The increasing share of renewable production like wind and PV poses new challenges to our energy system. The intermittent behavior and lack of controllability on these sources requires flexibility measures like storage and conversion. Production, consumption, transportation, storage and conversion systems become more intertwined. The increasing complexity of the system requires new control strategies to fulfill existing requirements.The SynergyS project addresses the main question how to operate increasingly complex energy systems in a controllable, robust, safe, affordable, and reliable way. Goal of the project is to develop and test a smart control system for a multi-commodity energy system (MCES), with electricity, hydrogen and heat. In scope are an industrial cluster (Chemistry Park Delfzijl) and a residential cluster (Leeuwarden) and their mutual interaction. Results are experimentally tested in two real-life demo-sites scale models: Centre of Expertise Energy (EnTranCe) and The Green Village (TU Delft) represent respectively the industrial and residential cluster.The result will be a market-driven control system to operate a multi-commodity energy system, integrating the industrial and residential cluster. The experimental setup is a combination of physical demo-site assets complemented with (digital) asset models. Experimental validation is based on a demo-scenario including real time data, simulated data and several stress tests.In this session we’ll elaborate more on the project and present (preliminary) results on the testing criteria, scenarios and experimental setup.
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The SynergyS project aims to develop and assess a smart control system for multi-commodity energy systems (SMCES). The consortium, including a broad range of partners from different sectors, believes a SMCES is better able to incorporate new energy sources in the energy system. The partners are Hanze, TU Delft, University of Groningen, TNO, D4, Groningen Seaports, Emerson, Gain Automation Technology, Energy21, and Enshore. The project is supported by a Energy Innovation NL (topsector energie) subsidy by the Ministry of Economic Affairs.Groningen Seaports (Eemshaven, Chemical Park Delfzijl) and Leeuwarden are used as case studies for respectively an industrial and residential cluster. Using a market-based approach new local energy markets have been developed complementing the existing national wholesale markets. Agents exchange energy using optimized bidding strategies, resulting in better utilization of the assets in their portfolio. Using a combination of digital twins and physical assets from two field labs (ENTRANCE, The Green Village) performance of the SMCES is assessed. In this talk the smart multi-commodity energy system is presented, as well as some first results of the assessment. Finally an outlook is given how the market-based approach can benefit the development of energy hubs.
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Methods to design viable business networks (BNs) treat the concept of viability merely in terms of profitability. Further, the methods are restricted to mono-commodity (a single product or a service) BNs. However, business literature suggests that besides economic value (profit), non-economic values (e.g. lowering CO2 emission) play an important role in making BNs viable. Furthermore, BNs can also be multi-commodity (e.g. electricity, gas, heat). Hence, we aim to develop an method to determine a viable configuration of services for multi-commodity BNs. In addition, the term viability is used in an extended scope to include non-economic values.
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The Heating Ventilation and Air Conditioning (HVAC) sector is responsible for a large part of the total worldwide energy consumption, a significant part of which is caused by incorrect operation of controls and maintenance. HVAC systems are becoming increasingly complex, especially due to multi-commodity energy sources, and as a result, the chance of failures in systems and controls will increase. Therefore, systems that diagnose energy performance are of paramount importance. However, despite much research on Fault Detection and Diagnosis (FDD) methods for HVAC systems, they are rarely applied. One major reason is that proposed methods are different from the approaches taken by HVAC designers who employ process and instrumentation diagrams (P&IDs). This led to the following main research question: Which FDD architecture is suitable for HVAC systems in general to support the set up and implementation of FDD methods, including energy performance diagnosis? First, an energy performance FDD architecture based on information embedded in P&IDs was elaborated. The new FDD method, called the 4S3F method, combines systems theory with data analysis. In the 4S3F method, the detection and diagnosis phases are separated. The symptoms and faults are classified into 4 types of symptoms (deviations from balance equations, operating states (OS) and energy performance (EP), and additional information) and 3 types of faults (component, control and model faults). Second, the 4S3F method has been tested in four case studies. In the first case study, the symptom detection part was tested using historical Building Management System (BMS) data for a whole year: the combined heat and power plant of the THUAS (The Hague University of Applied Sciences) building in Delft, including an aquifer thermal energy storage (ATES) system, a heat pump, a gas boiler and hot and cold water hydronic systems. This case study showed that balance, EP and OS symptoms can be extracted from the P&ID and the presence of symptoms detected. In the second case study, a proof of principle of the fault diagnosis part of the 4S3F method was successfully performed on the same HVAC system extracting possible component and control faults from the P&ID. A Bayesian Network diagnostic, which mimics the way of diagnosis by HVAC engineers, was applied to identify the probability of all possible faults by interpreting the symptoms. The diagnostic Bayesian network (DBN) was set up in accordance with the P&ID, i.e., with the same structure. Energy savings from fault corrections were estimated to be up to 25% of the primary energy consumption, while the HVAC system was initially considered to have an excellent performance. In the third case study, a demand-driven ventilation system (DCV) was analysed. The analysis showed that the 4S3F method works also to identify faults on an air ventilation system.
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Wat zijn belangrijke succesfactoren om onderzoek, onderwijs en ondernemen bij elkaar te brengen, zó dat 'het klikt'. De uitdaging voor de toekomst van bedrijven in de smart factoryligt bij data science: het omzetten van ruwe (sensor) data naar (zinnige) informatie en kennis, waarmee producten en diensten verbeterd kunnen worden. Tevens programma van het symposium t.g.l. inauguratie 3 december 2015
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The last decade has seen an increasing demand from the industrial field of computerized visual inspection. Applications rapidly become more complex and often with more demanding real time constraints. However, from 2004 onwards the clock frequency of CPUs has not increased significantly. Computer Vision applications have an increasing demand for more processing power but are limited by the performance capabilities of sequential processor architectures. The only way to get more performance using commodity hardware, like multi-core processors and graphics cards, is to go for parallel programming. This article focuses on the practical question: How can the processing time for vision algorithms be improved, by parallelization, in an economical way and execute them on multiple platforms?
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n the work package described in this report, members are investigating whether a cooperative of farmers can become self-sufficient in energy and fertilization by using manure and organic waste flows in combination with anaerobic fermentation. The aim is to link the nutrient cycle (from manure to digestate to green fertilizer consisting of, for example, nitrate, phosphate, potassium, and trace elements) to a self-sufficient energy system, by the combined production of electricity, green gas, green fuels, and green fertilizers. Within this research such a system is called a circular multi commodity system (CMCS). In effect linking, the nutrient cycle with an energy production chain. In addition, other energy sources and sinks can also play a role in the system such as wind, solar PV and storage (e.g. batteries or hydrogen). For this symbiosis of production techniques to succeed in practice, intensive cooperation between arable farmers and dairy farmers is needed. Farmers supply part of the input from the biofermenter and receive green fertilizers at the end of the process, which are used as a substitute for fertilizer. The case is based on a cooperative of farmers with a minimal geographical spread and maximum diversity in type of business. In this way, the current waste and nutrient chain is being replaced by a more sustainable and closed cycle. This could provide significant environmental benefits: reduction of the environmental impact through the use of fertilizer, reduction of dependence on fossil raw materials, and reduction of CO2 emissions.
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Summary Project objectives This study fits into a larger research project on logistics collaboration and outsourcing decisions. The final objective of this larger project is to analyze the logistics collaboration decision in more detail to identify thresholds in these decisions. To reach the overall objectives, the first step is to get a clearer picture on the chemical and logistics service providers industry, sectors of our study, and on logistics collaboration in these sectors. The results of this first phase are presented in this report. Project Approach The study consists of two parts: literature review and five case studies within the chemical industry. The literature covers three topics: logistics collaboration, logistics outsourcing and purchasing of logistics services. The five case studies are used to refine the theoretical findings of the literature review. Conclusions Main observations during the case studies can be summarized as follows: Most analyzed collaborative relationships between shippers and logistics service providers in the chemical industry are still focused on operational execution of logistics activities with a short term horizon. Supply management design and control are often retained by the shippers. Despite the time and cost intensive character of a logistics service buying process, shippers tendering on a very regular basis. The decision to start a new tender project should more often be based on an integral approach that includes all tender related costs. A lower frequency of tendering could create more stability in supply chains. Beside, it will give both, shippers and LSPs, the possibility to improve the quality of the remaining projects. Price is still a dominating decision criterion in selecting a LSP. This is not an issue as long as the comparison of costs is based on an integral approach, and when shippers balance the cost criterion within their total set of criteria for sourcing logistics services. At the shippers' side there is an increased awareness of the need of more solid collaboration with logistics service providers. Nevertheless, in many cases this increased awareness does not actually result in the required actions to establish more intensive collaboration. Over the last years the logistics service providers industry was characterized by low profit margins, strong fragmentation and price competition. Nowadays, the market for LSPs is changing, because of an increasing demand for logistics services. To benefit from this situation a more pro-active role of the service providers is required in building stronger relationships with their customers. They should pay more attention on mid and long term possibilities in a collaborative relation, in stead of only be focused on running the daily operation.
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Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduc- tion of spectral bands. Therefore, we present a methodology for per-patch classification combined with hyper-spectral band selection. In controlled experiments performed on a set of individual leaves, we measure the performance of five classifiers and three dimensionality-reduction methods with three patch sizes. With the best-performing classifier an error rate of 1.5% is achieved for distinguishing two important potato-plant diseases.
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