19 research outputs found

    Computational methods for exploiting image-based data in paper web profile control

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    Abstract Sheet and film forming processes such as paper manufacturing pose a challenging monitoring and control problem, where quality variations are classified into machine direction (MD), cross-machine direction (CD) and residual variation. The measurements are typically collected with a scanning sensor that covers only a small part of the paper web, and therefore provides a very limited view of the paper web, setting performance limitations on the online monitoring and control. The development of cameras, light sources and computation hardware enable the consideration of utilizing in-use web inspection systems in paper machines to measure the paper web variations with a considerably higher resolution, sampling rate and coverage. The light transmittance images captured with this kind of system need, however, to be converted into a controllable quality property, such as basis weight, in order to utilize the new measurement information for control purposes. In this thesis, computational methods are identified and developed that are capable of combining light transmittance and scanning measurements, and can efficiently utilize the combined information for control purposes. The possible benefits gained with these image-based measurements in paper machine online monitoring and profile control are evaluated in a simulation environment. In a real paper machine, the benefits are ultimately dependent on the machine configuration and the nature of paper variations therein. It was found that with a suitable estimation method, light transmittance could increase the awareness of basis weight variations such as fast MD variation, tilted waves and dynamic CD variation patterns, which are practically undetectable using scanner-based measurement. The enhanced basis weight estimation enables a considerable improvement in the dynamic performance of profile controls. CD control was able to handle fast variations earlier classified as uncontrollable residual variation. In MD control, enhanced estimation enabled the development of a control strategy that led to improved reference tracking and disturbance rejection properties.Tiivistelmä Paperinvalmistus on yksi esimerkki levyjen tai kalvojen valmistusprosesseista, jotka ovat tyypillisesti haasteellisia prosessin monitoroinnin ja säädön kannalta. Laatuvaihtelut näissä prosesseissa luokitellaan koneensuuntaisiin (MD), poikkisuuntaisiin (CD) ja jäännösvaihteluihin. Paperikoneella mittaukset kerätään tavallisesti radan yli liikkuvalla skannaavalla sensorilla, joka tarjoaa vain hyvin rajoitetun määrän informaatiota paperiradasta, asettaen siten rajoituksia online monitoroinnin ja säädön suorituskyvylle. Kameroiden ja valonlähteiden kehitys sekä laskentakapasiteetin kasvu mahdollistavat paperiradan vaihteluiden mittaamisen huomattavasti korkeammalla resoluutiolla ja näytteenottovälillä jo käytössä olevilla vianilmaisujärjestelmillä. Vianilmaisujärjestelmän keräämä valon transmittanssitieto pitää kuitenkin muuntaa esimerkiksi neliömassatiedoksi, jotta uutta mittausinformaatiota voitaisiin hyödyntää myös prosessin online säädössä nykyisillä toimilaitteilla. Tässä työssä on identifioitu ja kehitetty laskennallisia menetelmiä, jotka kykenevät yhdistämään kuvantavan ja skannaavan mittauksen sekä käyttämään tätä yhdistettyä tietoa säätötarkoituksissa. Kuvapohjaisen mittauksen mahdollisia hyötyjä online monitoroinnissa ja profiilien säädössä on arvioitu simulointiympäristössä. Saavutettavat hyödyt paperikoneella ovat lopulta riippuvaisia myös koneen konfiguraatiosta ja koneella ilmenevien laatuvaihteluiden luonteesta. Tulokset osoittavat, että transmittanssimittauksen ja tehokkaan estimointimenetelmän avulla kyetään lisäämään tietämystä neliömassamuutoksista, joita ei käytännössä voida havaita pelkän skannaavan mittauksen avulla. Estimoinnin parempi suorituskyky mahdollistaa myös profiilisäätöjen dynaamisen suorituskyvyn kasvattamisen. CD-säätö voitiin laajentaa kattamaan myös nopeita vaihteluita, jotka ovat aiemmin luokiteltu jäännösvaihteluksi. MD-säädölle voitiin kehittää säätöstrategia, jonka avulla sekä asetusarvojen seurantaa että häiriöiden vaimennusta pystyttiin parantamaan

    Hierarchical control of an integrated fuel processing and fuel cell system

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    Abstract An advanced model-based control method for the integrated fuel processing and a fuel cell system consisting of ethanol reforming, hydrogen purification, and a proton exchange membrane fuel cell is presented. For process identification, a physical model of the process chain was constructed. Subsequently, the simulated process was approximated with data-driven control models. Based on these control models, a hierarchical control framework consisting of model predictive controller and a global optimization algorithm was introduced. The performance of the new control method was evaluated with simulations. Results indicate that the new optimization concept enables resource efficient and fast control of the studied energy conversion process. Fast and efficient fuel cell process could then provide sustainable power source for autonomous and mobile applications in the future

    Control design tools for intensified solids handling process concepts

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    Abstract The Theory of Inventive Problem Solving (TRIZ) can be applied to generate new concepts for process intensification (PI). In order to meet the target performance of the intensified process and to avoid design bottlenecks due to process operation, the suggested concepts need to comprise a feasible control system. Therefore, a design step, where a systematic procedure for variable selection is performed, available measurement devices are mapped, and the control design is initialized, is needed. This chapter presents a systematic approach to tackle these issues in a structured manner in order to enable a smooth transfer from new innovative ideas into feasible process design from operation point of view

    Genetic algorithms in model structure identification for fuel cell polarization curve

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    Abstract Evolutionary optimizers, such as genetic algorithms, have earlier been successfully applied to find the parameter values for the fuel cell polarization curve models. The structure of these, typically semi-empirical, models have evolved during the decades. In this study, the model structures were reviewed and a new model structure was generated. Genetic algorithms were used to determine the optimized model structure with linear model parameters. Four different fuel cells, one with varying operating conditions, were studied. The results show that the model can outperform the semi-empirical model utilized in number of studies without increasing the model complexity

    Model structure optimization for fuel cell polarization curves

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    Abstract The applications of evolutionary optimizers such as genetic algorithms, differential evolution, and various swarm optimizers to the parameter estimation of the fuel cell polarization curve models have increased. This study takes a novel approach on utilizing evolutionary optimization in fuel cell modeling. Model structure identification is performed with genetic algorithms in order to determine an optimized representation of a polarization curve model with linear model parameters. The optimization is repeated with a different set of input variables and varying model complexity. The resulted model can successfully be generalized for different fuel cells and varying operating conditions, and therefore be readily applicable to fuel cell system simulations

    Model adaptation for dynamic flotation process simulation

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    Abstract Dynamic production processes in mineral beneficiation have complex flowsheets and non-linear, time-varying behavior. They have also limited measurement capabilities. Hence, virtual models are seen as important tools for assisting in design, planning and operation. The development of operational dimension requires not only a suitable software architecture and virtual model, but also continuous validation or adaptation of the virtual model. In this paper, a framework establishing a digital twin for a flotation process is presented. The model adaptation is treated as a trajectory matching problem and realized with a Differential Evolution algorithm. The results are demonstrating the applicability of the presented approach in simulation environment together with a discussion on additional challenges foreseen in implementations to the real processes

    Adaptation framework for an industrial digital twin

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    Abstract Digital twins for performance-oriented applications in industrial environments require systematic model maintenance. Model adaptation requires efficient optimization tools and continuous evaluation of measurement quality. The adaptation and model performance evaluation are based on the modeling error, making the adaptation prone also to the measurement errors. In this paper, a framework for combining model adaptation and measurement quality assurance are discussed. Two examples with simulated industrialscale biopharmaceutical penicillin fermentation are presented to illustrate the usability of the framework

    Model Predictive Control and Differential Evolution optimisation of the fuel cell process

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    Abstract In the future, the energy production will aim at more sustainable methods. Fuel cell technology is one alternative to the existing technology and its popularity in distributed energy systems comes from high efficiency, low risk to the environment, fast dynamic response, and its reliability and durability in different applications. It, however, requires a reliable source of pure hydrogen. This report introduces first the production routes of different fuel cell systems. Next, models of steam reformer, WGS reactor and PEM fuel cell are shortly presented together with the integrated model of the whole fuel cell system. The model is then utilised in process identification and control development comparing PI-control with Model Predictive Control (MPC). Finally, the upper level optimisation using Differential Evolution (DE) is tested with simulations in Matlab® Simulink® and results of the control simulations are briefly discussed

    Ex-post evaluation of data-driven decisions:conceptualizing design objectives

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    Abstract This paper addresses a need for developing ex-post evaluation for data-driven decisions resulting from collaboration between humans and machines. As a first step of a design science project, we propose four design objectives for an ex-post evaluation solution, from the perspectives of both theory (concepts from the literature) and practice (through a case of industrial production planning): (1) incorporate multi-faceted decision evaluation criteria across the levels of environment, organization, and decision itself and (2) acknowledge temporal requirements of the decision contexts at hand, (3) define applicable mode(s) of collaboration between humans and machines to pursue collaborative rationality, and (4) enable a (potentially automated) feedback loop for learning from the (discrete or continuous) evaluations of past decisions. The design objectives contribute by supporting the development of solutions for the observed lack of ex-post methods for evaluating data-driven decisions to enhance human-machine collaboration in decision making. Our future research involves design and implementation efforts through on-going industry-academia cooperation

    Development of a surrogate-model based energy efficiency estimator for a multi-step chemical process

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    Abstract Energy efficiency is increasingly being considered as a critical measure of process performance due to its importance both in production costs and in environmental footprint. In this work, an indirect energy efficiency estimator was developed for the Tennessee Eastman (TE) benchmark process for the first time. The TE model was first modified to provide the reference values of energy efficiency. A sophisticated model selection scheme was then applied to build the surrogate-model. The results indicate reasonable model performance with mean absolute prediction error around 1.7%. The results also highlight the limitations present in the training set, which are, together with other practical implementation issues, discussed in this work
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