Mixed extractant systems for metal recovery

Hoje em dia, a prevenção dos resíduos de metais é uma questão muito importante para um grande número de empresas, pois necessitam optimizar o seu sistema de tratamento de águas residuais a fim de alcançarem os limites legais dos teores em iões metálicos e poderem efectuar a descarga das águas residuais no domínio hídrico público. Devido a esta problemática foram efectuados estudos inovadores relacionados com a remoção de iões metálicos de águas residuais, verificando-se que as tecnologias de membrana oferecem uma série de vantagens para o efeito. Uma dessas tecnologias, referida como Membrana Líquida de Suporte (SLM), é baseada num mecanismo de extracção. A membrana hidrofóbica, impregnada com uma solução extractora, funciona como barreira entre a água residual e uma solução, geralmente ácida. A diferença de pH entre a água residual e a solução actua como força motriz para o transporte de iões metálicos da água residual para a referida solução. Poderá ocorrer um problema de falta de estabilidade, resultante da possível fuga da solução extractora para fora dos poros das membranas. Estudos anteriores mostraram que os ácidos alquilfosfóricos ou ácidos fosfónicos, como os reagentes D2EHPA e CYANEX e hidroxioximas como o LIX 860-I podem ser muito úteis para a extração de iões metálicos como ferro, cobre, níquel, zinco e outros. A clássica extracção líquido-líquido também tem mostrado que a mistura de diferentes extractores pode ter um efeito sinergético. No entanto, não é claro que haja um efeito óptimo da razão de extractor ou que tipo de complexo é formado durante o processo de extracção. O objectivo deste projecto é investigar este comportamento sinergético e as complexas formações por meio de um método espectrofotométrico, o “Job’s method” e “Mole-ratio method”. Estes métodos são utilizados para estimar a estequiometria dos vários complexos entre dois solutos, a partir da variação de absorvância dos complexos quando comparado com a absorvância do soluto. Com este projecto, o Job’s method e mole-ratio method serão aplicados a um sistema de três componentes, para conseguir mais informações sobre a complexação de níquel (II) e a fim de determinar a razão extractor: metal dos complexos formados durante a aplicação de mistura de extractores D2EHPA e LIX 860-I. Segundo Job’s method a elavada absorvância situa-se na região de 0,015-0,040 M de LIX 860-I e uma baixa concentração de D2EHPA. Quando as diferentes experiências são encontradas num conjunto experimental foram avaliadas de acordo com o método de trabalho, o valor máximo do gráfico foi encontrado para uma baixa fração molar do ião metálico e uma maior concentração de D2EHPA. Esta mudança foi encontrado de 0,50 até 0,30, que poderia apontar para a direção da formação de diferentes complexos. Para o Mole-Ratio method, a estequiometria dos complexos metal pode ser determinada a partir do ponto de intersecção das linhas tangente do gráfico da absorbância versus a concentração do ligante. Em todos os casos, o máximo foi obtido em torno de uma concentração total de 0,010 M. Quando D2EHPA foi aplicado sozinho, absorvâncias muito baixos foram obtidas.

Use of a fluorescence-based approach to assess short-term responses of the alga Pseudokirchneriella subcapitata to metal stress

This work explores the use of fluorescent probes to evaluate the responses of the green alga Pseudokirchneriella subcapitata to the action of three nominal concentrations of Cd(II), Cr(VI), Cu(II) and Zn(II) for a short time (6 h). The toxic effect of the metals on algal cells was monitored using the fluorochromes SYTOX Green (SG, membrane integrity), fluorescein diacetate (FDA, esterase activity) and rhodamine 123 (Rh123, mitochondrial membrane potential). The impact of metals on chlorophyll a (Chl a) autofluorescence was also evaluated. Esterase activity was the most sensitive parameter. At the concentrations studied, all metals induced the loss of esterase activity. SG could be used to effectively detect the loss of membrane integrity in algal cells exposed to 0.32 or 1.3 μmol L−1 Cu(II). Rh123 revealed a decrease in the mitochondrial membrane potential of algal cells exposed to 0.32 and 1.3 μmol L−1 Cu(II), indicating that mitochondrial activity was compromised. Chl a autofluorescence was also affected by the presence of Cr(VI) and Cu(II), suggesting perturbation of photosynthesis. In conclusion, the fluorescence-based approach was useful for detecting the disturbance of specific cellular characteristics. Fluorescent probes are a useful diagnostic tool for the assessment of the impact of toxicants on specific targets of P. subcapitata algal cells.

Responses of the alga Pseudokirchneriella subcapitata to long-term exposure to metal stress

The green alga Pseudokirchneriella subcapitata has been widely used in ecological risk assessment, usually based on the impact of the toxicants in the alga growth. However, the physiological causes that lead algal growth inhibition are not completely understood. This work aimed to evaluate the biochemical and structural modifications in P. subcapitata after exposure, for 72 h, to three nominal concentrations of Cd(II), Cr(VI), Cu(II) and Zn(II), corresponding approximately to 72 h-EC10 and 72 h-EC50 values and a high concentration (above 72 h-EC90 values). The incubation of algal cells with the highest concentration of Cd(II), Cr(VI) or Cu(II) resulted in a loss of membrane integrity of ~16, 38 and 55%, respectively. For all metals tested, an inhibition of esterase activity, in a dose-dependent manner, was observed. Reduction of chlorophyll a content, decrease of maximum quantum yield of photosystem II and modification of mitochondrial membrane potential was also verified. In conclusion, the exposure of P. subcapitata to metals resulted in a perturbation of the cell physiological status. Principal component analysis revealed that the impairment of esterase activity combined with the reduction of chlorophyll a content were related with the inhibition of growth caused by a prolonged exposure to the heavy metals.

Programas de self-management em pacientes com DPOC: uma revisão sistemática

Introdução: Programas de self-management têm como objectivo habilitar os pacientes com estratégias necessárias para levar a cabo procedimentos específicos para a patologia. A última revisão sistemática sobre selfmanagament em DPOC foi realizada em 2007, concluindo-se que ainda não era possível fornecer dados claros e suficientes acerca de recomendações sobre a estrutura e conteúdo de programas de self-managament na DPOC. A presente revisão tem o intuito de complementar a análise da revisão anterior, numa tentativa de inferir a influência do ensino do self-management na DPOC. Objectivos: verificar a influência dos programas de self-management na DPOC, em diversos indicadores relacionados com o estado de saúde do paciente e na sua utilização dos serviços de saúde. Estratégia de busca: pesquisa efectuada nas bases de dados PubMed e Cochrane Collaboration (01/01/2007 – 31/08/2010). Palavras-chave: selfmanagement education, self-management program, COPD e pulmonary rehabilitation. Critérios de Selecção: estudos randomizados sobre programas de selfmanagement na DPOC. Extracção e Análise dos Dados: 2 investigadores realizaram, independentemente, a avaliação e extracção de dados de cada artigo. Resultados: foram considerados 4 estudos randomizados em selfmanagement na DPOC nos quais se verificaram benefícios destes programas em diversas variáveis: qualidade de vida a curto e médio prazo, utilização dos diferentes recursos de saúde, adesões a medicação de rotina, controle das exacerbações e diminuição da sintomatologia. Parece não ocorrer alteração na função pulmonar e no uso de medicação de emergência, sendo inconclusivo o seu efeito na capacidade de realização de exercício. Conclusões: programas de self-management aparentam ter impacto positivo na qualidade de vida, recurso a serviços de saúde, adesão à medicação, planos de acção e níveis de conhecimento da DPOC. Discrepâncias nos critérios de selecção das amostras utilizadas, períodos de seguimento desiguais, consistência das variáveis mensuradas, condicionam a informação disponibilizada sobre este assunto.

Self-optimization module for scheduling using case-based reasoning

Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.

Meta-heuristics self-configuration for scheduling

Scheduling resolution requires the intervention of highly skilled human problemsolvers. This is a very hard and challenging domain because current systems are becoming more and more complex, distributed, interconnected and subject to rapidly changing. A natural Autonomic Computing evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference. This paper addresses the resolution of complex scheduling problems using cooperative negotiation. A Multi-Agent Autonomic and Meta-heuristics based framework with self-configuring capabilities is proposed.

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).

Case-based reasoning for meta-heuristics self-parameterization in a multi-agent scheduling system

A novel agent-based approach to Meta-Heuristics self-configuration is proposed in this work. Meta-heuristics are examples of algorithms where parameters need to be set up as efficient as possible in order to unsure its performance. This paper presents a learning module for self-parameterization of Meta-heuristics (MHs) in a Multi-Agent System (MAS) for resolution of scheduling problems. The learning is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. In the end, some conclusions are reached and future work outlined.

Negotiation mechanism for self-organized scheduling system

This paper presents a negotiation mechanism for Dynamic Scheduling based on Swarm Intelligence (SI). Under the new negotiation mechanism, agents must compete to obtain a global schedule. SI is the general term for several computational techniques which use ideas and get inspiration from the social behaviors of insects and other animals. This work is concerned with negotiation, the process through which multiple selfinterested agents can reach agreement over the exchange of operations on competitive resources.

Self-organization for scheduling in agile manufacturing

Agility refers to the manufacturing system ability to rapidly adapt to market and environmental changes in efficient and cost-effective ways. This paper addresses the development of self-organization methods to enhance the operations of a scheduling system, by integrating scheduling system, configuration and optimization into a single autonomic process requiring minimal manual intervention to increase productivity and effectiveness while minimizing complexity for users. We intend to conceptualize real manufacturing systems as interacting autonomous entities in order to build future Decision Support Systems (DSS) for Scheduling in agile manufacturing environments.

Case-based reasoning for self-optimizing behavior

In this paper we present a Self-Optimizing module, inspired on Autonomic Computing, acquiring a scheduling system with the ability to automatically select a Meta-heuristic to use in the optimization process, so as its parameterization. Case-based Reasoning was used so the system may be able of learning from the acquired experience, in the resolution of similar problems. From the obtained results we conclude about the benefit of its use.

Self-optimization aspects for dynamic scheduling

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. At this scenario, self-optimizing arise as the ability of the agent to monitor its state and performance and proactively tune itself to respond to environmental stimuli.

Self-optimizing through CBR learning

In this paper, we foresee the use of Multi-Agent Systems for supporting dynamic and distributed scheduling in Manufacturing Systems. We also envisage the use of Autonomic properties in order to reduce the complexity of managing systems and human interference. By combining Multi-Agent Systems, Autonomic Computing, and Nature Inspired Techniques we propose an approach for the resolution of dynamic scheduling problem, with Case-based Reasoning Learning capabilities. The objective is to permit a system to be able to automatically adopt/select a Meta-heuristic and respective parameterization considering scheduling characteristics. From the comparison of the obtained results with previous results, we conclude about the benefits of its use.

Self-managing agents for dynamic scheduling in manufacturing

The main purpose of this paper is to propose a Multi-Agent Autonomic and Bio-Inspired based framework with selfmanaging capabilities to solve complex scheduling problems using cooperative negotiation. Scheduling resolution requires the intervention of highly skilled human problem-solvers. This is a very hard and challenging domain because current systems are becoming more and more complex, distributed, interconnected and subject to rapidly changing. A natural Autonomic Computing (AC) evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference.