Quantifying the water–energy nexus in Greece

In this paper we provide an assessment of the water–energy nexus for Greece. More specifically, the amount of freshwater consumed per unit of energy produced is determined: for both conventional (lignite, diesel and fuel oil-fired) and advanced (combined operation of gas turbine) thermal power plants in the electricity generation sector; for extraction and refining activities in the primary energy production sector; and for the production of biodiesel that is used as a blend in the ultimately delivered automotive diesel fuel. In addition, the amount of electricity consumed for the purposes of water supply and sewerage is presented. In view of the expected effects of climate change in the Mediterranean region, the results of this study highlight the need for authorities to prepare a national strategy that will ensure climate resilience in both energy and water sectors of the country.     

Infrastructure,automation and model-based operation strategy in a stand-alone hydrolytic solar-hydrogen production unit

An autonomous power system that exploits solar energy for the production of hydrogen through water electrolysis is fully assessed in terms of system implementation and optimal operating strategy. A 10 kW_p photovoltaic array supplies energy to a PEM electrolyzer rated at 6.9 kW_p. In order to maintain a smooth operation regardless of the inherent weather fluctuations, a 1000 Ah/24 V lead–acid accumulator stores energy excess and provides it when needed. The monitoring and control of the system is implemented through a Supervisory Control and Data Acquisition system (SCADA), while the interactions between electrical and chemical subsystems are addressed by a complete automation infrastructure. The mathematical models of each subsystem are validated based on real operational data and a model-based power management strategy is proposed and assessed through a parameter sensitivity analysis. Further on, an off-line optimization framework is evaluated regarding the optimal operation of the system in two diverse, but representative time periods. The optimal parameters are identified and their effect on hydrogen production and accumulator utilization is reported.     

Automatic estimation of exercises' difficulty levels in a tutoring system for teaching the conversion of natural language into first‐order logic

The estimation of the difficulty level of exercises is a fundamental aspect of intelligent tutoring systems, and it is necessary in order to achieve better adaptation to the students' needs and maximize learning efficiency. In this article, we present an approach to automatically estimates the difficulty level of exercises in natural language (NL) to first‐order of logic (FOL). The estimation of an exercise's difficulty level is based on the complexity of the corresponding answer, that is the FOL formula, as well as the structure and the semantics of the exercise, that is a natural language sentence and it is carried out in two main steps. Initially, a preliminary estimation is performed based on the complexity of the FOL formula. The system takes as input parameters the number, the type and the order of quantifiers, the number of implications, and the number of different connectives. Afterwards, the final estimation is made based on both semantic aspects of the NL sentence and the structure of the FOL formula. An evaluation study was conducted to assess the system's performance, and the results are very encouraging.     

Human resource optimisation through semantically enriched data

The industrial domain is experiencing a so-called fourth industrial revolution in which the evergrowing complexity of manufacturing information, the increasing amount of knowledge and the use of web-oriented techniques, represent three crucial factors that are accelerating the growth of complexity of industrial systems. On the other hand, continuous-evolving requirements in industrial environments, due to technology outbreaks and a new global marketplace, have led to an on-going evolution of human resource management through the creation and adoption of alternative business models. In the past decade, semantic models such as ontologies have been proven to be effective for many knowledge-intensive applications, since they provide formal models of domain knowledge that can be exploited in different ways. For all these reasons, an innovative human resource optimisation (HRO) engine is introduced, which employs semantically enhanced information and conditional random field (CRFs) probabilistic models with knowledge derived from industrial shop floor level, and proposes the right person for the right job in real-time shop floor operations towards optimising decisions on how to implement and schedule either repeatedly or non-occurring tasks. Industrial information data flow and semantic enrichment were ensured through the combined use of a common interface data exchange model (CIDEM) and ontologies, after which a feasibility study at a chemical plant presented interesting preliminary results.     

Modeling, simulation and experimental validation of a PEM fuel cell system

The aim of this work is the development and experimental validation of a detailed dynamic fuel cell model using the gPROMS modeling environment. The model is oriented towards optimization and control and it relies on material and energy balances as well as electrochemical equations including semi-empirical equations. For the experimental validation of the model a fully automated and integrated hydrogen fuel cell testing unit was used. The predictive power of the model has been compared with the data obtained during load change experiments. A sensitivity analysis has been employed to reveal the most critical empirical model parameters that should be estimated using a systematic estimation procedure. Model predictions are in good agreement with experimental data under a wide range of operating conditions.     

Extending an embedded RISC microprocessor for efficient translation based Java execution

Java has gained great popularity in embedded appliances such as set-top boxes, smart phones and other hand held devices. In this paper we propose a translation based hw/sw codesigned Java virtual machine architecture, which extends a typical embedded RISC processor. The architectural extensions we propose include special instructions that accelerate translated blocks dispatch and security checks for arrays and objects. The extensions are done in a way that operating systems support is maintained, something that makes their adoption more attractive. Benchmarking using Embedded Caffeine Mark (ECM) benchmarks, showed significant speedups, especially when high performance RISC processors are employed.     

Enhancement of hybrid renewable energy systems control with neural networks applied to weather forecasting: the case of Olvio

In this paper, an intelligent forecasting model, a recurrent neural network (RNN) with nonlinear autoregressive architecture, for daily and hourly solar radiation and wind speed prediction is proposed for the enhancement of the power management strategies (PMSs) of hybrid renewable energy systems (HYRES). The presented model (RNN) is applicable to an autonomous HYRES, where its estimations can be used by a central control unit in order to create in real time the proper PMSs for the efficient subsystems’ utilization and overall process optimization. For this purpose, a flexible network-based design of the HYRES is used and, moreover, applied to a specific system located on Olvio, near Xanthi, Greece, as part of Systems Sunlight S.A. facilities. The simulation results indicated that RNN is capable of assimilating the given information and delivering some satisfactory future estimation achieving regression coefficient from 0.93 up to 0.99 that can be used to safely calculate the available green energy. Moreover, it has some sufficient for the specific problem computational power, as it can deliver the final results in just a few seconds. As a result, the RNN framework, trained with local meteorological data, successfully manages to enhance and optimize the PMS based on the provided solar radiation and wind speed prediction and make the specific HYRES suitable for use as a stand-alone remote energy plant.     

Early malfunction diagnosis of industrial process units utilizing online linear trend profiles and real‐time classification

The early detection of potential malfunctions at process systems can significantly reduce downtime and improve their overall operability. In that context, this paper demonstrates the behavior and response, through a comparative analysis, of novel data‐driven diagnosis methods for interdependent time series. The proposed real‐time slope statistic profile method utilizes a self‐adaptive sliding window based on a real‐time classification technique of linear trend profiles of both interdependent time series and internal condition so as to avoid misdetections. The calculation of the linear trend profile is based on a standard parametric linear trend test, and the selection of possible incidents is based on its two‐level cross‐checking. All possible combinations for the calculation of the trend test and cross‐checking are created to explore their efficiency. The proposed methods are tested against real data sets from a chemical process system of the Centre for Research and Technology Hellas/Chemical Process Energy and Resources Institute derived from specific scenarios during nominal operating conditions.     

Nonlinear stability analysis and a new design methodology for a PEM fuel cell fed DC–DC boost converter

The dynamical behaviour of a fuel cell feeding a boost converter is studied in this paper. A nonlinear model of the combined system is derived including the effect of the switching action of the converter. Using Filippov's theory, it is possible to analytically study the bifurcation patterns of the system and to demonstrate that the system loses stability through a period doubling bifurcation. To overcome this instability, we inject a high frequency sinusoidal signal into the system that forces the system to remain stable while at the same time retaining its basic slow scale properties (like the steady state error). This controller is simple to implement and does not require any special hardware. The stability analysis and new controller design method presented in this paper allow for the re-design of the converter to stabilize circuit operation with a substantially reduced inductor size, reducing the size and cost of the converter while maintaining its average currents and voltages and other circuit steady-state behaviour characteristics. The results are confirmed by using numerical and analytical tools.