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La riduzione dei consumi di combustibili fossili e lo sviluppo di tecnologie per il risparmio energetico sono una questione di centrale importanza sia per l’industria che per la ricerca, a causa dei drastici effetti che le emissioni di inquinanti antropogenici stanno avendo sull’ambiente. Mentre un crescente numero di normative e regolamenti vengono emessi per far fronte a questi problemi, la necessità di sviluppare tecnologie a basse emissioni sta guidando la ricerca in numerosi settori industriali. Nonostante la realizzazione di fonti energetiche rinnovabili sia vista come la soluzione più promettente nel lungo periodo, un’efficace e completa integrazione di tali tecnologie risulta ad oggi impraticabile, a causa sia di vincoli tecnici che della vastità della quota di energia prodotta, attualmente soddisfatta da fonti fossili, che le tecnologie alternative dovrebbero andare a coprire. L’ottimizzazione della produzione e della gestione energetica d’altra parte, associata allo sviluppo di tecnologie per la riduzione dei consumi energetici, rappresenta una soluzione adeguata al problema, che può al contempo essere integrata all’interno di orizzonti temporali più brevi. L’obiettivo della presente tesi è quello di investigare, sviluppare ed applicare un insieme di strumenti numerici per ottimizzare la progettazione e la gestione di processi energetici che possa essere usato per ottenere una riduzione dei consumi di combustibile ed un’ottimizzazione dell’efficienza energetica. La metodologia sviluppata si appoggia su un approccio basato sulla modellazione numerica dei sistemi, che sfrutta le capacità predittive, derivanti da una rappresentazione matematica dei processi, per sviluppare delle strategie di ottimizzazione degli stessi, a fronte di condizioni di impiego realistiche. Nello sviluppo di queste procedure, particolare enfasi viene data alla necessità di derivare delle corrette strategie di gestione, che tengano conto delle dinamiche degli impianti analizzati, per poter ottenere le migliori prestazioni durante l’effettiva fase operativa. Durante lo sviluppo della tesi il problema dell’ottimizzazione energetica è stato affrontato in riferimento a tre diverse applicazioni tecnologiche. Nella prima di queste è stato considerato un impianto multi-fonte per la soddisfazione della domanda energetica di un edificio ad uso commerciale. Poiché tale sistema utilizza una serie di molteplici tecnologie per la produzione dell’energia termica ed elettrica richiesta dalle utenze, è necessario identificare la corretta strategia di ripartizione dei carichi, in grado di garantire la massima efficienza energetica dell’impianto. Basandosi su un modello semplificato dell’impianto, il problema è stato risolto applicando un algoritmo di Programmazione Dinamica deterministico, e i risultati ottenuti sono stati comparati con quelli derivanti dall’adozione di una più semplice strategia a regole, provando in tal modo i vantaggi connessi all’adozione di una strategia di controllo ottimale. Nella seconda applicazione è stata investigata la progettazione di una soluzione ibrida per il recupero energetico da uno scavatore idraulico. Poiché diversi layout tecnologici per implementare questa soluzione possono essere concepiti e l’introduzione di componenti aggiuntivi necessita di un corretto dimensionamento, è necessario lo sviluppo di una metodologia che permetta di valutare le massime prestazioni ottenibili da ognuna di tali soluzioni alternative. Il confronto fra i diversi layout è stato perciò condotto sulla base delle prestazioni energetiche del macchinario durante un ciclo di scavo standardizzato, stimate grazie all’ausilio di un dettagliato modello dell’impianto. Poiché l’aggiunta di dispositivi per il recupero energetico introduce gradi di libertà addizionali nel sistema, è stato inoltre necessario determinare la strategia di controllo ottimale dei medesimi, al fine di poter valutare le massime prestazioni ottenibili da ciascun layout. Tale problema è stato di nuovo risolto grazie all’ausilio di un algoritmo di Programmazione Dinamica, che sfrutta un modello semplificato del sistema, ideato per lo scopo. Una volta che le prestazioni ottimali per ogni soluzione progettuale sono state determinate, è stato possibile effettuare un equo confronto fra le diverse alternative. Nella terza ed ultima applicazione è stato analizzato un impianto a ciclo Rankine organico (ORC) per il recupero di cascami termici dai gas di scarico di autovetture. Nonostante gli impianti ORC siano potenzialmente in grado di produrre rilevanti incrementi nel risparmio di combustibile di un veicolo, è necessario per il loro corretto funzionamento lo sviluppo di complesse strategie di controllo, che siano in grado di far fronte alla variabilità della fonte di calore per il processo; inoltre, contemporaneamente alla massimizzazione dei risparmi di combustibile, il sistema deve essere mantenuto in condizioni di funzionamento sicure. Per far fronte al problema, un robusto ed efficace modello dell’impianto è stato realizzato, basandosi sulla Moving Boundary Methodology, per la simulazione delle dinamiche di cambio di fase del fluido organico e la stima delle prestazioni dell’impianto. Tale modello è stato in seguito utilizzato per progettare un controllore predittivo (MPC) in grado di stimare i parametri di controllo ottimali per la gestione del sistema durante il funzionamento transitorio. Per la soluzione del corrispondente problema di ottimizzazione dinamica non lineare, un algoritmo basato sulla Particle Swarm Optimization è stato sviluppato. I risultati ottenuti con l’adozione di tale controllore sono stati confrontati con quelli ottenibili da un classico controllore proporzionale integrale (PI), mostrando nuovamente i vantaggi, da un punto di vista energetico, derivanti dall’adozione di una strategia di controllo ottima. Fossil fuels consumption reduction and the development of energy saving technologies are becoming a central topic for both the industry and the academic word, due to the drastic effects of anthropogenic emissions on the environment. As an increasing number of measures and regulations are being issued to cope with such issue, the development of low emission technologies is guiding the research in many different industrial sectors. While the realization of clean carbon free energy sources is regarded as one of the most promising solution in the long term, it is widely accepted that for a short-medium horizon an efficient and effective integration of such technologies is highly infeasible, due to technical limitations and the magnitude of the energy share, actually provided by traditional fossil sources, that these alternative technologies should cover. The optimization of the energy production and management processes, associated to the development of energy consumption reduction technologies, is on the other hand regarded as an adequate solution that can be more easily exploited to promptly cope with the problem. The objective of this thesis is to investigate, develop, and apply a set of numerical tools for the optimal design and management of energy processes, which can be exploited to achieve a minimization of the fuel consumptions and an increase of the energetic efficiencies. The devised methodologies rely on a model based approach, which takes advantage of the predictions ability deriving from a mathematical representation of the examined system, to provide a framework for the estimation and optimization of the system behavior under realistic operating conditions. When optimizing the systems, particular emphasis has been given to the necessity of deriving a proper management strategy, accounting for the dynamic properties of the examined plant, which is necessary to achieve the best performance during the effective operating phase. Throughout the thesis, the problem of energy efficiency optimization is investigated with reference to three different technological applications. In the first one, a multi-source plant for the fulfillment of the energetic demand of a building is considered. As the plant exploits multiple different technologies for the provision of the required electric and thermal power, it is necessary to derive a proper scheduling policy, determining how the loads have to be effectively divided between the different sources, in order to obtain the maximal plant efficiency. Based on a simplified plant model, the problem is efficiently solved by applying the deterministic Dynamic Programming algorithm, and the results are compared to those attained by the adoption of a simpler rule-based policy, proving the advantages deriving from the adoption of an optimal control strategy. In the second application, the design of a hybrid solution for energy recovery from a hydraulic excavator is investigated. As different plant technological layouts may be conceived and the additional components introduced require to be properly sized, a methodology to evaluate the benchmark potentiality of each different solution needs to be derived. The comparison between the different layouts is based on the predicted performance of the machine during a standardized digging duty cycle, which are estimated with the help of a detailed plant model. As the introduction of energy recovery devices introduces additional degrees of freedom to the system, it is necessary to derive the optimal management strategies for such devices in order to derive the maximum attainable performance from each layout solution. This task is again carried out with the help of the deterministic Dynamic Programming algorithm, which exploits a control oriented simplified model of the plant, designed for the sake of the optimization. Once the best achievable performance for each design solution is obtained, it is possible to carry out a fair comparison between the available alternatives. In the third and final application, an Organic Rankine Cycle plant for the Waste Heat Recovery from the exhausts from a light vehicle application is investigated. The ORC plant has the potential to deliver considerable increases in the overall vehicle efficiency but it requires the development of a complex control algorithm, which must be able to comply with the variability of the energy source for the process. Moreover, the maximization of fuel economy must be carried out while keeping the plant in safe operating conditions. A robust and efficient system model, based on the Moving Boundary Methodology, has been developed to simulate the performance of the plant and to account for the effect of the phase changing in the used fluid. This model has been subsequently used to design a Model Predictive Controller which estimates the optimal control inputs for the system, in order to achieve the desired performance. An original optimization algorithm, based on the Particle Swarm Optimization, has been conceived to solve the connected nonlinear dynamic optimization problem. The results obtained from the adoption of the devised controller are compared to those that can be reached with a classic PI based controller, showing again the advantages, from an energetic efficiency point of view, deriving from the adoption of an optimal control strategy. The objective of this thesis is to investigate, develop, and apply a set of numerical tools for the optimal design and management of energy processes, which can be exploited to achieve a minimization of the fuel consumptions and an increase of the energetic efficiencies. The devised methodologies rely on a model based approach, which takes advantage of the predictions ability deriving from a mathematical representation of the examined system, to provide a framework for the estimation and optimization of the system behavior under realistic operating conditions. When optimizing the systems, particular emphasis has been given to the necessity of deriving a proper management strategy, accounting for the dynamic properties of the examined plant, which is necessary to achieve the best performance during the effective operating phase. Throughout the thesis, the problem of energy efficiency optimization is investigated with reference to three different technological applications. In the first one, a multi-source plant for the fulfillment of the energetic demand of a building is considered. As the plant exploits multiple different technologies for the provision of the required electric and thermal power, it is necessary to derive a proper scheduling policy, determining how the loads have to be effectively divided between the different sources, in order to obtain the maximal plant efficiency. Based on a simplified plant model, the problem is efficiently solved by applying the deterministic Dynamic Programming algorithm, and the results are compared to those attained by the adoption of a simpler rule-based policy, proving the advantages deriving from the adoption of an optimal control strategy. In the second application, the design of a hybrid solution for energy recovery from a hydraulic excavator is investigated. As different plant technological layouts may be conceived and the additional components introduced require to be properly sized, a methodology to evaluate the benchmark potentiality of each different solution needs to be derived. The comparison between the different layouts is based on the predicted performance of the machine during a standardized digging duty cycle, which are estimated with the help of a detailed plant model. As the introduction of energy recovery devices introduces additional degrees of freedom to the system, it is necessary to derive the optimal management strategies for such devices in order to derive the maximum attainable performance from each layout solution. This task is again carried out with the help of the deterministic Dynamic Programming algorithm, which exploits a control oriented simplified model of the plant, designed for the sake of the optimization. Once the best achievable performance for each design solution is obtained, it is possible to carry out a fair comparison between the available alternatives. In the third and final application, an Organic Rankine Cycle plant for the Waste Heat Recovery from the exhausts from a light vehicle application is investigated. The ORC plant has the potential to deliver considerable increases in the overall vehicle efficiency but it requires the development of a complex control algorithm, which must be able to comply with the variability of the energy source for the process. Moreover, the maximization of fuel economy must be carried out while keeping the plant in safe operating conditions. A robust and efficient system model, based on the Moving Boundary Methodology, has been developed to simulate the performance of the plant and to account for the effect of the phase changing in the used fluid. This model has been subsequently used to design a Model Predictive Controller which estimates the optimal control inputs for the system, in order to achieve the desired performance. An original optimization algorithm, based on the Particle Swarm Optimization, has been conceived to solve the connected nonlinear dynamic optimization problem. The results obtained from the adoption of the devised controller are compared to those that can be reached with a classic PI based controller, showing again the advantages, from an energetic efficiency point of view, deriving from the adoption of an optimal control strategy. The objective of this thesis is to investigate, develop, and apply a set of numerical tools for the optimal design and management of energy processes, which can be exploited to achieve a minimization of the fuel consumptions and an increase of the energetic efficiencies. The devised methodologies rely on a model based approach, which takes advantage of the predictions ability deriving from a mathematical representation of the examined system, to provide a framework for the estimation and optimization of the system behavior under realistic operating conditions. When optimizing the systems, particular emphasis has been given to the necessity of deriving a proper management strategy, accounting for the dynamic properties of the examined plant, which is necessary to achieve the best performance during the effective operating phase. Throughout the thesis, the problem of energy efficiency optimization is investigated with reference to three different technological applications. In the first one, a multi-source plant for the fulfillment of the energetic demand of a building is considered. As the plant exploits multiple different technologies for the provision of the required electric and thermal power, it is necessary to derive a proper scheduling policy, determining how the loads are to be effectively divided between the different sources, in order to obtain the maximal plant efficiency. Based on a simplified plant model, the problem is efficiently solved by applying the deterministic Dynamic Programming algorithm, and the results are compared to those attained by the adoption of a simpler rule-based policy, proving the advantages deriving from the adoption of an optimal control strategy. In the second application, the design of a hybrid solution for energy recovery from a hydraulic excavator is investigated. As different plant technological layouts may be conceived and the additional components introduced require to be properly sized, a methodology to evaluate the benchmark potentiality of each different solution needs to be derived. The comparison between the different layouts is based on the predicted performance of the machine during a standardized digging duty cycle, which are estimated with the help of a detailed plant model. As the introduction of energy recovery devices introduces additional degrees of freedom to the system, it is necessary to derive the optimal management strategies for such devices in order to derive the maximum attainable performance from each layout solution. This task is again carried out with the help of the deterministic Dynamic Programming algorithm, which exploits a control oriented simplified model of the plant, designed for the sake of the optimization. Once the best achievable performance for each design solution is obtained, it is possible to carry out a fair comparison between the available alternatives. In the third and final application, an Organic Rankine Cycle plant for the Waste Heat Recovery from the exhausts from a light vehicle application is investigated. The ORC plant has the potential to deliver considerable increases in the overall vehicle efficiency but it requires the development of a complex control algorithm, which must be able to comply with the variability of the energy source for the process; moreover, the maximization of fuel economy must be carried out while keeping the plant in safe operating conditions. A robust and efficient system model, based on the Moving Boundary Methodology, has been developed to simulate the performance of the plant and to account for the effect of the phase changing fluid used. This model has been subsequently used to design a Model Predictive Controller which estimates the optimal control inputs to feed to the system, in order to achieve the desired performance. An original optimization algorithm, based on the Particle Swarm Optimization, has been conceived to solve the connected nonlinear dynamic optimization problem. The results obtained from the adoption of the devised controller are compared to those that can be reached with a classic PI based controller, showing again the advantages, from an energetic efficiency point of view, deriving from the adoption of an optimal control strategy. |
