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.

The Cristo Rei section (Lower Miocene). Distal fluviatile environments in a marine series, plants, vertebrates and other evidence, age

The section at Cristo Rei shows sandy beds with intercalated clayey lenses (IVb division from the Lisbon Miocene series) that correspond to a major regression event dated from between ca. 17.6 and 17 Ma. They also correspond to a distal position (relatively to the typical fluviatile facies in Lisbon), nearer the basin's axis. Geologic data and paleontological analysis (plant fossils, fishes, crocodilians, land mammals) allow the reconstruction of environments that were represented in the concerned area: estuary with channels and ox-bows; upstream, areas occupied by brackish waters where Gryphaea griphoides banks developped; still farther upstream, freshwaters sided by humid forests and low mountain subtropical forests under warm temperate and rainy conditions, as well as not far away, seasonally dry environments (low density tree or shrub cover, or steppe).

Double coverage and demand for health care: Evidence from quantile regression

An individual experiences double coverage when he bene ts from more than one health insurance plan at the same time. This paper examines the impact of such supplementary insurance on the demand for health care services. Its novelty is that within the context of count data modelling and without imposing restrictive parametric assumptions, the analysis is carried out for di¤erent points of the conditional distribution, not only for its mean location. Results indicate that moral hazard is present across the whole outcome distribution for both public and private second layers of health insurance coverage but with greater magnitude in the latter group. By looking at di¤erent points we unveil that stronger double coverage e¤ects are smaller for high levels of usage. We use data for Portugal, taking advantage of particular features of the public and private protection schemes on top of the statutory National Health Service. By exploring the last Portuguese Health Survey, we were able to evaluate their impacts on the consumption of doctor visi

Análise espacial dos resultados eleitorais em Portugal – Aplicação do Geographically Weighted Regression

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação.

Análise espacial dos resultados eleitorais em Portugal – Aplicação do Geographically Weighted Regression

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação.