Remote Optimization in Petrochemistry

Transcription

1 Remote Optimization in Petrohemistry Cornel Resteanu and Marin Andreia Abstrat In order to extend worldwide the proessing business of the petrohemial plant, its potential business partners an themselves onurrently simulate prodution plans with high quality tehnial and eonomi features. In the simulation proess, a large number of divergent goals are under attention. Therefore, the plant omputer will use the multiobetive linear programming as a tool for negotiations. The dialog between a partner and the plant omputer onsists in two steps, namely proessing demand and plant response, performed repeatedly until the business makes sense or it shows unaeptable. In the first ase, an be signed the proessing ontrat. Keywords EBusiness, Large Sale Systems, Web Enabled Simulation and Optimization, MultiObetive Linear Programming. I. INTRODUCTION s opposed to disrete proess plants that an adopt, beside Anew tehnology, the reorganization of their internal funtions and an better realize the horizontal integration with their business partners, possibly by onsidering the onept of extended enterprise, ontinuous proess plants, like petrohemial ones, an only resort, at this stage of tehnologial development, to reengineering the business proesses [1] [3]. Obviously, the approahing of the business proesses reengineering are linked to the development of new software for prodution ontrol [4] [8]. As it is known, e business is now the most important way to determine hanges in the ating manner of businesspersons. Beause of sarity of raw materials, a onsequene of shrinking finane, the plant must proess the raw materials belonging to various partners in order to avoid working below its atual apaities. Petrohemial plants yield a good deal of their output under proessing regime. This signifies that the raw materials are not purhased by the plant, but are owned by a business partner whose intention is to squeeze maximum out of their treatment. The drawbak in this ase is bombarding the Manusript reeived Marh 7, 2006: Revised version reeived Marh 31, This work was finaned in part by the Romanian Researh and Eduation Ministry see the 463/ ontrat. Ones bring many thanks to the Petrobrazi Informatis Department, espeially to Mr. Dorian Cuuteanu, the department head. He allowed us to work there with a big team, omposed by researhers but also by students from Politehnia University and Eonomi Studies Aademy, leading us among the inherent diffiulties in stating and solving the real plant's SCM problems. Many thanks also to the Supply Department staff for understanding our sientifi goal and for the help in gathering the data neessary for onduting eloquent experiments. C. Resteanu is with the National Institute for Researh and Development in Informatis, 810 Averesu Avenue, , Buharest 1, Romania, phone /ext 162, fax , resteanu@ii.ro M. Andreia is with the Eonomi Studies Aademy, 6 Romana Square, , Buharest 1, Romania, phone , fax , marinandreia@yahoo.om plant with lots of proessing proposals, not to be finalized ever beause of their overrating by business people. In order to negotiate a mutual advantageous business, the solution ould be a dialogue via Internet between potential partners and the plant omputer. One founds that business makes sense, the ontrat signing an be done. The simulating of optimal plans in these irumstanes must be very fast and it must allow the onurrent work for a large number of plant partners. For this purpose, ICI has developed the software named Tele PROCESSING. II. PLANT MATHEMATICAL MODEL The software designed for prodution planning in petrohemial industry ahieves both strutural and funtional desription of the plant [8], [9]. The plant anatomy is given by desribing the struture direted multigraph, verties being the individual prodution ells (IPCs) and ars being the pipelines (Ps) between these. The plant physiology is given by desribing the IPCs and Ps behavior. From this, it is possible to generate and solve planning problems as Multi Obetive Linear Programming (MOLP) problems. A. Syntheti Plant Model The planning model is: min f ( x ), F { x g( x ) b, x 0 }, where: (0) x F f ( x ) ( f ( x ),.., f ( x )), g( x ) ( g ( x ),.., g ( x )), 1 o 1 m x ( x,.., x ), b ( b,..,b ), 0 ( 0,.., 0 ), 1 n 1 m 1 n n n f ( x ) x, l 1,h, g ( x ) a x, 1, m, l i1 li i i1 i i li,a i, b, ( )l 1, o, i 1, n, 1, m. In onlusion, there exist: n deision variables, namely the yields of prodution system, m onstraints ( m m 1 m2, m 1 are tehnologial onstraints and m 2 are ommerial onstraints), o obetives ( o o 1 o2, o1 are plant obetives and o 2 are partner obetives). B. Analytial Plant Model Let C 6 C k be the set of IPCs, deomposed into six k 1 lasses: 1) system inputs, 2) effiienytype relations based IPCs, 3) blendingtype relations based IPCs, 4) speifi onsumptions relations based IPCs, 5) flow merging / distribution nodes, 6) system outputs. In the multigraph Issue 2, Volume 1,

2 terminology all the elements of C are verties, while for C1 and C 6 only outgoing and inoming ars respetively exist, for the remaining C (( )k 2, 5 ) both ar types being k possible. For a ertain vertex, let id () be the indegree and od () be the outdegree. Some harateristi features of the model, whih represents the industrial omplex, an be identified: 1) ) 0 ( ) C1, od ( ) 0 ( ) C6, and 1 ),od( ) 20, ( ) 5 C k ; k 2 2) no (self)loops are present; 3) all iruits have more than two ars; 4) dimensions are rather large, the number of verties, ars and iruits typially amounts to hundreds, thousands and tens, respetively and so, suh a kind of plant is a largesale system (LSS). Eah IPCs lass has its own behavior, whih is presented in the following: For C 1 and quantities ( ) 1,, outputs Q min, Q max, fulfill: 1 O i available C AVA and Q min AVA Q max (limiting of the available quantity relation) (1) od( ) O AVA (relation for the distribution of the available i1 i quantity) (2) For C2 and ( ) 1, 2, inputs I, outputs Oi, apaities CAP min and CAP max, inputs I in outputs Oi effiieny transformation oeffiients R i, quantities Q min, Q max and weights p k, qk for inputs and outputs respetively, fulfill: id ( ) Oi R i I i ( ) i 1, od( ) (effiieny relations) 1 id ( ) where R i fulfills R i 1 ( ) i 1, od( ) (3) CAP min id ( ) 1 I 1 CAP max (refining apaity relation) (4) ( ) i {1, 2,......, od ( )} suh that Q min O i Q max (limitation relations for some inputs) (5) ISUB ({ 1, 2,..., )} \ { }) () pk ( ) k ISUB for fixed suh that I pk I k (6) kisub OSUB ({ 1, 2,..., od( )} \ { i}) & () qk ( ) k OSUB for fixed i suh that Oi kosub qkok (the inputs and outputs interondition relation) For C 3 and 3 (7) ( ) 1,, inputs I, output O, apaities CAP min and CAP max, blending oeffiients r, qualities min max QUALl, QUALl of output and qualities QUAl l whih define the ontribution of input to the orresponding quality of the output QUAL l, fulfill: id ( ) O I (free blend relation) (8) 1 I r O, ( ) 1, ) (blending reipe relation) with id ( ) 0 r 1 ( ) 1, ) and 1 r (9) id ( ) 1 min O QUALl QUALl I,( ) l 1, l( O ) 1 (10) id ( ) max & O QUAL,( ) 1, ( l QUAL I l l O ) (11) 1 l (quality standards relations) CAPmin O CAPmax (apaity relation) (12) For C 4 and ( ) 1, 4 inputs I, prinipal / seondary outputs PRI _ O and SEC_ O, apaities CAP i p is min and CAP max, quantities Q i p min, Q, speifi onsumptions i p max for obtaining the prinipal outputs SC and the ratios for i p obtaining the seondary outputs from the first prinipal output RA is 1, fulfill: id ( ) I PRI _ O SEC _ O i i 1 PRI OSUB p SEC OSUB s ip _ is _ (inputs outputs balane relation) (13) where: PRI _ OSUB, SEC_ OSUB { 1, 2,..., od ( )} PRI _ OSUB, SEC_ OSUB PRI _ OSUB SEC_ OSUB PRI _ OSUB SEC_ OSUB { 1, 2,..., od ( )} ard ( PRI _ OSUB ) ard ( SEC_ OSUB ) ip PRI _ OSUB PRI _ O CS I, ( ) i p i p 1, id ( ) (speifi onsumptions relations) (14) SEC _ O RA PRI _ O1, ( ) i SEC _ OSUB (ratio is is1 relations) (15) s Issue 2, Volume 1,

3 id ( ) CAPmin 1 I CAPmax (apaity relation) (16) ( ) i PRI _ OSUB suh that Q Q Q p i p min i p i p max (limitation for prinipal outputs) (17) For C 5 and () 1, 5, inputs I, outputs Oi and weights p k, q k for inputs and outputs respetively, fulfill: id ( ) od ( ) I O i (inputsoutputs balane relation) (18) 1 i1 ISUB ({ 1, 2,.., )} \ { }) () pk k ( ) ISUB I pk I k ISUB k OSUB ({ 1, 2,.., od ( )} \ { i}) &() qk k ( ) OSUB Oi qko k OSUB k for fixed (the inputs and outputs interondition relations) suh that (19) for fixed i suh that (20) For C 6 and ( ) 1, 6, inputs I, deliverable DEL, minimum needed quantity O min and maximum quantity that an be stored / transported S / T _ O max, fulfill: id ( ) DEL I Q min 1 (deliverable quantity aumulation relation) (21) DEL S / T _ Q (relation that limits the max deliverable quantity) (22) Remarks: For the Ps with aumulation buffers, input in aumulation buffer I, output from aumulation buffer O, apaity CAP max, initial stok I _ S, final stok F _ S and quantities before / after the aumulation buffer A_ Q and P_ Q, fulfill: A_ Q O P_ Q I (flow balane relation) (23) I I _ S O F _ S (aumulation buffer balane relation) (24) I I _ S CAP O I _ S max, (aumulation buffer apaity relations) (25) therefore, the links have their own behavior; The IPCs belonging to C 2 and C 4 an have several running modes. Thus, the relations (3), (13), (14), (15), (16) and (17) should be written adding a supersript r that denotes the running mode. As a result, the following relations must be added: r( ) r1 r( ) r O r1 r I I, ( ) 1, ) O,( ) 1, ) (26) (27) At any given moment, the IPCs belonging to C an funtion 3 aording to only one set of the relations: (8) / (9) / (10) & (11); The deision maker an define a set of linear obetive funtions, denoted by f f ( x), f ( ),......, ( x)), where ( 1 2 x n x. For instane, the value of final produts is: DEL _ PRICE DEL (28) C7 ' ' '' C C7 C7 7 ' '' where, C 7 being deliverable outputs and C 7 ' '' C7 C7 being remaining outputs (side effets). The parameters and variables desribing the IPCs and Ps behavior are in. C. Model Charateristis In this field, MOLP problems have the following harateristis: huge dimensions (thousands of rows and olumns), predominane of equality relations (following from Kirkhoff s first law), emphasized sparsity, wide differenes in the order of magnitude of the various oeffiients of the model (leading to internal saling), tight feasibility domains (sometimes leading to the absene of a feasible solution). This is true mainly for the tehnologial onstraints. Nevertheless, the problem also ontains ommerial onstraints that are usually stated by the business partner. They are limits for the end produts. Main obetives inluded in the plant obetives set are the market value of the end produts, the value of the net prodution, proessing osts, labor osts, stoks of end produts, on flow stoks, profit and pollution prevention osts. The partner obetives set usually ontains profit, rude oil value, shipping osts, ingredients ost, proessing ost and end produts value. To generate and solve suh kind of problems is a very diffiult task by itself. In nowadays, many welltrained teams in prodution departments of petrohemial plants are designated to simulate diverse variants of prodution plans using the PIMS software. However, to aomplish this task from remote loations, worldwide situated, by people without a speial training, it is a true hallenge. In the following, it is shown how this thing is possible. III. TELEPROCESSING SOFTWARE Like any advaned software working from a plant eletroni site [10], TelePROCESSING has two funtional bloks. The f l Issue 2, Volume 1,

4 first blok addresses the Prodution Department in plant, therefore, works on the Intranet and is written in MSVC 6.0. The seond blok addresses the partners of proessing business, therefore, works on the Internet and is written in ASP. An automation blok, whose role is to exlude plant speialists intervention in the simulation proess, links these two bloks. It is a supervisor program, developed in the dormant tehnique that automatially runs, at a given tat, the optimizations / reoptimizations as well as other ations suh as optimization data management. Obviously, there an be some exeptions that must be treated by human intervention, the omputer breakdowns only! It is to notie that TelePROCESSING works as a shell over the PIMS software whose database, ontain rude oils and their harateristis, raw materials, ingredients, onflow produts, end produts, apaities, tehnologial data (regarding effiieny transformation oeffiients, speifi onsumptions, blending reipes, inputs / outputs interonditions relations, stoking apaities, funtioning regime et.), repairs, system timetable, is available. Ones must onsider a proessing demand only for a rude oil that the prodution system an proess it and only for the free apaities in a ertain time level (deade, month, quarter, and year). Even for the same time level, in various time horizons, free apaities map an be very different beause the regular prodution plans an differ. Adding, to this bakoffie, strutured information about plant partners (users, ompanies and their banks) and assoiated simulated prodution plans one obtains the information framework for TelePROCESSING software well funtioning. The supplementary information is managed using SQL Server 7.0 Enterprise Edition. This is the ensemble of information neessary to start simulating the proessing business, i.e. defining an optimization planning problem, solving it, reading the result and the assoiated eonomi indiators, (repeat the proess if is neessary) and deiding on business opportunity. A. Partners Registration Using the Registration site setion, the potential partners must register in the system. Only thus, they beome benefiiaries of the mehanism that is able to make a remote plan simulation and an estimation of the profit that an be obtained in the proessing business. The seurity is a very serious problem, solved in an elaborate manner. For exterior, there is the PKI omponent that assures aess ontrol, user authentiation, nonrepudiation, integrity of data transfer, digital signature, et. For interior, a speial omponent manages the database integrity. Every unauthorized aess, even those made by programmers diretly on the database tables, provoke system blokage until the system automatially regains its integrity. The supervisor also runs this funtion. Therefore, the partners an trust that they work in an environment haraterized by orretness and loyalty. The trust s problem was very well solved. The partners risk is minimized beause, from the very beginning, they are not finanially involved. The organizer risk involves the ompetitors earnestness. The business is based, as always, on partners honor. The size of the business is not a limitation. B. Proessing Proposals It is now possible to aess the eproessing site using Mirosoft Internet Explorer or Netsape Navigator browsers. As a rule, the mathematial model data are the plant knowhow and are seret. To protet them, the plant makes publi on the Internet only the data referring the ingredients and the end produts. For a partner it is normal to give the quantity of rude oil to be proessed with its physialhemial properties, the data levels assoiated with the desired produts and the proessing time horizon. These are the data that the business partners find to be of paramount importane and only they are taken into onsideration. Aessing the site setion named Proessing proposals, the business partner an make a proessing demand by speifying: proessing demand s name, rude oil name, measurement unit, prie and quantity. To desribe the rude oil it is also of outer importane. Ones an do this ob by filling up the table ontaining: attribute name, measurement unit and level for eah physialhemial harateristi. Shipping ost is information of low importane. It is neessary to ompute the partner profit (Capture 1). A simple pushing on submitting button triggers the taking over of the proessing demand by the TelePROCESSING supervisor. In order to deide whether the rude oil an be proessed or not, the supervisor heks the rude oil pattern. If it is tehnologially unaeptable, the partner is invited to give another rude oil with harateristis that fits to the plant prodution system. If it is aeptable, the harateristis are frozen, they annot be afterworlds hanged, and the proessing proposal is passed, for optimizing, to the sheduling mehanism. This inserts the demand into the waiting queue. The insertion is made aording to a priority oeffiient omputed taking into aount the value of the business and the number of the previous optimizations. Even a powerful omputer annot effiiently solve more than five optimizations at the same time. So, the launhings in exeutions are made gradually in time. The partner is informed about the plant response by onsulting the optimization status. One the optimization is finished, one offers three ategories of information: a) Inputs (ingredient name, measure unit, quantity, prie and value); b) Outputs (end produt name, measure unit, lower limit, optimal quantity, upper limit, prie and value); ) Eonomi indiators (rude oil value, shipping ost, ingredients ost, proessing ost, end produts value and profit) (Capture 2). At this moment, the business partner an establish some onditions for the end produts, maybe influened by the market and ask for a new optimization The number of the optimizations, as well as the time allowed for negotiations, is limited. As it is shown before, the business partner analyzes the results of any optimization work, i.e. the end produts levels attained through prodution optimization and the values of obetive funtions. The partner may onsider it neessary that the plant omputer must make the iterations of the business optimization for as many times as it takes to reah the best result, or otherwise he may wish to get out of business. Issue 2, Volume 1,

5 Capture 1. Proessing proposal Capture 2. Input and output data of an optimized proessing Issue 2, Volume 1,

6 A. Best Proposals Taking into aount that a multitude of potential proessing partners an onurrently make tempting proposals for the same time level and horizon, TelePROCESSING software arranges the proposals, for eah time level and horizon, into three lasses: big, medium and small businesses. Every business lass benefits from a MultiAttribute Deision Making (MADM) type optimization. If in the MLP problem there exist only ten obetives, in the MADM problem are stated over two hundred attributes, expressed by more than one expert (tehnologists, eonomists, bankers, eologists et.) in more than one state of nature (eonomi stability, inflation, deflation et.) Thus, the plant managers an appreiate on solid basses, whih is the best proposal. The pratie shows that it is possible to exist one or many winners for every business lass. They are invited for a last disussion and ontrat signing. The prodution department staff, to whom some IT people will oin, will take the responsibility for wathing the system and answering to the partners eletroni letters. The plant omputer automatially solves even the proessing demands that present problems, like void feasibility domain, aused by overrating of plant apaities. The funtion that ensures the manmahine interfae on the Intranet is analogous with the funtion that works on the Internet. In addition, also a funtion manages the partners setion of the database. The main goal of this funtion is to ompute, in time, based on statistial data, the partners allowane. As ones an see in this paper, the designer preoupation was the optimization proess automation. Only the eonomi ontrat signing is out of the system, mainly beause of the bureauray aepted as neessary in this field. Thus, the plant will be able to improve the quality of the proessing business. Extending worldwide the business of a plant is one of the globalization ommandments. Another, very important too, is to promote mutual advantageous business. TelePROCESING will be apable to support suh kind of business. This is possible by making use of advaned simulation and optimization tehniques. Web enabled simulation and optimization is a new trend in treating the omplex industrial problems. Moreover, the designing team preoupation is to extend the failities of treating the onurrent optimizations by using GRID solutions. IV. CONCLUSION The Internet reated the support for a new range of appliations. In our days, eappliations undergo a fast development. In the authors opinion, elaborate ebusiness appliations will soon beome fats on the IT market. TelePROCESSING software is under final testing; launhing it is thought to be a real event and to have a signifiant impat at level of petrohemial industry. A series of Romanian petrohemial plants have supported testing in real onditions and the onlusions have been enouraging. The software arhiteture is general for a advaned e appliations but, as TelePROCESSING is not business to onsumer but businesstobusiness software and its appliability is with petrohemial plants, some partiularities individualizes the manner of doing eletroni business with this software. It really ontributes to the modernization of the proessing business. Its apabilities related to genuine e business make it suitable to ontribute to the invigoration of the business. With this software, petrohemial plants may beome more ative on the world market via Internet. The strong point of TelePROCESSING software is the fat that the proessing business is optimal for both the plant and its business partners. Please remark that although the mathematial model shows high omplexity, Internet developed interfae is a narrow one, beause it onerns reasonable demands. Its interfae is very human beause people without a speial training in mathematis and informatis an easily use the software. TelePROCESSING delivery onditions and servies inluded are software and doumentation on CD, autodemonstration, onall / onsite assistane in use and short time training ourse (16 40 hours) for initiation in eletroni business. Further developments refer to extensions for other ontinuous proess industries. Ones suppose that the Internet modules and the simulation mehanism will be the same, only the Intranet modules and the optimization will hange. Therefore, a lot of programming work will be save with good onsequenes for delivery time and software ost. REFERENCES [1] Limam Mansar, S. and H.A. Reiers (2005). Best Praties in Business Proess Redesign: Validation of a Redesign Framework. Computers in Industry, 56(5) pp [2] JosseyBass (2002). The New Proet Management: Tools for an Age of Rapid Change, Complexity and Other Business Realities. Business & Management Series by Frame, J. Davidson. San Franiso, CA John Wiley & Sons, In, US. [3] Dennis, A. R., Carte, T.A. and G. G. Kelly (2003). 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A ooperative prodution planning method in the field of ontinuous proess plants. Elsevier Siene, International Journal of Prodution Eonomis 64, pp [9] Draman, M., A. Kuban, N. Bagori, A. T. Unal and B. Birgoren (2002). A lonebased graphial modeler and mathematial model generator for optimal prodution planning in proess industries. European Journal of Operational Researh Volume 137, Issue 3, pp [10] Resteanu, C., N. Andrei and E. Mitan (2000). Continuous proess plant as a system open to business. In: Dietmar P.F. Moller Ed., Proeedings of the ESS 2000, 12th European Simulation Symposium, Simulation in Industry. (Hamburg, Germany, September 2830), pp Issue 2, Volume 1,