Mathematical modelling of Portuguese hydroelectric energy system

Hydropower is one of the most traditional renewable energy source and a major contributor for renewable energy production inmany countries. In Portugal it was the only renewable energy source for many years but nowadays wind presents similar production levels and for example in 2015 wind was the main source producing 45.5 % of the total renewable energy. However hydro energy will continue to be important in the renewable energy production and in this work ranking of nine models for hydro energy production with various numbers of parameters was done using adjusted R-squared and corrected Akaike information criterion (AICc).info:eu-repo/semantics/publishedVersio

Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method

Over the past two decades, neural networks have been applied to develop short-term traffic flow predictors. The past traffic flow data, captured by on-road sensors, is used as input patterns of neural networks to forecast future traffic flow conditions. The amount of input patterns captured by the on-road sensors is usually huge, but not all input patterns are useful when trying to predict the future traffic flow. The inclusion of useless input patterns is not effective to developing neural network models. Therefore, the selection of appropriate input patterns, which are significant for short-term traffic flow forecasting, is essential. This can be conducted by setting an appropriate configuration of input nodes of the neural network; however, this is usually conducted by trial and error. In this paper, the Taguchi method, which is a robust and systematic optimization approach for designing reliable and high-quality models, is proposed for the purpose of determining an appropriate neural network configuration, in terms of input nodes, in order to capture useful input patterns for traffic flow forecasting. The effectiveness of the Taguchi method is demonstrated by a case study, which aims to develop a short-term traffic flow predictor based on past traffic flow data captured by on-road sensors located on a Western Australia freeway. Three advantages of using the Taguchi method were demonstrated: 1) short-term traffic flow predictors with high accuracy can be designed; 2) the development time for short-term traffic flow predictors is reasonable; and 3) the accuracy of short-term traffic flow predictors is robust with respect to the initial settings of the neural network parameters during the learning phase

Predicción de lecturas de aforos de filtraciones de presas bóveda mediante redes neuronales artificiales

Las redes neuronales artificiales son estructuras matemáticas inspiradas en el cerebro de los seres vivos, capaces de generar modelos numéricos no lineales de calibración relativamente sencilla. En el presente trabajo se modela el caudal de agua filtrado a través del cimiento rocoso de una presa bóveda piloto con una red neuronal tipo perceptrón multicapa. La filtración a través de un macizo rocoso es un fenómeno difícil de modelar debido a la imposibilidad de caracterizar con detalle el medio en el que discurre y por la complejidad del propio fenómeno. El resultado final es un modelo compuesto por tres neuronas ocultas agrupadas en una capa y cuyas variables de entrada son el nivel de agua en el embalse y tres velocidades de la misma en periodos anteriores. La estructura de la red neuronal se determina teniendo en cuenta la influencia de cada una de las variables de entrada sobre las variables de salida. Para ello, se parte de un conjunto extenso de posibles variables de entrada extraídas de los modelos analíticos o conceptuales del fenómeno físico a modelar

Novel approaches for effective design of controlled drug release systems, employing hybrid semi-parametric mathematical systems

The controlled release of a drug from a carrier into a medium over a defined period of time is referred to as Controlled Drug Release (CDR). A major challenge for a sustainable and reproducible CDR is the unintentional initial burst, which occurs in the first hours/days of immersion and during which a large amount of drug is released. Also it can have deleterious effects on the host. Burst release happens with both small drug molecules and large proteins and for both drug-loaded PLGA micro- and nanoparticles. Particle design can, in principal, be used to control the amount of burst but no systematic methods are to date available and the design process is governed by trial and error. One reason might be that the available models for burst release do not explicitly account for the particle design parameters. This thesis proposes novel methodologies that allow for rational design of drug-loaded PLGA micro- and nanoparticles. It is divided in three main parts. Firstly, a quantitative analysis of the physicochemical factors that impact on the amount of burst release and the burst release rate using partial least squares and decision tree methods is performed. The factors with the greatest impact are selected for the subsequent modelling activities. Next, a bootstrap aggregated hybrid model (HM) is developed, which can successfully predict the cumulative drug release of an independent set of CDR experiments. Lastly, a new rational design method is presented for the optimization of the formulation characteristics of protein-loaded PLGA nanoparticles. The method is successfully applied to design the carrier of a mock-protein, α- chymotrypsin, yielding a close to desired release profile. The method can also help to judge upon the similarity of the mock protein with a target protein in terms of their similarities in burst release behavior. This thesis proposes the first rational PLGA particle design method requiring only the specification of the drug and the desired burst release profile. The application of the method can be expected to significantly reduce the time for PLGA particle development. With the increasing availability of CDR data the predictive power of the method can be further improved towards a systematic and reliable tool. The engine of the method is the hybrid model which links the release profile to the design parameters and is the first of its kind in drug release modeling