This study models different hybrid systems based on renewable energies that can be supported by diesel generators to meet the energy needs of isolated homes in the Canary Islands. The research will cover the energy requirements of a residential house, including the production of fresh water using a reverse osmosis desalination plant. The system is designed to operate independently of the electrical grid. The HOMER software package was used to model and optimize the hybrid systems. The model was fed with data on the electrical demands of residential homes (including the consumption by the small reverse osmosis desalination plant) as well as the technical specifications of the various devices and renewable energy sources, such as solar radiation and wind speed potentials. The software considers various configurations to optimize hybrid systems, selecting the most suitable one based on the available renewable energy sources at the selected location. The data used in the research were collected on the eastern islands of the Canary Islands (Gran Canaria, Lanzarote and Fuerteventura). Based on the system input parameters, the simulation and optimization performed in HOMER, taking into account the lowest “Levelized Cost of Energy”, it can be concluded that the preferred hybrid renewable energy system for this region is a small wind turbine with a nominal power of 1.9 kW, eight batteries, and a small diesel generator with a nominal power of 1.0 kW. The knowledge from this research could be applied to other geographical areas of the world that have similar conditions, namely a shortage of water and plentiful renewable energy sources.

In this research, a renewable energy hybrid system (PV-Wind) is modeled to compare different design options based on their economic and technical features. The energy requirements of a Reversible Osmosis desalination plant located on the island of Tenerife with a water production capacity of up to 20,000 m3/day was considered. The system is connected to the electricity grid. The HOMER software, version 2.75 was used to produce optimum strategies for renewable energy. The assumptions input into the model were: the technical specifications of the devices, electricity demand of the desalination plant, as well as the solar radiation and the wind speed potentials. Numerous arrangements were considered by the software, version 2.75. The optimal results were obtained based on the use of renewable energy. The data used in the study were recorded in Tenerife in the Canary Islands. The experience of this research could be transferred to other Atlantic islands with similar renewable energy sources (specifically the wind) and water scarce conditions.

Condition Monitoring is key to predictive maintenance and especially in the operational efficiency of the Marine Oil Separation System. These systems are crucial for environmental protection and compliance with international maritime regulations. Therefore, it is necessary to design a technique capable of analyzing the signals from sensors and estimating the remaining useful life in order to avoid breakage or unnecessary replacement. This work presents an intelligent method with signal processing based on Wavelet Packets Transform that provides energy data from vibration measurements as characteristic parameters. These values can be related to its RUL, and they are used as inputs for the training process. In particular, a Genetic Neuro-Fuzzy system is proposed as an intelligent classification technique. Once the training process is completed, it can be concluded that a good classifier has been built, since it relates the energy state of the oil separation system with its remaining useful life, and therefore, the necessary information for efficient predictive maintenance is achieved. Furthermore, a mechanism to obtain the final set of fuzzy rules has been developed, showing the correspondence between these fuzzy rules and the neural network structure.

The automation of maintenance is a growing field and consequently, predictive maintenance is achieving more importance. The main objective is to predict a breakage before it happens. In order to reach this, it is necessary to have an intelligent classification technique that analyzes the state of the key breakage elements and evaluates whether a replacement is necessary or not. This work presents a study to classify belts according to their state of use. For training, vibration data have been collected on a test bench using new belts, belts with half use and belts near the breaking point. The processing of these vibrations allows for extracting the characteristic parameters that can be related to its state of use, and then, after the initial analysis, these values are used as inputs for training the intelligent system. In particular, the Genetic Neuro-Fuzzy (GNF) technique has been chosen and, with the proposed algorithm, more detailed Fuzzy rules are obtained. Once the algorithm has been trained, it is possible to establish a relationship between the vibration shown by the belt and its state of use. The achieved results show that a good classifier has been built.

This work presents a novel intelligent control system based on a Genetic Neuro-Fuzzy tool to optimize and improve the performance of a seawater reverse osmosis desalination plant (SWRO) on board a marine vessel. This investigation pays special attention to minimizing energy consumption to improve the energy efficiency of this marine installation. The system analyzes measurements of different variables-seawater pH, seawater conductivity, permeate flow rate, permeate conductivity, and total energy consumed-in order to provide the most appropriate value of permeate flow rate control and operating pressure of the high-pressure pump (HPP). This intelligent method allows the plant to achieve output values nearer to the desired setpoints set by the plant operators.

The European Solar Telescope (EST) is a 4.2-metre telescope that will be equipped with a multi-conjugate adaptive optics (MCAO) system to provide a high spatial resolution over a circular field of view with 60 '' diameter. The EST-MCAO testbed is a downscaled demonstrator of solar MCAO with similar requirements to those defined for EST. It has been designed and developed to identify and better understand the capabilities of this system. Before MCAO implementation, different AO configurations must be first evaluated in the testbed. Single conjugate adaptive optics for point-like sources has been identified as the first step towards MCAO for the EST. This configuration is based on the use of a deformable mirror with 820 actuators and a Shack-Hartmann wavefront sensor with 33x33 subapertures. The main objective of this paper is to present the results obtained in the EST-MCAO testbed when testing different phase reconstruction algorithms and closed-loop schemes to deal with SCAO for point-like sources. Center of gravity technique was proposed for local slope computation when using point-like sources. Both zonal and modal approaches, including SVD and their regularized least-squares versions, were evaluated and tested for the reconstruction. A closed-loop strategy based on a proportional-integral controller was initially proposed. The benefits of pseudo open-loop control were also evaluated. The performance of the algorithms was tested considering different seeing conditions. Finally, a comparison based on RMS wavefront error and Strehl Ratio was conducted.

Este trabajo presenta el empleo de técnicas de Inteligencia Artificial para obtener una clasificación del estado de desgaste de correas, motivado principalmente por el funcionamiento de estos equipos en las bombas de alta presión de plantas desaladoras

Currently, the problem of water shortages is presented in many regions around the world. Because of this, desalination has been taken as an unconventional source for the shortage of drinking water. The purpose of this work is to model hybrid systems based on renewable energy, in order to compare different options of design analyzing their economic and technical qualities for energy supply to isolated desalination systems. To creating the optimum design for hybrid renewable energy systems, HOMER software has been used. The possible inputs in the software were technical devices' specifications, the electric demand of the desalination plant, and the renewable source potentials (wind speeds and solar radiation). With the intention of evaluating different renewable energy possibilities, a multicriteria calculation is carried out using a number of criteria that are probable to be essential in application decisions. The software has considered several configurations and different optimum outputs according to the use of renewable energies. Real data from Canary Islands have been used in this study, taking into consideration the high solar and wind potentials in the region and extended the use of the desalination in the Canarian archipelago. The outcomes of this work could be an extent to other analogous scenarios in the North Atlantic Ocean or throughout the world.

Reducing contaminant emissions is an important task of any industry, included the maritime one. In fact, in April 2018, IMO (International Maritime Organization) adopted an Initial Strategy on reduction of Greenhouse gas (GHG) emissions from ships. An essential part responsible for producing these emissions is the diesel engine. For that reason vessels include separation systems for heavy fuel oils. The purpose of this work is to improve the predictive maintenance techniques incorporating new intelligent approaches. An analysis of vibrations of this separation system was made and their characteristics were used in a Genetic Neuro-Fuzzy System in order to design an intelligent maintenance based on condition monitoring. The achieved results show that the proposed method provides an improvement since it indicates if a maintenance operation is necessary before the schedule one or if it could be possible extend the next maintenance service.

En el siguiente trabajo de investigacion se realiza una caracterizacion de los potenciales del oleaje en las islas de Fuerteventura y Lanzarote, llevando a cabo una modelizacion de los mismos. Para alcanzar dicho objetivo se utilizan las series temporales del oleaje para aguas profundas facilitadas por “Puertos del Estado” (1996- 2016). El estudio establece un mapa de recursos undimotrices a partir de la informacion disponible, determinando zonas de interes que posteriormente podran ser analizadas con mayor precision. Se presenta un sistema NeuroFuzzy genetico con el objetivo de predecir los valores energeticos undimotrices de ciertos puntos, a partir de los valores conocidos de los puntos WANA cercanos.

In this work two mathematical models based on soft computing techniques for the forecasting of the wave energy in the Macaronesian region are exposed. The intelligent systems proposed for the wave energy prediction are Fuzzy Inference Systems (FIS) and Artificial Neural Networks (ANN). The models were implemented and validated thanks to the dataset of deep waters buoys belonging to Spain's State Ports, in several places near the Canary Islands. The buoys dataset covered a total period of 18 years. Once this research finished, it was possible to conclude that there is an excellent correspondence between annual wave energy predicted by ANN- and FIS-based models with respect to both buoys. These models constitute an effective tool to compute the wave power quickly and accurately at any point in oceanic deep waters, which allows for an optimal use of the dataset from the buoys even with only a few months of measurements.

A great investment is made in maintenance of machinery in any industry. A big percentage of this is spent both in workers and in materials in order to prevent potential issues with said devices. In order to avoid unnecessary expenses, this article presents an intelligent method to detect incipient faults. Particularly, this study focuses on bearings due to the fact that they are the mechanical elements that are most likely to break down. In this article, the proposed method is tested with data collected from a quasi-real industrial machine, which allows for the measurement of the behaviour of faulty bearings with incipient defects. In a second phase, the vibrations obtained from healthy and defective pieces are processed with a multiresolution analysis with the purpose of extracting the most interesting characteristics. Particularly, a Wavelet Packets Transform processing is carried out. Finally, these parameters are used as Genetic Neuro-Fuzzy inputs; this way, once it has been trained, it will indicate whether the analyzed mechanical element is faulty or not.

In this article, a study of characteristic vibrations of marine oils separation system is presented. Vibrations analysis allows for the extraction of representative features that could be related to the lifetime of their pieces. Actual measurements were carried out on these systems on Ro-Pax vessels to transport passengers and freight. The vibrations obtained were processed in the frequency domain and following this, they were used in a Genetic Neuro-Fuzzy System in order to design new predictive maintenance strategies. The obtained results show that these techniques as a promising strategy can be utilized to determine incipient faults.

An assistant onboard system is proposed in order to provide help to the captain. It is a Genetic Neuro-Fuzzy system which is able to advices about how the maneuver should be done depending on each particular situation. Particularly the casting-off is taking into account. In this way, the obstacles situated around the vessel like others boats or the port structures are considered when the maneuver is in process. Depending on the distances the system provides adequate values to the three control parameters: angle turn of tiller, principal engine power and lateral engine power.

In this paper, an intelligent control approach based on Neuro-Fuzzy systems performance is presented with the objective of counteracting the vibrations that affect the low-cost vision platform onboard an unmanned aerial system of rotating nature. A scaled dynamical model of a helicopter is used to simulate vibrations on its fuselage. The impact of these vibrations on the low-cost vision system will be assessed and an intelligent control approach will be derived in order to reduce its detrimental influence. Different trials that consider a Neuro-Fuzzy approach as a fundamental part of an intelligent semi-active control strategy have been carried out. Satisfactory results have been achieved compared to those obtained by means of vibration reduction passive techniques.

This paper focuses on the design of an intelligent control system. The used techniques are based on Neuro Fuzzy approaches applied to a magnetorheological damper in order to reduce the vibrations over footbridges; it has been applied to the Science Museum Footbridge of Valladolid, particularly. A model of the footbridge and of the damper has been built using different simulation tools, and a successful comparison with the real footbridge and the real damper has been carried out. This simulated model has allowed the reproduction of the behaviour of the footbridge and damper when a pedestrian walks across the footbridge. Once it is determined that the simulation results are similar to real data, the control system is introduced into the model. In this sense, different strategies based on Neuro Fuzzy systems have been studied. In fact, an ANFIS (Artificial Neuro Fuzzy Inference System) method has also been used, in addition to an alternative Neuro Fuzzy approach. Several trials have been carried out, using both techniques, obtaining satisfactory results after using these techniques.

In this paper, an intelligent control approach based on neuro-fuzzy systems performance is presented, with the objective of counteracting the vibrations that affect the low-cost vision platform onboard an unmanned aerial system of rotating nature. A scaled dynamical model of a helicopter is used to simulate vibrations on its fuselage. The impact of these vibrations on the low-cost vision system will be assessed and an intelligent control approach will be derived in order to reduce its detrimental influence. Different trials that consider a neuro-fuzzy approach as a fundamental part of an intelligent semi-active control strategy have been carried out. Satisfactory results have been achieved compared to those obtained by means of vibration reduction passive techniques.