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	'longtitle_de' => 'Ship2Fair: Solare Wärme für industrielle Prozesse im Hinblick auf das Engagement der Lebensmittel- und Agroindustrie für erneuerbare Energien',
	'longtitle_en' => 'Ship2Fair: Solar Heat for Industrial Process towards Food and Agro Industries commitment in Renewables',
	'content_de' => '<p>SHIP2FAIR (Solar Heat for Industrial Process towards Food and Agro Industries Commitment in Renewables) ist ein HORIZON 2020-Projekt mit dem Ziel, die Integration von Solarw&auml;rme in industrielle Prozesse der Agrar- und -Nahrungsmittelindustrie zu f&ouml;rdern. Zu diesem Zweck wird im Projekt SHIP2FAIR eine Reihe von Tools (Replication- and Control-Tool) und Methoden entwickelt, welche die Entwicklung von industriellen Solarthermieprojekten w&auml;hrend ihres gesamten Lebenszyklus unterst&uuml;tzen und im Projekt demonstriert werden. Konkret soll dabei die Planung, die Regelung als auch die energetische und wirtschaftliche Bewertung von Solarthermieprojekten ma&szlig;geblich verbessert werden.</p>

<p>Die Demonstration und Validierung findet an 4 realen Industriestandorten statt, die f&uuml;r den Agrar- und Lebensmittelsektor repr&auml;sentativ sind: Spirituosendestillation (Martini &amp; Rossi, Italien), Fleischtrocknung (LARNAUDIE, Frankreich), Zuckertrocknung (RAR Group, Portugal) und Weinverg&auml;rung und -stabilisierung (RODA Wineries, Spanien).</p>

<p>Als Ergebnis dieser Demonstration zielt SHIP2FAIR darauf ab, einen Solaranteil von bis zu 40 % mit insgesamt 2,9 MW installierter Kollektorleistung zur Erzeugung von 4,04 GWhth zu erreichen und somit 403 m3 an fossilen Brennstoffen und 1.145 t CO2-&Auml;quivalente pro Jahr einzusparen.</p>

<p>SHIP2FAIR ist ein Projekt, das von 15 Partnern aus ganz Europa und mit Unterst&uuml;tzung der Europ&auml;ischen Kommission entwickelt wurde.</p>

<p><a href="http://ship2fair-h2020.eu/" target="_blank">http://ship2fair-h2020.eu/</a><br />
<a href="https://youtu.be/s-s8PuPR7eI" target="_blank">https://youtu.be/s-s8PuPR7eI</a></p>

<p>Webinar: <a href="https://www.youtube.com/watch?v=AL01tNZiNz4" target="_blank">Solar goes Digital</a></p>

<h4>Pressemitteilungen</h4>

<p><a href="https://www.best-research.eu/webroot/files/pressreleases/Presseaussendung_BEST_Ship2Fair.pdf">https://www.best-research.eu/webroot/files/pressreleases/Presseaussendung_BEST_Ship2Fair.pdf</a></p>

<p><a href="https://www.best-research.eu/webroot/files/pressreleases/Presseaussendung_Projektstart_SHIP2FAIR_final.pdf">https://www.best-research.eu/webroot/files/pressreleases/Presseaussendung_Projektstart_SHIP2FAIR_final.pdf</a></p>
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	'content_en' => '<p>SHIP2FAIR (Solar Heat for Industrial Process towards Food and Agro Industries commitment in Renewables) aims to foster the integration of solar heat in industrial processes of the agro&ndash;food industry. With this purpose, SHIP2FAIR will develop and demonstrate a set of tools (Replication- and Control-Tool) and methods for the development of industrial solar heat projects during their whole life-cycle.</p>

<p>Demonstration and validation will take place at four real industrial sites, representative of the agro-food sector:&nbsp;spirits distillation (Martini &amp; Rossi, Italy), meat-cooking (LARNAUDIE, France), sugar boiling (RAR Group, Portugal) and wine fermentation and stabilization (RODA Wineries, Spain).</p>

<p>SHIP2FAIR is a project developed by 15 partners from all over Europe and with the support of the European Commission (as part of the HORIZON 2020 program). As a result of this demonstration, SHIP2FAIR, aims to achieve up to a 40% of solar fraction with a total of 2.9 MW of installed power for producing 4.04 GWh and allowing 403 m3 of fossil fuels and 1,145 TeqCO2 per year.</p>

<p><a href="http://ship2fair-h2020.eu/" target="_blank">http://ship2fair-h2020.eu/</a><br />
<a href="https://youtu.be/s-s8PuPR7eI" target="_blank">https://youtu.be/s-s8PuPR7eI </a></p>

<p>Webinar: <a href="https://www.youtube.com/watch?v=AL01tNZiNz4" target="_blank">Solar goes Digital</a></p>

<p>&nbsp;</p>
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	'finanzierung' => '<p>The project has received funding from the European Union&acute;s Horizon 2020 research and innovation programme 2014-2018 under grant agreement n&deg; 792276.</p>
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				'abstract' => '<p>The number of large-scale solar thermal installations has increased rapidly in Europe in recent years, with 70 % of these systems operating with flat-plate solar collectors. Since these systems cannot be easily switched on and off but directly depend on the solar radiation, they have to be combined with other technologies or integrated in large energy systems. In order to most efficiently integrate and operate solar systems, it is of great importance to consider their expected energy yield to better schedule heat production, storage and distribution. To do so the availability of accurate forecasting methods for the future solar energy yield are essential. Currently available forecasting methods do not meet three important practical requirements: simple implementation, automatic adaption to seasonal changes and wide applicability. For these reasons, a simple and adaptive forecasting method is presented in this paper, which allows to accurately forecast the solar heat production of flat-plate collector systems considering weather forecasts. The method is based on a modified collector efficiency model where the parameters are continuously redetermined to specifically consider the influence of the time of the day. In order to show the wide applicability the method is extensively tested with measurement data of various flat-plate collector systems covering different applications (below 200 Celsius), sizes and orientations. The results show that the method can forecast the solar yield very accurately with a Mean Absolute Range Normalized Error (MARNE) of about 5 % using real weather forecasts as inputs and outperforms common forecasting methods by being nearly twice as accurate.</p>
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				'citation' => 'Feierl L, Unterberger V, Rossi C, Gerardts B, Gaetani M. Fault detective: Automatic fault-detection for solar thermal systems based on artificial intelligence. Solar Energy Advances 2023;3:100033. https://doi.org/10.1016/j.seja.2023.100033.',
				'abstract' => '<p>Fault-Detection (FD) is essential to ensure the performance of solar thermal systems. However, manually analyzing the system can be time-consuming, error-prone, and requires extensive domain knowledge. On the other hand, existing FD algorithms are often too complicated to set up, limited to specific system layouts, or have only limited fault coverage. Hence, a new FD algorithm called Fault-Detective is presented in this paper, which is purely data-driven and can be applied to a wide range of system layouts with minimal configuration effort. It automatically identifies correlated sensors and models their behavior using Random-Forest-Regression. Faults are then detected by comparing predicted and measured values.</p>

<p>The algorithm is tested using data from three large-scale solar thermal systems to evaluate its applicability and performance. The results are compared to manual fault detection performed by a domain expert. The evaluation shows that Fault-Detective can successfully identify correlated sensors and model their behavior well, resulting in coefficient-of-determination scores between R&sup2;=0.91 and R&sup2;=1.00. In addition, all faults detected by the domain experts were correctly spotted by Fault-Detective. The algorithm even identified some faults that the experts missed. However, the use of Fault-Detective is limited by the low precision score of 30% when monitoring temperature sensors. The reason for this is a high number of false alarms raised due to anomalies (e.g., consecutive days with bad weather) instead of faults. Nevertheless, the algorithm shows promising results for monitoring the thermal power of the systems, with an average precision score of 91%.</p>
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				'titel' => 'FAULT DETECTIVE: FAULT DETECTION FOR SOLAR THERMAL SYSTEMS ',
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				'autor' => 'Feierl L, Bolognesi T, Unterberger V, Geatani M, Gerardts B. ',
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				'titel' => 'Solar goes Digital: Wie Solarwärme selbstlernende Algorithmen nutzt (Austria Solar Webinar 26)',
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				'titel' => 'MIMO state feedback control for redundantly-actuated LiBr/H O absorption heat pumping devices and experimental validation',
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				'autor' => 'Staudt S, Unterberger V, Muschick D, Gölles M, Horn M, Wernhart M, Rieberer R',
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				'abstract' => '<p>Absorption heat pumping devices (AHPDs, comprising absorption heat pumps and chillers) use mainly <a class="topic-link" href="https://www.sciencedirect.com/topics/engineering/thermal-energy" title="Learn more about thermal energy from ScienceDirect's AI-generated Topic Pages">thermal energy</a> instead of electricity as the driving energy to provide resource-efficient heating and cooling when using waste heat or renewable heat sources. Despite this benefit, AHPDs are still not a very common technology due to their complexity. However, better modulation and part-load capability, which can be achieved through advanced control strategies, can simplify the use of AHPDs and help to better integrate them into complex <a class="topic-link" href="https://www.sciencedirect.com/topics/engineering/energy-systems" title="Learn more about energy systems from ScienceDirect's AI-generated Topic Pages">energy systems</a>. Therefore, this paper presents a new, dynamic model-based control approach for single-stage AHPDs that can extend an AHPD&rsquo;s operating range by employing multi-input-multi-output (MIMO) control methods. The control approach can be used for different AHPD applications and thus <a class="topic-link" href="https://www.sciencedirect.com/topics/engineering/configuration-control" title="Learn more about control configurations from ScienceDirect's AI-generated Topic Pages">control configurations</a>, i.e., different combinations of manipulated and controlled variables, and can also be used for redundantly-actuated configurations with more manipulated than controlled variables. It consists of an observer for the state variables and unknown disturbances, a <a class="topic-link" href="https://www.sciencedirect.com/topics/engineering/state-feedback-controller" title="Learn more about state feedback controller from ScienceDirect's AI-generated Topic Pages">state feedback controller</a> and, in case of redundantly-actuated configurations, a dynamic control allocation algorithm. The proposed control approach is experimentally validated with a representative AHPD for two different control configurations and compared to two benchmark control approaches &ndash; single-input-single-output (SISO) PI control representing the state-of-the-art, and model-predictive control (MPC) as an alternative advanced control concept. The experimental validation shows that the two MIMO control approaches (the proposed state feedback and the MPC approach) allow for a wider operating range and hence better part load capability compared to the SISO PI control approach. While MPC generally results in a comparably high computational effort due to the necessity of continuously solving an optimization problem, the proposed state <a class="topic-link" href="https://www.sciencedirect.com/topics/engineering/feedback-control-systems" title="Learn more about feedback control from ScienceDirect's AI-generated Topic Pages">feedback control</a> approach is mathematically simple enough to be implemented on a conventional programmable logic controller. It is therefore considered a promising new control approach for AHPDs with the ability to extend their operating range and improve their part load capability, which in turn facilitates their implementation and thus the use of sustainable heat sources in heating and cooling systems.</p>
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