GreenDisc: a HW/SW energy optimization framework in globally distributed computation

In recent future, wireless sensor networks (WSNs) will experience a broad high-scale deployment (millions of nodes in the national area) with multiple information sources per node, and with very specific requirements for signal processing. In parallel, the broad range deployment of WSNs facilitates the definition and execution of ambitious studies, with a large input data set and high computational complexity. These computation resources, very often heterogeneous and driven on-demand, can only be satisfied by high-performance Data Centers (DCs). The high economical and environmental impact of the energy consumption in DCs requires aggressive energy optimization policies. These policies have been already detected but not successfully proposed. In this context, this paper shows the following on-going research lines and obtained results. In the field of WSNs: energy optimization in the processing nodes from different abstraction levels, including reconfigurable application specific architectures, efficient customization of the memory hierarchy, energy-aware management of the wireless interface, and design automation for signal processing applications. In the field of DCs: energy-optimal workload assignment policies in heterogeneous DCs, resource management policies with energy consciousness, and efficient cooling mechanisms that will cooperate in the minimization of the electricity bill of the DCs that process the data provided by the WSNs.

GreenDisc: a HW/SW energy optimization framework in globally distributed computation

In recent future, wireless sensor networks ({WSNs}) will experience a broad high-scale deployment (millions of nodes in the national area) with multiple information sources per node, and with very specific requirements for signal processing. In parallel, the broad range deployment of {WSNs} facilitates the definition and execution of ambitious studies, with a large input data set and high computational complexity. These computation resources, very often heterogeneous and driven on-demand, can only be satisfied by high-performance Data Centers ({DCs}). The high economical and environmental impact of the energy consumption in {DCs} requires aggressive energy optimization policies. These policies have been already detected but not successfully proposed. In this context, this paper shows the following on-going research lines and obtained results. In the field of {WSNs}: energy optimization in the processing nodes from different abstraction levels, including reconfigurable application specific architectures, efficient customization of the memory hierarchy, energy-aware management of the wireless interface, and design automation for signal processing applications. In the field of {DCs}: energy-optimal workload assignment policies in heterogeneous {DCs}, resource management policies with energy consciousness, and efficient cooling mechanisms that will cooperate in the minimization of the electricity bill of the DCs that process the data provided by the WSNs.

UPM Activities on the STLs processing (Task 6.2) and the generation of DPA cross section library up to 150 MeV (Task 4.1)

Generation of a complete damage energy and dpa cross section library up to 150 MeVbased on JEFF- 3.1.1 and suitable approximations (UPM) Postprocessing of photonuclear libraries (by CCFE) and thermal scattering  tables (by UPM) at the backend of the calculational system (CCFE/UPM)

Laser Shock Processing influence on local properties and overall tensile behavior of friction stir welded joints

Based on laser beam intensities above 109 W/cm2 with pulse energy of several Joules and duration of nanoseconds, Laser Shock Processing (LSP) is capable of inducing a surface compressive residual stress field. The paper presents experimental results showing the ability of LSP to improve the mechanical strength and cracking resistance of AA2024-T351 friction stir welded (FSW) joints. After introducing the FSW and LSP procedures, the results of microstructural analysis and micro-hardness are discussed. Video Image Correlation was used to measure the displacement and strain fields produced during tensile testing of flat specimens; the local and overall tensile behavior of native FSW joints vs. LSP treated were analyzed. Further, results of slow strain rate tensile testing of the FSW joints, native and LSP treated, performed in 3.5% NaCl solution are presented. The ability of LSP to improve the structural behavior of the FSW joints is underscored.

Analysis of a Spanish energy scenario with Generation IV nuclear reactors

The advantages of fast-spectrum reactors consist not only of an efficient use of fuel through the breeding of fissile material and the use of natural or depleted uranium, but also of the potential reduction of the amount of actinides such as americium and neptunium contained in the irradiated fuel. The first aspect means a guaranteed future nuclear fuel supply. The second fact is key for high-level radioactive waste management, because these elements are the main responsible for the radioactivity of the irradiated fuel in the long term. The present study aims to analyze the hypothetical deployment of a Gen-IV Sodium Fast Reactor (SFR) fleet in Spain. A nuclear fleet of fast reactors would enable a fuel cycle strategy different than the open cycle, currently adopted by most of the countries with nuclear power. A transition from the current Gen-II to Gen-IV fleet is envisaged through an intermediate deployment of Gen-III reactors. Fuel reprocessing from the Gen-II and Gen-III Light Water Reactors (LWR) has been considered. In the so-called advanced fuel cycle, the reprocessed fuel used to produce energy will breed new fissile fuel and transmute minor actinides at the same time. A reference case scenario has been postulated and further sensitivity studies have been performed to analyze the impact of the different parameters on the required reactor fleet. The potential capability of Spain to supply the required fleet for the reference scenario using national resources has been verified. Finally, some consequences on irradiated final fuel inventory are assessed. Calculations are performed with the Monte Carlo transport-coupled depletion code SERPENT together with post-processing tools.

Localization and Tracking in Energy-Constrained Networks with Dynamic Connectivity

Systems used for target localization, such as goods, individuals, or animals, commonly rely on operational means to meet the final application demands. However, what would happen if some means were powered up randomly by harvesting systems? And what if those devices not randomly powered had their duty cycles restricted? Under what conditions would such an operation be tolerable in localization services? What if the references provided by nodes in a tracking problem were distorted? Moreover, there is an underlying topic common to the previous questions regarding the transfer of conceptual models to reality in field tests: what challenges are faced upon deploying a localization network that integrates energy harvesting modules? The application scenario of the system studied is a traditional herding environment of semi domesticated reindeer (Rangifer tarandus tarandus) in northern Scandinavia. In these conditions, information on approximate locations of reindeer is as important as environmental preservation. Herders also need cost-effective devices capable of operating unattended in, sometimes, extreme weather conditions. The analyses developed are worthy not only for the specific application environment presented, but also because they may serve as an approach to performance of navigation systems in absence of reasonably accurate references like the ones of the Global Positioning System (GPS). A number of energy-harvesting solutions, like thermal and radio-frequency harvesting, do not commonly provide power beyond one milliwatt. When they do, battery buffers may be needed (as it happens with solar energy) which may raise costs and make systems more dependent on environmental temperatures. In general, given our problem, a harvesting system is needed that be capable of providing energy bursts of, at least, some milliwatts. Many works on localization problems assume that devices have certain capabilities to determine unknown locations based on range-based techniques or fingerprinting which cannot be assumed in the approach considered herein. The system presented is akin to range-free techniques, but goes to the extent of considering very low node densities: most range-free techniques are, therefore, not applicable. Animal localization, in particular, uses to be supported by accurate devices such as GPS collars which deplete batteries in, maximum, a few days. Such short-life solutions are not particularly desirable in the framework considered. In tracking, the challenge may times addressed aims at attaining high precision levels from complex reliable hardware and thorough processing techniques. One of the challenges in this Thesis is the use of equipment with just part of its facilities in permanent operation, which may yield high input noise levels in the form of distorted reference points. The solution presented integrates a kinetic harvesting module in some nodes which are expected to be a majority in the network. These modules are capable of providing power bursts of some milliwatts which suffice to meet node energy demands. The usage of harvesting modules in the aforementioned conditions makes the system less dependent on environmental temperatures as no batteries are used in nodes with harvesters--it may be also an advantage in economic terms. There is a second kind of nodes. They are battery powered (without kinetic energy harvesters), and are, therefore, dependent on temperature and battery replacements. In addition, their operation is constrained by duty cycles in order to extend node lifetime and, consequently, their autonomy. There is, in turn, a third type of nodes (hotspots) which can be static or mobile. They are also battery-powered, and are used to retrieve information from the network so that it is presented to users. The system operational chain starts at the kinetic-powered nodes broadcasting their own identifier. If an identifier is received at a battery-powered node, the latter stores it for its records. Later, as the recording node meets a hotspot, its full record of detections is transferred to the hotspot. Every detection registry comprises, at least, a node identifier and the position read from its GPS module by the battery-operated node previously to detection. The characteristics of the system presented make the aforementioned operation own certain particularities which are also studied. First, identifier transmissions are random as they depend on movements at kinetic modules--reindeer movements in our application. Not every movement suffices since it must overcome a certain energy threshold. Second, identifier transmissions may not be heard unless there is a battery-powered node in the surroundings. Third, battery-powered nodes do not poll continuously their GPS module, hence localization errors rise even more. Let's recall at this point that such behavior is tight to the aforementioned power saving policies to extend node lifetime. Last, some time is elapsed between the instant an identifier random transmission is detected and the moment the user is aware of such a detection: it takes some time to find a hotspot. Tracking is posed as a problem of a single kinetically-powered target and a population of battery-operated nodes with higher densities than before in localization. Since the latter provide their approximate positions as reference locations, the study is again focused on assessing the impact of such distorted references on performance. Unlike in localization, distance-estimation capabilities based on signal parameters are assumed in this problem. Three variants of the Kalman filter family are applied in this context: the regular Kalman filter, the alpha-beta filter, and the unscented Kalman filter. The study enclosed hereafter comprises both field tests and simulations. Field tests were used mainly to assess the challenges related to power supply and operation in extreme conditions as well as to model nodes and some aspects of their operation in the application scenario. These models are the basics of the simulations developed later. The overall system performance is analyzed according to three metrics: number of detections per kinetic node, accuracy, and latency. The links between these metrics and the operational conditions are also discussed and characterized statistically. Subsequently, such statistical characterization is used to forecast performance figures given specific operational parameters. In tracking, also studied via simulations, nonlinear relationships are found between accuracy and duty cycles and cluster sizes of battery-operated nodes. The solution presented may be more complex in terms of network structure than existing solutions based on GPS collars. However, its main gain lies on taking advantage of users' error tolerance to reduce costs and become more environmentally friendly by diminishing the potential amount of batteries that can be lost. Whether it is applicable or not depends ultimately on the conditions and requirements imposed by users' needs and operational environments, which is, as it has been explained, one of the topics of this Thesis.

Thermal Scattering Libraries Processing (F4E, Task 6.2)

Presentación del trabajo realizado en el marco del proyecto F4E, sobre el procesamiento de librerías de dispersión térmica de neutrones en formato ACE para su uso con el código MCNP. Se presentan tanto los métodos y procedimientos empleados, como los resultados y diferencias entre las distintas fuentes de datos.

Utilizing the Internet of Things to promote energy awareness and efficiency at discrete production processes: Practices and methodology

El actual contexto de fabricación, con incrementos en los precios de la energía, una creciente preocupación medioambiental y cambios continuos en los comportamientos de los consumidores, fomenta que los responsables prioricen la fabricación respetuosa con el medioambiente. El paradigma del Internet de las Cosas (IoT) promete incrementar la visibilidad y la atención prestada al consumo de energía gracias tanto a sensores como a medidores inteligentes en los niveles de máquina y de línea de producción. En consecuencia es posible y sencillo obtener datos de consumo de energía en tiempo real proveniente de los procesos de fabricación, pero además es posible analizarlos para incrementar su importancia en la toma de decisiones. Esta tesis pretende investigar cómo utilizar la adopción del Internet de las Cosas en el nivel de planta de producción, en procesos discretos, para incrementar la capacidad de uso de la información proveniente tanto de la energía como de la eficiencia energética. Para alcanzar este objetivo general, la investigación se ha dividido en cuatro sub-objetivos y la misma se ha desarrollado a lo largo de cuatro fases principales (en adelante estudios). El primer estudio de esta tesis, que se apoya sobre una revisión bibliográfica comprehensiva y sobre las aportaciones de expertos, define prácticas de gestión de la producción que son energéticamente eficientes y que se apoyan de un modo preeminente en la tecnología IoT. Este primer estudio también detalla los beneficios esperables al adoptar estas prácticas de gestión. Además, propugna un marco de referencia para permitir la integración de los datos que sobre el consumo energético se obtienen en el marco de las plataformas y sistemas de información de la compañía. Esto se lleva a cabo con el objetivo último de remarcar cómo estos datos pueden ser utilizados para apalancar decisiones en los niveles de procesos tanto tácticos como operativos. Segundo, considerando los precios de la energía como variables en el mercado intradiario y la disponibilidad de información detallada sobre el estado de las máquinas desde el punto de vista de consumo energético, el segundo estudio propone un modelo matemático para minimizar los costes del consumo de energía para la programación de asignaciones de una única máquina que deba atender a varios procesos de producción. Este modelo permite la toma de decisiones en el nivel de máquina para determinar los instantes de lanzamiento de cada trabajo de producción, los tiempos muertos, cuándo la máquina debe ser puesta en un estado de apagada, el momento adecuado para rearrancar, y para pararse, etc. Así, este modelo habilita al responsable de producción de implementar el esquema de producción menos costoso para cada turno de producción. En el tercer estudio esta investigación proporciona una metodología para ayudar a los responsables a implementar IoT en el nivel de los sistemas productivos. Se incluye un análisis del estado en que se encuentran los sistemas de gestión de energía y de producción en la factoría, así como también se proporcionan recomendaciones sobre procedimientos para implementar IoT para capturar y analizar los datos de consumo. Esta metodología ha sido validada en un estudio piloto, donde algunos indicadores clave de rendimiento (KPIs) han sido empleados para determinar la eficiencia energética. En el cuarto estudio el objetivo es introducir una vía para obtener visibilidad y relevancia a diferentes niveles de la energía consumida en los procesos de producción. El método propuesto permite que las factorías con procesos de producción discretos puedan determinar la energía consumida, el CO2 emitido o el coste de la energía consumida ya sea en cualquiera de los niveles: operación, producto o la orden de fabricación completa, siempre considerando las diferentes fuentes de energía y las fluctuaciones en los precios de la misma. Los resultados muestran que decisiones y prácticas de gestión para conseguir sistemas de producción energéticamente eficientes son posibles en virtud del Internet de las Cosas. También, con los resultados de esta tesis los responsables de la gestión energética en las compañías pueden plantearse una aproximación a la utilización del IoT desde un punto de vista de la obtención de beneficios, abordando aquellas prácticas de gestión energética que se encuentran más próximas al nivel de madurez de la factoría, a sus objetivos, al tipo de producción que desarrolla, etc. Así mismo esta tesis muestra que es posible obtener reducciones significativas de coste simplemente evitando los períodos de pico diario en el precio de la misma. Además la tesis permite identificar cómo el nivel de monitorización del consumo energético (es decir al nivel de máquina), el intervalo temporal, y el nivel del análisis de los datos son factores determinantes a la hora de localizar oportunidades para mejorar la eficiencia energética. Adicionalmente, la integración de datos de consumo energético en tiempo real con datos de producción (cuando existen altos niveles de estandarización en los procesos productivos y sus datos) es esencial para permitir que las factorías detallen la energía efectivamente consumida, su coste y CO2 emitido durante la producción de un producto o componente. Esto permite obtener una valiosa información a los gestores en el nivel decisor de la factoría así como a los consumidores y reguladores. ABSTRACT In today‘s manufacturing scenario, rising energy prices, increasing ecological awareness, and changing consumer behaviors are driving decision makers to prioritize green manufacturing. The Internet of Things (IoT) paradigm promises to increase the visibility and awareness of energy consumption, thanks to smart sensors and smart meters at the machine and production line level. Consequently, real-time energy consumption data from the manufacturing processes can be easily collected and then analyzed, to improve energy-aware decision-making. This thesis aims to investigate how to utilize the adoption of the Internet of Things at shop floor level to increase energy–awareness and the energy efficiency of discrete production processes. In order to achieve the main research goal, the research is divided into four sub-objectives, and is accomplished during four main phases (i.e., studies). In the first study, by relying on a comprehensive literature review and on experts‘ insights, the thesis defines energy-efficient production management practices that are enhanced and enabled by IoT technology. The first study also explains the benefits that can be obtained by adopting such management practices. Furthermore, it presents a framework to support the integration of gathered energy data into a company‘s information technology tools and platforms, which is done with the ultimate goal of highlighting how operational and tactical decision-making processes could leverage such data in order to improve energy efficiency. Considering the variable energy prices in one day, along with the availability of detailed machine status energy data, the second study proposes a mathematical model to minimize energy consumption costs for single machine production scheduling during production processes. This model works by making decisions at the machine level to determine the launch times for job processing, idle time, when the machine must be shut down, ―turning on‖ time, and ―turning off‖ time. This model enables the operations manager to implement the least expensive production schedule during a production shift. In the third study, the research provides a methodology to help managers implement the IoT at the production system level; it includes an analysis of current energy management and production systems at the factory, and recommends procedures for implementing the IoT to collect and analyze energy data. The methodology has been validated by a pilot study, where energy KPIs have been used to evaluate energy efficiency. In the fourth study, the goal is to introduce a way to achieve multi-level awareness of the energy consumed during production processes. The proposed method enables discrete factories to specify energy consumption, CO2 emissions, and the cost of the energy consumed at operation, production and order levels, while considering energy sources and fluctuations in energy prices. The results show that energy-efficient production management practices and decisions can be enhanced and enabled by the IoT. With the outcomes of the thesis, energy managers can approach the IoT adoption in a benefit-driven way, by addressing energy management practices that are close to the maturity level of the factory, target, production type, etc. The thesis also shows that significant reductions in energy costs can be achieved by avoiding high-energy price periods in a day. Furthermore, the thesis determines the level of monitoring energy consumption (i.e., machine level), the interval time, and the level of energy data analysis, which are all important factors involved in finding opportunities to improve energy efficiency. Eventually, integrating real-time energy data with production data (when there are high levels of production process standardization data) is essential to enable factories to specify the amount and cost of energy consumed, as well as the CO2 emitted while producing a product, providing valuable information to decision makers at the factory level as well as to consumers and regulators.

New strategies in laser processing of TCOs for light management improvement in thin-film silicon solar cells

Light confinement strategies play a crucial role in the performance of thin-film (TF) silicon solar cells. One way to reduce the optical losses is the texturing of the transparent conductive oxide (TCO) that acts as the front contact. Other losses arise from the mismatch between the incident light spectrum and the spectral properties of the absorbent material that imply that low energy photons (below the bandgap value) are not absorbed, and therefore can not generate photocurrent. Up-conversion techniques, in which two sub-bandgap photons are combined to give one photon with a better matching with the bandgap, were proposed to overcome this problem. In particular, this work studies two strategies to improve light management in thin film silicon solar cells using laser technology. The first one addresses the problem of TCO surface texturing using fully commercial fast and ultrafast solid state laser sources. Aluminum doped Zinc Oxide (AZO) samples were laser processed and the results were optically evaluated by measuring the haze factor of the treated samples. As a second strategy, laser annealing experiments of TCOs doped with rare earth ions are presented as a potential process to produce layers with up-conversion properties, opening the possibility of its potential use in high efficiency solar cells.

Measurement of plasma electron density generated in an experiment of laser shock processing, utilizing the H­α­-line

In this work we have realized plasma diagnosis produced by Laser (LPP), by means of emission spectroscopy in a Laser Shock Processing (LSP). The LSP has been proposed as an alternative technology, competitive with classical surface treatments. The ionic species present in the plasma together with electron density and its temperature provide significant indicators of the degree of surface effect of the treated material. In order to analyze these indicators, we have realized spectroscopic studies of optical emission in the laser-generated plasmas in different situations. We have worked focusing on an aluminum sample (Al2024) in air and/or in LSP conditions (water flow) a Q-switched laser of Nd:YAG (λ = 1.06 μm, 10 ns of pulse duration, running at 10 Hz repetition rate). The pulse energy was set at 2,5 J per pulse. The electron density has been measured using, in every case, the Stark broadening of H Balmer α line (656.27 nm). In the case of the air, this measure has been contrasted with the value obtained with the line of 281.62 nm of Al II. Special attention has been paid to the self-absorption of the spectral lines used. The measures were realized with different delay times after the pulse of the laser (1–8 μs) and with a time window of 1 μs. In LSP the electron density obtained was between 1017 cm−3 for the shortest delays (4–6 μs), and 1016 cm−3 for the greatest delays (7,8 μs).

Development of a fuzzy control based self-adaptive system for energy saving in constrained networks for the Internet of Things

Las redes del futuro, incluyendo las redes de próxima generación, tienen entre sus objetivos de diseño el control sobre el consumo de energía y la conectividad de la red. Estos objetivos cobran especial relevancia cuando hablamos de redes con capacidades limitadas, como es el caso de las redes de sensores inalámbricos (WSN por sus siglas en inglés). Estas redes se caracterizan por estar formadas por dispositivos de baja o muy baja capacidad de proceso y por depender de baterías para su alimentación. Por tanto la optimización de la energía consumida se hace muy importante. Son muchas las propuestas que se han realizado para optimizar el consumo de energía en este tipo de redes. Quizás las más conocidas son las que se basan en la planificación coordinada de periodos de actividad e inactividad, siendo una de las formas más eficaces para extender el tiempo de vida de las baterías. La propuesta que se presenta en este trabajo se basa en el control de la conectividad mediante una aproximación probabilística. La idea subyacente es que se puede esperar que una red mantenga la conectividad si todos sus nodos tienen al menos un número determinado de vecinos. Empleando algún mecanismo que mantenga ese número, se espera que se pueda mantener la conectividad con un consumo energético menor que si se empleara una potencia de transmisión fija que garantizara una conectividad similar. Para que el mecanismo sea eficiente debe tener la menor huella posible en los dispositivos donde se vaya a emplear. Por eso se propone el uso de un sistema auto-adaptativo basado en control mediante lógica borrosa. En este trabajo se ha diseñado e implementado el sistema descrito, y se ha probado en un despliegue real confirmando que efectivamente existen configuraciones posibles que permiten mantener la conectividad ahorrando energía con respecto al uso de una potencia de transmisión fija. ABSTRACT. Among the design goals for future networks, including next generation networks, we can find the energy consumption and the connectivity. These two goals are of special relevance when dealing with constrained networks. That is the case of Wireless Sensors Networks (WSN). These networks consist of devices with low or very low processing capabilities. They also depend on batteries for their operation. Thus energy optimization becomes a very important issue. Several proposals have been made for optimizing the energy consumption in this kind of networks. Perhaps the best known are those based on the coordinated planning of active and sleep intervals. They are indeed one of the most effective ways to extend the lifetime of the batteries. The proposal presented in this work uses a probabilistic approach to control the connectivity of a network. The underlying idea is that it is highly probable that the network will have a good connectivity if all the nodes have a minimum number of neighbors. By using some mechanism to reach that number, we hope that we can preserve the connectivity with a lower energy consumption compared to the required one if a fixed transmission power is used to achieve a similar connectivity. The mechanism must have the smallest footprint possible on the devices being used in order to be efficient. Therefore a fuzzy control based self-adaptive system is proposed. This work includes the design and implementation of the described system. It also has been validated in a real scenario deployment. We have obtained results supporting that there exist configurations where it is possible to get a good connectivity saving energy when compared to the use of a fixed transmission power for a similar connectivity.

Piezoelectric sensing and non-parametric statistical signal processing for health monitoring of hysteretic dampers used in seismic-resistant structures

The paper proposes a new application of non-parametric statistical processing of signals recorded from vibration tests for damage detection and evaluation on I-section steel segments. The steel segments investigated constitute the energy dissipating part of a new type of hysteretic damper that is used for passive control of buildings and civil engineering structures subjected to earthquake-type dynamic loadings. Two I-section steel segments with different levels of damage were instrumented with piezoceramic sensors and subjected to controlled white noise random vibrations. The signals recorded during the tests were processed using two non-parametric methods (the power spectral density method and the frequency response function method) that had never previously been applied to hysteretic dampers. The appropriateness of these methods for quantifying the level of damage on the I-shape steel segments is validated experimentally. Based on the results of the random vibrations, the paper proposes a new index that predicts the level of damage and the proximity of failure of the hysteretic damper

Microspectroscopic imaging tracks the intracellular processing of a signal transduction protein: fluorescent-labeled protein kinase C beta I.

We have devised a microspectroscopic strategy for assessing the intracellular (re)distribution and the integrity of the primary structure of proteins involved in signal transduction. The purified proteins are fluorescent-labeled in vitro and reintroduced into the living cell. The localization and molecular state of fluorescent-labeled protein kinase C beta I isozyme were assessed by a combination of quantitative confocal laser scanning microscopy, fluorescence lifetime imaging microscopy, and novel determinations of fluorescence resonance energy transfer based on photobleaching digital imaging microscopy. The intensity and fluorescence resonance energy transfer efficiency images demonstrate the rapid nuclear translocation and ensuing fragmentation of protein kinase C beta I in BALB/c3T3 fibroblasts upon phorbol ester stimulation, and suggest distinct, compartmentalized roles for the regulatory and catalytic fragments.

Conversion of low density polyethylene into fuel through co-processing with vacuum gas oil in a fluid catalytic cracking riser reactor

In this work, mixtures of vacuum gas oil and low density polyethylene, a major component of common industrial and consumer household plastics, were pyrolytically co-processed in a fluid catalytic cracking (FCC) riser reactor as a viable alternative for the energy and petrochemical revalorisation of plastic wastes into valuable petrochemical feedstocks and fuel within an existing industrial technology. Using equilibrium FCC catalyst, the oil–polymer blends were catalytically cracked at different processing conditions of temperatures between 773 K and 973 K and catalyst feed ratios of 5:1, 7:1 and 10:1. The influence of each of these processing parameters on the cracking gas and liquid yield patterns were studied and presented. Further analysed and presented are the different compositional distributions of the obtained liquids and gaseous products. The analysis of the results obtained revealed that with very little modifications to existing process superstructure, yields and compositional distributions of products from the fluid catalytic cracking of the oil–polymer blend in many cases were very similar to those of the processed oil feedstock, bringing to manifest the viability of the feedstock co-processing without significant detriments to FCC product yields and quality.

Impact of the Decarbonisation of the Energy System on Employment in Europe. CEPS Special Report No. 82, February 2014

This paper presents a methodology for calculating the potential impact of the new socio-ecological transition away from fossil fuels on employment in EU energy supply. The methodology is based on “employment factors” (i.e. labour intensities) of different energy technologies. These employment factors are applied to changing energy mixes as projected by the decarbonisation scenarios of the European Commission’s Energy Roadmap 2050. In particular, we analyse quantitative (number of jobs) and qualitative (qualification levels) impacts on employment in extraction and processing of primary (fossil) fuels and in the power sector for the years 2020, 2030 and 2050. The results show that the energy sector will provide not only more jobs as the new socio-ecological transition unfolds, but also jobs requiring higher-level qualifications when compared with the current energy sector.