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Exergy-Based Investigation and Optimization of a Heat-Driven Polygeneration System Using Carbon Dioxide as Working Fluid = = Exergiebasierte Untersuchung und Optimierung Eines Abwarmebetriebenen Polygenerationssystems Mit Kohlendioxid als Arbeitsflussigkeit.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Exergy-Based Investigation and Optimization of a Heat-Driven Polygeneration System Using Carbon Dioxide as Working Fluid =/
其他題名:	Exergiebasierte Untersuchung und Optimierung Eines Abwarmebetriebenen Polygenerationssystems Mit Kohlendioxid als Arbeitsflussigkeit.
作者:	Luo, Jing.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	183 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Software. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28642032
ISBN:	9798522951689

Exergy-Based Investigation and Optimization of a Heat-Driven Polygeneration System Using Carbon Dioxide as Working Fluid = = Exergiebasierte Untersuchung und Optimierung Eines Abwarmebetriebenen Polygenerationssystems Mit Kohlendioxid als Arbeitsflussigkeit.
Luo, Jing.

Exergy-Based Investigation and Optimization of a Heat-Driven Polygeneration System Using Carbon Dioxide as Working Fluid =Exergiebasierte Untersuchung und Optimierung Eines Abwarmebetriebenen Polygenerationssystems Mit Kohlendioxid als Arbeitsflussigkeit. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 183 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--Technische Universitaet Berlin (Germany), 2021.

This item must not be sold to any third party vendors.

To achieve global sustainable development goals, efficiency improvement of sustainable energy systems is the most cost-effective and near-term option. Polygeneration systems improve system efficiency significantly and minimize the discharge of system pollutants. In this work, a novel small-scale waste heat-driven polygeneration system using CO2 as its working fluid was proposed for local power and thermal energy supply. Such a stand-alone system can easily be integrated with other facilities and harness different available heat sources. The novel polygeneration system's conceptual design, including system simulation, investigation, and optimization with computer-aid software, was addressed. Exergy-based methods, including conventional and advanced exergetic and exergoeconomic analyses, were implemented to examine the system performance and identify the magnitude, location, and cause of component-wise and system-wise real inefficiencies. Moreover, Aspen HYSYS® was connected to a Python program to achieve calculation and optimization automation. The automation allows the "communication" between these two programs; thus, importing and exporting values manually can be avoided. To solve multimodal optimization problems, including single-objective, multi-objective, and superstructure-based optimization, population-based stochastic algorithms (differential evolution and particle swarm optimization) were implemented to find the global optimal results by overcoming local minima. The system investigation results showed that temperatures and costs of the available heat sources affect system product costs notably, especially the heating cost. The pressure designed for merging two subsystems is a crucial decision parameter. Lower product costs can be obtained in cases with a lower ambient temperature. In the case study of recovering the exhaust gas from a 2 MW natural gas engine, the overall system efficiency, including the natural gas engine and the polygeneration system, is around 45% regardless of the case scenarios. However, by focusing only on the polygeneration system, the system exergetic efficiencies are 31% and 47% in the Case hot climate and the Case cold climate, respectively; while the average system product cost in the Case cold climate is 41% lower than that in the Case hot climate. The similarities observed in both cases are: the electric power has the lowest price, while the refrigeration capacity is more expensive than the electricity by a factor of ranging from 3-4; for the heating price, the factor decreases to approximately between 1.5-2. For system optimization, the differential evolution and particle swarm optimization algorithms generally obtained consistent results. For better implementing these stochastic algorithms for thermal system design, many efforts were needed for designing reliable simulations in Aspen HYSYS® and tuning the optimization algorithms. A robust superstructure-based simulation ensures the smooth execution of the system structural optimization. Amongst 2304 alternative configurations, the new configuration of adding the recuperation and preheating design features to the initial system design was selected for the Case hot climate; in contrast, the initially proposed system structure is the best system configuration for the Case cold climate.

ISBN: 9798522951689Subjects--Topical Terms:

619355
Software.

Exergy-Based Investigation and Optimization of a Heat-Driven Polygeneration System Using Carbon Dioxide as Working Fluid = = Exergiebasierte Untersuchung und Optimierung Eines Abwarmebetriebenen Polygenerationssystems Mit Kohlendioxid als Arbeitsflussigkeit.
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245 1 0 $a Exergy-Based Investigation and Optimization of a Heat-Driven Polygeneration System Using Carbon Dioxide as Working Fluid = $b Exergiebasierte Untersuchung und Optimierung Eines Abwarmebetriebenen Polygenerationssystems Mit Kohlendioxid als Arbeitsflussigkeit.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 183 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Morozyuk, Tetyana;Tsatsaronis, George;Tashtoush, Bourhan Mohammad;Stanek, Wojciech.
502 $a Thesis (Ph.D.)--Technische Universitaet Berlin (Germany), 2021.
506 $a This item must not be sold to any third party vendors.
520 $a To achieve global sustainable development goals, efficiency improvement of sustainable energy systems is the most cost-effective and near-term option. Polygeneration systems improve system efficiency significantly and minimize the discharge of system pollutants. In this work, a novel small-scale waste heat-driven polygeneration system using CO2 as its working fluid was proposed for local power and thermal energy supply. Such a stand-alone system can easily be integrated with other facilities and harness different available heat sources. The novel polygeneration system's conceptual design, including system simulation, investigation, and optimization with computer-aid software, was addressed. Exergy-based methods, including conventional and advanced exergetic and exergoeconomic analyses, were implemented to examine the system performance and identify the magnitude, location, and cause of component-wise and system-wise real inefficiencies. Moreover, Aspen HYSYS® was connected to a Python program to achieve calculation and optimization automation. The automation allows the "communication" between these two programs; thus, importing and exporting values manually can be avoided. To solve multimodal optimization problems, including single-objective, multi-objective, and superstructure-based optimization, population-based stochastic algorithms (differential evolution and particle swarm optimization) were implemented to find the global optimal results by overcoming local minima. The system investigation results showed that temperatures and costs of the available heat sources affect system product costs notably, especially the heating cost. The pressure designed for merging two subsystems is a crucial decision parameter. Lower product costs can be obtained in cases with a lower ambient temperature. In the case study of recovering the exhaust gas from a 2 MW natural gas engine, the overall system efficiency, including the natural gas engine and the polygeneration system, is around 45% regardless of the case scenarios. However, by focusing only on the polygeneration system, the system exergetic efficiencies are 31% and 47% in the Case hot climate and the Case cold climate, respectively; while the average system product cost in the Case cold climate is 41% lower than that in the Case hot climate. The similarities observed in both cases are: the electric power has the lowest price, while the refrigeration capacity is more expensive than the electricity by a factor of ranging from 3-4; for the heating price, the factor decreases to approximately between 1.5-2. For system optimization, the differential evolution and particle swarm optimization algorithms generally obtained consistent results. For better implementing these stochastic algorithms for thermal system design, many efforts were needed for designing reliable simulations in Aspen HYSYS® and tuning the optimization algorithms. A robust superstructure-based simulation ensures the smooth execution of the system structural optimization. Amongst 2304 alternative configurations, the new configuration of adding the recuperation and preheating design features to the initial system design was selected for the Case hot climate; in contrast, the initially proposed system structure is the best system configuration for the Case cold climate.
520 $a Um die globale Ziele der nachhaltigen Entwicklung zu erreichen, ist die Effizienzsteigerung nachhaltiger Energiesysteme die kostengunstigste und kurzfristigste Option. Polygenerationssysteme verbessern die Systemeffizienz erheblich und minimieren den Ausstos von Systemschadstoffen. In dieser Arbeit wurde ein neuartiges durch Abwarme betriebenes kleinausgelegtes Polygenerationssystem mit CO2 als Arbeitsflussigkeit vorgeschlagen, das fur die lokale Strom- und Warmeenergieversorgung verwendet wird. Ein solches eigenstandiges System kann leicht mit anderen Anlagen integriert werden und die verschiedene verfugbare Warmequellen nutzen. Der konzeptionelle Entwurf des neuartigen Polygenerationssystems, einschlieslich der Systemsimulation, Systemuntersuchung und Systemoptimierung mit rechnergestutzter Software, wurde konzentriert. Exergie-basierte Methoden, einschlieslich konventioneller und fortgeschrittener exergetischer und exergookonomischer Analysen, wurden eingesetzt, um die System zu bewerten und das Ausmas, den Ort und die Ursache komponenten- und systemweise realer Ineffizienzen zu identifizieren. Daruber hinaus wurde Aspen HYSYS® mit einem Python-Programm verbunden, um eine Automatisierung der Berechnung und Optimierung zu erreichen. Die Automatisierung ermoglicht die "Kommunikation" zwischen diesen beiden Programmen; so kann der manuelle Import und Export von Werten vermieden werden. Zur Losung multimodaler Optimierungsprobleme, einschlieslich der Einzelziel-, Mehrziel- und Superstruktur-basierten Optimierung, wurden populationsbasierte stochastische Algorithmen (differential evolution and particle swarm optimization) implementiert, um durch Uberwindung lokaler Minima die global optimalen Ergebnisse zu finden. Die Ergebnisse der Systemuntersuchung zeigten, dass die Temperaturen und Kosten der verfugbaren Warmequellen die Produktkosten des Systems, insbesondere die Heizkosten, deutlich beeinflussen. Der Druck, der fur die Verschmelzung zweier Subsysteme ausgelegt ist, ist ein kritischer Entscheidungsparameter. Niedrigere Produktkosten konnen in Fallen mit einer niedrigeren Umgebungstemperatur erzielt werden. In der Beispielstudie zur Ruckgewinnung des Abgases eines 2 MW Erdgasmotors liegt der Gesamtsystemwirkungsgrad, einschlieslich des Erdgasmotors und des Polygenerationssystems, unabhangig von den Beispielszenarien bei etwa 45%. Wenn man sich jedoch nur auf das Polygenerationssystem konzentriert, betragen die exergetischen Systemwirkungsgrade 31% bzw. 47% in der Fallstudie hot climate und in der Fallstudie cold climate; wahrend die durchschnittlichen Systemproduktkosten in der Fallstudie old climate 41% niedriger sind als in der Fallstudie hot climate. Die in beiden Fallen kann beobachtet werden, dass der Strom den niedrigsten Preis hat, wahrend die Kalteleistung um einen Faktor von drei- bis vierfach teurer ist als der Strom; fur den Warmepreis sinkt der Faktor auf etwa 1,5 bis zwei. Bei der Systemoptimierung erzielten die differential evolution and particle swarm optimization Algorithmen im Allgemeinen konsistente Ergebnisse. Fur eine bessere Implementierung dieser stochastischen Algorithmen fur das thermische Systemdesign waren viele Anstrengungen erforderlich, um die zuverlassige Simulationen in Aspen HYSYS® zu entwerfen und die Optimierungsalgorithmen abzustimmen. Eine robuste Superstruktur-basierte Simulation gewahrleistet die reibungslose Ausfuhrung der strukturellen Systemoptimierung. Unter 2304 alternativen Konfigurationen wurde fur die Fallstudie hot climate die neue Konfiguration ausgewahlt, bei der die Rekuperations- und Vorwarm-Designmerkmale zum ursprunglichen Systementwurf hinzugefugt wurden; im Gegensatz dazu ist die ursprunglich vorgeschlagene Systemstruktur die beste Systemkonfiguration fur die Fallstudie cold climate.
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