Study of materials and interphases for electrochemical energy storage from renewable sources

Batteries should be refined depending on their application for a future in which the sustainable energy demand increases. On the one hand, it is fundamental to improve their safety, prevent failures, increase energy density, and reduce production costs. On the other hand, new battery materials and architecture are required to satisfy the growing demand. This thesis explores different electrochemical energy storage systems and new methodologies to investigate complex and dynamic processes. Lithium-ion batteries are described in all their cell components. In these systems, this thesis investigates negative electrodes. Both the development of new sustainable materials and new in situ electrode characterization methods were explored. One strategy to achieve high-energy systems is employing lithium metal anodes. In this framework, ammonium hexafluorophosphate is demonstrated to be a suitable additive for stabilizing the interphase and preventing uncontrolled dendritic deposition. Deposition/stripping cycles, electrochemical impedance spectroscopy, in situ optical microscopy, and operando confocal Raman spectroscopy have been used to study lithium metal-electrolyte interphase in the presence of the additive. Redox Flow Batteries (RFBs) are proposed as a sustainable alternative for stationary applications. An all-copper aqueous RFB (CuRFB) has been studied in all its aspects. For the electrolyte optimization, spectro-electrochemical tests in diluted solution have been used to get information on the electrolyte’s electrochemical behaviour with different copper complexes distributions. In concentrated solutions, the effects of copper-to-ligand ratios, the concentration, and the counter-ion of the complexing agent were evaluated. Electrode thermal treatment was optimized, finding a compromise between the electrochemical performance and the carbon footprint. On the membrane side, a new method for permeability studies was designed using scanning electrochemical microscopy (SECM). The Cu(II) permeability of several membranes was tested, obtaining direct visualization of Cu(II) concentration in space. Also, two spectrophotometric approaches were designed for SoC monitoring systems for negative and positive half-cells.

Efficient and low-computational predictive models for spectral sensors

Spectral sensors are a wide class of devices that are extremely useful for detecting essential information of the environment and materials with high degree of selectivity. Recently, they have achieved high degrees of integration and low implementation cost to be suited for fast, small, and non-invasive monitoring systems. However, the useful information is hidden in spectra and it is difficult to decode. So, mathematical algorithms are needed to infer the value of the variables of interest from the acquired data. Between the different families of predictive modeling, Principal Component Analysis and the techniques stemmed from it can provide very good performances, as well as small computational and memory requirements. For these reasons, they allow the implementation of the prediction even in embedded and autonomous devices. In this thesis, I will present 4 practical applications of these algorithms to the prediction of different variables: moisture of soil, moisture of concrete, freshness of anchovies/sardines, and concentration of gasses. In all of these cases, the workflow will be the same. Initially, an acquisition campaign was performed to acquire both spectra and the variables of interest from samples. Then these data are used as input for the creation of the prediction models, to solve both classification and regression problems. From these models, an array of calibration coefficients is derived and used for the implementation of the prediction in an embedded system. The presented results will show that this workflow was successfully applied to very different scientific fields, obtaining autonomous and non-invasive devices able to predict the value of physical parameters of choice from new spectral acquisitions.

Acquisition of Vibration Signals for Predictive Maintenance: a Practical Case

As predictive maintenance becomes more and more relevant in industrial environment, the possible range of applications for this maintenance strategy grows. The progresses in components technology and their reduction in price, together with the late years' advances in machine learning and in computational power, are making the implementation of predictive maintenance possible in plants where it would have previously been unreasonably costly. This is leading major pharmaceutical industries to explore the possibility of the application of condition monitoring systems on progressively less and less critical equipment. The focus of this thesis is on the implementation of a system to gather vibrational data from the motors installed in a pre-existing machine using off-the-shelf components. The final goal for the system is to provide the necessary vibration data, in the form of frequency spectra, to a machine learning system developed by IMA Digital, which will be leveraging such data to predict possible upcoming faults and to give the final client all the information necessary to plan maintenance activity according to the estimated machine condition.

A New Approach to Monitoring Touristic and Cultural Routes: The Challenge of Development and Use of Indicators-Based Systems

The present paper results of an ongoing research project were it is expected to develop an information system to monitoring a cultural-touristic route. The route to monitor is the Romanesque Route of Tâmega. This Route is composed of 58 monuments located in the region of Tâmega in the North of Portugal. Due to the particular location of this region, that is between coastal zone, but not yet in the inland, it has a weak political influence, and it is reflected in the low levels of development at several levels, observed. The Romanesque Route was implemented in a part of this region in 1998, and enlarged to the all-region in 2010. In order to evaluate the socio-ecomonic impact of this route in the region a research project is being developed. The main goal of this paper is to open a discussion on the elements that must be taken into consideration to evaluate the economic and social impact of a touristic cultural route within a region and this one in particular.

Run-time monitoring in real-time operating systems

Embedded systems are increasingly complex and dynamic, imposing progressively higher developing time and costs. Tuning a particular system for deployment is thus becoming more demanding. Furthermore when considering systems which have to adapt themselves to evolving requirements and changing service requests. In this perspective, run-time monitoring of the system behaviour becomes an important requirement, allowing to dynamically capturing the actual scheduling progress and resource utilization. For this to succeed, operating systems need to expose their internal behaviour and state, making it available to external applications, and a runtime monitoring mechanism must be available. However, such mechanism can impose a burden in the system itself if not wisely used. In this paper we explore this problem and propose a framework, which is intended to provide this run-time mechanism whilst achieving code separation, run-time efficiency and flexibility for the final developer.

Mechanisms for reflection-based monitoring of real- time systems

Monitoring is a very important aspect to consider when developing real-time systems. However, it is also important to consider the impact of the monitoring mechanisms in the actual application. The use of Reflection can provide a clear separation between the real-time application and the implemented monitoring mechanisms, which can be introduced (reflected) into the underlying system without changing the actual application part of the code. Nevertheless, controlling the monitoring system itself is still a topic of research. The monitoring mechanisms must contain knowledge about “how to get the information out”. Therefore, this paper presents the ongoing work to define a suitable strategy for monitoring real-time systems through the use of Reflection.

Forest cover monitoring in the Bara district (Nepal) with remote sensing and geographic information systems

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Monitoring the ex-vivo expansion of human mesenchymal stem/stromal cells in xeno-free microcarrier-based reactor systems by MIR spectroscopy

Human mesenchymal stem/stromal cells (MSCs) have received considerable attention in the field of cell-based therapies due to their high differentiation potential and ability to modulate immune responses. However, since these cells can only be isolated in very low quantities, successful realization of these therapies requires MSCs ex-vivo expansion to achieve relevant cell doses. The metabolic activity is one of the parameters often monitored during MSCs cultivation by using expensive multi-analytical methods, some of them time-consuming. The present work evaluates the use of mid-infrared (MIR) spectroscopy, through rapid and economic high-throughput analyses associated to multivariate data analysis, to monitor three different MSCs cultivation runs conducted in spinner flasks, under xeno-free culture conditions, which differ in the type of microcarriers used and the culture feeding strategy applied. After evaluating diverse spectral preprocessing techniques, the optimized partial least square (PLS) regression models based on the MIR spectra to estimate the glucose, lactate and ammonia concentrations yielded high coefficients of determination (R2 ≥ 0.98, ≥0.98, and ≥0.94, respectively) and low prediction errors (RMSECV ≤ 4.7%, ≤4.4% and ≤5.7%, respectively). Besides PLS models valid for specific expansion protocols, a robust model simultaneously valid for the three processes was also built for predicting glucose, lactate and ammonia, yielding a R2 of 0.95, 0.97 and 0.86, and a RMSECV of 0.33, 0.57, and 0.09 mM, respectively. Therefore, MIR spectroscopy combined with multivariate data analysis represents a promising tool for both optimization and control of MSCs expansion processes.

Run-time Monitoring Architecture for RealTime Systems

Presented at INForum - Simpósio de Informática (INFORUM 2015). 7 to 8, Sep, 2015. Portugal.

Monitoring morphisms to support sustainable interoperability of enterprise systems

Dissertation to obtain the Master degree in Electrical Engineering and Computer Science

An agent based architecture to support monitoring in plug and produce manufacturing systems using knowledge extraction

In recent years a set of production paradigms were proposed in order to capacitate manufacturers to meet the new market requirements, such as the shift in demand for highly customized products resulting in a shorter product life cycle, rather than the traditional mass production standardized consumables. These new paradigms advocate solutions capable of facing these requirements, empowering manufacturing systems with a high capacity to adapt along with elevated flexibility and robustness in order to deal with disturbances, like unexpected orders or malfunctions. Evolvable Production Systems propose a solution based on the usage of modularity and self-organization with a fine granularity level, supporting pluggability and in this way allowing companies to add and/or remove components during execution without any extra re-programming effort. However, current monitoring software was not designed to fully support these characteristics, being commonly based on centralized SCADA systems, incapable of re-adapting during execution to the unexpected plugging/unplugging of devices nor changes in the entire system’s topology. Considering these aspects, the work developed for this thesis encompasses a fully distributed agent-based architecture, capable of performing knowledge extraction at different levels of abstraction without sacrificing the capacity to add and/or remove monitoring entities, responsible for data extraction and analysis, during runtime.

Development of sensing systems for environmental monitoring

This project focused on the investigation and the development of a chemical sensing system for the determination of chromium Cr6+ and a bio-reactor followed by electrochemical detection at a glassy carbon electrode, for the determination of organochlorine compounds. The conjugation of Cr6+ with 1,5-diphenylcarbazide was studied at various types of electrodes such as glassy carbon, ultra-trace epoxy-graphite, chemically or un-modified carbon-paste and dropping-mercury. The cyclic voltammetric behaviour of the complex was also investigated. In addition, the possibility of developing a chemical sensor, Le. an electrochemical probe capable of sensing Cr6+ through its complexation with 1,5-diphenylacarbazide was studied. The conjugations of l-chloro-2,4-dinitrobenzene, 2,4-dichloronitrobenzene and ethacrynic, which are electrophilic organochlorine compounds, with reduced glutathione, were studied in order to test the bioreactor developed, based on the immobilisation of glutathione s-transferase. This was carried out at different types of electrodes such as glassy-carbon, gold, silver, platinum, epoxy-graphite, hangingmercury, and ferrocene-modified rotating-disc electrodes.

A Feasibility Study on Embedded Micro-Electromechanical Sensors and Systems (MEMS) for Monitoring Highway Structures, TR-575, 2011

Micro-electromechanical systems (MEMS) provide vast improvements over existing sensing methods in the context of structural health monitoring (SHM) of highway infrastructure systems, including improved system reliability, improved longevity and enhanced system performance, improved safety against natural hazards and vibrations, and a reduction in life cycle cost in both operating and maintaining the infrastructure. Advancements in MEMS technology and wireless sensor networks provide opportunities for long-term continuous, real-time structural health monitoring of pavements and bridges at low cost within the context of sustainable infrastructure systems. The primary objective of this research was to investigate the use of MEMS in highway structures for health monitoring purposes. This study focused on investigating the use of MEMS and their potential applications in concrete through a comprehensive literature review, a vendor survey, and a laboratory study, as well as a small-scale field study. Based on the comprehensive literature review and vendor survey, the latest information available on off-the-shelf MEMS devices, as well as research prototypes, for bridge, pavement, and traffic applications were synthesized. A commercially-available wireless concrete monitoring system based on radio-frequency identification (RFID) technology and off-the-shelf temperature and humidity sensors were tested under controlled laboratory and field conditions. The test results validated the ability of the RFID wireless concrete monitoring system in accurately measuring the temperature both inside the laboratory and in the field under severe weather conditions. In consultation with the project technical advisory committee (TAC), the most relevant MEMS-based transportation infrastructure research applications to explore in the future were also highlighted and summarized.

Energy-efficient control strategies for variable speed driven parallel pumping systems based on pump operation point monitoring with frequency converters

The pumping processes requiring wide range of flow are often equipped with parallelconnected centrifugal pumps. In parallel pumping systems, the use of variable speed control allows that the required output for the process can be delivered with a varying number of operated pump units and selected rotational speed references. However, the optimization of the parallel-connected rotational speed controlled pump units often requires adaptive modelling of both parallel pump characteristics and the surrounding system in varying operation conditions. The available information required for the system modelling in typical parallel pumping applications such as waste water treatment and various cooling and water delivery pumping tasks can be limited, and the lack of real-time operation point monitoring often sets limits for accurate energy efficiency optimization. Hence, alternatives for easily implementable control strategies which can be adopted with minimum system data are necessary. This doctoral thesis concentrates on the methods that allow the energy efficient use of variable speed controlled parallel pumps in system scenarios in which the parallel pump units consist of a centrifugal pump, an electric motor, and a frequency converter. Firstly, the suitable operation conditions for variable speed controlled parallel pumps are studied. Secondly, methods for determining the output of each parallel pump unit using characteristic curve-based operation point estimation with frequency converter are discussed. Thirdly, the implementation of the control strategy based on real-time pump operation point estimation and sub-optimization of each parallel pump unit is studied. The findings of the thesis support the idea that the energy efficiency of the pumping can be increased without the installation of new, more efficient components in the systems by simply adopting suitable control strategies. An easily implementable and adaptive control strategy for variable speed controlled parallel pumping systems can be created by utilizing the pump operation point estimation available in modern frequency converters. Hence, additional real-time flow metering, start-up measurements, and detailed system model are unnecessary, and the pumping task can be fulfilled by determining a speed reference for each parallel-pump unit which suggests the energy efficient operation of the pumping system.

Data assimilation for marine monitoring and prediction: The MERCATOR operational assimilation systems and the MERSEA developments

During the past 15 years, a number of initiatives have been undertaken at national level to develop ocean forecasting systems operating at regional and/or global scales. The co-ordination between these efforts has been organized internationally through the Global Ocean Data Assimilation Experiment (GODAE). The French MERCATOR project is one of the leading participants in GODAE. The MERCATOR systems routinely assimilate a variety of observations such as multi-satellite altimeter data, sea-surface temperature and in situ temperature and salinity profiles, focusing on high-resolution scales of the ocean dynamics. The assimilation strategy in MERCATOR is based on a hierarchy of methods of increasing sophistication including optimal interpolation, Kalman filtering and variational methods, which are progressively deployed through the Syst`eme d’Assimilation MERCATOR (SAM) series. SAM-1 is based on a reduced-order optimal interpolation which can be operated using ‘altimetry-only’ or ‘multi-data’ set-ups; it relies on the concept of separability, assuming that the correlations can be separated into a product of horizontal and vertical contributions. The second release, SAM-2, is being developed to include new features from the singular evolutive extended Kalman (SEEK) filter, such as three-dimensional, multivariate error modes and adaptivity schemes. The third one, SAM-3, considers variational methods such as the incremental four-dimensional variational algorithm. Most operational forecasting systems evaluated during GODAE are based on least-squares statistical estimation assuming Gaussian errors. In the framework of the EU MERSEA (Marine EnviRonment and Security for the European Area) project, research is being conducted to prepare the next-generation operational ocean monitoring and forecasting systems. The research effort will explore nonlinear assimilation formulations to overcome limitations of the current systems. This paper provides an overview of the developments conducted in MERSEA with the SEEK filter, the Ensemble Kalman filter and the sequential importance re-sampling filter.