne l to montor the progresson of chemcal processes. In ths paper, Self-Organzng Map s (SOM) has been used for the purpose of montorng of chemcal processes. SOM s a powerful projecton method whch s capable of projectng hgh dmensonal data onto a lower two dmensonal grd whle preservng the metrc relatonshps of the orgnal data. The effort to perform process montorng s reduced many fold by SOM as compared to conventonal methods snce vsualzat on s performed on a much lower two dmensonal grd. The organzaton of ths paper s as follows: A bref descrpton of the underlyng concepts behnd SOM s presented n Secton 2. Secton 3 presents the revew of SO M-based process analyss whle Secton 4 presents the method of usng SOM for process montorng and state dentfcaton. Montorng and fault detecton through SOM for a lab -scale dstllaton column s presented n Secton THE SELF ORGANIZING-MAP SOM was frst proposed by Kohonen (Kohonen, 1981) and has been wdely used snce then. SOM algorthm employs nonparametrc regresson

2 technque whch nvolves the fttng of dscrete, ordered reference vectors, to the dstrbuton of nput feature vectors. The dmensons of the prototype vectors are equal to the dmensons of nput vectors. The hexagonal lattce type has been employed as t s more effectve for vsualzaton. Consder an nput vector, x, gven by x = { x x, x,..., } 1, 2 3 x n (1) x wll be compared wth all reference vectors, say m, m = m m, m,..., (2) Each { } 1, 2 3 m n m also represents a node on a hexagonal m whch gves the smallest output grd. The map Eucldean dstance wth x s defned as the bestmatchng unt (BMU), represented here as c, c = arg mn x m (3) { } Durng the tranng of the SOM, the reference weght vector m wll be updated and the topologcal neghbors around the BMU are updated as well by movng them towards the tranng sample x. The SOM learnng rule s gven by m ( t + 1) = m ( t) + α ( t) h ( t)[ x( t) m ( t)] (4) where h c (t) s the neghborhood functon and α (t) s the learnng rate factor. It s necessary that h c reduces to zero as the tranng proceeds to guarantee convergence. A large value of α s employed ntally and usually decreases monotoncally wth tme (Kohonen, 2000). Durng the tranng phase, SO M wll fold onto the pattern formed by the tranng data and neghborng unts are pulled nearer together because of the neghborhood relatons. Thus neghborng unts are n a sense, smlar to each other. The SOM s usually vsualzed usng the U-matrx (Ultsch, 1990), where dstances of each map unt to ts correspondng neghbors are calculated and dsplayed through gray or color - scales. A smlar technque can also be appled to vsualze the ndvdual components (or varables) so that correlatons am ong components can be nvestgated and abnormalty can be detected at the varable level. The combnatory use of U-matrx and component planes can so fulfll the objectves of montorng of chemcal processes on both a plant wde and varable level. c used component plane presentaton of SOM for mcroarray data analyss of yeast cells and human breast tumors, permttng the vsualzaton of transcrptonal changes of tumor sample at a genome scale. López-Rubo, et al. (2003) proposed a selforganzng neural model that performs prncpal components analyss. Kolehmanen, et al.(2003) used SOM and Sammon s mappng to dentfy the growth phases of brewer s yeast. In ther work, the exhaust gas from the fermenter was measured wth on moblty spectrometry and a new growth phase for the fermentaton process has been dentfed n addton to the four phases reported n the lterature. Whle the prevous applcatons of SOM are manly on data clusterng, some recent applcatons of SOM on process montorng and fault dagnoss are presented n the next subsecton. Deventer, et al. (1996) demonstrated how dsturbances n a plant for froth flotaton of mnerals can be vsualzed wth SOM. They track the changes n operatng condtons through an on-lne computer system utlzng features extracted from froth mages and vsualze the degree of dsperson of the varous nput feature vectors through SOM. Chan, et al. (2001) presented a modfed verson of Kohonen SOM called constraned Kohonen networks (CKN) to overcome the problem of redundant sensors by constranng the weght vectors n the party space. Srnvasan and Gopal (2002) showed how SOM can be used to extract operatng nformaton from operatng data from a fludzed catalytc crackng unt. Jämsä-Jounela, et al. (2003) presented a SOM based fault dagnoss system for a smelter based on heurstc rules. SOM has been used to determne the coeffcent for oxygen enrchment and detecton of aggregatons n varous part of the plant after prncpal component analyss. Abony, et al., (2003) appled SOM to a polyethylene process for product qualty estmaton. They developed multple local lnear model for the Phlps polyethylene process through pecewse lnear regresson wth SOM. More than 4000 scentfc publcaton on SOM have been wrtten to date (Kohonen, 2001) and the majorty of them deal wth the classfcaton of nput feature vectors and data mnng. A comprehensve reference of SOM research has been compled by Kask, et al., (1998). In ths work, we propose a methodology for process montorng and state dentfcaton through the use of SOM. 3. SOM-BASED PROCESS ANALYSIS SOM has been wdely used as a vsualzaton tool for unsupervsed learnng. (Vesanto, 2002). It has several powerful features whch make t popular for the task of process montorng. SOM mplements an ordered dmensonalty reducton through the mappng of the nput feature vectors whle preservng the most crucal topologcal and metrc relatonshps of the orgnal data, hence producng a smlarty map of the nput feature vectors The self-organzng maps s readly explanable, smple and easy to vsualze (Vesanto, 1999). SOM has found successes n dverse felds of applcatons. L Xao, et al. (2003) 4. PROPOSED SOM -BASED MONITORING METHODOLOGY The proposed methodology can be summarzed as follows. Measurements of the normal operatng process wll frst be collected and range normalzed before beng projected onto the U-matrx of SOM. The clusters formed on the U-matrx are nterpreted and dfferent states, whch correspond to dfferent operatng phases s dentfed. Dfferent clusters wll be separated by large dstances, modeled n the SOM s U-matrx by a border of darker color, predefned by the user. Durng the on-lne montorng phase, the trajectory beng formed by the

3 real tme data wll follow the trajectory of the normal operatng data and sgnfcant devaton from the normal operatng trajectory can be regarded as a fault. Duraton of operaton can be read from the sze of the hexagon on the U-matrx, n whch the sze of the hexagon s drectly proportonal to the duraton of operaton. State dentfcaton of the process s done through the dentfcaton of the locaton of the projected operatng data (data ht) on SOM. The proposed methodology s llustrated next through a case study. 5. MONITORING OF DISTILLATION-COLUMN OPERATION The proposed method has been tested on an Armfeld lab-scale dstllaton column. The schematc of the dstllaton column s shown n Fgure 1. The dstllaton column s of 2 meters heght and 20cm wdth and has 10 trays, where the feed enters at tray 4. The system s well ntegrated wth a control console for controllers settng and data recordng. Cold startup of the dstllaton column wth ethanol - water at 30% v/v as bottoms s performed followng the standard operatng procedu re (SOP) as shown n Table 1. The feed passes through a heat exchanger before beng fed to the column. 19 varables --- all tray temperatures, reboler and condenser temperature, reflux rato, top and bottom column temperature, feed pump power, reboler heat duty, coolng water nlet and outlet temperature - are measured at 10-second nterval. The startup normally takes two hours and dfferent faults such as sensor fault, falure to open pump, too hgh a reflux rato etc., can be ntroduced at dfferent states of operaton. The constructed SOM for a normal startup of dstllat on column s shown n Fgure 2. The fully traned SOM conssts of 30 x 8 map unts and ts correspondng bar-plane s shown n Fgure 3. Each unt of the bar-plane s a representaton of the magntude of the 19 varables of the dstllaton column after normalzaton. Two observatons can be seen from Fgure 2. Frstly, the startup process has been observed to follow a trajectory on the U-matrx. Each label represents the correspondng operatng tme (x 10 seconds). Secondly, t has been observed that any sgnfcant devaton from the normal operatng trajectory s an ndcaton of a faulty operaton. By comparng Fgure 2 and Fgure 3, even wthout any pror knowledge on the process, one can deduce that durng the normal run, most of the varables are ncreasng over tme as represented by the trajectory on the U-matrx as ndcated n Fgure 2. Dfferent clusters whch correspond to dfferent operatng phases can be defned for each U-matrx or component plane constructed. As can be seen from the U-matrx on Fgure 2, four clusters of operatons can be dentfed for the startup of dstllaton column, each representng dfferent phases of the start -up of dstllaton column. The border of each cluster s separated by map unts of darker color Fgure 1. Schematc of the lab scale dstllaton column. Table 1 Standard operatng procedures (SOP) for the startup of dstllaton column Dstllaton column startup SOP 1. Set all controllers to manual 2. Fll reboler wth lqud bottom product 3. Open reflux valve and operate the column on full reflux 4. Establsh coolng water flow to condenser 5. Start the reboler heatng col power 6. Wat for all of the temperatures to stablze 7. Start feed pump 8. Actvate reflux control and set reflux rato 9. Open bottom valve to collect product 10. Wat for all the temperatures to stablze whch can be nterpreted as a boundary marker between dfferent clusters. The color code on the rght tells the exact dstance among neghborng unts, whch were traned usng Gaussan neghborhood functon durng the batch tranng phase. Operatng trajectory from the startng of the heatng process tll tme 3630s can be regarded as a separate state from the rest of the data ponts reboler heatng phase. Tme seres 3630s to 3820s s the bolng phase where evaporaton of the bottom lqud takes place and there s an abrupt rse of temperature n the dstllaton tower. Tme seres 3840s to 4800s correspond to the full reflux heatng phase before the feed was ntroduced and thus causng a swtch n operatng phase as shown from tme trajectory from 4930s to 6340s, whch s representng the steady state operatng phase of the tower. The use of SOM to montor faulty operatons s shown n Fgure 4 and Fgure 5. Scenaro 1: Fgure 4

4 Fgure 2. Trajectory of a normal run on U-matrx. Labels ndcate the correspondng operatng tme (x10 seconds). Fgure 4. Trajectory of Scenaro 1 (Dark sold lne represents the faulty run whle lght sold lne represents the normal run). Fgure 3. Bar-plane for the traned self-organzng maps. shows the trajectory of a faulty run. Two trajectores can be observed from Fgure 4, the lght sold trajectory s the trajectory of a normal run whereas the dark sold lne s the trajectory of the faulty run. The frst fault occurred from 2650s (shown as 265 n Fgure 4), where operator faled to turn on the feed pump and thus causng devaton from normal run. The stuaton s later rectfed by the operator and the recovery can be seen on the U-matrx where the Fgure 5. Trajectory of Scenaro 2 (Dark sold lne represents the faulty run whle lght sold lne represents the normal run). trajectory returns to follow the trajectory of a normal run at 3570s. A second fault occurred at tme 6520s,

5 when a feed flow of 1.5 tmes the normal value was ntroduced. The correspondng trajectory was found to devate from normal operatng trajectory from 6520s to 6770s. Secnaro 2: The second scenaro conssts of hgh reflux rato and temperature sensor fault as shown n Fgure 5. At 4900s, the operator acc dentally set the reflux rato to 2 tmes hgher as compared to the normal run and the column s operated n ths condton for a long perod of tme (as seen from the sze of the data hts), before a temperature sensor break down at 5600s. Whle U-matrx s a smple 2-dmensonal plot for operators to perform process montorng, t does not contan suffcent nformaton for dagnosng the fault. The component planes of the correspondng run have to be nvestgated n order to locate the fault. For the second scenaro (Fgure 5), varable 6 can be dentfed to be the man cause of devaton as t moves nto a cluster of lower value (see Fgure 6). The correspondng operatng chart for varable 6 s gven n Fgure 7. Smlar analyss can also be appled to other scenaros for fault dentfcaton (not shown n ths paper). The knowledge nferred from the normal operatng run n Fgure 2 can also be appled to other cases such as the one n Fgure 4 and Fgure 5. The operatng state of a dfferent run can be drectly obtaned from the locaton of the data pont on the self-organzng maps. As an llustratve example, for the operaton of dstllaton column shown n Fgure 4, even though there are faults n the system, the process of state dentfcaton can stll be done farly Fgure 7. Tme varatons of varable 6 (5 th tray temperature sensor). successfully. Reboler heatng phase can be observed from tme s. Evaporaton of bottom s has been observed to take place from 3680s 3870s whle full reflux operaton pror to feed s from 3890s 5220s, and lastly the steady state operaton can be seen to take place from 5230s 6770s. Smlar nterpretatons can be appled to other case studes as well. 6. CONCLUSIONSAND DISCUSSION Ths paper llustrates how Kohonen SOM can be used for montorng of process dynamc transtons, n ths case the operaton of a dstllaton column. Hgh dmensonal data has been projected ont o a lower two dmensonal grd maps, consequently the task of process montorng has been smplfed as compared to conventonal control charts approach. One only needs to keep track of the trajectory n order to deduce whether the operaton s normal or abnormal. A faulty run wll result n devaton from normal trajectory, and component planes can then be analyzed to locate faulty varables for root cause dentfcaton. Ths paper has also shown that SOM can be used for state-dentfcaton. The exact phase of dfferent run at any nstant can be nferred from the locaton of the data ht on the self-organzng maps. Our current research s targeted at automatng the SOM -based approach to mprove the effcency of montorng. Two methods that use the results from SOM for automated abnormalty detecton and dagnoss have been formulated. Frstly, statstcal analyss has been used to generate varable specfc resdual by usng SOM as local model durng the transton. Secondly, a pattern recognton approach s beng explored for dagnoss and fault solaton purposes. By usng a fault database and machne learnng approaches, faults can be dentfed n a short amount of tme. A robust formulaton of normal trajectores s also beng nvestgated to accommodate multple normal operatng condtons. Fgure 6. Component plane of varable 6 (5 th tray temperature sensor).

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