Browsing by Author "Komulainen, Tiina"

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Control of an industrial copper solvent extraction process
(2009) Komulainen, Tiina; Doyle III, Francis J.; Rantala, Ari; Jämsä-Jounela, Sirkka-Liisa
School of Chemical Technology | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
A two level control strategy that stabilizes and optimizes the production of an industrial copper solvent extraction process is presented. The stabilizing layer consists of a multi-input multi-output controller or two single-input single-output controllers with additional four feedforward compensators that regulate the flow rates in the copper solvent extraction process. The optimization layer consists of an optimizer that maximizes the production of the copper solvent extraction process and gives setpoints to the controllers at the stabilizing level. The mechanistic plant models, verified with industrial data, are linearized by identifying first and higher order transfer function models from simulated PRBS data. On the basis of the linear models, the interactions of the controlled variables, and the pairing of the controlled and manipulated variables are studied and the optimizer and the controllers designed. The control strategy employing two PI-control loops or a model predictive controller and additionally four feedforward control loops is successfully tested against simulated disturbances and setpoint changes. The control strategy is also compared to the data collected from the industrial plant under manual control. With this two level control strategy the production of the copper solvent extraction process is increased by 3-5% and the process variation is decreased by 70-90% compared to the manual operation of the case industrial plant. The results gained in simulation environment are successful and encouraging for further testing in an industrial plant.
Control of Industrial Copper Solvent Extraction Process
(2009) Komulainen, Tiina; Rantala, Ari; Doyle, F.; Jämsä-Jounela, Sirkka-Liisa
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
A two level control strategy that stabilizes and optimizes the production of an industrial copper solvent extraction process is presented. The stabilizing layer consists of a multi-input multi-output controller or two single-input single-output controllers with additional four feedforward compensators that regulate the flow rates in the copper solvent extraction process. The optimization layer consists of an optimizer that maximizes the production of the copper solvent extraction process and gives setpoints to the controllers at the stabilizing level. The mechanistic plant models, verified with industrial data, are linearized by identifying first and higher order transfer function models from simulated PRBS data. On the basis of the linear models, the interactions of the controlled variables, and the pairing of the controlled and manipulated variables are studied and the optimizer and the controllers designed. The control strategy employing two PI-control loops or a model predictive controller and additionally four feedforward control loops is successfully tested against simulated disturbances and setpoint changes. The control strategy is also compared to the data collected from the industrial plant under manual control. With this two level control strategy the production of the copper solvent extraction process is increased by 3-5% and the process variation is decreased by 70-90% compared to the manual operation of the case industrial plant. The results gained in simulation environment are successful and encouraging for further testing in an industrial plant. Copyright © 2006
Dynamic modelling of an industrial copper solvent extraction process
(2006) Komulainen, Tiina; Pekkala, Pertti; Rantala, Ari; Jämsä-Jounela, Sirkka-Liisa
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The dynamic behaviour of an industrial copper solvent extraction mixer–settler cascade is modelled to develop an advanced process control system. First, the process is introduced and the dynamical models are formulated. The testing environment is described and the successful results presented. Only industrially measured variables are required and plant-specific McCabe-Thiele diagrams are utilized to predict copper concentrations. The results with constant and adapted parameters are compared and the importance of parameter adaptation is discussed. Testing the simulator with adapted parameters over a period of 1 month of industrial operating data gave data that followed the real process measurements closely. In the future, the mechanistic models will be used for control system development and testing. The model can be used on all copper solvent extraction plants by modifying the flow configuration and adapting parameters.
Fault detection and isolation of an online analyzer for an ethylene cracking process
(2008) Kämpjärvi, Petteri; Sourander, Mauri; Komulainen, Tiina; Nikus, Mats; Vatanski, Nikolai; Jämsä-Jounela, Sirkka-Liisa
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Fault diagnosis methods based on process history data have been studied widely in recent years, and several successful industrial applications have been reported. Improved data validation has resulted in more stable processes and better quality of the products. In this paper, an on-line fault detection and isolation system consisting of a combination of principal component analysis (PCA) and two neural networks (NNs), radial basis function network (RBFN) and self-organizing map (SOM), is presented. The system detects and isolates faulty operation of the analyzers in an ethylene cracking furnace. The test results with real-time process data are presented and discussed.
Integrating Process Indicators with Monitoring Method Hybrids
(2004) Jämsä-Jounela, Sirkka-Liisa; Komulainen, Tiina
A4 Artikkeli konferenssijulkaisussa
In this article the benefits of process monitoring are discussed. The different aspects of data-based monitoring methods and their combinations are reviewed. The role of process indicators is discussed, and the concept of combining a monitoring method library with the process indicators is presented. Finally, the benefits of process indicators and monitoring method hybrids are demonstrated with five industrial process monitoring applications created by the Laboratory of Process Control and Automation, Helsinki University of Technology.
An online application of dynamic PLS to a dearomatization process
(2004) Komulainen, Tiina; Sourander, Mauri; Jämsä-Jounela, Sirkka-Liisa
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Early detection of process disturbances and prediction of malfunctions in process equipment improve the safety of the process, minimize the time and resources needed for maintenance, and increase the uniform quality of the products. The objective of online-monitoring is to trace the state of the process and the condition of process equipment in real-time, and to detect faults as early as possible. In this article the different properties of the online-monitoring methods applied in the process industries are first reviewed. A description of the systematic development of the online-monitoring system for an industrial dearomatization process, specifically for flash point and distillation curve analysers, is then presented. Finally, the results of offline and online tests of the monitoring system using real industrial data from the Fortum Naantali Refinery in Finland, are described and discussed. The developed online-monitoring application was successful in real-time process monitoring and it fulfilled the industrial requirements. PACS: 07.05.Mh; 07.05.Tp; 83.85.Ns
Liuottimien aromaattien poistoprosessin online-monitorointi
(2003) Komulainen, Tiina
Helsinki University of Technology | Master's thesis
Prosessihäiriöiden aikainen tunnistus ja prosessilaitteiden vikaantumisen ennakointi parantavat lopputuotteen laadun tasaisuutta, parantavat prosessin turvallisuutta ja minimoivat laitteiden kunnossapitoon tarvittavaa aikaa ja resursseja. Online-monitoroinnin tavoitteena on seurata reaaliaikaisesti prosessin tilaa ja prosessilaitteiden kuntoa ja tunnistaa mahdollisimman aikaisessa vaiheessa häiriö. Tämän diplomityön tarkoituksena oli kartoittaa prosessiteollisuudessa sovellettuja online-monitorointimenetelmiä ja soveltaa yhtä menetelmistä liuottimien aromaattien poistoprosessin analysaattoreiden monitorointiin. Diplomityön kirjallisuusosassa on kartoitettu prosessiteollisuuteen sovellettuja online-monitorointimenetelmiä ja sovellusten tuloksia, sekä esitelty menetelmien matemaattista taustaa. Lähes kaikki löydetyistä online-monitorointimenetelmien sovelluksista olivat historiatietoon perustuvia tilastollisia monimuuttujamenetelmiä tai neuroverkkomenetelmiä. Diplomityön kokeellisessa osassa selvitettiin liuottimien aromaattien poistoprosessin monitorointitarpeita. Monitorointikohteeksi valittiin prosessin loppupään leimahduspiste- ja tislausalueanalysaattorit. Analysaattoreiden kunnon arvioimiseen ja analysaattorimittauksia vastaavien ennusteiden laskemiseen valittiin osittaisten neliösummien menetelmä, PLS. Aluksi kaikki prosessimuuttujat aikaskaalattiin. Prosessimuuttujien perusteella kehitettiin yksinkertaisia laskennallisia muuttujia, joiden avulla pyrittiin saamaan prosessin ominaispiirteet paremmin esille datasta. Seuraavaksi valittiin prosessi- ja laskennallisten muuttujien kombinaatio, jolla monitorointijärjestelmän offline-testaus suoritettiin. PLS-mallin yleistyskyvyn takaamiseksi opetusdata koostettiin esitutkimusten perusteella kaikkia kuutta laatua sisältävästä datasta. Offline-testidataa kerättiin noin 455 tunnin ajan minuuttiresoluutiolla. Menetelmällä havaittiin noin 96 - 99 % analysaattoreiden normaalitiloista oikein ja vikatiloista noin 67 - 97 %. Monitorointijärjestelmää testattiin online kuuden vuorokauden ajan. Online-testauksen aikana liuottimen laatu vaihtui kaksi kertaa ja kerran prosessi joutui epänormaaliin tilaan. Monitorointijärjestelmä havaitsi laadunvaihdot normaalitiloiksi ja hälytti oikein prosessihäiriöstä.