An SEM investigation of the pozzolanic activity of a waste catalyst oil refinery

The most active phase of the fluid catalytic cracking (FCC) catalyst, used in oil refinery, is zeolite-Y which is an aluminosilicate with a high internal and external surface area responsible for its high reactivity. Waste FCC catalyst is potentially able to be reused in cement-based materials - as an additive - undergoing a pozzolanic reaction with calcium hydroxide (Ca(OH)2) formed during cement hydration [1-3]. This reaction produces additional strength-providing reaction products i.e., calcium silicate hydrate (C-S-H) and hydrous calcium aluminates (C-A-H) which exact chemical formula and structure are still unknown. Partial replacement of cement by waste FCC catalyst has two key advantages: (1) lowering of cement production with the associated pollution reduction as this industry represents one of the largest sources of man-made CO2 emissions, and (2) improving the mechanical properties and durability of cement-based materials. Despite these advantages, there is a lack of fundamental knowledge on pozzolanic reaction mechanisms as well as spatial distribution of porosity and solid phases interactions at the microstructural level and consequently their relationship with macroscopical engineering properties of catalyst/cement blends. Within this scope, backscattered electron (BSE) images acquired in a scanning electron microscope (SEM) equipped with Energy-Dispersive Spectroscopy (EDS) and by X-ray diffraction were used to investigate chemical composition of hydration products and to analyse spatial information of the microstructure of waste FCC catalyst blended cement mortars. For this purpose mortars with different levels of cement substitution by waste catalyst as well as with different hydration ages, were prepared. The waste FCC catalyst used is produced by the Portuguese refinery company Petrogal S.A.

Efeito da incorporação de resíduo da indústria petrolífera nas propriedades mecânicas de argamassas

O presente artigo apresenta resultados preliminares de um projeto, em curso, no qual o objetivo final é investigar a viabilidade da utilização de um resíduo produzido na refinação do petróleo em materiais à base de cimento. Os valores do Índíce de Atividade - determinados com base nos valores da resistência à compressão de argamassas com substituição parcial de cimento pelo resíduo - mostraram que, ao fim de 7 dias de hidratação, o resíduo já apresenta atividade pozolânica em argamassas com incorporação de resíduo até 15%, e que esta atividade é também evidente nas argamassas com incorporação de 20% de resíduo ao fim de 28 dias de hidratação.

Low-carbon cement with waste oil-craking catalyst incorporation

The present paper shows preliminary results of an ongoing project which one of the goals is to investigate the viability of using waste FCC catalyst (wFCC), originated from Portuguese oil refinery, to produce low carbon blended cements. For this purpose, four blended cements were produced by substituting cement CEM I 42.5R up to 20% (w/w) by waste FCC catalyst. Initial and final setting times, consistency of standard paste, soundness and compressive strengths after 2, 7 and 28 days were measured. It was observed that the wFCC blended cements developed similar strength, at 28 days, compared to the reference cement, CEM I 42.5R. Moreover, cements with waste FCC catalyst incorporation up to 15% w/w meet European Standard EN 197-1 specifications for CEM II/A type cement, in the 42.5R strength class.

Machine learning Vasicek model calibration with Gaussian processes

In this article, we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. The only prices needed for calibration are zero coupon bond prices and the parameters are directly obtained in the arbitrage free risk neutral measure.

Optimal offering strategies for wind power producers considering uncertainty and risk

This paper provides a two-stage stochastic programming approach for the development of optimal offering strategies for wind power producers. Uncertainty is related to electricity market prices and wind power production. A hybrid intelligent approach, combining wavelet transform, particle swarm optimization and adaptive-network-based fuzzy inference system, is used in this paper to generate plausible scenarios. Also, risk aversion is explicitly modeled using the conditional value-at-risk methodology. Results from a realistic case study, based on a wind farm in Portugal, are provided and analyzed. Finally, conclusions are duly drawn.

Scheduling of a hydro producer considering head-dependency, price scenarios and risk-aversion

In this paper, a mixed-integer quadratic programming approach is proposed for the short-term hydro scheduling problem, considering head-dependency, discontinuous operating regions and discharge ramping constraints. As new contributions to earlier studies, market uncertainty is introduced in the model via price scenarios, and risk aversion is also incorporated by limiting the volatility of the expected profit through the conditional value-at-risk. Our approach has been applied successfully to solve a case Study based on one of the main Portuguese cascaded hydro systems, requiring a negligible computational time.

Optimal design for active damping in sandwich structures using the Direct MultiSearch method

This paper addresses the problem of optimal positioning of surface bonded piezoelectric patches in sandwich plates with viscoelastic core and laminated face layers. The objective is to maximize a set of modal loss factors for a given frequency range using multiobjective topology optimization. Active damping is introduced through co-located negative velocity feedback control. The multiobjective topology optimization problem is solved using the Direct MultiSearch Method. An application to a simply supported sandwich plate is presented with results for the maximization of the first six modal loss factors. The influence of the finite element mesh is analyzed and the results are, to some extent, compared with those obtained using alternative single objective optimization.

Gestão integrada de ativos num contexto real

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Manutenção

Aplicação do princípio das ondas de Elliott à bolsa portuguesa

Tese para a obtenção do grau de Doutor em Economia, especialidade de Economia da Empresa

Com Hélder Costa: o sábio prático d’A Barraca: relatório de estágio

Relatório de Estágio submetido à Escola Superior de Teatro e Cinema para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Teatro - especialização em Artes Performativas (Interpretação).

Otimização de placas compósitas laminadas utilizando redes neuronais e enxames de partículas

Trabalho Final de mestrado para obtenção do grau de Mestre em engenharia Mecância

Multiobjective optimization of viscoelastic laminated sandwich structures using the Direct MultiSearch method

A multiobjective approach for optimization of passive damping for vibration reduction in sandwich structures is presented in this paper. Constrained optimization is conducted for maximization of modal loss factors and minimization of weight of sandwich beams and plates with elastic laminated constraining layers and a viscoelastic core, with layer thickness and material and laminate layer ply orientation angles as design variables. The problem is solved using the Direct MultiSearch (DMS) solver for derivative-free multiobjective optimization and solutions are compared with alternative ones obtained using genetic algorithms.

Feature Discretization with Relevance and Mutual Information Criteria

Feature discretization (FD) techniques often yield adequate and compact representations of the data, suitable for machine learning and pattern recognition problems. These representations usually decrease the training time, yielding higher classification accuracy while allowing for humans to better understand and visualize the data, as compared to the use of the original features. This paper proposes two new FD techniques. The first one is based on the well-known Linde-Buzo-Gray quantization algorithm, coupled with a relevance criterion, being able perform unsupervised, supervised, or semi-supervised discretization. The second technique works in supervised mode, being based on the maximization of the mutual information between each discrete feature and the class label. Our experimental results on standard benchmark datasets show that these techniques scale up to high-dimensional data, attaining in many cases better accuracy than existing unsupervised and supervised FD approaches, while using fewer discretization intervals.