DECHAMP, L

DRANSART, P

Maenhout, Greet

THEVENON, J

Undetermined

Ghent University Academic Bibliography

CHISA Conference, Prague, August 20041/8Auditing of Batch ProcessesG. Janssens-Maenhout1, L. Dechamp, J. B. Thevenon2, P. Dransart2Institute for the Protection and the Security of the Citizen, Nuclear Safeguards UnitJoint Research Centre Ispra, Via E. Fermi 1, TP 421, I-21020 ISPRA (VA), Italy;tel: +39 0332 78 5831; +39 0332 78 5076; fax: +39 0332 78 9216; e-mail:greet.maenhout@jrc.it; luc.dechamp@jrc.it1docent University Ghent, Engineering Faculty, Electrical Energy Systems and Automation2Euriware, West Region, AREVA group, 25 av de Tourville, 50120 Equeurdreville, France;tel: +33 6 83 44 45 42; +33 6 83 44 56 13; email: jbtheven@euriware.fr; pdransa@euriware.frAbstractA software tool for  auditing the successive batch process steps at the La Hague reprocessingplant is developed in a support to Euratom inspectors. This tool compares the observed data ofa batch with a description of the process cycle in order to analyse and to interpret the data ofthe batch cycle.The "Data Analysis and Interpretation" monitoring software "DAI" detects important eventsin the process cycle by means of the start/exit of functional behaviours. Comparison of theobserved data with the process model means that deviations from expected behaviour can bedetected. The DAI software verifies (1) incoherency with the prescribed goals by checking therequired complementarity between linked signals, and (2) the conformity with the predefineddesign by checking the completeness in the succession of functional behaviours.The system applied at the reprocessing plant especially allows monitoring of the solution flowthrough the process by analysing the required coherence in complementary data ofcommunicating tanks. This example demonstrates the usefulness of a simplified syntacticalpattern recognition methodology to follow-up the complete transfer of different materialflows. This software may be applied for the follow-up of other processes, like in thepetrochemical or food industry.1. IntroductionThe need for a monitoring tool, which manages the material flow, is subject to a normunder development of the American Instrumentation Society (ISA), as announced by Vieille(2002).The software tool, which is described in this paper, is meant for auditing the successiveproduction process steps of a batch of liquor on two levels:· The batch is submitted to general constraints such as the density, the temperature-dependency, the absence of remarkable mass loss and impurities, which have tobe monitored globally.· At each production step there exist also specific constraints such as pressure,temperature and level limits or selective transfers between communicating tanks,which need a local monitoring.CHISA Conference, Prague, August 20042/8It especially allows a near-real time follow-up, as addressed by Janssens-Maenhout et al(2004) and an overview of a batch process under the specified constraints. The specificconstraints can be of different types:· the authorised transfers between tanks,· the allowed transfer time,· the physico-chemical relations of the liquid, and· the exploitation constraintsEach liquor production process is characterized by well-instrumented tanks for the liquorcontrol and inventory tanks for the process control. The process is for each batch repeatedand each process cycle is characterized with a set of prescriptions. The liquid has to be tracedthrough the all process steps in quantity (mass) and in quality (density and chemicalcomposition).Automated supervision and process control is abundantly described in the literature by,for example, Amaral et al. (1992) or more recently Lee et al. (2001). However, this tool onlycompares the observed data of a batch with a description of the process cycle in order toanalyse and to interpret the data of the batch cycle. The intelligent process monitoring systemcan be defined as a system, which neither supervises nor controls but analyses the measureddata points and interprets the required coherency with the prescriptions without intervening inthe process by a feedback function.2. Overview of the process monitoring systemThe software tool bases:· on a data historian for data acquisition with compression and· on an analyzing kernel for interpreting the data, which is called “Data Analysisand Interpretation” or “DAI” software.Various data historians are available on the market for the efficient data acquisition andcompression. Commonly the PI data historian is still selected in chemical process plants aswell-validated data acquisition and compression software tool according to Harrold (2004).This paper does not focus on this part as it is already well resolved by the industry but it focuson the analyzing kernel, for which no standard well-validated industrial software tools areavailable.The central task of the analyzing kernel DAI is the recognition of exploited processcycles. Information is collected from the actuators and sensors for direct control andsupervision and is analyzed with the following objectives:· to survey in real time the correct operation of the production process· to diagnose abnormal situations· to evaluate the process performance indicators and inventory balances of thebatch cycles in real timeCHISA Conference, Prague, August 20043/82.1. Recognition of batch cyclesVarious techniques exist to recognise a pattern:· the template matching,· the statistical approach and· the syntactical approach.Industrial processes with high production availability are commonly characterized by signalswith a certain repetitive form but not mathematically periodical. Therefore the syntacticapproach was chosen to analyse the signals’ repetition with a given set of predefinedfunctional behaviours.The DAI software detects important events in the process cycle by means of the start/exitof functional behaviours. Comparison of the observed data with the process model means thatdeviations from expected behaviour can be detected. The DAI software verifies (1) flowincoherency with the prescribed goals by checking the required complementarity betweenlinked signals, and (2) the conformity with the predefined design by checking thecompleteness in the succession of functional behaviours. The syntactical pattern recognition isbased on expertise analysis, encoded with local functional behaviours of the signal2.2. Local monitoring by detection of predefined functional behavioursThe DAI software analyzes efficiently the data with all underlaying information andyields continuously updated synthesis of so-called events, which represent the entering /exiting of a functional behaviour. To detect the event in time, an observation window movesfrom one data point to the next and calculates the average of the variation in data point value.This characterizes each data point with a mean slope. Experience with the online processmonitoring has indicated that an algorithm of first order with an observation window of fixedtime and with a predefined cone of allowed mean slope variation are fast and sufficientlyprecise for the recognition and confirmation of an event.Fig. 1: Slope calculation for the detection of a functional filling behaviourEach functional behaviour is characterized by the mean slope value and the predefinedcone of allowed slope variation. The larger the cone, the more noisy the signal, however theexit time identified is less accurate. The observation window with fixed time width for thewhole process monitoring slides normally its center to each data point, as shown in Fig. 1. Todetect the starting point of a new functional behaviour the slope evaluation is repeated for aCHISA Conference, Prague, August 20044/8SkimmingTransfer to Vessel XFilling from Vessel ZFilling from Vessel YBack skimming(filling from Vessel V)Mixingcertain number of previous points with the left border of the observation window positioned ateach of the previous points. Analogously to detect the exit point of a functional behaviour theslope evaluation is repeated with the right border of the observation window positioned at thepoints. More information can be found in the paper by Thevenon et al. (2003)Experience with those algorithms in the online monitoring of a process has revealed thefollowing weaknesses:· the recognition of a functional behaviour is sensitive tot the parameters definingthe cone of allowed slope variation· the recognition of the main functions in a process cycle on a tank may becomplicated by superposed oscillations due to liquid agitation· the identification of the start and end points of a functional behaviour is veryimportant as these dates and times determine the total duration of the functionalbehaviour and the total mass transferFor the example of a solution tank cycle the principal functional behaviours includefilling/emptying at know or unknown flow, plateau at known or unknown level and steps atknown amplitude or known limit. The monitoring tool allows visualisation of the nuclearmaterial flow through the plant by means of two couple layers: a fixed design work sheet anda real-time updated process signal sheet.2.3. Global monitoring of a complete batch cycleOn the design work sheet of a plant, all process cycles are modeled with the involvedfunctional behaviours. An example of a tank cycle with filling -  mixing - emptying - is shownin Fig. 2. Not only the signal profile of a tank in operation in a process cycle can be identified,which is referred to as signal auto-correlation, but also the total material transfer betweentanks can be traced, which is referred to as signal cross-correlation. Moreover the design worksheet guides the detection of functional behaviours and is therefore also a diagnostic aid in thecase of abnormal exit of a cycle.Fig. 2: Design work sheet of a prototype cycle for an inventory tank. The sequence ofexpected level slopes is described in detail with three different possibilities: (i) filling in 2steps, (ii) filling in one single step, (iii) filling in three steps.CHISA Conference, Prague, August 20045/8Each design work sheet connects to a process signal sheet, which visualises in real-timeall recorded signals for the selected process cycle. On this process signal sheet all importantevents are displayed and the coherency in material flow is verified. Fig. 3 shows an exampleof the level signal in red and volume signal in green for four tank cycles with filling – mixing– sampling – skimming - emptying. The events (starting points and end points of thefunctional behaviour are indicated in red and are utilized to calculate the total materialtransfer and to verify coherency between transferred material. Once all functional behavioursas defined in the design work sheet of the plant are encountered, the conformity with acomplete cycle is confirmed and a new cycle is initiated.Fig. 3: Process signal sheet with real-time representation of the level (red) and volume(green) signal for four tank cycles.CHISA Conference, Prague, August 20046/82.3. Verification method for the transition between successive functional behavioursTo facilitate the verification of incoherence in data from communicating tanks and ofconformity in procedure, the monitoring system applies a formalism, which allocates to eachfunctional behaviour one of the five distinct modes: Passive, Ready, Active, Checking andCompleted.At the initialization all functional behaviours are in passive mode. Their mode can betransformed to ready if the prescribed model considers them as one of the possible functionalbehaviours in the following time steps. In case the functional behaviour can be immediatelyrecognized the ready mode passes to active, and after reaching its exit point, the mode finallypasses to completed. In case the functional behaviour is not immediately detectable, the readymode is first submitted to a checking mode before passing to active and completed.3. Application on an inventory tank in a reprocessing plantA solution monitoring tool with a modular structure and an analyzing kernel in C# isrealized, which runs under an XP environment and which is validated for the UP2reprocessing facility at the La Hague site. The solution monitoring tool has similar features tothe one described by Howell and Scothern (1997) but operates in a more pragmatic way on alarger scale.No real data from the UP2 in La Hague could be shown here, because they are strictlyproprietary information. Instead a fictitious but representative example of the most importantinventory tank of a reprocessing plant, the input accountancy tank is presented in Fig. 4. Inthe example the accountancy tank (AT) is connected to three similar feeding tanks (FT) and toone succeeding process tank (PT). The accountancy tank knows a filling in three partscorresponding to each of the three upstream tanks with a simple emptying. The fillingfunctional behaviour of the AT is correlated with the emptying functional behaviours of thethree FT, which are successively assigned the active status.Fig 4: Example of the modeling of an accountancy tank with three feeding tanks and onesuccessive process tank.CHISA Conference, Prague, August 20047/8The inspector follows in real-time the collected measurement data on the process signalsheet. In case an anomaly is noticed, the subsequent events are marked on the signal profilewith a symbol, which indicates the type of error. Additionally an alarm is generated and themonitoring may be interrupted. The monitoring is automatically reinitiated with thesynchronisation point modeled in the design work sheet.4. ConclusionA stand-alone software tool DAI (for Data Analysis and Interpretation) has been realized,which monitors the liquid in a reprocessing plant according to the described algorithms andformalism. The system applied at the reprocessing plant especially allows monitoring of thesolution flow through the process by analysing the required coherence in complementary dataof communicating tanks and the required conformity with the process procedure and cyclecompleteness. The signals from a central accountancy tank are not only interpreted withidentification of all functional behaviours but also evaluated carefully in order to asses theintegrity of the material flow from and to the connected tanks.The inspectors are satisfied with this tool, which serves for the verification of all cyclicfunctional behaviours as prescribed in the design. At each point where a non-conformity isdetected, the cycle is interrupted and an alarm for the inspector is switched on. The tool thenlists all possible procedural errors or constraint violations. It is left to the inspector to decideon the real case, consistent with their defined responsibility in the follow-up of the process.This example demonstrates the usefulness of a simplified approach to follow-up thecomplete transfer of different material flows. The syntactical pattern recognition based onexpertise analysis appeared to be a very appropriate methodology to follow batch processes.This methodology and to some extend also this software may be applied in other processfacilities, such as petrochemical plants, milk or sugar production facilities.ReferencesJ. Vieille (2000), Repères Batch : Regard S88 / S95 sur le(s) cycle(s) de vie du systèmede production, Applicatif J’automatise, No. 9, April 2000, p. 82-90Janssens-Maenhout G. and Dechamp, L. (2004), Process Monitoring Appropriate forNear Real Time Accountancy, J. Nuclear Material Management, Vol 32, No. 3, Spring issue2004Amaral W., Arruda L. and Bueno S. (1992), CAD package for industrial processidentification, J. Process Control, Vol. 2, No. 3, p. 155-161Lee J., Natarajan S. and Lee K. (2001), A model-based predictive control approach torepetitive control of continuous processes with periodic operations, J. Process Control, Vol.11, No. 2, p. 195-207Harrold, D. (2004), Historian holds key to performance, Control Engineering, January2004CHISA Conference, Prague, August 20048/8Thevenon J.-B., Janssens-Maenhout G., Dransart P. and Dechamp L. (2003), Uneapproche du monitoring d’installation industrielle, Proc. Optimation Automation Congress,Paris, October 2003Scothern S.J. and Howell J. (1997), A physical-model-based diagnostic aid forsafeguarding nuclear material in a liquor storage facility, J. Nuclear Materials Management,Vol. 25, No.4

Auditing of batch processes

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Auditing of batch processes

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