Fast pyrolysis of agricultural residues: Reaction mechanisms and effects of feedstock properties and Microwave operating conditions on the yield and product composition

Fundamental understanding of the pyrolysis process plays an indispensable role in valorization of wastes and the development of novel sustainable technologies. This study introduces a novel approach by investigating the reaction mechanisms involve in Microwave-Assisted Fast Pyrolysis (MAFP) to unveil the thermal decomposition of agricultural residues: pecan nutshell (NS), sugarcane bagasse (SB), and orange seed (OS) biomasses. The holistic understanding of the pyrolysis process for these biomasses was analyzed based on the final chemical compositions and yields of bio-oil, biochar and biogas and correlated to the microwave processing conditions and feedstock’s chemical composition. The findings revealed that the bio-oil is enhanced at moderated microwave energy (<5 GJ/t) as result of endothermic reactions such as heterolytic fragmentation, Maccoll elimination, Friedel-Craft acylation, Piancatelli rearrangement and methoxylation. The maximum yield of bio-oil for protein-rich biomass was due to selective heating (Paal-Knorr pyrrole synthesis, Baeyer-Villiger oxidation, Maillard reaction, and ring conversion of γ-butyrolactone). The formation of biochar and biogas is attributed to the repolymerization of aromatic aldehydes, hydrocarbons, amines, and ethers, as well as dehydroxymethylation and dealkylation processes. This study provides a comprehensive understanding of the reaction mechanisms for several wastes using microwave pyrolysis, to establish the bases for effective valorization and agricultural waste management

Towards Visualization of Discrete Optimization Problems and Search Algorithms

Diskrete Optimierung beschäftigt sich mit dem Identifizieren einer Kombination oder Permutation von Elementen, die im Hinblick auf ein gegebenes quantitatives Kriterium optimal ist. Anwendungen dafür entstehen aus Problemen in der Wirtschaft, der industriellen Fertigung, den Ingenieursdisziplinen, der Mathematik und Informatik. Dazu gehören unter anderem maschinelles Lernen, die Planung der Reihenfolge und Terminierung von Fertigungsprozessen oder das Layout von integrierten Schaltkreisen. Häufig sind diskrete Optimierungsprobleme NP-hart. Dadurch kommt der Erforschung effizienter, heuristischer Suchalgorithmen eine große Bedeutung zu, um für mittlere und große Probleminstanzen überhaupt gute Lösungen finden zu können. Dabei wird die Entwicklung von Algorithmen dadurch erschwert, dass Eigenschaften der Probleminstanzen aufgrund von deren Größe und Komplexität häufig schwer zu identifizieren sind. Ebenso herausfordernd ist die Analyse und Evaluierung von gegebenen Algorithmen, da das Suchverhalten häufig schwer zu charakterisieren ist. Das trifft besonders im Fall von emergentem Verhalten zu, wie es in der Forschung der Schwarmintelligenz vorkommt. Visualisierung zielt auf das Nutzen des menschlichen Sehens zur Datenverarbeitung ab. Das Gehirn hat enorme Fähigkeiten optische Reize von den Sehnerven zu analysieren, Formen und Muster darin zu erkennen, ihnen Bedeutung zu verleihen und dadurch ein intuitives Verstehen des Gesehenen zu ermöglichen. Diese Fähigkeit kann im Speziellen genutzt werden, um Hypothesen über komplexe Daten zu generieren, indem man sie in einem Bild repräsentiert und so dem visuellen System des Betrachters zugänglich macht. Bisher wurde Visualisierung kaum genutzt um speziell die Forschung in diskreter Optimierung zu unterstützen. Mit dieser Dissertation soll ein Ausgangspunkt geschaffen werden, um den vermehrten Einsatz von Visualisierung bei der Entwicklung von Suchheuristiken zu ermöglichen. Dazu werden zunächst die zentralen Fragen in der Algorithmenentwicklung diskutiert und daraus folgende Anforderungen an Visualisierungssysteme abgeleitet. Mögliche Forschungsrichtungen in der Visualisierung, die konkreten Nutzen für die Forschung in der Optimierung ergeben, werden vorgestellt. Darauf aufbauend werden drei Visualisierungssysteme und eine Analysemethode für die Erforschung diskreter Suche vorgestellt. Drei wichtige Aufgaben von Algorithmendesignern werden dabei adressiert. Zunächst wird ein System für den detaillierten Vergleich von Algorithmen vorgestellt. Auf der Basis von Zwischenergebnissen der Algorithmen auf einer Probleminstanz wird der Suchverlauf der Algorithmen dargestellt. Der Fokus liegt dabei dem Verlauf der Qualität der Lösungen über die Zeit, wobei die Darstellung durch den Experten mit zusätzlichem Wissen oder Klassifizierungen angereichert werden kann. Als zweites wird ein System für die Analyse von Suchlandschaften vorgestellt. Auf Basis von Pfaden und Abständen in der Landschaft wird eine Karte der Probleminstanz gezeichnet, die strukturelle Merkmale intuitiv erfassbar macht. Der zweite Teil der Dissertation beschäftigt sich mit der topologischen Analyse von Suchlandschaften, aufbauend auf einer Schwellwertanalyse. Ein Visualisierungssystem wird vorgestellt, dass ein topologisch equivalentes Höhenprofil der Suchlandschaft darstellt, um die topologische Struktur begreifbar zu machen. Dieses System ermöglicht zudem, den Suchverlauf eines Algorithmus direkt in der Suchlandschaft zu beobachten, was insbesondere bei der Untersuchung von Schwarmintelligenzalgorithmen interessant ist. Die Berechnung der topologischen Struktur setzt eine vollständige Aufzählung aller Lösungen voraus, was aufgrund der Größe der Suchlandschaften im allgemeinen nicht möglich ist. Um eine Anwendbarkeit der Analyse auf größere Probleminstanzen zu ermöglichen, wird eine Methode zur Abschätzung der Topologie vorgestellt. Die Methode erlaubt eine schrittweise Verfeinerung der topologischen Struktur und lässt sich heuristisch steuern. Dadurch können Wissen und Hypothesen des Experten einfließen um eine möglichst hohe Qualität der Annäherung zu erreichen bei gleichzeitig überschaubarem Berechnungsaufwand.Discrete optimization deals with the identification of combinations or permutations of elements that are optimal with regard to a specific, quantitative criterion. Applications arise from problems in economy, manufacturing, engineering, mathematics and computer sciences. Among them are machine learning, scheduling of production processes, and the layout of integrated electrical circuits. Typically, discrete optimization problems are NP hard. Thus, the investigation of efficient, heuristic search algorithms is of high relevance in order to find good solutions for medium- and large-sized problem instances, at all. The development of such algorithms is complicated, because the properties of problem instances are often hard to identify due to the size and complexity of the instances. Likewise, the analysis and evaluation of given algorithms is challenging, because the search behavior of an algorithm is hard to characterize, especially in case of emergent behavior as investigated in swarm intelligence research. Visualization targets taking advantage of human vision in order to do data processing. The visual brain possesses tremendous capabilities to analyse optical stimulation through the visual nerves, perceive shapes and patterns, assign meaning to them and thus facilitate an intuitive understanding of the seen. In particular, this can be used to generate hypotheses about complex data by representing them in a well-designed depiction and making it accessible to the visual system of the viewer. So far, there is only little use of visualization to support the discrete optimization research. This thesis is meant as a starting point to allow for an increased application of visualization throughout the process of developing discrete search heuristics. For this, we discuss the central questions that arise from the development of heuristics as well as the resulting requirements on visualization systems. Possible directions of research for visualization are described that yield a specific benefit for optimization research. Based on this, three visualization systems and one analysis method are presented. These address three important tasks of algorithm designers. First, a system for the fine-grained comparison of algorithms is introduced. Based on the intermediate results of algorithm runs on a given problem instance the search process is visualized. The focus is on the progress of the solution quality over time while allowing the algorithm expert to augment the depiction with additional domain knowledge and classification of individual solutions. Second, a system for the analysis of search landscapes is presented. Based on paths and distances in the landscape, a map of the problem instance is drawn that facilitates an intuitive cognition of structural properties. The second part of this thesis focuses on the topological analysis of search landscapes, based on barriers. A visualization system is presented that shows a topological equivalent height profile of the search landscape. Further, the system facilitates to observe the search process of an algorithm directly within the search landscape. This is of particular interest when researching swarm intelligence algorithms. The computation of topological structure requires a complete enumeration of all solutions which is not possible in the general case due to the size of the search landscapes. In order to enable an application to larger problem instances, we introduce a method to approximate the topological structure. The method allows for an incremental refinement of the topological approximation that can be controlled using a heuristic. Thus, the domain expert can introduce her knowledge and also hypotheses about the problem instance into the analysis so that an approximation of good quality is achieved with reasonable computational effort

Investigando o desenvolvimento do processo de abstração na resolução de problemas de geometria molecular mediada pela realidade aumentada

A aplicação de conhecimentos e habilidades voltados à resolução de problemas de geometria molecular é uma tarefa considerada complexa, porém, essencial para o processo de aprendizagem. Normalmente, os estudantes direcionam a atenção para as características superficiais dos problemas, demonstrando dificuldades em atribuir significados a símbolos e a operações submicroscópicas. Como agravante, muitas vezes, somente compreendem tarefas iguais às que realizaram em aula, sendo incapazes de propor soluções adequadas às tarefas diferentes das já praticadas. Dessa forma, para que os alunos tenham sucesso, devem ser competentes em extrair características relevantes, vincular semelhanças ou diferenças a problemas anteriormente vistos e realizar generalizações, enquanto resolvem problemas. Em suma, a aprendizagem de geometria molecular requer dos alunos a capacidade de reconhecer e gerar abstrações. Assim, esta tese investigou como ocorre o desenvolvimento do processo de abstração na resolução de problemas de geometria molecular, mediada pela tecnologia de realidade aumentada (RA). Metodologicamente, a pesquisa caracteriza-se como de abordagem mista. No que concerne à natureza, trata-se de pesquisa aplicada, de objetivo descritivo. Quanto aos procedimentos, baseia-se em um desenho quasiexperimental, no qual a principal estratégia de coleta de dados consistiu em entrevistas semiestruturadas, gravadas em áudio e vídeo e transcritas para posterior análise de conteúdo, com codificação a priori. Deste modo, ao resolver problemas, analisou-se o pensamento de cada participante, sendo definida a representação do conhecimento armazenado, empregada como um recurso, bem como a representação de nova instância, caracterizada pelo processo de resolução do problema em questão. A partir desse procedimento, definiu-se a ocorrência da abstração considerando o nível de abstração entre as representações, bem como o modo de abstração durante o uso das representações. Com a aplicação de um modelo de mapeamento de representação, caracterizou-se o raciocínio como sendo do tipo: baseado em regras, baseado em banco de memória, baseado em similaridade ou protótipo. Como resultado, descobriu-se indícios de que a RA atua no nível de abstração diferente com representação do conhecimento armazenado maior que a representação de nova instância e, ainda, que há influência do nível de abstração no acerto de resoluções de problemas. Encontraramse, igualmente, evidências da existência de uma relação peculiar entre a RA e o modo de abstração parcial, e que esta tecnologia influencia na ocorrência dos tipos de raciocínio de protótipo e baseado em similaridade. Do mesmo modo, descobriu-se indicativos de que a capacidade de visualização molecular mental influi no acerto de resoluções de problemas de geometria molecular. Ao mesmo tempo que existem tendências da abstração, do processo de raciocínio e da capacidade de visualização influenciarem na média de notas.The use of knowledge and acquired skills to solve molecular geometry problems is a complex task, however, essential for the learning process. Usually, students direct their attention to the superficial characteristics of the problems, demonstrating difficulties in assigning meaning to symbols and submicroscopic operations. As an aggravating factor, they often only understand tasks that are similar to those they perform in class, being unable to propose adequate solutions to tasks different from those already practiced. Thus, for students to succeed, they must be competent in extracting relevant features, linking similarities or differences to previously seen problems, and making generalizations while solving problems. In short, learning molecular geometry requires students to be able to recognize and generate abstractions. This thesis investigates the development of abstract representations when students solve molecular geometry problems using augmented reality (AR). The research has been conducted using a quali-quantitative approach, being characterized as an applied research work with a descriptive objective. Regarding its procedures, they were based on a quasiexperimental design, in which the main data collection strategy consisted of semistructured interviews, recorded in audio and video and transcribed for later content analysis, with a priori coding. Students were asked to verbalize their problem solving strategies, and these interviews were analyzed to define the knowledge representation used, the representation of new instances and problem solving process. Based on this procedure, the occurrence of abstraction evidence was defined considering the level of abstraction between the representations, as well as the mode of abstraction during the use of the representations. With the use of a representation mapping model, the students’ reasoning process was characterized as being based on rules, based on memory bank, based on similarity or prototype. As a result, evidence was found that AR operates at a different level of abstraction with a representation of stored knowledge greater than the representation of a new instance, and also that there is an influence of the level of abstraction on the success of problem resolutions. Evidence was also found regarding the existence of a particular relationship between AR and the partial abstraction mode, and that this technology influences the occurrence of prototype and similarity-based reasoning types. Likewise, evidence was found that the students’ capacity for mental molecular visualization influences the correct resolution of molecular geometry problems. At the same time, there is a trend that the average grade of students be influenced by the level of abstraction and the reasoning process used, and the ability to visualize molecular structures