From the article The paradigm shift towards competency-based education in the Netherlands has a logical counterpart: the need for more flexibility in the curricula. After all, in competency-based education it is recognized that learning not only takes place in designated places (school, university), but may happen every time when the learner is confronted with a challenge. This observation leads to the necessity to incorporate the learning outcomes of formal and informal education in one curriculum. As a result, the educational process becomes more complex and must be better structured to control the individual learning outcomes. In this paper we discuss this paradox: how more flexibility in the program creates the need for more control in the process. We also discuss what kind of IT-tools are helpful in controlling flexibility in curricula for higher professional education.
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BACKGROUND: Dissatisfaction is being voiced with the generally used way joint flexibility problems are defined (operationalised), i.e. as a range of motion (ROM) one or more degrees lower than normative ROM of healthy subjects. Other, specifically more function-related operationalisations have been proposed. The current study evaluated the effect of applying different operationalisations of joint flexibility problems on its prevalence.METHOD: ROM data of 95 joints affected by burns of 23 children were used, and data on 18 functional activities (Burn Outcome Questionnaire (BOQ)). Five methods were used to operationalise joint flexibility problems: (1) ROM below normative ROM, (2) ROM below normative ROM minus 1SD, (3) ROM below normative ROM minus 2SD, (4) ROM below functional ROM, and (5) a score of 2 or more on the Likert Scale (BOQ).RESULTS: Prevalence of joint flexibility problems on a group level ranged from 13 to 100% depending on the operationalisation used. Per joint and movement direction, prevalence ranged from 40% to 100% (Method 1) and 0% to 80% (Methods 2-4). 18% of the children received '2' on the Likert Scale (Method 5).CONCLUSION: The operationalisation of joint flexibility problems substantially influences prevalence, both on group and joint level. Changing to a function-related operationalisation seems valuable; however, international consensus is required regarding its adoption.TRIAL REGISTRATION: The study is registered in the National Academic Research and Collaborations Information System of the Netherlands (OND1348800).
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We use a longitudinal examination of the production of a complex vessel to develop theory concerning operational flexibility behaviors within interorganizational projects. We find that operational flexibility behaviors are enabled by trust between project participants, sense of urgency, and the availability of resources. These enablers are in turn positively influenced by positive experiences in previous interactions (“shadow of the past”) and expectations of possible future collaboration (“shadow of the future”), the temporary nature of interorganizational projects and slack in project tasks, respectively. The positive effect of enablers on operational flexibility is weakened by the time pressure project participants experience. The latter is also caused by the temporariness of interorganizational projects. Based on our findings, we propose that the different time dimensions play a crucial role in explaining flexibility behaviors in interorganizational projects: the temporariness that is an essential characteristic of interorganizational projects has two potentially opposite effects on the behavior of its participants, and we argue that shadows of the past and future play a decisive role in which of the two effects will dominate. The theoretical framework based on our case study suggests that the temporariness of interorganizational projects is indeed important—as acknowledged in the literature—but that its effect is contingent on shadows of past and future.
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This research presents a case study exploring the potential for demand side flexibility at a cluster of university buildings. The study investigates the potential of a collection of various electrical devices, excluding heating and cooling systems. With increasing penetration of renewable electricity sources and the phasing out of dispatchable fossil sources, matching grid generation with grid demand will become difficult using traditional grid management methods alone. Additionally, grid congestion is a pressing problem. Demand side management in buildings may contribute to a solution to these problems. Currently demand response is, however, not yet exploited at scale. In part, this is because it is unclear how this flexibility can be translated into successful business models, or whether this is possible under the current market regime. This research gives insight into the potential value of energy demand flexibility in reducing energy costs and increasing the match between electricity demand and purchased renewable electricity. An inventory is made of on-site electrical devices that offer load flexibility and the magnitude and duration of load shifting is estimated for each group of devices. A demand response simulation model is then developed that represents the complete collection of flexible devices. This model, addresses demand response as a ‘distribute candy’ problem and finds the optimal time-of-use for shiftable electricity demand whilst respecting the flexibility constraints of the electrical devices. The value of demand flexibility at the building cluster is then assessed using this simulation model, measured electricity consumption, and data regarding the availability of purchased renewables and day-ahead spot prices. This research concludes that coordinated demand response of large variety of devices at the building cluster level can improve energy matching by 0.6-1.5% and reduce spot market energy cost by 0.4-3.2%.
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The future energy system could benefit from the integration of the independent gas, heat and electricity infrastructures. In addition to an increase in exergy efficiency, such a Hybrid Energy Network (HEN) could support the increase of intermittent renewable energy sources by offering increased operational flexibility. Nowadays, the expectations on Natural Gas resources forecast an increase in the application of Liquefied Natural Gas (LNG), as a means of storage and transportation, which has a high exergy value due to the low temperature. Therefore, we analysed the integration of a decentralized LNG regasification with a CHP (Waste-to-Energy) plant, to determine whether the integration could offer additional operational flexibility for the future energy network with intermittent renewable energy sources, under optimized exergy efficient conditions. We compared the independent system with two systems integrated by means of 1) Organic Rankine Cycle and 2) Stirling Engine using the cold of the LNG, that we analysed using a simplified deterministic model based on the energy hub concept. We use the hourly measured electricity and heat demand patterns for 200 households with 35% of the households producing electricity from PV according to a typical measured solar insolation pattern in The Netherlands. We found that for both systems the decentralized LNG regasification integrated with the W2E plant affects the imbalance of the system for electricity and heat, due to the additional redundant paths to produced electricity. The integration of the systems offers additional operational flexibility depending on the means of integration and its availability to produce additional energy carriers. For our future work, we will extend the model, taking into account the variability and randomness in the different parameters, which may cause significant changes in the performance and reliability of the model.
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The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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In the field of ‘renewable energy resources’ formation of biogas Biomass and biogas: potentials, efficiencies and flexibility is an important option. Biogas can be produced from biomass in a multistep process called anaerobic digestion (AD) and is usually performed in large digesters. Anaerobic digestion of biomass is mediated by various groups of microorganisms, which live in complex community structures. However, there is still limited knowledge on the relationships between the type of biomass and operational process parameters. This relates to the changes within the microbial community structure and the resulting overall biogas production efficiency. Opening this microbial black box could lead to an better understanding of on-going microbial processes, resulting in higher biogas yields and overall process efficiencies.
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Author supplied Combining Performance and Flexibility for RMS with a Hybrid Architecture Dani¨el Telgen 12? , Leo van Moergestel 1 , Erik Puik 1 , Pascal Muller 1 , Arjan Groenewegen 1 , Dick van der Steen 1 , Dennis Koole 1 , Patrick de Wit 1 , Arjen van Zanten 1 , and John-Jules Meyer 2 1 Department of Micro Systems Technology and Embedded Systems HU University of Applied Sciences Utrecht Nijenoord 1, 3552AS Utrecht, The Netherlands Reconfigurable Manufacturing Systems (RMS) provide a new step for Agile Manufacturing. RMS consist of modular manufacturing systems that can be customized for the latest product demand. There are many research projects concerning RMS. However, not many imple- mentations have made it into industry. The requirements of the control software is an important aspect, which has unique aspects to create max- imum flexibility that becomes very complex for the software to control. To deal with this challenge, the software needs to have high performance and show intelligent properties to create more flexibility. This paper dis- cusses these properties, the basic performance, and it shows how a hy- brid system, using Robot Operating System (ROS), MongoDB, and Java Agent Development Framework (JADE) could be the basis for further development and be made feasible for industrial use
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© 2016, © Emerald Group Publishing Limited. Purpose – Despite their growing popularity among organisations, satisfaction with activity-based work (ABW) environments is found to be below expectations. Research also suggests that workers typically do not switch frequently, or not at all, between different activity settings. Hence, the purpose of this study is to answer two main questions: Is switching behaviour related to satisfaction with ABW environments? Which factors may explain switching behaviour? Design/methodology/approach – Questionnaire data provided by users of ABW environments (n = 3,189) were used to carry out ANOVA and logistic regression analyses. FindingsSatisfaction ratings of the 4 per cent of the respondents who switched several times a day appeared to be significantly above average. Switching frequency was found to be positively related to heterogeneity of the activity profile, share of communication work and external mobility. Practical implicationsOur findings suggest that satisfaction with ABW environments might be enhanced by stimulating workers to switch more frequently. However, as strong objections against switching were observed and switching frequently does not seem to be compatible with all work patterns, this will presumably not work for everyone. Many workers are likely to be more satisfied if provided with an assigned (multifunctional) workstation. Originality/value – In a large representative sample, clear evidence was found for relationships between behavioural aspects and appreciation of ABW environments that had not been studied previously.
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Biogas can be seen as a flexible and storable energy carrier, capable of absorbing intermittent energy production and demand. However, the sustainability and efficiency of biogas production as a flexible energy provider is not fully understood. This research will focus on simulating biogas production within decentralised energy systems. Within these system several factors need to be taken into account, including, biomass availability, energy demand, energy production from other decentralised energy sources and factors influencing the biogas production process. The main goal of this PhD. research is to design and develop a method capable of integrating biomass availability, energy demand, biogas production, in a realistic dynamic geographical model, such that conclusions can be drawn on mainly the sustainability, and additionally on the efficiency, flexibility and economy of biogas production in the near and far future (2012 to 2050), within local decentralised smart energy grids. Furthermore. This research can help determining the best use of biogas in the near and far future.
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