Persona: Gaudioso Vázquez, Elena
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Gaudioso Vázquez
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Publicación Machine Learning Weather Soft-Sensor for Advanced Control ofWastewater Treatment Plants(MDPI, 2019) Hernández del Olmo, Félix; Gaudioso Vázquez, Elena; Duro Carralero, Natividad; Dormido Canto, RaquelControl of wastewater treatment plants (WWTPs) is challenging not only because of their high nonlinearity but also because of important external perturbations. One the most relevant of these perturbations is weather. In fact, different weather conditions imply different inflow rates and substance (e.g., N-ammonia, which is among the most important) concentrations. Therefore, weather has traditionally been an important signal that operators take into account to tune WWTP control systems. This signal cannot be directly measured with traditional physical sensors. Nevertheless, machine learning-based soft-sensors can be used to predict non-observable measures by means of available data. In this paper, we present novel research about a new soft-sensor that predicts the current weather signal. This weather prediction differs from traditional weather forecasting since this soft-sensor predicts the weather conditions as an operator does when controling the WWTP. This prediction uses a model based on past WWTP influent states measured by only a few physical and widely applied sensors. The results are encouraging, as we obtained a good accuracy level for a relevant and very useful signal when applied to advanced WWTP control systems.Publicación Supporting Teachers to Monitor Student’s Learning Progress in an Educational Environment With Robotics Activities(IEEE, 2020-03-06) Orlando, Samantha; Gaudioso Vázquez, Elena; Paz López, Félix de laEducational robotics has proven its positive impact on the performances and attitudes of students. However, the educational environments that employ them rarely provide teachers with relevant information that can be used to make an effective monitoring of the student learning progress. To overcome these limitations, in this paper we present IDEE (Integrated Didactic Educational Environment), an educational environment for physics, that uses EV3 LEGO Mindstorms R© educational kit as robotic component. To provide support to teachers, IDEE includes a dashboard that provides them with information about the students’ learning process. This analysis is done by means of an Additive Factor Model (AFM). That is a well-known technique in the educational data mining research area. However, it has been usually employed to carry out analysis about students’ performance data outside the system. This can be a burden for the teacher who, in most cases, is not an expert in data analysis. Our goal in this paper is to show how the coefficients of AFM provide valuable information to the teacher without requiring any deep expertise in data analysis. In addition, we show an improved version of the AFM that provides a deeper understanding about the students’ learning process.Publicación Advanced Control by Reinforcement Learning for Wastewater Treatment Plants: A Comparison with Traditional Approaches(MDPI, 2023) Gorrotxategi Zipitria, Mikel; Hernández del Olmo, Félix; Gaudioso Vázquez, Elena; Duro Carralero, Natividad; Dormido Canto, RaquelControl mechanisms for biological treatment of wastewater treatment plants are mostly based on PIDS. However, their performance is far from optimal due to the high non-linearity of the biological and changing processes involved. Therefore, more advanced control techniques are proposed in the literature (e.g., using artificial intelligence techniques). However, these new control techniques have not been compared to the traditional approaches that are actually being used in real plants. To this end, in this paper, we present a comparison of the PID control configurations currently applied to control the dissolved oxygen concentration (in the active sludge process) against a reinforcement learning agent. Our results show that it is possible to have a very competitive operating cost budget when these innovative techniques are applied.Publicación Tackling the start-up of a reinforcement learning agent for the control of wastewater treatment plants(Elsevier, 2018) Hernández del Olmo, Félix; Gaudioso Vázquez, Elena; Dormido Canto, Raquel; Duro Carralero, NatividadReinforcement learning problems involve learning by doing. Therefore, a reinforcement learning agent will have to fail sometimes (while doing) in order to learn. Nevertheless, even with this starting error, introduced at least during the non-optimal learning stage, reinforcement learning can be affordable in some domains like the control of a wastewater treatment plant. However, in wastewater treatment plants, trying to solve the day-to-day problems, plant operators will usually not risk to leave their plant in the hands of an inexperienced and untrained reinforcement learning agent. In fact, it is somewhat obvious that plant operators will require firstly to check that the agent has been trained and that it works as it should at their particular plant. In this paper, we present a solution to this problem by giving a previous instruction to the reinforcement learning agent before we let it act on the plant. In fact, this previous instruction is the key point of the paper. In addition, this instruction is given effortlessly by the plant operator. As we will see, this solution does not just solve the starting up problem of leaving the plant in the hands of an untrained agent, but it also improves the future performance of the agent.