Identification of low order models for large scale processes

Many industrial chemical processes are complex, multi-phase and large scale in nature. These processes are characterized by various nonlinear physiochemical effects and fluid flows. Such processes often show coexistence of fast and slow dynamics during their time evolutions. The increasing demand for a flexible operation of a complex process, a pressing need to improve the product quality, an increasing energy cost and tightening environmental regulations make it rewarding to automate a large scale manufacturing process. Mathematical tools used for process modeling, simulation and control are useful to meet these challenges. Towards this purpose, development of process models, either from the first principles (conservation laws) i.e. the rigorous models or the input-output data based models constitute an important step. Both types of models have their own advantages and pitfalls. Rigorous process models can approximate the process behavior reasonably well. The ability to extrapolate the rigorous process models and the physical interpretation of their states make them more attractive for the automation purpose over the input-output data based identified models. Therefore, the use of rigorous process models and rigorous model based predictive control (R-MPC) for the purpose of online control and optimization of a process is very promising. However, due to several limitations e.g. slow computation speed and the high modeling efforts, it becomes difficult to employ the rigorous models in practise. This thesis work aims to develop a methodology which will result in smaller, less complex and computationally efficient process models from the rigorous process models which can be used in real time for online control and dynamic optimization of the industrial processes. Such methodology is commonly referred to as a methodology of Model (order) Reduction. Model order reduction aims at removing the model redundancy from the rigorous process models. The model order reduction methods that are investigated in this thesis, are applied to two benchmark examples, an industrial glass manufacturing process and a tubular reactor. The complex, nonlinear, multi-phase fluid flow that is observed in a glass manufacturing process offers multiple challenges to any model reduction technique. Often, the rigorous first principle models of these benchmark examples are implemented in a discretized form of partial differential equations and their solutions are computed using the Computational Fluid Dynamics (CFD) numerical tools. Although these models are reliable representations of the underlying process, computation of their dynamic solutions require a significant computation efforts in the form of CPU power and simulation time. The glass manufacturing process involves a large furnace whose walls wear out due to the high process temperature and aggressive nature of the molten glass. It is shown here that the wearing of a glass furnace walls result in change of flow patterns of the molten glass inside the furnace. Therefore it is also desired from the reduced order model to approximate the process behavior under the influence of changes in the process parameters. In this thesis the problem of change in flow patterns as result of changes in the geometric parameter is treated as a bifurcation phenomenon. Such bifurcations exhibited by the full order model are detected using a novel framework of reduced order models and hybrid detection mechanisms. The reduced order models are obtained using the methods explained in the subsequent paragraphs. The model reduction techniques investigated in this thesis are based on the concept of Proper Orthogonal Decompositions (POD) of the process measurements or the simulation data. The POD method of model reduction involves spectral decomposition of system solutions and results into arranging the spatio-temporal data in an order of increasing importance. The spectral decomposition results into spatial and temporal patterns. Spatial patterns are often known as POD basis while the temporal patterns are known as the POD modal coefficients. Dominant spatio-temporal patterns are then chosen to construct the most relevant lower dimensional subspace. The subsequent step involves a Galerkin projection of the governing equations of a full order first principle model on the resulting lower dimensional subspace. This thesis can be viewed as a contribution towards developing the databased nonlinear model reduction technique for large scale processes. The major contribution of this thesis is presented in the form of two novel identification based approaches to model order reduction. The methods proposed here are based on the state information of a full order model and result into linear and nonlinear reduced order models. Similar to the POD method explained in the previous paragraph, the first step of the proposed identification based methods involve spectral decomposition. The second step is different and does not involve the Galerkin projection of the equation residuals. Instead, the second step involves identification of reduced order models to approximate the evolution of POD modal coefficients. Towards this purpose, two different methods are presented. The first method involves identification of locally valid linear models to represent the dynamic behavior of the modal coefficients. Global behavior is then represented by ‘blending’ the local models. The second method involves direct identification of the nonlinear models to represent dynamic evolution of the model coefficients. In the first proposed model reduction method, the POD modal coefficients, are treated as outputs of an unknown reduced order model that is to be identified. Using the tools from the field of system identification, a blackbox reduced order model is then identified as a linear map between the plant inputs and the modal coefficients. Using this method, multiple local reduced LTI models corresponding to various working points of the process are identified. The working points cover the nonlinear operation range of the process which describes the global process behavior. These reduced LTI models are then blended into a single Reduced Order-Linear Parameter Varying (ROLPV) model. The weighted blending is based on nonlinear splines whose coefficients are estimated using the state information of the full order model. Along with the process nonlinearity, the nonlinearity arising due to the wear of the furnace wall is also approximated using the RO-LPV modeling framework. The second model reduction method that is proposed in this thesis allows approximation of a full order nonlinear model by various (linear or nonlinear) model structures. It is observed in this thesis, that, for certain class of full order models, the POD modal coefficients can be viewed as the states of the reduced order model. This knowledge is further used to approximate the dynamic behavior of the POD modal coefficients. In particular, reduced order nonlinear models in the form of tensorial (multi-variable polynomial) systems are identified. In the view of these nonlinear tensorial models, the stability and dissipativity of these models is investigated. During the identification of the reduced order models, the physical interpretation of the states of the full order rigorous model is preserved. Due to the smaller dimension and the reduced complexity, the reduced order models are computationally very efficient. The smaller computation time allows them to be used for online control and optimization of the process plant. The possibility of inferring reduced order models from the state information of a full order model alone i.e. the possibility to infer the reduced order models in the absence of access to the governing equations of a full order model (as observed for many commercial software packages) make the methods presented here attractive. The resulting reduced order models need further system theoretic analysis in order to estimate the model quality with respect to their usage in an online controller setting

Vetikad Pseudokircheriella subcapitata kemikaalide ja sünteetiliste nanoosakeste keskkonnaohtlikkuse hindamisel

Vetikad on peamised fotosünteesivad organismid veekogudes, tootes umbes poole Maa primaarproduktsioonist. Seeõttu on oluline keskkonnasaastuse mõju vetikate kasvule. Vetikad on ka head keskkonnatoksikoloogia mudelorganismid, kuna nad on tundlikud paljudele kemikaalidele ja neid on lihtne laboratoorsetes tingimustes kasvatada. Käesolevas doktoritöös täiustati OECD 201 suuniseid järgivat vetikate P. subcapitata kasvu pärssimise testi. Täiustamisel peeti silmas, et meetod sobiks lisaks vees hästi lahustuvate kemikaalide toksilisuse analüüsiks ka vees halvasti lahustuvate ainete ja osakesi sisaldavate keskkonnaproovide uurimiseks, sh ka suuremahuliseks testimiseks. Täiustatud testmetoodikat rakendati kolmes uuringus: (i) Raskemetallidega (Zn, Cd, Pb) saastunud metallisulatustehaste ümbruse muldade uuring näitas, et vaatamata kõrgetele Zn kontsentratsioonidele mulla vee-ekstraktides, mis pidanuksid olema vetikale mürgised, oli P. subcapitata kasv neis võrreldav kontrolliga. Mullasuspensioonid, mis sisaldasid tunduvalt enam raskemetalle kui vastavad vee-ekstraktid isegi stimuleerisid vetikate kasvu võrreldes vetikate kasvuga kontroll-lahuses. Samas, kui analüüsiti puhtast ja metallidega saastatud mullast valmistatud segusid, ilmnes mullaosakestele seotud metallide doosist sõltuv kahjulik mõju vetikatele. Seega tuleks mullaproovide toksilisuse hindamisel vetikatele võrdluseks kasutada samade füüsikalis-keemiliste omadustega puhast mulda. (ii) ZnO, TiO2 ja CuO nanoosakeste mõjude hindamine vetikate P. subcapitata kasvule näitas, et ZnO ja CuO nanoosakesed olid vetikatele ’väga toksilised’, kuna pärssisid vetikate kasvu juba väga madalates kontsentratsioonides: vastavad 72-tunni EC50 väärtused olid 0,052 mg/l ja 0,89 mg/l. TiO2 nanoosaksesed inhibeerisid vetikate kasvu tunduvalt kõrgemates kontsentratsioonides (EC50=9,9 mg/l), olles vetikatele umbes 6 korda toksilisemad kui vastavad mikroosakesed, pärssides vetikate kasvu tänu nende seondumisele vetikarakkudele. Nominaalsete kontsentratsioonide põhjal olid CuO nanoosakesed 16 korda toksilisemad kui vastavad mikroosakesed, samas kui ZnO puhul olid ühtviisi väga mürgised nii nano- kui mikroosakesed. Lahustunud Zn ja Cu analüüs vetikate kasvukeskkonnast näitas, et ZnO ja CuO osakeste vetikate kasvu pärssiv toime oli seotud osakestest leostunud metalli-ioonidega. Kirjeldatud töö oli esimene CuO nanoosakeste toksilisuse uuring vetikatele. (iii) Konstrueeriti struktuur-aktiivsussõltuvuste (QSAR) analüüsi võimaldav kemikaalipaneel, mis koosnes 58st eri positsioonides erinevalt asendatud aniliinist ja fenoolist. Analüüsides nende toksilisust vetikatele P. subcapitata saadi väikese katseveaga andmestik, mis sisaldas 48 seni avaldamata EC50 väärtust. Ehkki analüüsitud molekulid olid struktuurselt sarnased, erines nende toksilisus vetikatele kahe suurusjärgu võrra, jäädes vahemikku 1,43 mg/l (3,4,5-trikloroaniliin) kuni 197 mg/l (fenool). Uuritud ainete toksilisus sõltus asendajate tüübist (kloro-, metüül-, etüül-), arvust (mono-, di-, tri-) ja asendist (orto-, meta-, para-). Reeglina kaasnes suurema asendajate arvuga ka suurem toksilisus. Kloro-asendatud molekulid olid üldiselt toksilisemad kui alküülrühmadega asendatud analoogid. Andmetest selgusid ka asendajate positsioonist tulenevad efektid, nii näiteks suurendas asendaja para-asendis peaagu kõigi ainete toksilisust, samas kui orto-asendusega analoogid olid kõige vähem toksilised. Näidati ka, et hüdrofoobsuse ja toksilisuse seos on aniliinide ja fenoolide puhul erinev. Antud töös täiustatud vetikate kasvu inhibitsoonil põhinev ainete toksilisuse hindamise meetod osutus sobivaks ka vees lahustumatuid osakesi sisaldavate proovide hindamiseks ning võimaldas väikese töökuluga analüüsida suurt hulka proove. See omakorda võimaldab struktuur-aktiivsus sõltuvuste täiustamist, kuna vetika kasvu inhibitsiooni andmeseeriad on seni olnud küllaltki piiratud. Mainitud meetodid on vajalikud kemikaalide keskkonnaohtuse hindamiseks ning möödapääsmatud kemikaale reguleeriva määruse REACH rakendamiseks.Publication of the thesis is supported by Estonian University of Life Sciences, by the Doctoral School of Earth Sciences and Ecology created under the auspices of European Social Fund and by the EU 6th Framework Integrated Project OSIRIS, contract no. GOCE-CT-2007-03701

Digital health technologies:Potential tools for promoting adherence to antiretroviral therapy among people living with HIV in Tanzania

Sufficiently high levels of adherence to ART are needed to achieve and maintain suppressed viral load. Despite the vast majority of people living with HIV(PLHIV) in Tanzania have access to ART, maintaining adherence to lifelong treatment thus seems to be a major challenge. Given the great penetration of mobile technology in Tanzania, digital health technology provides a unique opportunity to improve adherence to ART treatment. Therefore, this thesis investigates the impact of digital health technology as a potential tool to primarily support ART adherence and secondarily improve clinical outcomes among PLHIV in Tanzania. The first objective is to compare the effectiveness of two DATs, SMS and Real Time Medication Monitoring(RTMM), with standard care in promoting treatment adherence, using a randomized clinical trial, the so-called REMIND-HIV trial. Additionally, to examine the barriers and challenges participants encounter when using the DAT strategies. Lastly, to examine to what extent the adherence measures, including pharmacy refill counts, self-reported adherence and RTMM, predict virologic suppression. This thesis produced important findings on the effectiveness and implementation of DATs in influencing adherence to ART treatment. In our investigation, DATs have the potential to enhance the delivery of quality HIV care, particularly for communities that are hard-to-reach and having limited resources-settings. Furthermore, our studies indicate that DAT have the potential to raise awareness about the importance of ART adherence and improve patient-nurse communication during clinic visits. We recommend that several challenges learned from this study need to be considered to ensure that DATs are sustainable, feasible and acceptable

Validation practices for satellite based earth observation data across communities

Assessing the inherent uncertainties in satellite data products is a challenging task. Different technical approaches have been developed in the Earth Observation (EO) communities to address the validation problem which results in a large variety of methods as well as terminology. This paper reviews state-of-the-art methods of satellite validation and documents their similarities and differences. First the overall validation objectives and terminologies are specified, followed by a generic mathematical formulation of the validation problem. Metrics currently used as well as more advanced EO validation approaches are introduced thereafter. An outlook on the applicability and requirements of current EO validation approaches and targets is given

Equation of State of Nuclear Matter at high baryon density

A central issue in the theory of astrophysical compact objects and heavy ion reactions at intermediate and relativistic energies is the Nuclear Equation of State (EoS). On one hand, the large and expanding set of experimental and observational data is expected to constrain the behaviour of the nuclear EoS, especially at density above saturation, where it is directly linked to fundamental processes which can occur in dense matter. On the other hand, theoretical predictions for the EoS at high density can be challenged by the phenomenological findings. In this topical review paper we present the many-body theory of nuclear matter as developed along different years and with different methods. Only nucleonic degrees of freedom are considered. We compare the different methods at formal level, as well as the final EoS calculated within each one of the considered many-body schemes. The outcome of this analysis should help in restricting the uncertainty of the theoretical predictions for the nuclear EoS.Comment: 51 pages, to appear in J. Phys. G as Topical Revie

Articles indexats publicats per investigadors del Campus de Terrassa: 2020

Aquest informe recull els 314 treballs publicats per 242 investigadors/es del Campus de Terrassa en revistes indexades al Journal Citation Report durant el 2020Postprint (author's final draft

時間と周波数領域情報に基づいたシステムモデリングとその応用

System modeling is required to deal with the time-varying system dynamics or the experimental data with insufficient information. However, the existing methods cannot construct satisfactory models for rapidly varying systems or severely band-limited signals. This thesis focuses on the new approaches to solve such system modeling problems based on time and frequency-domain information and illustrates their applications in time-varying channel identification and localization system. For the rapid time-varying systems, parameters can be approximated by the cosine series using virtual even periodic functions. Following the orthogonality of the trigonometric functions, the parameter estimation is recursively implemented by estimating the coefficients of each degree of the cosine harmonic term. For the localization system with insufficient frequency components, the spectral characteristics including phase information in frequency domain and the information evaluation in time domain are applied to improve the convergence performance. Numerical simulations demonstrate the effectiveness of the new approaches.北九州市立大