Esteban Jove

Power Cell SOC Modelling for Intelligent Virtual Sensor Implementation

Batteries are one of the principal components in electric vehicles and mobile electronic devices. They operate based on electrochemical reactions, which are exhaustively tested to check their behavior and to determine their characteristics at each working point. One remarkable issue of batteries is their complex behavior. The power cell type under analysis in this research is a LFP (Lithium Iron Phosphate LiFePO4). The purpose of this research is to predict the power cell State of Charge (SOC) by creating a hybrid intelligent model. All the operating points measured from a real system during a capacity confirmation test make up the dataset used to obtain the model. This dataset is clustered to obtain different behavior groups, which are used to develop the final model. Different regression techniques such as polynomial regression, support vector regression (SVR), and artificial neural networks (ANN) have been implemented for each cluster. A combination of these methods is performed to achieve an intelligent model. The SOC of the power cell can be predicted by this hybrid intelligent model, and good results are achieved.

Modeling of Bicomponent Mixing System Used in the Manufacture of Wind Generator Blades

The clean energy use has increased during the last years, especially, electricity generation through wind energy. Wind generator blades are usually made by bicomponent mixing machines. With the aim to predict the behavior of this type of manufacturing systems, it has been developed a model that allows to know the performance of a real bicomponent mixing equipment. The novel approach has been obtained by using clustering combined with regression techniques with a dataset obtained during the system operation. Finally, the created model has been tested with very satisfactory results.

An Intelligent Model to Predict ANI in Patients Undergoing General Anesthesia

One of the main challenges in anesthesia is the proposal of safe and efficient methods to administer drugs to regulate the pain that the patient is sufffering during the surgical process. First steps towards this objective is the proposal of adequate indexes that correlate well with analgesia. One of the most promising index is ANI (Antinociception Index). This research focuses on the modelling of the ANI response in patients undergoing general anesthesia with intravenous drug infusion. The aim is to predict the ANI response in terms of the analgesic infusion rate. For this a model based on intelligent regression techniques is proposed. To create the model, it has been checked Artificial Neural Networks (ANN) and Support Vector Regression (SVR). Results were validated using data from patients in the operating room. The measured performance attest for the potential of the proposed technique.

Attempts Prediction by Missing Data Imputation in Engineering Degree

Nowadays, both students performance and its evaluation are important challenges and play a significant role, in general terms. Frequently, the students attempts to pass a specific curriculum subjects, have several fails due to different reasons and, in this context, lack of data adversely affects interesting future analysis for achieving conclusions. As a consequence, data imputation processes must be performed in order to substitute the missing data for estimated values. This paper presents a comparison between two data imputation methods developed by the authors in previous researches, the Adaptive Assignation Algorithm (AAA) based on Multivariate Adaptive Regression Splines (MARS), and the Multivariate Imputation by Chained Equations methodology (MICE). The results obtained demonstrate that both proposed methods achieve good results, specially AAA algorithm.

A New Approach for System Malfunctioning over an Industrial System Control Loop Based on Unsupervised Techniques

Systems optimization is one of the great challenges to improve the industry plants performance. From an economical point of view, a proper optimization means, among others, energy, material and maintenance savings. Furthermore, the quality of the final product is improved. So fault detection techniques development plays a very important role to achieve the system optimization. Under this topic, the present research shows the developed work over a real common system, the level control. A new proposal based on unsupervised techniques were used to detect the system malfunction states, taking into account a dataset collected during the right operation. The proposal is validated with ad-hoc created faults for the different system operation points. The performance is very satisfactory in general terms.

Sensor Fault Detection and Recovery Methodology for a Geothermal Heat Exchanger

This research addresses a sensor fault detection and recovery methodology oriented to a real system as can be a geothermal heat exchanger installed as part of the heat pump installation at a bioclimatic house. The main aim is to stablish the procedure to detect the anomaly over a sensor and recover the value when it occurs. Therefore, some experiments applying a Multi-layer Perceptron (MLP) regressor, as modelling technique, have been made with satisfactory results in general terms. The correct election of the input variables is critical to get a robust model, specially, those features based on the sensor values on the previous state.

Prediction of the Energy Demand of a Hotel Using an Artificial Intelligence-Based Model

The growth of the hotel industry in the world, is a reality that increasingly needs a greater use of energy resources, and their optimal management. Of all the available energy resources, renewable energies can give greater economic efficiency and lower environmental impact. To manage these resources it is important the availability of energy prediction models. This allows managing the demand for power and the available energy resources, to obtain maximum efficiency and stability, with the consequent economic savings. This paper focuses in the use of Artificial Intelligence methods for energy prediction in luxury hotels. As a case of study, the energy performance data used were taken from the hotel complex The Ritz-Carlton, Abama, located in the South of the island of Tenerife, in the Canary Islands, Spain. This is a high complexity infrastructure with many services that require a lot of energy, such as restaurants, kitchens, swimming pools, vehicle fleet, etc., which make the hotel a good study model for other resorts. The model developed for the artificial intelligence system is based on a hybrid topology with artificial neural networks. In this paper, the daily power demand prediction using information of last 24 h is presented. This prediction allows the development of appropriate actions to optimize energy management.

Remifentanil Dose Prediction for Patients During General Anesthesia

In the anesthesia field there are some challenges, such as achieving new methods to control, and, of course, for reducing the pain suffered for the patients during surgeries. The first steps in this field were focused on obtaining representative measurements for pain measurement. Nowadays, one of the most promiser index is the ANI (Antinociception Index). This research works deals the model for the remifentanil dose prediction for patients undergoing general anesthesia. To do that, a hybrid model based on intelligent techniques is implemented. The model was trained using Support Vector Regression (SVR) and Artificial Neural Networks (ANN) algorithms. Results were validated with a real dataset of patients. It was possible to check the really successful model performance.

Hybrid model for the ANI index prediction using Remifentanil drug and EMG signal

With the aim to control and reduce the pain of patients during a surgery with general anesthesia, one of the main challenges is the proposal of safe an optimal and efficient methods of drugs administering. First step to achieve this goal is the proposal and development of right indexes that correlate satisfactory with analgesia. One of this index gives the most hopeful results is the Analgesia Nociception Index (ANI). The present research work deals the ANI response of patients during surgeries with general anesthesia with intravenous drug infusion. The main aim is to predict the ANI signal behavior regarding of the analgesic infusion rate. To do that, a hybrid intelligent model is developed, using clustering and regression techniques based on artificial neural networks and support vector regression. The proposal was validated with a dataset of surgeries real cases of patients undergoing general anesthesia. The achieved results attest for the potential of the proposed technique.

Bioclimatic House Heat Exchanger Behavior Prediction with Time Series Modeling

The Heat Pump with geothermal exchanger is one of the best methods to heat up a building. The heat exchanger is one of the most representative elements when a heat pump is employed as building heating system. In the present study, a novel intelligent system was designed to predict the performance of on this kind of heating equipment. The novel approach has been successfully empirically tested under a real dataset obtained during measurements along one year. It was based on time series modeling. Then, the model was validated and verified; it obtains good results in all the operating conditions ranges.