Heat Transfer Engineering in Waste to Energy Systems

2007 ◽  
Vol 28 (5) ◽  
pp. 381-382
Author(s):  
Petr Stehlik
2018 ◽  
Vol 6 (6) ◽  
pp. 7960-7968 ◽  
Author(s):  
Małgorzata Musiał ◽  
Michał Kuczak ◽  
Anna Mrozek-Wilczkiewicz ◽  
Robert Musiol ◽  
Edward Zorębski ◽  
...  

2020 ◽  
Vol 20 (5) ◽  
pp. 04020041 ◽  
Author(s):  
Klementyna A. Gawecka ◽  
David M. G. Taborda ◽  
David M. Potts ◽  
Eleonora Sailer ◽  
Wenjie Cui ◽  
...  

2003 ◽  
Vol 125 (3) ◽  
pp. 331-342 ◽  
Author(s):  
Moncef Krarti

An overview of commonly used methodologies based on the artificial intelligence approach is provided with a special emphasis on neural networks, fuzzy logic, and genetic algorithms. A description of selected applications to building energy systems of AI approaches is outlined. In particular, methods using the artificial intelligence approach for the following applications are discussed: Prediction energy use for one building or a set of buildings (served by one utility), Modeling of building envelope heat transfer, Controlling central plants in buildings, and Fault detection and diagnostics for building energy systems.


Author(s):  
Matteo Morandin ◽  
Andrea Toffolo ◽  
Andrea Lazzaretto

The search for increasing performance and efficiency in energy system analysis leads to complex and highly integrated systems configurations. In a wide variety of energy systems the high integration among components derives from the need of correctly exploiting all the internal heat sources by a proper matching with the internal heat sinks. To address this problem in a general way, in previous works it was suggested to extract from the system flowsheet a “basic configuration” including the components different from the heat exchangers (named “basic” components) and a set of hot and cold thermal flows (without considering the heat exchangers that realize the heat transfer among them). It was also shown how the comprehension of the processes occurring within the system can be strongly facilitated by analyzing separately the elementary thermodynamic cycles involved in the system processes. In this paper, a further step is done by considering the overall efficiency as a baseline efficiency, obtained from the contributions of the separate elementary cycles, with the additional contribution given by the thermal coupling (i.e. the internal heat transfer) among the cycles themselves. The advantages of this analysis are shown using the evolution of the STIG cycle towards more complex system configurations as an example of application.


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