Intelligent decision making in electricity markets: simulated annealing Q-Learning

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 simulator 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 is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties

A novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS), Case-based Reasoning (CBR), and Bio-Inspired Optimization Techniques (BIT) will be described. AC has emerged as a paradigm aiming at incorporating applications with a management structure similar to the central nervous system. The main intentions are to improve resource utilization and service quality. In this paper we envisage the use of MAS paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with AC properties, in order to reduce the complexity of managing manufacturing systems and human interference. The proposed CBR based Intelligent Scheduling System was evaluated under different dynamic manufacturing scenarios.

Integração energética unidades 0300 e 0400 da Fábrica de Aromáticos Refinaria de Matosinhos

Mestrado em Engenharia Química - Ramo optimização energética na indústria química

Towards a reliable technology for antioxidant capacity and oxidative damage evaluation: Electrochemical (bio)sensors

To counteract and prevent the deleterious effect of free radicals the living organisms have developed complex endogenous and exogenous antioxidant systems. Several analytical methodologies have been proposed in order to quantify antioxidants in food, beverages and biological fluids. This paper revises the electroanalytical approaches developed for the assessment of the total or individual antioxidant capacity. Four electrochemical sensing approaches have been identified, based on the direct electrochemical detection of antioxidant at bare or chemically modified electrodes, and using enzymatic and DNA-based biosensors.

Emerging (Bio)Sensing Technology for Assessing and Monitoring Freshwater Contamination – Methods and Applications

Ecological Water Quality - Water Treatment and Reuse