1,721,080 research outputs found
Efficient Management of HVAC Systems
No full textIn HVAC (Heating, Ventilation and Air Conditioning) plants of medium-high cooling capacity, multiple-chiller systems are often employed. In such systems, chillers are independent of each other in order to provide standby capacity, operational exibility, and less disruption maintenance. However, the problem of an eciently managing of multiple-chiller systems is complex in many respects. In particular, the electrical energy consumption in the chiller plant markedly increases if the chillers are managed improperly, therefore signicant energy savings can be achieved by optimizing the chiller operations of HVAC systems.
In this Thesis an unied method for Multi-Chiller Management optimization is presented, that deals simultaneously with the Optimal Chiller Loading and Optimal Chiller Sequencing problems. The main objective is that of reducing both power consumption and operative costs. The approach is based on a cooling load estimation algorithm, and the optimization step is performed by means of a multi-phase genetic algorithm, that provides an ecient and suitable approach to solve this kind of complex multi-objective optimization problem. The performance of the algorithm is evaluated by resorting to a dynamic simulation environment, developed in Matlab and Simulink, where the plant dynamics are accurately described. It is shown that the proposed algorithm gives superior performance with respect to standard approaches, in terms of both energy performance and load prole tracking.Negli impianti HVAC di capacità frigorifera medio-grande vengono spesso impiegati sistemi con più refrigeratori di liquido (chiller) in parallelo. Il problema della gestione eciente di tali sistemi è complesso sotto diversi punti di vista. In particolare, il consumo di energia elettrica dell'impianto aumenta notevolmente allorché i refrigeratori siano gestiti scorrettamente. In questa Tesi viene presentato un metodo unicato per l'ottimizzazione della gestione di chiller in parallelo che risolve simultaneamente i problemi del carico ottimo e della sequenza ottima di accensioni/spegnimenti relativi ai refrigeratori. L'obiettivo principale è quello ridurre il consumo energetico ed abbassare i costi di esercizio. L'approccio si basa su un algoritmo di stima del carico frigorifero richiesto e l'ottimizzazione è realizzata attraverso l'impiego di un algoritmo genetico multi-fase; quest'ultimo fornisce un approccio eciente per risolvere questo genere di problema di ottimo multi-obiettivo. Le prestazioni dell'algoritmo sono valutate ricorrendo ad un ambiente di simulazione dinamico, sviluppato in Matlab e Simulink, dove le dinamiche del sistema sono accuratamente descritte.
Si evince che l'algoritmo proposto fornisce prestazioni superiori, rispetto agli approcci standard, sia in termini di soddisfacimento del carico che di prestazione energetica
Automatic Regulation of Anesthesia via Ultra-Local Model Control
Full text linkAs a part of the BMS2021 Benchmark Challenge, this paper deals with the design and testing of a closed-loop anesthesia delivery regulation system by exploiting the open-source Matlab-based patient simulator. Because of system inherent complexity together with intra-and inter-patient parameters variability and partially unknown disturbances, traditional model-based approaches may suffer. To overcome these limitations, we opt for a data-driven approach using real-time ultra-local models coupled with the corresponding so-called intelligent controllers. In this way, one maintains the hemodynamic variables while regulating the levels of hypnosis, analgesia, and neuromuscular blockade in anesthesia by automatic delivery of drugs. The performance of the proposed approach has been evaluated in silico by considering a representative dataset composed of 24 patients, the presence of disturbances mimicking both surgical stimulations and actions of “anesthesiologist in the loop”, including also noise effects and time-varying system delays
Designing and teaching of an effective engineering continuing education course: Modeling and simulation of HVAC systems
No full textDetection of Glucose Sensor Faults in an Artificial Pancreas via Whiteness Test on Kalman Filter Residuals
Full text linkContinuous Glucose Monitoring (CGM) sensors are key components in an artificial pancreas, an emerging tool for type 1 diabetes treatment. Malfunctioning of this component might reduce the efficacy of glucose control achieved by the system and even pose the safety of the patient at risk. Therefore, accurate and prompt detection of these anomalies is an important problem. This paper investigates a model-based method to detect CGM failures. Based on an individualized linear model of the subject, identified on hystorical data, the method predicts future glucose concentration through a one-step ahead Kalman predictor. The correct functioning of the system is then monitored using two different criteria: the first checks the magnitude of prediction residuals. The second checks the whiteness of the residuals through a correlogram test. The effectiveness of the two criteria is investigated and compared by performing tests on an in-silico dataset obtained by means of UVA/Padova Type 1 Diabetes simulator, accepted by the US Food and Drug Administration as a substitute of animal testing prior to artificial pancreas clinical trials on humans
Thermal Comfort Control in Air-Conditioned Buildings: new data-driven approaches to Neutral Temperature estimation
No full textElastic Shape Analysis for Anomaly Detection in Fabric Images
Full text linkIn this paper, the problem of quality control in the textile industrial field is addressed. Because of the general unavailability of labelled data from real production plants and the imbalanced nature of the problem, this task is faced with novelty detection methods that monitor the behaviour of the system and identify whether shifts from the nominal conditions arise. In particular, we utilize techniques from Elastic Shape Analysis to analyse the shapes created by the yarns intersections of the fabrics and to extract features used to define distance metrics that quantify the shapes variability. The proposed approach is applied to images of four different textiles, where only some defect free images are needed for the training phase. The results of this preliminary study confirm the effectiveness of the proposed approach
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