Context-based identification of energy consumption in industrial plants

Nowadays, reducing energy consumption is one of the highest priorities and biggest challenges faced worldwide and in particular in the industrial sector. Given the increasing trend of consumption and the current economical crisis, identifying cost reductions on the most energy-intensive sectors has become one of the main concerns among companies and researchers. Particularly in industrial environments, energy consumption is affected by several factors, namely production factors(e.g. equipments), human (e.g. operators experience), environmental (e.g. temperature), among others, which influence the way of how energy is used across the plant. Therefore, several approaches for identifying consumption causes have been suggested and discussed. However, the existing methods only provide guidelines for energy consumption and have shown difficulties in explaining certain energy consumption patterns due to the lack of structure to incorporate context influence, hence are not able to track down the causes of consumption to a process level, where optimization measures can actually take place. This dissertation proposes a new approach to tackle this issue, by on-line estimation of context-based energy consumption models, which are able to map operating context to consumption patterns. Context identification is performed by regression tree algorithms. Energy consumption estimation is achieved by means of a multi-model architecture using multiple RLS algorithms, locally estimated for each operating context. Lastly, the proposed approach is applied to a real cement plant grinding circuit. Experimental results prove the viability of the overall system, regarding both automatic context identification and energy consumption estimation.

Production and characterization of electrospun composite fibers: confinement of thermosensitive microgels

Materials engineering focuses on the assembly of materials´ properties to design new products with the best performance. By using sub-micrometer size materials in the production of composites, it is possible to obtain objects with properties that none of their compounds show individually. Once three-dimensional materials can be easily customized to obtain desired properties, much interest has been paid to nanostructured poly-mers in order to build biocompatible devices. Over the past years, the thermosensitive microgels have become more common in the framework of bio-materials with potential applicability in therapy and/or diagnostics. In addition, high aspect ratio biopolymers fibers have been produced using the cost-effective method called electrospinning. Taking advantage of both microgels and electrospun fibers, surfaces with enhanced functionalities can be obtained and, therefore employed in a wide range of applications. This dissertation reports on the confinement of stimuli-responsive microgels through the colloidal electro-spinning process. The process mainly depends on the composition, properties and patterning of the precur-sor materials within the polymer jet. Microgels as well as the electrospun non-woven mats were investigated to correlate the starting materials with the final morphology of the composite fibers. PNIPAAm and PNIPAAm/Chitosan thermosensitive microgels with different compositions were obtained via surfactant free emulsion polymerization (SFEP) and characterized in terms of chemical structure, morphology, thermal sta-bility, swelling properties and thermosensitivity. Finally, the colloidal electrospinning method was carried out from spinning solutions composed of the stable microgel dispersions (up to a concentration of about 35 wt. % microgels) and a polymer solution of PEO/water/ethanol mixture acting as fiber template solution. The confinement of microgels was confirmed by Scanning Electron Microscopy (SEM). The electrospinning process was statistically analysed providing the optimum set of parameters aimed to minimize the fiber diameter, which give rise to electrospun nanofibers of PNIPAAm microgels/PEO with a mean fiber diameter of 63 ± 25 nm.

Damage propagation in composite Materials meso-mechanical models

Composite materials have a complex behavior, which is difficult to predict under different types of loads. In the course of this dissertation a methodology was developed to predict failure and damage propagation of composite material specimens. This methodology uses finite element numerical models created with Ansys and Matlab softwares. The methodology is able to perform an incremental-iterative analysis, which increases, gradually, the load applied to the specimen. Several structural failure phenomena are considered, such as fiber and/or matrix failure, delamination or shear plasticity. Failure criteria based on element stresses were implemented and a procedure to reduce the stiffness of the failed elements was prepared. The material used in this dissertation consist of a spread tow carbon fabric with a 0°/90° arrangement and the main numerical model analyzed is a 26-plies specimen under compression loads. Numerical results were compared with the results of specimens tested experimentally, whose mechanical properties are unknown, knowing only the geometry of the specimen. The material properties of the numerical model were adjusted in the course of this dissertation, in order to find the lowest difference between the numerical and experimental results with an error lower than 5% (it was performed the numerical model identification based on the experimental results).