Investigation of electrical conductivity as a function of dopant-ion radius in the systems Zr0.75Ce0.08M0.17O1.92 (M=Nd, Sm, Gd, Dy, Ho, Y, Er, Yb, Sc)

Electrical conductivity versus dopant ionic radius studies in zirconia- and ceria-based, solid oxide fuel cell (SOFC) electrolyte systems have shown that oxygen-ion conductivity is highest when the host and dopant ions are similar in size [J. Am. Ceram. Soc. 48 (1965) 286; Solid State Ionics 37 (1989) 67; Solid State Ionics 5 (1981) 547]. Under these conditions, it is thought that the conduction paths within the crystal lattice become less distorted [Solid State Ionics 8 (1983) 201]. In this study, binary ZrO2-M2O3 unit cells were expanded, via the partial substitution of Ce+4 for Zr+4 into the lattice, in an attempt to identify new, ternary, zirconia/ceria-based electrolyte systems with enhanced electrical conductivity. The compositions Zr0.75Ce0.08M0.17O1.92 (M = Nd, Sm, Gd, Dy, Ho, Y, Yb, Sc) were prepared using traditional solid state techniques. Bulk phase characterisation and precise lattice parameter measurements were performed with X-ray diffraction techniques. Four-probe DC conductivity measurements between 400 and 900 degreesC showed that the dopant-ion radius influenced electrical conductivity. The conductivity versus dopant-ion radius trends previously observed in zirconia-based, binary systems are clearly apparent in the ternary systems investigated in this study. The addition of ceria was found to have a negative influence on the electrical conductivity over the temperature range 400-900 degreesC. It is suggested that distortion of the oxygen-ion conduction path by the presence of the larger M+3 and Ce+4 species (relative to Zr+4) is the reason for the decreasing electrical conductivity as a function of increasing dopant size and ceria addition, respectively. (C) 2002 Elsevier Science B.V. All rights reserved.

Control of nitrate recirculation flow in predenitrification systems

The control of the nitrate recirculation flow in a predenitrification system is addressed. An elementary mass balance analysis on the utilisation efficiency of the influent biodegradable COD (bCOD) for nitrate removal indicates that the control problem can be broken down into two parts: maintaining the anoxic zone anoxic (i.e. nitrate is present throughout the anoxic zone) and maximising the usage of influent soluble bCOD for denitrification. Simulation studies using the Simulation Benchmark developed in the European COST program show that both objectives can be achieved by maintaining the nitrate concentration at the outlet of the anoxic zone at around 2 mgN/L. This setpoint appears to be robust towards variations in the influent characteristics and sludge kinetics.

A uniform white-noise model for fixed-point roundoff errors in digital systems

Fixed-point roundoff noise in digital implementation of linear systems arises due to overflow, quantization of coefficients and input signals, and arithmetical errors. In uniform white-noise models, the last two types of roundoff errors are regarded as uniformly distributed independent random vectors on cubes of suitable size. For input signal quantization errors, the heuristic model is justified by a quantization theorem, which cannot be directly applied to arithmetical errors due to the complicated input-dependence of errors. The complete uniform white-noise model is shown to be valid in the sense of weak convergence of probabilistic measures as the lattice step tends to zero if the matrices of realization of the system in the state space satisfy certain nonresonance conditions and the finite-dimensional distributions of the input signal are absolutely continuous.

Phenomenological turbulence and innovation in knowledge systems

Most considerations of knowledge management focus on corporations and, until recently, considered knowledge to be objective, stable, and asocial. In this paper we wish to move the focus away from corporations, and examine knowledge and national innovation systems. We argue that the knowledge systems in which innovation takes place are phenomenologically turbulent, a state not made explicit in the change, innovation and socio-economic studies of knowledge literature, and that this omission poses a serious limitation to the successful analysis of innovation and knowledge systems. To address this lack we suggest that three evolutionary processes must be considered: self-referencing, self-transformation and self-organisation. These processes, acting simultaneously, enable system cohesion, radical innovation and adaptation. More specifically, we argue that in knowledge-based economies the high levels of phenomenological turbulence drives these processes. Finally, we spell out important policy principles that derive from these processes.

Method for determining the shear stress in cylindrical systems

We develop a method for determining the elements of the pressure tensor at a radius r in a cylindrically symmetric system, analogous to the so-called method of planes used in planar systems [B. D. Todd, Denis J. Evans, and Peter J. Daivis, Phys. Rev. E 52, 1627 (1995)]. We demonstrate its application in determining the radial shear stress dependence during molecular dynamics simulations of the forced flow of methane in cylindrical silica mesopores. Such expressions are useful for the examination of constitutive relations in the context of transport in confined systems.

Radiological imaging in digital systems: the effect of exposure parameters in diagnostic quality and patient dose

Esta tese pretende contribuir para o estudo e análise dos factores relacionados com as técnicas de aquisição de imagens radiológicas digitais, a qualidade diagnóstica e a gestão da dose de radiação em sistema de radiologia digital. A metodologia encontra-se organizada em duas componentes. A componente observacional, baseada num desenho do estudo de natureza retrospectiva e transversal. Os dados recolhidos a partir de sistemas CR e DR permitiram a avaliação dos parâmetros técnicos de exposição utilizados em radiologia digital, a avaliação da dose absorvida e o índice de exposição no detector. No contexto desta classificação metodológica (retrospectiva e transversal), também foi possível desenvolver estudos da qualidade diagnóstica em sistemas digitais: estudos de observadores a partir de imagens arquivadas no sistema PACS. A componente experimental da tese baseou-se na realização de experiências em fantomas para avaliar a relação entre dose e qualidade de imagem. As experiências efectuadas permitiram caracterizar as propriedades físicas dos sistemas de radiologia digital, através da manipulação das variáveis relacionadas com os parâmetros de exposição e a avaliação da influência destas na dose e na qualidade da imagem. Utilizando um fantoma contraste de detalhe, fantomas antropomórficos e um fantoma de osso animal, foi possível objectivar medidas de quantificação da qualidade diagnóstica e medidas de detectabilidade de objectos. Da investigação efectuada, foi possível salientar algumas conclusões. As medidas quantitativas referentes à performance dos detectores são a base do processo de optimização, permitindo a medição e a determinação dos parâmetros físicos dos sistemas de radiologia digital. Os parâmetros de exposição utilizados na prática clínica mostram que a prática não está em conformidade com o referencial Europeu. Verifica-se a necessidade de avaliar, melhorar e implementar um padrão de referência para o processo de optimização, através de novos referenciais de boa prática ajustados aos sistemas digitais. Os parâmetros de exposição influenciam a dose no paciente, mas a percepção da qualidade de imagem digital não parece afectada com a variação da exposição. Os estudos que se realizaram envolvendo tanto imagens de fantomas como imagens de pacientes mostram que a sobreexposição é um risco potencial em radiologia digital. A avaliação da qualidade diagnóstica das imagens mostrou que com a variação da exposição não se observou degradação substancial da qualidade das imagens quando a redução de dose é efectuada. Propõe-se o estudo e a implementação de novos níveis de referência de diagnóstico ajustados aos sistemas de radiologia digital. Como contributo da tese, é proposto um modelo (STDI) para a optimização de sistemas de radiologia digital.

Surge damping analysis in pipe systems: modelling and experiments

The current study focuses on the analysis of pressure surge damping in single pipeline systems generated by a fast change of flow, conditions. A dimensionless form of pressurised transient flow equations was developed. presenting the main advantage of being independent of the system characteristics. In lack of flow velocity profiles. the unsteady friction in turbulent regimes is analysed based on two new empirical corrective-coefficients associated with local and convective acceleration terms. A new, surge damping approach is also presented taking into account the pressure peak time variation. The observed attenuation effect in the pressure wave for high deformable pipe materials can be described by a combination of the non-elastic behaviour of the pipe-wall with steady and unsteady friction effects. Several simulations and experimental tests have been carried out. in order to analyse the dynamic response of single pipelines with different characteristics, such as pipe materials. diameters. thickness. lengths and transient conditions.

Assessment of patient dose in digital systems

The assessment of patient dose has gained increased attention, still being an issue of concern that arises from the use of digital systems. The development of digital technology offers the possibility for a reduction of radiation dose around 50% without loss in image quality when compared to a conventional screen–film system. Digital systems give an equivalent or superior diagnostic performance and also several other advantages, but the risk of overexposure with no adverse effect on image quality could be present. This chapter refers to the management of patient dose and provides an explanation of dose-related concepts. In this chapter, exposure influence in dose and image representation and the effects of radiation exposure are also discussed.

Self-optimization for dynamic scheduling in manufacturing systems

Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. In this paper, we describe a Self-Optimizing Mechanism for Scheduling System through Nature Inspired Optimization Techniques (NIT).

The verification of temporal KBS: SPARSE - a case study in power systems

In this paper we present VERITAS, a tool that focus time maintenance, that is one of the most important processes in the engineering of the time during the development of KBS. The verification and validation (V&V) process is part of a wider process denominated knowledge maintenance, in which an enterprise systematically gathers, organizes, shares, and analyzes knowledge to accomplish its goals and mission. The V&V process states if the software requirements specifications have been correctly and completely fulfilled. The methodologies proposed in software engineering have showed to be inadequate for Knowledge Based Systems (KBS) validation and verification, since KBS present some particular characteristics. VERITAS is an automatic tool developed for KBS verification which is able to detect a large number of knowledge anomalies. It addresses many relevant aspects considered in real applications, like the usage of rule triggering selection mechanisms and temporal reasoning.

Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.

Data mining applications in power systems - case-studies and future trends

Presently power system operation produces huge volumes of data that is still treated in a very limited way. Knowledge discovery and machine learning can make use of these data resulting in relevant knowledge with very positive impact. In the context of competitive electricity markets these data is of even higher value making clear the trend to make data mining techniques application in power systems more relevant. This paper presents two cases based on real data, showing the importance of the use of data mining for supporting demand response and for supporting player strategic behavior.

Cyber-ambient intelligent training of operators in power systems control centres

Cyber-Physical Systems and Ambient Intelligence are two of the most important and emerging paradigms of our days. The introduction of renewable sources gave origin to a completely different dimension of the distribution generation problem. On the other hand, Electricity Markets introduced a different dimension in the complexity, the economic dimension. Our goal is to study how to proceed with the Intelligent Training of Operators in Power Systems Control Centres, considering the new reality of Renewable Sources, Distributed Generation, and Electricity Markets, under the emerging paradigms of Cyber-Physical Systems and Ambient Intelligence. We propose Intelligent Tutoring Systems as the approach to deal with the intelligent training of operators in these new circumstances.

Autonomic computing for scheduling in manufacturing systems

We describe a novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Autonomic Computing has emerged as paradigm aiming at embedding applications with a management structure similar to a central nervous system. A natural Autonomic Computing evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference. In this paper we envisage the use of Multi-Agent Systems paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with Autonomic properties, in order to reduce the complexity of managing systems and human interference. Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems.

AI in power systems and energy markets

In a world increasingly conscientious about environmental effects, power and energy systems are undergoing huge transformations. Electric energy produced from power plants is transmitted and distributed to end users through a power grid. The power industry performs the engineering design, installation, operation, and maintenance tasks to provide a high-quality, secure energy supply while accounting for its systems’ abilities to withstand uncertain events, such as weather-related outages. Competitive, deregulated electricity markets and new renewable energy sources, however, have further complicated this already complex infrastructure.Sustainable development has also been a challenge for power systems. Recently, there has been a signifi cant increase in the installation of distributed generations, mainly based on renewable resources such as wind and solar. Integrating these new generation systems leads to more complexity. Indeed, the number of generation sources greatly increases as the grid embraces numerous smaller and distributed resources. In addition, the inherent uncertainties of wind and solar energy lead to technical challenges such as forecasting, scheduling, operation, control, and risk management. In this special issue introductory article, we analyze the key areas in this field that can benefi t most from AI and intelligent systems now and in the future.We also identify new opportunities for cross-fertilization between power systems and energy markets and intelligent systems researchers.