https://hdl.handle.net/2072/453038 2026-04-04T05:27:23Z https://hdl.handle.net/10256/19954 Multi-vector energy optimization tools for energy islands Puranik, Sanket; Tuiskula, Heidi; Ilieva, Iliana; Torrent-Fontbona, Ferran; Colomer Llinàs, Joan; Meléndez i Frigola, Joaquim Synergies between multiple energy vectors can support decarbonization of local energy islands and, at the same time, relieve stress from the electricity grid. Multi-vector energy systems offer flexibility to integrate variable and economic local energy generation. However, the implementation and operation of multi-vector energy systems face technical, societal and business-related challenges. This paper identifies these challenges and proposes different tools to tackle them. These tools are to be developed in the E-LAND project funded by European Union under the H2020 scheme. Special focus of the paper is on developing a method for optimal planning and operation of multi-vector grid considering robustness. Another novelty of technical tool lies in making scheduling decisions on long-term and short-term storage considering demand response as flexibility resource. By combining technological, societal and business tools, the project expects to create multi-vector energy systems accepted by citizens and with viable business model; E-LAND project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824388 2019-06-23T00:00:00Z https://hdl.handle.net/10256/18584 A Novel Meal Detection Algorithm for an Artificial Pancreas [Pòster] Ramkissoon, Charrise Mary; Herrero i Viñas, Pau; Bondia, Jorge; Vehí, Josep Pòster de congrés presentat a: 10th International Conference on Advanced Technologies and Treatments for Diabetes (ATTD 2017), celebrat a París del 15 al 18 de febrer de 2017; Postprandial glucose fluctuations are a challenge to daytime closed-loop control1,2 in type 1 diabetes (T1D). It is predicted that the high number of missed meal boluses experienced during insulin pump therapy3 will carry over to artificial pancreas therapy. Therefore, a means to reduce poor outcomes due to unannounced meals must be developed. The aim of this study is to implement an algorithm to detect meals using data from a continuous glucose monitor (CGM) and the insulin delivered to the subject; DPI2013-46982-1-R/2-R, R, Feder Funds 2017-02-01T00:00:00Z https://hdl.handle.net/10256/18583 Automatic Detection of Exercise in People with Type 1 Diabetes Using an Unscented Kalman Filter [Pòster] Ramkissoon, Charrise Mary; Herrero i Viñas, Pau; Bondia, Jorge; Georgiou, Pantelis; Oliver, Nick; Vehí, Josep Pòster de congrés presentat a: 9th International Conference on Advanced Technologies & Treatments for Diabetes (ATTD 2016 ), celebrat a Milà (Itàlia), del 3 a 6 de febrer de 2016; Physical activity in type 1 diabetes mellitus has been found to have varying degrees of effect on glycaemic control depending on the type, intensity and duration of the exercise. Such effect is associated with an imbalance between hepatic glucose production and glucose disposal into the muscle, increased insulin sensitivity and impaired counter-regulatory hormonal response. In the context of an artificial pancreas, automatic detection of exercise has the potential to significantly improve glycaemic control. This work aims to develop a new methodology for automatically detecting exercise that only requires data from a continuous glucose monitor (CGM) and the insulin delivered to the subject. Method: The glucose-insulin Minimal Model was extended, by adding an insulin absorption model and an auxiliary parameter used to describe disturbances. Increases in this parameter may indicate meal ingestions whereas decreases may indicate exercise. The disturbance parameter was estimated using an Unscented Kaman Filter. Two thresholds were introduced to detect exercise: a first threshold to indicate the possibility of an abnormal event; and a second threshold, based on an area-under-the-curve, to indicate exercise. The method was tested on data from 7 closed-loop trials including a period of moderate intensity structured exercise. Results: Overall, the results obtained were satisfactory with an average detection time of 22 minutes, accuracy of 96%, sensitivity of 100% and a specificity of 96%. Conclusion: The presented technique has the potential to be a viable approach to detect physical exercise in the context of an artificial pancreas. Improvements and further testing are necessary to confirm such hypothesis (Diabetes Technology & Therapeutics, 2016, vol. 18, núm. S1, https://doi.org/10.1089/dia.2016.2525); This project has been funded by the Spanish Government through grant DPI2013-46982-C2-R and the Wellcome Trust 2016-02-01T00:00:00Z https://hdl.handle.net/10256/18582 A Taxonomy of Safety Issues to be Overcome in the Artificial Pancreas [Pòster] Ramkissoon, Charrise Mary; Vehí, Josep; Aufderheide, Brian; Bequette, B. Wayne; Palerm, César C. Pòster de congrés presentat a: 8th International Conference on Advanced Technologies and Treatments for Diabetes (ATTD 2015), celebrat a París del 18 al 21 de febrer de 2015; Currently, research on the artificial pancreas (AP) has increased exponentially. This is due to a number of factors including the significant advancement of both the hardware and algorithms associated with the AP. Several AP prototypes have been validated in controlled inpatient studies and a number of trials in home setting are currently being conducted. The domiciliary use of the AP requires, besides an efficient controller, the ability to face the daily life conditions with the safety mechanisms that will allow it to be used without additional risk for the patient. Objective: This study aims to provide the basic infrastructure required by the AP regarding safety. This involves the classification of the various safety issues encountered by AP and a hierarchical system for resolving these issues. Methodology: All safety issues regarding the AP were assessed inclusive of anomalies that may occur due to changing physiological conditions within the patient, as well as, hardware malfunctions, maintenance, and inherent error. Issues were then categorized based on time span, the ability to be detected with available sensors and, more challenging situations that require additional information to be detected. Conclusion: The taxonomy provided will allow subsequent investigations to be done in a structured format. Dealing with the most impending safety issues and then following with less risky situations. It should be noted that all these safety mechanisms should be functional in an AP suitable for unsupervised use 2015-02-01T00:00:00Z