Bioproduction of succinic acid from renewable feedstocks by Actinobacillus succinogenes 130Z

Succinic acid (SA) is a highly versatile building block that is used in a wide range of industrial applications. The biological production of succinic acid has emerged in the last years as an efficient alternative to the chemical production based on fossil fuels. However, in order to fully replace the competing petro-based chemical process from which it has been produced so far, some challenges remain to be surpassed. In particular, one main obstacle would be to reduce its production costs, mostly associated to the use of refined sugars. The present work is focused on the development of a sustainable and cost-e↵ective microbial production process based on cheap and renewable resources, such as agroindustrial wastes. Hence, glycerol and carob pods were identified as promising feedstocks and used as inexpensive carbon sources for the bioproduction of succinic acid by Actinobacillus succinogenes 130Z, one of the best naturally producing strains. Even though glycerol is a highly available carbon source, as by-product of biodiesel production, its consumption by A. succinogenes is impaired due to a redox imbalance during cell growth. However, the use of an external electron acceptor such as dimethylsulfoxide (DMSO) may improve glycerol metabolism and succinic acid production by this strain. As such, DMSO was tested as a co-substrate for glycerol consumption and concentrations of DMSO between 1 and 4% (v/v) greatly promoted glycerol consumption and SA production by this biocatalyst. Aiming at obtaining higher succinic acid yield and production rate, batch and fed-batch experiments were performed under controlled cultivation conditions. Batch experiments resulted in a succinic acid yield on glycerol of 0.95 g SA/g GLY and a production rate of 2.13 g/L.h, with residual production of acetic and formic acids. In fed-batch experiment, the SA production rate reached 2.31 g/L.h, the highest value reported in the literature for A. succinogenes using glycerol as carbon source. DMSO dramatically improved the conversion of glycerol by A. succinogenes and may be used as a co-substrate, opening new perspectives for the use of glycerol by this biocatalyst. Carob pods, highly available in Portugal as a residue from the locust bean gum industry, contain a significant amount of fermentable sugars such as sucrose, glucose and fructose and were also used as substrate for succinic acid production. Sugar extraction from raw and roasted carobs was optimized varying solid/water ratio and extraction time, maximizing sugar recovery while minimizing the extraction of polyphenols. Kinetic studies of glucose, fructose and sucrose consumption by A. succinogenes as individual carbon sources till 30 g/L were first determined to assess possible metabolic diferences. Results showed no significant diferences related to sugar consumption and SA production between the diferent sugars. Carob pods water extracts were then used as carbon source during controlled batch cultivations. (...)

Semantic relations extraction from unstructured information for domain ontologies enrichment

Based in internet growth, through semantic web, together with communication speed improvement and fast development of storage device sizes, data and information volume rises considerably every day. Because of this, in the last few years there has been a growing interest in structures for formal representation with suitable characteristics, such as the possibility to organize data and information, as well as the reuse of its contents aimed for the generation of new knowledge. Controlled Vocabulary, specifically Ontologies, present themselves in the lead as one of such structures of representation with high potential. Not only allow for data representation, as well as the reuse of such data for knowledge extraction, coupled with its subsequent storage through not so complex formalisms. However, for the purpose of assuring that ontology knowledge is always up to date, they need maintenance. Ontology Learning is an area which studies the details of update and maintenance of ontologies. It is worth noting that relevant literature already presents first results on automatic maintenance of ontologies, but still in a very early stage. Human-based processes are still the current way to update and maintain an ontology, which turns this into a cumbersome task. The generation of new knowledge aimed for ontology growth can be done based in Data Mining techniques, which is an area that studies techniques for data processing, pattern discovery and knowledge extraction in IT systems. This work aims at proposing a novel semi-automatic method for knowledge extraction from unstructured data sources, using Data Mining techniques, namely through pattern discovery, focused in improving the precision of concept and its semantic relations present in an ontology. In order to verify the applicability of the proposed method, a proof of concept was developed, presenting its results, which were applied in building and construction sector.

An agent based architecture to support monitoring in plug and produce manufacturing systems using knowledge extraction

In recent years a set of production paradigms were proposed in order to capacitate manufacturers to meet the new market requirements, such as the shift in demand for highly customized products resulting in a shorter product life cycle, rather than the traditional mass production standardized consumables. These new paradigms advocate solutions capable of facing these requirements, empowering manufacturing systems with a high capacity to adapt along with elevated flexibility and robustness in order to deal with disturbances, like unexpected orders or malfunctions. Evolvable Production Systems propose a solution based on the usage of modularity and self-organization with a fine granularity level, supporting pluggability and in this way allowing companies to add and/or remove components during execution without any extra re-programming effort. However, current monitoring software was not designed to fully support these characteristics, being commonly based on centralized SCADA systems, incapable of re-adapting during execution to the unexpected plugging/unplugging of devices nor changes in the entire system’s topology. Considering these aspects, the work developed for this thesis encompasses a fully distributed agent-based architecture, capable of performing knowledge extraction at different levels of abstraction without sacrificing the capacity to add and/or remove monitoring entities, responsible for data extraction and analysis, during runtime.