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Creative Industries and New Materials

Op 27 januari 2006 is dr. Michiel Scheffer geïnstalleerd als lector Fashion Materials Design bij Saxion in Enschede. Het lectoraat en de bijbehorende kenniskring is gericht op het versterken van de wisselwerking tussen creativiteit, technologie en economie op het gebied van mode en textiel. Deze wisselwerking moet sterker tot uiting komen in het onderwijs en dient ook in samenwerking met het bedrijfsleven tot uiting te komen in een onderzoeksprogramma. Het lectoraat is ondergebracht bij de opleiding Fashion en Textiel Management binnen de Academie voor Kunst en Toegepaste Techniek in Enschede. Dr. Michiel Scheffer is economisch geograaf en is al vijftien jaar actief in de Europese kleding en textielbranche als onderzoeker, consultant en branchemanager. Dit boek bevat de tekst van de lectorale rede van Michiel Scheffer.

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Enabling Data Informed Health: Improving data accessibility and standardization using openEHR

Data is widely recognized as a potent catalyst for advancing healthcare eﬀectiveness, increasing worker satisfaction, and mitigating healthcare costs. The ongoing digital transformation within the healthcare sector promises to usher in a new era of ﬂexible patient care, seamless inter-provider communication, and data-informed healthcare practices through the application of data science. However, more often than not data lacks interoperability across diﬀerent healthcare institutions and are not readily available for analysis. This inability to share data leads to a higher administrative burden for healthcare providers and introduces risks when data is missing or when delays occur. Moreover, medical researchers face similar challenges in accessing medical data due to thediﬃculty of extracting data from applications, a lack of standardization, and the required data transformations before it can be used for analysis. To address these complexities, a paradigm shift towards a data-centric application landscape is essential, where data serves as the bedrock of the healthcare infrastructure and is application agnostic. In short, a modern way to think about data in general is to go from an application driven landscape to a data driven landscape, which will allow for better interoperability and innovative healthcare solutions.In the current project the research group Digital Transformation at Hanze University of Applied Sciences works together with industry partners to build an openEHR implementation for a Groningen-based mental healthcare provider.

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Enabling Data Informed Health: Improving data accessibility and standardization using openEHR

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Sustainability in Project Management: Reality Bites

The relationship between project management and sustainable development is rapidly gaining interest from both practitioners and academics. Studies on the integration of the concepts of sustainability into project management, approach this topic mostly from a conceptual, logical or moral point of view. Given the fact that the relationship between sustainability and project management is still an emerging field of study, these approaches make sense. However, they do not diminish the need for more empirical studies to understand how the concepts of sustainable development are implemented in practice. This paper reports an analysis of 56 case studies on the integration of the concepts of sustainability in the way organizations initiate, develop and manage projects. The research question of the study was: To what extent, do organizations consider the concepts of sustainability in the initiation, development and management of projects? The study uses the maturity model for sustainability integration that was presented at the 2010 IPMA World Congress for the assessment of the level of sustainability consideration. The study found an overall average level of sustainability consideration in the actual situation of 25.9%. For the desired situation, this score is almost 10 percent higher, showing an ambition to take sustainability more into consideration. The study also showed that the way sustainability currently is considered, shows the traditional ‘less bad’ approach to sustainability integration and not a more modern social responsibility approach.

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Sustainability in Project Management: Reality Bites

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Innovative large-scale energy storagetechnologies and Power-to-Gas concepts after optimisation

New technologies or approaches are being widely developed and proposed to be deployed in real energy systems to improve desired objectives; however, supporting decision making processes to select best solutions in terms of performance and efficiently following cost-benefit analysis require some sort of scientific evidence based tools. These tools should be reliable, robust, and capable of demonstrating the behaviour and impact of newly developed devices or algorithms in different pre- defined scenarios. Therefore, new approaches and technologies need to be tested and verified using a safe laboratory test environment.This report is about the development and realisation of some major tools and reliable methods to calculate risks and opportunities for integrating of new energy resources into the European electricity grid. Hanze University Groningen and Politecnico di Torino worked together within the STORE&GO project sharing laboratories, knowledge, hardware facilities and researchers for the realisation of the characterisation and mathematical modelling of renewable resources. Needed to realize a stable and reliable environment for remote physical hardware in the loop simulations.For this realisation we started with the local characterisation of a PV-Field and a PEM electrolyser at Entrance Groningen by logging and measuring the electric behaviour and specific device parameters to integrate and convert these into working mathematical models of a PV-Field and electrolyser prosumer. After testing and evaluating these models by comparing the results with the real-time measurements, these test and modelling is also realised from the remote laboratory in Torino. To achieve dynamical physical hardware we also realised dynamic mathematical model(s) with real-time functionality to interact directly with the remote electrolyser. To connect both the laboratories with full duplex communication functionalities between physical hardware and models we have also realized a network which is able to share network resources on both local and remote sites.

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Innovative large-scale energy storagetechnologies and Power-to-Gas concepts after optimisation

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Building and building systems energy prediction models to enhance energy flexibility control

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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Building and building systems energy prediction models to enhance energy flexibility control

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Marnix van Gisbergen

Professor Digital Media Concepts

Projects 7

project

Climate change adaptation under multiple interpretations of reality

Climate change adaptation has influenced river management through an anticipatory governance paradigm. As such, futures and the power of knowing the future has become increasingly influential in water management. Yet, multiple future imaginaries co-exist, where some are more dominant that others. In this PhD research, I focus on deconstructing the future making process in climate change adaptation by asking ‘What river imaginaries exist and what future imaginaries dominate climate change adaptation in riverine infrastructure projects of the Meuse and Magdalena river?’. I firstly explore existing river imaginaries in a case study of the river Meuse. Secondly, I explore imaginaries as materialised in numerical models for the Meuse and Magdalena river. Thirdly, I explore the integration and negotiation of imaginaries in participatory modelling practices in the Magdalena river. Fourthly, I explore contesting and alternative imaginaries and look at how these are mobilised in climate change adaptation for the Magdalena and Meuse river. Multiple concepts stemming from Science and Technology Studies and Political Ecology will guide me to theorise the case study findings. Finally, I reflect on my own positionality in action-research which will be an iterative process of learning and unlearning while navigating between the natural and social sciences.

Ongoing

project

GITools: a toolbox to assess gastrointestinal health effects of natural products

In the last decade, the concept on interactions between humans, animals and their environment has drastically changed, endorsed by the One Health approach that recognizes that health of humans and animals are inextricably linked. Consideration of welfare of livestock has increased accordingly and with it, attention into the possibilities to improve livestock health via natural, more balanced nutrition is expanding. Central to effects of healthy nutrition is an optimal gastrointestinal condition which entails a well-balanced functional local immune system leading to a resilient state of well-being. This project proposal, GITools, aims to establish a toolbox of in vitro assays to screen new feed ingredients for beneficial effects on gastrointestinal health and animal well-being. GITools will focus on pig and chicken as important livestock species present in high quantities and living in close proximity to humans. GITools builds on intestinal models (intestinal cell lines and stem cell-derived organoids), biomarker analysis, and in vitro enzymatic and microbial digestion models of feed constituents. The concept of GITools originated from various individual contacts and projects with industry partners that produce animal feed (additives) or veterinary medicines. Within these companies, an urgent need exists for straightforward, well-characterized and standardized in vitro methods that provide results translatable to the in vivo situation. This to replace testing of new feed concepts in live animal. We will examine in vitro methods for their applicability with feed ingredients selected based on the availability of data from (previous) in vivo studies. These model compounds will include long and short chain fatty acids, oligosaccharides and herbal-derived components. GITools will deliver insights on the role of intestinal processes (e.g. dietary hormone production, growth of epithelial cells, barrier function and innate immune responses) in health and well-being of livestock animals and improve the efficiency of testing new feed products.

Ongoing

project

Living Lab Sustainable Supply Chain Management in Healthcare (SSCMH)

English: This living lab aims to support the creation, development and implementation of next generation concepts for sustainable healthcare logistics, with special attention for last mile solutions. Dutch healthcare providers are on the verge of a transition towards (more) sustainable business models, spurred by e.g., increasing healthcare costs, ongoing budget cuts, tight labor market conditions and increasing ecological awareness. Consequently, healthcare providers need to improve and innovate their business model and underlying logistics concept(s). Simultaneously, many cities are struggling with congestion in traffic, air quality and liveability in general. This calls for Last Mile Logistics (LML) concepts that can address challenges like effective and efficient resource planning, scheduling and utilization and, particularly, sustainability goals. LML can reduce environmental and social impact by decreasing emissions, congestion and pollution through effectively consolidating in-flows of goods and providing innovative solutions for care, wellbeing and related services. The research and initiatives in the living lab will address the following challenges: reducing the ecological footprint, reducing (healthcare-related) costs, improving service quality, decreasing loneliness of frail citizens and improving the livability of urban areas (reducing congestion and emissions). Given the scarcity and fragmentation of knowledge on healthcare logistics in organizations the living lab will also act as a learning community for (future) healthcare- and logistics professionals, thereby supporting the development of human capital. By working closely with related stakeholders and using a transdisciplinary research approach it is ensured that the developed knowledge and solutions deliver a contribution to societal challenges and have sound business potential.

Finished

project

XAIPre: Explainable AI for Predictive Maintenance

Predictive maintenance, using data of thousands of sensors already available, is key for optimizing the maintenance schedule and further prevention of unexpected failures in industry.Current maintenance concepts (in the maritime industry) are based on a fixed maintenance interval for each piece of equipment with enough safety margin to minimize incidents. This means that maintenance is most of the time carried out too early and sometimes too late. This is in particular true for maintenance on maritime equipment, where onshore maintenance is strongly preferred over offshore maintenance and needs to be aligned with the vessel’s operations schedule. However, state-of-the-art predictive maintenance methods rely on black-box machine learning techniques such as deep neural networks that are difficult to interpret and are difficult to accept and work with for the maintenance engineers. The XAIPre project (pronounce Xyper) aims at developing Explainable Predictive Maintenance algorithms that do not only provide the engineers with a prediction but in addition, with a risk analysis on the components when delaying the maintenance, and what the primary indicators are that the algorithms use to create inference. To use predictive maintenance effectively in Maritime operations, the predictive models and also the optimization of the maintenance schedule using these models, need to be aware of the past and planned vessel activities, since different activities affect the lifetime of the machines differently. For example, the degradation of a hydraulic pump inside a crane depends on the type of operations the crane but also the vessel is performing. Thus the models do not only need to be explainable but they also need to be aware of the context which is in this case the vessel and machinery activity. Using sensor data processing and edge-computing technologies that will be developed and applied by the Hanze University of Applied Sciences in Groningen (Hanze UAS), context information is extracted from the raw sensor data. The XAIPre project combines these Explainable Context Aware Machine Learning models with state-of-the-art optimizers, that are already developed and available from the NWO CIMPLO project at LIACS, in order to develop optimal maintenance schedules for machine components. The resulting XAIPre prototype offers significant competitive advantages for maritime companies such as Heerema, by increasing the longevity of machine components, increasing worker safety and decreasing maintenance costs.

Ongoing

project

XAIPre: Explainable AI for Predictive Maintenance

Predictive maintenance, using data of thousands of sensors already available, is key for optimizing the maintenance schedule and further prevention of unexpected failures in industry. Current maintenance concepts (in the maritime industry) are based on a fixed maintenance interval for each piece of equipment with enough safety margin to minimize incidents. This means that maintenance is most of the time carried out too early and sometimes too late. This is in particular true for maintenance on maritime equipment, where onshore maintenance is strongly preferred over offshore maintenance and needs to be aligned with the vessel’s operations schedule. However, state-of-the-art predictive maintenance methods rely on black-box machine learning techniques such as deep neural networks that are difficult to interpret and are difficult to accept and work with for the maintenance engineers. The XAIPre project (pronounce “Xyper”) aims at developing Explainable Predictive Maintenance (XPdM) algorithms that do not only provide the engineers with a prediction but in addition, with 1) a risk analysis on the components when delaying the maintenance, and 2) what the primary indicators are that the algorithms used to create inference. To use predictive maintenance effectively in Maritime operations, the predictive models and the optimization of the maintenance schedule using these models, need to be aware of the past and planned vessel activities, since different activities affect the lifetime of the machines differently. For example, the degradation of a hydraulic pump inside a crane depends on the type of operations the crane performs. Thus, the models do not only need to be explainable but they also need to be aware of the context which is in this case the vessel and machinery activity. Using sensor data processing and edge-computing technologies that will be developed and applied by the Hanze UAS in Groningen, context information is extracted from the raw sensor data. The XAIPre project combines these Explainable Context Aware Machine Learning models with state-of-the-art optimizers, that we already developed in the NWO CIMPLO project at LIACS, in order to develop optimal maintenance schedules for machine components. The optimizers will be adapted to fit within XAIPre. The resulting XAIPre prototype offers significant competitive advantages for companies such as Heerema, by increasing the longevity of machine components, increasing worker safety, and decreasing maintenance costs. XAIPre will focus on the predictive maintenance of thrusters, which is a key sub-system with regards to maintenance as it is a core part of the vessels station keeping capabilities. Periodic maintenance is currently required in fixed intervals of 5 years. XPdM can provide a solid base to deviate from the Periodic Maintenance prescriptions to reduce maintenance costs while maintaining quality. Scaling up to include additional components and systems after XAIPre will be relatively straightforward due to the accumulated knowledge of the predictive maintenance process and the delivered methods. Although the XAIPre system will be evaluated on the use-cases of Heerema, many components of the system can be utilized across industries to save maintenance costs, maximize worker safety and optimize sustainability.

Ongoing

Products 4.276

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Creative Industries and New Materials

MULTIFILE

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Enabling Data Informed Health: Improving data accessibility and standardization using openEHR

PDF

product

Sustainability in Project Management: Reality Bites

PDF

product

Innovative large-scale energy storagetechnologies and Power-to-Gas concepts after optimisation

PDF

product

Building and building systems energy prediction models to enhance energy flexibility control

PDF

People 1

person

Marnix van Gisbergen

Professor Digital Media Concepts

Projects 7

project

Climate change adaptation under multiple interpretations of reality

Ongoing

project

GITools: a toolbox to assess gastrointestinal health effects of natural products

Ongoing

project

Living Lab Sustainable Supply Chain Management in Healthcare (SSCMH)

Finished

project

XAIPre: Explainable AI for Predictive Maintenance

Ongoing

project

XAIPre: Explainable AI for Predictive Maintenance

Predictive maintenance, using data of thousands of sensors already available, is key for optimizing the maintenance schedule and further prevention of unexpected failures in industry. Current maintenance concepts (in the maritime industry) are based on a fixed maintenance interval for each piece of equipment with enough safety margin to minimize incidents. This means that maintenance is most of the time carried out too early and sometimes too late. This is in particular true for maintenance on maritime equipment, where onshore maintenance is strongly preferred over offshore maintenance and needs to be aligned with the vessel’s operations schedule. However, state-of-the-art predictive maintenance methods rely on black-box machine learning techniques such as deep neural networks that are difficult to interpret and are difficult to accept and work with for the maintenance engineers. The XAIPre project (pronounce “Xyper”) aims at developing Explainable Predictive Maintenance (XPdM) algorithms that do not only provide the engineers with a prediction but in addition, with 1) a risk analysis on the components when delaying the maintenance, and 2) what the primary indicators are that the algorithms used to create inference. To use predictive maintenance effectively in Maritime operations, the predictive models and the optimization of the maintenance schedule using these models, need to be aware of the past and planned vessel activities, since different activities affect the lifetime of the machines differently. For example, the degradation of a hydraulic pump inside a crane depends on the type of operations the crane performs. Thus, the models do not only need to be explainable but they also need to be aware of the context which is in this case the vessel and machinery activity. Using sensor data processing and edge-computing technologies that will be developed and applied by the Hanze UAS in Groningen, context information is extracted from the raw sensor data. The XAIPre project combines these Explainable Context Aware Machine Learning models with state-of-the-art optimizers, that we already developed in the NWO CIMPLO project at LIACS, in order to develop optimal maintenance schedules for machine components. The optimizers will be adapted to fit within XAIPre. The resulting XAIPre prototype offers significant competitive advantages for companies such as Heerema, by increasing the longevity of machine components, increasing worker safety, and decreasing maintenance costs. XAIPre will focus on the predictive maintenance of thrusters, which is a key sub-system with regards to maintenance as it is a core part of the vessels station keeping capabilities. Periodic maintenance is currently required in fixed intervals of 5 years. XPdM can provide a solid base to deviate from the Periodic Maintenance prescriptions to reduce maintenance costs while maintaining quality. Scaling up to include additional components and systems after XAIPre will be relatively straightforward due to the accumulated knowledge of the predictive maintenance process and the delivered methods. Although the XAIPre system will be evaluated on the use-cases of Heerema, many components of the system can be utilized across industries to save maintenance costs, maximize worker safety and optimize sustainability.

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Creative Industries and New Materials

Enabling Data Informed Health: Improving data accessibility and standardization using openEHR

Sustainability in Project Management: Reality Bites

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Teaching Software Architecture Concepts with HUSACCT

Innovative large-scale energy storagetechnologies and Power-to-Gas concepts after optimisation

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Business leadership concepts exemplified by the two exceptional leaders Daniel Vasella and Ricardo Semler

People 1

Marnix van Gisbergen

Projects 7

Climate change adaptation under multiple interpretations of reality

GITools: a toolbox to assess gastrointestinal health effects of natural products

Living Lab Sustainable Supply Chain Management in Healthcare (SSCMH)

Living Lab Sustainable Supply Chain Management in Healthcare (SSCMH)

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XAIPre: Explainable AI for Predictive Maintenance

XAIPre: Explainable AI for Predictive Maintenance

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Products 4.276

Creative Industries and New Materials

Enabling Data Informed Health: Improving data accessibility and standardization using openEHR

Sustainability in Project Management: Reality Bites

Quick Scan Circular Business Models

Circular Business Models

Teaching Software Architecture Concepts with HUSACCT

Innovative large-scale energy storagetechnologies and Power-to-Gas concepts after optimisation

Building and building systems energy prediction models to enhance energy flexibility control

Business leadership concepts exemplified by the two exceptional leaders Daniel Vasella and Ricardo Semler

People 1

Marnix van Gisbergen

Projects 7

Climate change adaptation under multiple interpretations of reality

GITools: a toolbox to assess gastrointestinal health effects of natural products

Living Lab Sustainable Supply Chain Management in Healthcare (SSCMH)

Living Lab Sustainable Supply Chain Management in Healthcare (SSCMH)

USER EXPERIENCES IN VIRTUAL WORLDS

XAIPre: Explainable AI for Predictive Maintenance

XAIPre: Explainable AI for Predictive Maintenance