Data-driven, mechanistic and hybrid modelling for statistical fault detection and diagnosis in chemical processes

Research and applications of multivariate statistical process monitoring and fault diagnostic techniques for performance monitoring of continuous and batch processes continue to be a very active area of research. Investigations into new statistical and mathematical methods and there applicability to chemical process modelling and performance monitoring is ongoing. Successive researchers have proposed new techniques and models to address the identified limitations and shortcomings of previously applied linear statistical methods such as principal component analysis and partial least squares. This thesis contributes to this volume of research and investigation into alternative approaches and their suitability for continuous and batch process applications. In particular, the thesis proposes a modified canonical variate analysis state space model based monitoring scheme and compares the proposed scheme with several existing statistical process monitoring approaches using a common benchmark simulator – Tennessee Eastman benchmark process. A hybrid data driven and mechanistic model based process monitoring approach is also investigated. The proposed hybrid scheme gives more specific considerations to the implementation and application of the technique for dynamic systems with existing control structures. A nonmechanistic hybrid approach involving the combination of nonlinear and linear data based statistical models to create a pseudo time-variant model for monitoring of large complex plants is also proposed. The hybrid schemes are shown to provide distinct advantages in terms of improved fault detection and reliability. The demonstration of the hybrid schemes were carried out on two separate simulated processes: a CSTR with recycle through a heat exchanger and a CHEMCAD simulated distillation column. Finally, a batch process monitoring schemed based on a proposed implementation of interval partial least squares (IPLS) technique is demonstrated using a benchmark simulated fed-batch penicillin production process. The IPLS strategy employs data unfolding methods and a proposed algorithm for segmentation of the batch duration into optimal intervals to give a unique implementation of a Multiway-IPLS model. Application results show that the proposed method gives better model prediction and monitoring performance than the conventional IPLS approach.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multianalyte Quantifications by Means of Integration of Artificial Neural Networks, Genetic Algorithms and Chemometrics for Time-Resolved Analytical Data

During the last decade the application of sensors for the detection and determination of various substances has gained an increasing popularity not only in the field of analytical chemistry but also in our daily life. Most sensor systems such as exhaust gas sensors for automobiles are based on single sensors, which are as selective as possible for the analyte of interest. The problems of interfering cross-reactive analytes and the lack of specific sensors for many analytes have ended up in the development of so-called sensor-arrays. Thereby, several analytes can be simultaneously quantified by the multivariate data analysis of the signal patterns of several cross-reactive sensors. Yet, this approach is also limited since the number of sensors in the array has to exceed the number of cross-reacting analytes. In this work, a new approach is presented, which allows multi-analyte quantifications on the basis of single-sensor systems. Thereby, differences of interaction kinetics of the analytes and sensor are exploited using time-resolved measurements and time-resolved data analyses. This time-resolved evaluation of sensor signals together with suitable sensor materials combines the sensory principle with the chromatographic principle of separating analytes in space or time. The main objectives of this work can be subsumed into two focuses concerning the measurement principle and the data analysis. The first focus is the introduction of time-resolved measurements in the field of chemical sensing. In this work the time-resolved measurements are based on the microporous polymer Makrolon as sensitive sensor coating, which allows a kinetic separation of the analytes during the sorption and desorption on the basis of the size of analytes. Multi-analyte determinations using single sensors are successfully performed for three different setups and for many multicomponent mixtures of the low alcohols and the refrigerants R22 and R134a. The second focus concerns the multivariate data analysis of the data. It is demonstrated that a highest possible scanning rate of the time-resolved sensor responses is desirable resulting in a high number of variables. It is shown that wide-spread data analysis methods cannot cope with the amount of variables and with the nonlinear relationship between the sensor responses and the concentrations of the analytes. Thus, three different algorithms are innovated and optimized in this study to find a calibration with the highest possible generalization ability. These algorithms perform a simultaneous calibration and variable selection exploiting a data set limited in size to a maximum extend. One algorithm is based on many parallel runs of genetic algorithms combined with neural networks, one algorithm bases on many parallel runs of growing neural networks and the third algorithm uses several runs of the growing neural networks in a loop. All three algorithms show by far better calibrations than all common methods of multivariate calibration and than simple non-optimized neural networks for all data sets investigated. Additionally, the variable selection of these algorithms allows an insight into the relationship between the time-resolved sensor responses and the concentrations of the analytes. The variable selections also suggest optimizations in terms of shorter measurements for several data sets. All three algorithms successfully solve the problems of too many variables for too few samples and the problems caused by the nonlinearities present in the data with practically no input needed by the analyst. Together, both main focuses of this work impressively demonstrate how the combination of an advanced measurement principle and of an intelligent data analysis can improve the results of measurements at reduced hardware costs. Thereby the principle of single-sensor setups or few-sensor setups is not only limited to a size-selective recognition but can be extended to many analyte discriminating principles such as temperature-resolved measurements leaving room for many further investigations.Während des letzten Jahrzehnts haben Sensoren zur Detektion und Bestimmung von verschiedenen Substanzen nicht nur auf dem Gebiet der analytischen Chemie sondern auch im täglichen Leben rasend Verbreitung gefunden. Die meisten Sensorsysteme, wie zum Beispiel Abgasdetektoren für Automobile beruhen auf einzelnen Sensoren, welche möglichst spezifisch für den interessanten Analyten sind. Probleme auf Grund störender kreuzreaktiver Analyte und auf Grund eines Mangels an spezifischen Sensoren für viele Analyte führten zur Entwicklung so genannter Sensor-Arrays. Dabei können mehrere Analyte gleichzeitig quantifiziert werden, indem die Signalmuster von mehreren kreuzreaktiven Sensoren ausgewertet werden. Dieser Ansatz ist jedoch auch limitiert, da die Anzahl der Sensoren im Array größer als die Anzahl der kreuzreaktiven Analyte sein muss. In dieser Arbeit wird ein neuer Ansatz präsentiert, welcher es erlaubt, Multi-Analyt Quantifizierungen mit einem Einsensor-System durchzuführen. Hierbei werden Unterschiede der Wechselwirkungskinetiken zwischen den Analyten und dem Sensor mit Hilfe von zeitaufgelösten Messungen und zeitaufgelösten Datenauswertungen ausgenutzt. Zusammen mit geeigneten Sensormaterialien kombiniert die zeitaufgelöste Auswertung das Prinzip der Sensoren mit dem Prinzip der Chromatographie, welche Analyte räumlich oder zeitlich trennt. Die wichtigsten Zielsetzungen dieser Arbeit können unter den zwei Hauptgesichtspunkten Messprinzip und die Datenauswertung gestellt werden. Der erste Hauptgesichtspunkt ist die Einführung der zeitaufgelösten Messungen in die Sensorik. In dieser Arbeit basieren die zeitaufgelösten Messungen auf dem mikroporösen Polymer Makrolon als sensitive Sensorbeschichtung, welches eine kinetische Trennung der Analyte während der Sorption und der Desorption auf Grund der Analytgröße erlaubt. Es werden mit drei verschiedenen Einsensor-Aufbauten und vielen Mischungen der niederen Alkohole und der Kühlmittel R22 und R134a erfolgreich Mehrkomponentenanalysen erfolgreich durchgeführt. Der zweite Hauptgesichtspunkt betrifft die multivariate Datenauswertung. Es wird gezeigt, dass eine höchstmögliche Scanrate der zeitaufgelösten Sensorantworten wünschenswert ist, was zu einer hohen Anzahl an Variablen führt. Es wird demonstriert, dass weit verbreitete Datenauswertungsmethoden nicht mit der großen Anzahl an Variablen und mit dem nichtlinearen Zusammenhang zwischen den Sensorsignalen und den Analytkonzentrationen zurechtkommen. Deshalb werden in dieser Arbeit drei verschiedene Algorithmen entwickelt und optimiert, um eine Kalibration mit der höchstmöglichen Generalisierung zu finden. Diese Algorithmen führen eine gleichzeitige Kalibrierung und Variablenselektion durch, wobei sie einen Datensatz, welcher in der Größe limitiert ist, bestmöglich ausnutzen. Ein Algorithmus basiert auf vielen parallelen Läufen von genetischen Algorithmen kombiniert mit neuronalen Netzen. Der zweite Algorithmus beruht auf vielen parallelen Läufen von wachsenden neuronalen Netzen, während der dritte Algorithmus mehrere wachsende neuronale Netze in einer Schleife benutzt. Alle drei Algorithmen zeigen eine bei weitem bessere Kalibration als gewöhnliche Methoden der multivariaten Kalibration und als einfache nicht optimierte neuronale Netze für alle Datensätze, welche untersucht wurden. Zusätzlich erlaubt die Variablenselektion einen Einblick in den Zusammenhang zwischen den zeitaufgelösten Sensorantworten und den Konzentrationen der verschiedenen Analyte. Außerdem schlägt die Variablenselektion Optimierungen bezüglich kürzerer Messungen für mehrere Datensätze vor. Alle drei Algorithmen meistern erfolgreich das Problem von zu vielen Variablen für zu wenige Proben und die Probleme, welche von den in den Daten vorhandenen Nichtlinearitäten verursacht werden. Dabei sind praktisch keine Eingaben des Benutzers nötig. Zusammen liefern beide Hauptaspekte dieser Arbeit eine beeindruckende Demonstration, wie die Kombination eines fortschrittlichen Messprinzips mit einer intelligenten Datenauswertung die Ergebnisse von Messungen bei reduzierten Kosten für die Hardware verbessern kann. Dabei ist das Prinzip der Einsensor-Aufbauten beziehungsweise der Aufbauten mit wenigen Sensoren nicht auf ein größenselektives Erkennungsprinzip limitiert, sondern kann auf viele Prinzipien der Unterscheidung von Analyten wie zum Beispiel temperaturaufgelöste Messungen erweitert werden, was weiteren Untersuchungen ein nahezu endloses Feld eröffnet

Development of an optical fiber probe for mercury detection

El mercurio presenta una alta toxicidad, pudiendo causar adversos efectos en la salud humana. Los procedimientos recomendados para la detección analítica de metales pesados no son apropiados para aquellas aplicaciones donde se requiere un bajo coste y equipos ligeros que permitan realizar medidas de campo. Por el contrario, los sensores opto-químicos son una tecnología con un alto potencial para el desarrollo de dispositivos detectores de bajo coste y dimensiones reducidas. Esta Tesis describe los aspectos más relevantes en el desarrollo de una sonda opto-química basada en fibra óptica para la determinación de mercurio en agua. La investigación se plantea a partir de un compuesto de rutenio(II) descubierto recientemente que presenta un cambio de color desde un rojo púrpura oscuro hasta el naranja en solución orgánica ante la exposición a iones de mercurio(II). Otro factor importante es la capacidad de anclaje de la molécula a finas capas de óxidos metálicos como puede ser el TiO2. Los logros principales alcanzados en este trabajo de investigación son: (1) un software de análisis espectral multi-variable que reduce notablemente las interferencias principales del reactivo en fase líquida. El modelo matemático se basa en la regresión lineal por mínimos cuadrados parciales (PLS), y además se ha optimizado el resultado mediante una comparativa entre diferentes modelos PLS que incluyen un tratamiento previo de los datos mediante wavelet, corrección ortogonal de la señal, algoritmos genéticos, y selección de características estadísticas. (2) Una mejora de la estabilidad acuosa de la molécula al soportarla sobre una matriz de nanopartículas de Al2O3. Algunos trabajos preliminares con el complejo de Ru(II) se centraron en la inmovilización de la molécula sobre capas finas mesoporosas de TiO2. No obstante, pérdidas del colorante son apreciables cuando se analizan muestras acuosas. Resultados de la presente investigación garantizan la estabilidad acuosa de la molécula soportada sobre películas de Al2O3 y tratadas con ácido sulfúrico, con pérdidas por debajo del 2% (durante 3 horas). (3) La construcción del transductor basado en fibra óptica consiste en la sustitución de un trozo de cubrimiento (2 cm) del núcleo de la fibra óptica por el material sensibilizado compuesto por las nanopartículas de Al2O3. El principio de funcionamiento se basa en los cambios ópticos del material reactivo ante la exposición a iones de Hg2+, modulando así la intensidad de luz que se transmite a través del núcleo óptico. Se intuye a priori que la configuración del dispositivo conlleva a admitir que la propagación de la luz en la interfase núcleo y película de alumina tratada es mediante la aparición del campo evanescente. Sin embargo, al tener la cubierta de alumina un mayor índice de refracción que el núcleo, la condición de reflexión interna total no se satisface completamente, y como resultado se tiene una respuesta de la sonda óptica a la que contribuye tanto el campo evanescente como el modo de radiación generado por la porción de luz que se refracta a través de la cubierta de alumina-molécula. Finalmente, si tenemos en cuenta que la respuesta de este tipo de sondas ópticas varía significativamente de sonda a sonda, la cuantificación de mercurio ha sido posible a través de una calibración multivariable. Se ha logrado un error en la predicción de mercurio de un 11.5 por ciento, considerando un rango de 0 a 6 mg L-1 de iones de Hg2+. De este modo, se ha conseguido una sonda opto-química basada en fibra óptica cuyo modo de funcionamiento no es muy habitual en la literatura. La originalidad del presente trabajo se fundamenta en los pocos ejemplos de dispositivos ópticos de estas características que existen para la detección de metales pesados. En lo referente al autor, este es el primer dispositivo con configuración de fibra óptica evanescente destinado a la determinación de mercurio en medio acuoso.The organic form of mercury (methylmercury) is highly toxic, affecting the nervous system and even causing death. In the last years, human activities on coal combustion, waste incineration, gold mining and other industrial processes have raised the level of mercury in the atmosphere, rivers and other sources. Several public bodies have demonstrated that the direct detection of inorganic mercury (the precursor of mehtylmercury) will be beneficial in order to prevent mercury contamination. The detection of inorganic mercury through simple and low cost systems is possible by using colorimetric chemical sensors.Thus, several research groups worldwide have shown that the use of molecular probes, which change their optical properties upon the binding of inorganic mercury, is a promising topic for the development of detector devices for pollutant species.This Thesis describes the most remarkable aspects in the development of an optical fiber probe designed for mercury determination in aqueous samples. The research arises from the discovery of a novel molecule (IUPAC name bis(2,2'-bipyridyl-4,4'-dicarboxylato) ruthenium(II) bistetrabutylammonium bis-thiocyanate) that upon mercury binding induces a color change from dark red-purple to orange in solution. The selectivity towards mercury of this ruthenium complex is high when compared to other known chemical reagents. Yet, in this work, we have been able to increase the selectivity through a fully multivariate calibration of the absorbance measurements. We have analyzed the mercury-containing solutions under the co-existence of higher concentrations (19.5 mg L-1) of other potential competitors such as Cd2+, Pb2+, Cu2+ and Zn2+ ions. Our experimental model is based on partial least squares (PLS) linear regression and other general techniques as wavelet, orthogonal signal correction, genetic algorithm and statistical feature selection that have been used to refine, a priori, the analytical data. In summary, we have demonstrated that the root mean square error of mercury prediction with statistical feature selection, as compared to the absence of pre-treatment, can be reduced from 10.5 to 5.2 percent, which improves the prediction ability of the calibration model by a factor of 2.On the other hand, the possibility of working in solid-liquid phase increases the integration ability of the molecule in a device, making easier the measurement process. Nevertheless, the immobilization of the molecule onto a surface constitutes one of the challenges of this Thesis.Some preliminary works with the Ru(II) complex focussed on the immobilization of the molecule onto TiO2 mesoporous thin films.However, some leaching problems were apparent when aqueous samples were analysed. Accordingly, we have improved the water stability of the molecule by anchoring the dye onto Al2O3 nanoparticles thin films treated with sulphuric acid. Moreover, the good optical properties of the alumina support allow a better transparency of the films, which translates in a higher amount of available spectral absorbance information.A compact mercury read-out system has been achieved by coating an unclad optical fiber piece with Al2O3 paste. The proof-of-principle is based on the optical changes of the reagent upon Hg2+ ions exposure, which modulates the light intensity transmitted through the optical core.There are many theoretical studies that explore a particular research case of the evanescent optical fibers. As the alumina cladding has higher refractive index than the core, both evanescent field and radiative mode may appear in the modified cladding. This Thesis exposes a brief explanation of this behavior in order to understand the mechanisms of the response of our mercury optical fiber probe. Moreover, several experiments have been carried out in mercury aqueous samples so as to find the proper working conditions, such as the optimum dye concentration adsorbed onto the alumina cladding, which has a great effect on the device performance. Finally, mercury quantification has been possible through multivariate calibration, direct partial least squares being the most robust procedure if we take into account the fact that the response of this kind of optical probes varies significantly from one to another. A root mean square error for mercury predictions of 11.5 percent has been achieved within a range from 0 to 6 mg L-1 of Hg2+ ions.Overall, this thesis work has illustrated all the steps that come into play in the design of an optical fiber chemical-based probe, providing a simplified measurement process and a lower cost if it is compared to traditional analysis equipment. As far as the author is concerned, an optical fiber probe for mercury determination is presented for the first time

Modelling, Simulation, and Control of Polymorphic Crystallization

Ph.DDOCTOR OF PHILOSOPH

Réagir et s’adapter à son environnement: Concevoir des méthodes autonomes pour l’optimisation combinatoire à plusieurs objectifs

Large-scale optimisation problems are usually hard to solve optimally. Approximation algorithms such as metaheuristics, able to quickly find sub-optimal solutions, are often preferred. This thesis focuses on multi-objective local search (MOLS) algorithms, metaheuristics able to deal with the simultaneous optimisation of multiple criteria. As many algorithms, metaheuristics expose many parameters that significantly impact their performance. These parameters can be either predicted and set before the execution of the algorithm, or dynamically modified during the execution itself.While in the last decade many advances have been made on the automatic design of algorithms, the great majority of them only deal with single-objective algorithms and the optimisation of a single performance indicator such as the algorithm running time or the final solution quality. In this thesis, we investigate the relations between automatic algorithm design and multi-objective optimisation, with an application on MOLS algorithms.We first review possible MOLS strategies ans parameters and present a general, highly configurable, MOLS framework. We also propose MO-ParamILS, an automatic configurator specifically designed to deal with multiple performance indicators. Then, we conduct several studies on the automatic offline design of MOLS algorithms on multiple combinatorial bi-objective problems. Finally, we discuss two online extensions of classical algorithm configuration: first the integration of parameter control mechanisms, to benefit from having multiple configuration predictions; then the use of configuration schedules, to sequentially use multiple configurations.Les problèmes d’optimisation à grande échelle sont généralement difficiles à résoudre de façon optimale. Des algorithmes d’approximation tels que les métaheuristiques, capables de trouver rapidement des solutions sous-optimales, sont souvent préférés. Cette thèse porte sur les algorithmes de recherche locale multi-objectif (MOLS), des métaheuristiques capables de traiter l’optimisation simultanée de plusieurs critères. Comme de nombreux algorithmes, les MOLS exposent de nombreux paramètres qui ont un impact important sur leurs performances. Ces paramètres peuvent être soit prédits et définis avant l’exécution de l’algorithme, soit ensuite modifiés dynamiquement.Alors que de nombreux progrès ont récemment été réalisés pour la conception automatique d’algorithmes, la grande majorité d’entre eux ne traitent que d’algorithmes mono-objectif et l’optimisation d’un unique indicateur de performance. Dans cette thèse, nous étudions les relations entre la conception automatique d’algorithmes et l’optimisation multi-objective.Nous passons d’abord en revue les stratégies MOLS possibles et présentons un framework MOLS général et hautement configurable. Nous proposons également MO-ParamILS, un configurateur automatique spécialement conçu pour gérer plusieurs indicateurs de performance. Nous menons ensuite plusieurs études sur la conception automatique de MOLS sur de multiples problèmes combinatoires bi-objectifs. Enfin, nous discutons deux extensions de la configuration d’algorithme classique : d’abord l’intégration des mécanismes de contrôle de paramètres, pour bénéficier de multiples prédictions de configuration; puis l’utilisation séquentielle de plusieurs configurations

Development of genetic algorithm based classification and cluster analysis methods for analytical data

Thesis (Doctoral)--İzmir Institute of Technology, Chemistry, İzmir, 2009Includes bibliographical references (leaves: 151-158)Text in English; Abstract: Turkish and Englishxviii, 158 leavesIn this study genetic algorithm based classification and clustering methods were aimed to develop for the spectral data. The developed methods were completely achieved hybridization of nature inspired algorithm (genetic algorithms, GAs) to other classification or clustering methods. The first method was genetic algorithm based principal component analysis (GAPCAD), and the second was genetic algorithm based discriminant analysis (GADA). Both methods were performed to achieve the best discrimination between the olive oil and vegetable oil samples. The classifications of samples were examined directly from their spectral data obtained from using near infrared spectrometry, Fourier transform infrared (FTIR) spectrometry, and spectrofluorometry. The GA was used to optimize the performance of classification or clustering techniques. on training set in order to maximize the correct classification of acceptable and unacceptable samples or samples of dissimilar properties and to reduce the spectral data by wavelength selection. After GA optimization the classification results of training set were controlled by validation set. Lastly, the success of both algorithms was compared to the results of PCA and SIMCA

Novel strategies for process control based on hybrid semi-parametric mathematical systems

Tese de doutoramento. Engenharia Química. Universidade do Porto. Faculdade de Engenharia. 201

Data-Based Modeling: Application in Process Identification, Monitoring and Fault Detection

Present thesis explores the application of different data based modeling techniques in identification, product quality monitoring and fault detection of a process. Biodegradation of an organic pollutant phenol has been considered for the identification and fault detection purpose. A wine data set has been used for demonstrating the application of data based models in product quality monitoring. A comprehensive discussion was done on theoretical and mathematical background of different data based models, multivariate statistical models and statistical models used in the present thesis.The identification of phenol biodegradation was done by using Artificial Neural Networks (namely Multi Layer Percetprons) and Auto Regression models with eXogenious inputs (ARX) considering the draw backs and complications associated with the first principle model. Both the models have shown a good efficiency in identifying the dynamics of the phenol biodegradation process. ANN has proved its worth over ARX models when trained with sufficient data with an efficiency of almost 99.99%. A Partial Least Squares (PLS) based model has been developed which can predict the process outcome at any level of the process variables (within the range considered for the development of the model) at steady state. Three continuous process variables namely temperature, pH and RPM were monitored using statistical process monitoring. Both univariate and multivariate statistical process monitoring techniques were used for the fault detection purpose. X-bar charts along with Range charts were used for univariate SPM and Principal Component Analysis (PCA) has been used for multivariate SPM. The advantage of multivariate statistical process monitoring over univariate statistical process monitoring has been demonstrated

Molecular Phylogeny, Evolution and Biogeography of the Andean Gynoxyoid Group (Compositae, Asteroideae-Senecioneae).

This study is focused on the Gynoxyoid group a species‐rich lineage with low genetic distances within the subtribe Tussilaginineae. The Andean clade within the Asteraceae family comprises four genera and ca. 160 species. The genus Paracalia represents the smallest genus within the Gynoxyoid comprising only two species, the genera Aequatorium and Paragynoxys include 12 and 13 species respectively, and the largest genus Gynoxys contains 131 species. This group includes shrubs to big trees and eventually scandent shrubs (meanly Paracalia) which are distributed from north to south Andes, representing important components of the Andean vegetation. Analyses on plastid and nuclear data have retrieved this group as monophyletic, nevertheless these studies include only some representatives of this clade as part of their investigations. In that sense, the phylogenetic relationships at inter- and infrageneric level remained largely unresolved. Likewise, the morphological studies within this group is limited to close-distributed group of species. The aim of this study is to elucidate the phylogenetic relationships of the Gynoxyoid clade and define genera and (for a reduced group) species limits within it. In that sense, 21 complete annotated chloroplast genomes from a representative subgroup of the Gynoxyoids and four related members of the Tussilaginineae were generated. Thereafter, phylogenetic analyses were performed under maximum likelihood and Bayesian approaches. In order to estimate the strength of the phylogenetic signal in each genome partition trees topologies supported by gene, intron, and intergenic spacer partitions were compared. In a second instance, the impact of indel coding as well as manual adjustments of multiple automatic DNA sequence on the reconstruction of phylogenetic tree was evaluated. Given the phylogenetic backbone retrieved in the first phase of this study, genera delimitation was evaluated. Morphological variation among genera was evaluated by selecting a representative group of members of the Gynoxyoids and searching for discontinuities. The set of characters contributing to the discontinuities and already stated diagnostic characters in prologues were evaluated with a character reconstruction analysis. A second evaluation of the potential set of morphological characters retrieved in the previous analysis was further extended to all remaining members of the Gynoxyoids. Consequently, a revision of the current genera and species circumscription is made and a checklist including all members of the clade is provided. When necessary, new circumscriptions were proposed. Finally, species delimitation was evaluated for a reduced number of species distributed exclusively in Bolivia. The Asteraceae checklist for Bolivia was the basis for the species selection. A morphological revision as described for the genera evaluation was carried out at species level. Based on the discontinuities, morpho-species were defined. Additionally, phylogenetic inferences were reconstructed on the nuclear markers ETS and ITS. The set of putative characters suitable for morpho-species was tested with a principal component analysis in order to test the clustering of morpho-species and phylogenetic clades. The results of all analyses resulted in the elaboration of a taxonomic treatment for all supported Bolivian species. The phylogenetic results resolve the Gynoxyoid group as monophyletic. Phylogenetic trees on all three plastid genomic partitions retrieved well-supported clades. Nevertheless, incongruences in tree topologies were found among all three partitions. Moreover, significant differences were found among tree inferences before and after the manual curation of the alignments, meaning that the automatic multiple sequence alignment failed at the assessment of homology. Furthermore, the results show that a manual alignment correction is essential for phylogeny reconstruction, specially for closely related taxa. The phylogeny retrieved in this study is partially incongruent with the current generic classification, which was purely based on morphological data. The genus Aequatorium was represented by only one representative and its monophyly could not be tested, nevertheless, this unique specimen was retrieved as an independent clade. The genus Paragynoxys was retrieved as monophyletic. All members of the genus Nordenstamia were retrieved as being part of the Gynoxys clade, as well as one of the two members of the genus Paracalia. The second member of the genus Paracalia was retrieved as basal clade of the tree inference. In the second part of the study, the ancestral character reconstruction suggested a set of potential characters for genera delimitation. After testing the validity of this characters set in all members of the Gynoxyoids the delimitation and characterization of four genera was achieved. In that sense, the genus Aequatorium comprises all species with radiate white capitula, Paragynoxys includes trees with discoid white capitula distributed in Colombia and Venezuela, Paracalia characterizes by the absence of outer phyllaries and a central (Bolivia and Peru) distribution, and finally all members with yellow capitula are included in Gynoxys. The checklist resulted in a total of 158 species belonging to the four previously mentioned genera. The genus Nordenstamia was synonymized under Gynoxys and so all its members were newly synonymized in this study. Finally, although the phylogeny on the ETS and ITS markers of the Bolivian species retrieved incongruent topologies, it supported the circumscription of some species. The results of the principal component analysis supported most of the morpho-species, but vaguely the clades retrieved in the phylogenetic inference. Interestingly, both molecular and morphological analysis suggested the presence of at least one putative hybrid species. Based on these results, the taxonomic treatment for the Bolivian species of the Gynoxyoid clade resulted in 14 species belonging to Paracalia (1 sp) and Gynoxys (13 sp). This represented a reduction on the species number stated in the Asteraceae checklist for Bolivia as seven names were synonymized, and three names were excluded because of misidentifications. This investigation represents the first comprehensive study on the Gynoxyoid clade. It includes a phylogenetic backbone, character states reconstruction and a morphological analysis. The molecular datasets reveled closely relationships among species, suggesting rapid-radiating evolution, which was supported by morphological data. Furthermore, based on this analysis we were able to delimit genera and in a more reduced geographic scale, at species level. This study aims to contribute to the Flora de Bolivia which is part of the cooperation agreement between the Botanic Garden and Botanical Museum Berlin (Germany), the “Herbario Nacional de Bolivia” and the “Instituto de Ecología” de La Paz-Bolivia. That aims of this association are to collaborate in the formation of professionals in the area of botany for the subsequent application of knowledge