Thermoeconomic Diagnosis: Zooming Strategy Applied to Highly Complex Energy Systems. Part 2: On the Choice of the Productive Structure*

2005 ◽  
Vol 127 (1) ◽  
pp. 50-58 ◽  
Author(s):  
Vittorio Verda ◽  
Luis Serra ◽  
Antonio Valero
Keyword(s):  
Energy Systems ◽  
Complex Energy ◽  
Number Of Components ◽  
Productive Structure ◽  
Control Volumes ◽  
Detection And Localization ◽  
Specific Part ◽  
Different Levels

The thermoeconomic diagnosis strategy introduced in the accompanying paper [Verda, V., Serra, L., Valero, A. 2004. Thermoeconomic Diagnosis: Zooming Strategy Applied to Highly Complex Energy Systems. Part 1: Detection and Localization of Anomalies. Part 1: The diagnosis procedure. ASME J. Energy Resour. Technol. 127(1), pp. 42–49. This issue.] is a zooming technique consisting of a successive localization of anomalies. At each step the required productive structure to be adopted becomes even more detailed, focusing the analysis on a more specific part of the system. The detail of a productive structure has two different levels: the number of components and the number of productive flows. The first one is selected according to the precision desired in locating the anomalies. A larger number of components (or subsystems) allows one to locate the anomalies in smaller control volumes, providing more precise indications for maintenance. The number of flows is partially dependent on the number of components. Once the number of components is fixed, the productive flows can be increased by separating exergy into its components or introducing fictitious flows, such as negentropy [see, for example, C. A. Frangopoulos, Energy, The International Journal 12(7), pp. 563–571 (1987)]. This decision also affects the results of the thermoeconomic analysis when it is adopted for diagnosis purposes. In this paper, the effects of the productive structure on the diagnosis results are carefully analyzed. Depending on the selected productive structure, the accuracy of the diagnosis results can be significantly improved.

Thermoeconomic Diagnosis: Zooming Strategy Applied to Highly Complex Energy Systems — Part 2: On the Choice of the Productive Structure

2002 ◽  
Author(s):  
Vittorio Verda ◽  
Luis Serra ◽  
Antonio Valero
Keyword(s):  
Energy Systems ◽  
Complex Energy ◽  
Number Of Components ◽  
Productive Structure ◽  
Control Volumes ◽  
Specific Part ◽  
Different Levels

The thermoeconomic diagnosis strategy introduced in the accompanying paper (part 1 [1]) is a zooming technique consisting of a successive localization of anomalies. At each step the required productive structure to be adopted becomes even more detailed, focusing the analysis on a more specific part of the system. The detail of a productive structure has two different levels: the number of components and the number of productive flows. The first one is selected according to the precision desired in locating the anomalies. A larger number of components (or subsystems) allows one to locate the anomalies in smaller control volumes, providing more precise indications for maintenance. The number of flows is partially dependent on the number of components. Once the number of components is fixed, the productive flows can be increased by separating exergy into its components or introducing fictitious flows, such as negentropy (see for example [2]). This decision also affects the results of the thermoeconomic analysis when it is adopted for diagnosis purposes. In this paper, the effects of the productive structure on the diagnosis results are carefully analyzed. Depending on the selected productive structure, the accuracy of the diagnosis results can be significantly improved.

Thermoeconomic Diagnosis: Zooming Strategy Applied to Highly Complex Energy Systems: Part 1 — Detection and Localization of Anomalies

2002 ◽  
Author(s):  
Vittorio Verda ◽  
Luis Serra ◽  
Antonio Valero
Keyword(s):  
Inverse Problem ◽  
Complex Systems ◽  
Reference Conditions ◽  
Energy Systems ◽  
Combined Cycle ◽  
Operating Conditions ◽  
The Other ◽  
Complex Energy ◽  
Operational Diagnosis ◽  
Detection And Localization

This paper presents a summary of our most recent advances in Thermoeconomic Diagnosis, developed during the last three years [1–3], and how they can be integrated in a zooming strategy oriented towards the operational diagnosis of complex systems. In fact, this paper can be considered a continuation of the work presented at the International Conference ECOS’99 [4–6] in which the concepts of malfunction (intrinsic and induced) and dysfunction [7] were analyzed in detail. These concepts greatly facilitate and simplify the analysis, the understanding and the quantification of how the presence of an anomaly, or malfunction, affects the behavior of the other plant devices and of the whole system. However, what remains unresolved is the so-called inverse problem of diagnosing [3], i.e. given two states of the plant (actual and reference operating conditions), find the causes of deviation of the actual conditions with respect to the reference conditions. The present paper tackles this problem and describes significant advances in addressing how to locate the actual causes of malfunctions, based on the application of procedures for filtering induced effects that hide the real causes of degradation. In this paper a progressive zooming thermoeconomic diagnosis procedure, which allows one to concentrate the analysis in an ever more specific zone is described and applied to a combined cycle. In an accompanying paper (part 2 [8]) the accuracy of the diagnosis results is discussed, depending on choice of the thermoeconomic model.

Thermoeconomic Diagnosis: Zooming Strategy Applied to Highly Complex Energy Systems. Part 1: Detection and Localization of Anomalies*

2005 ◽  
Vol 127 (1) ◽  
pp. 42-49 ◽  
Author(s):  
Vittorio Verda ◽  
Luis Serra ◽  
Antonio Valero
Keyword(s):  
Inverse Problem ◽  
Complex Systems ◽  
Reference Conditions ◽  
Energy Systems ◽  
Combined Cycle ◽  
Operating Conditions ◽  
The Other ◽  
Complex Energy ◽  
Operational Diagnosis ◽  
Detection And Localization

This paper presents a summary of our most recent advances in Thermoeconomic Diagnosis, developed during the last three years, and how they can be integrated in a zooming strategy oriented toward the operational diagnosis of complex systems. In fact, this paper can be considered a continuation of the work presented at the International Conference ECOS’99 in which the concepts of malfunction (intrinsic and induced) and dysfunction were analyzed in detail. These concepts greatly facilitate and simplify the analysis, the understanding, and the quantification of how the presence of an anomaly, or malfunction, affects the behavior of the other plant devices and of the whole system. However, what remains unresolved is the so-called inverse problem of diagnosing, i.e., given two states of the plant (actual and reference operating conditions), find the causes of deviation of the actual conditions with respect to the reference conditions. The present paper tackles this problem and describes significant advances in addressing how to locate the actual causes of malfunctions, based on the application of procedures for filtering induced effects that hide the real causes of degradation. In this paper a progressive zooming thermoeconomic diagnosis procedure, which allows one to concentrate the analysis in an ever more specific zone is described and applied to a combined cycle. In an accompanying paper the accuracy of the diagnosis results is discussed, depending on choice of the thermoeconomic model.

scholarly journals Orchard Mapping with Deep Learning Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3813
Author(s):  
Athanasios Anagnostis ◽  
Aristotelis C. Tagarakis ◽  
Dimitrios Kateris ◽  
Vasileios Moysiadis ◽  
Claus Grøn Sørensen ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Automated Detection ◽  
Aerial Images ◽  
Training Dataset ◽  
Field Boundary ◽  
Different Seasons ◽  
Detection And Localization ◽  
Different Levels

This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.

Generation of Complex Energy Systems by Combination of Elementary Processes

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
A. Toffolo ◽  
S. Rech ◽  
A. Lazzaretto
Keyword(s):  
Design Optimization ◽  
Ad Hoc ◽  
Energy Systems ◽  
Optimization Procedure ◽  
Thermodynamic Cycle ◽  
Energy System ◽  
Design Parameters ◽  
System Configuration ◽  
Complex Energy ◽  
Synthesis Design

The fundamental challenge in the synthesis/design optimization of energy systems is the definition of system configuration and design parameters. The traditional way to operate is to follow the previous experience, starting from the existing design solutions. A more advanced strategy consists in the preliminary identification of a superstructure that should include all the possible solutions to the synthesis/design optimization problem and in the selection of the system configuration starting from this superstructure through a design parameter optimization. This top–down approach cannot guarantee that all possible configurations could be predicted in advance and that all the configurations derived from the superstructure are feasible. To solve the general problem of the synthesis/design of complex energy systems, a new bottom–up methodology has been recently proposed by the authors, based on the original idea that the fundamental nucleus in the construction of any energy system configuration is the elementary thermodynamic cycle, composed only by the compression, heat transfer with hot and cold sources and expansion processes. So, any configuration can be built by generating, according to a rigorous set of rules, all the combinations of the elementary thermodynamic cycles operated by different working fluids that can be identified within the system, and selecting the best resulting configuration through an optimization procedure. In this paper, the main concepts and features of the methodology are deeply investigated to show, through different applications, how an artificial intelligence can generate system configurations of various complexity using preset logical rules without any “ad hoc” expertise.

Complex Energy Networks Optimization: Part II — Software Application to a Case Study

2020 ◽  
Author(s):  
M. A. Ancona ◽  
M. Bianchi ◽  
L. Branchini ◽  
A. De Pascale ◽  
F. Melino ◽  
...  
Keyword(s):  
Energy Production ◽  
Internal Combustion Engines ◽  
Energy Systems ◽  
Distribution Networks ◽  
Optimal Scheduling ◽  
Small Scale ◽  
Complex Energy ◽  
Energy Networks ◽  
Energy Network

Abstract In order to increase the exploitation of the renewable energy sources, the diffusion of the distributed generation systems is grown, leading to an increase in the complexity of the electrical, thermal, cooling and fuel energy distribution networks. With the main purpose of improving the overall energy conversion efficiency and reducing the greenhouse gas emissions associated to fossil fuel based production systems, the design and the management of these complex energy grids play a key role. In this context, an in-house developed software, called COMBO, presented and validated in the Part I of this study, has been applied to a case study in order to define the optimal scheduling of each generation system connected to a complex energy network. The software is based on a non-heuristic technique which considers all the possible combination of solutions, elaborating the optimal scheduling for each energy system by minimizing an objective function based on the evaluation of the total energy production cost and energy systems environmental impact. In particular, the software COMBO is applied to a case study represented by an existing small-scale complex energy network, with the main objective of optimizing the energy production mix and the complex energy networks yearly operation depending on the energy demand of the users. The electrical, thermal and cooling needs of the users are satisfied with a centralized energy production, by means of internal combustion engines, natural gas boilers, heat pumps, compression and absorption chillers. The optimal energy systems operation evaluated by the software COMBO will be compared to a Reference Case, representative of the current energy systems set-up, in order to highlight the environmental and economic benefits achievable with the proposed strategy.

Solar Energy in Agro-Ecologic Micrometeorology Measurements

2018 ◽  
pp. 141-148
Author(s):  
Ildus Saetgalievich Nurgaliev
Keyword(s):  
Energy Transfer ◽  
Energy Supply ◽  
Material Point ◽  
Diffusion Reaction ◽  
Solar Panels ◽  
New Approach ◽  
Number Of Components ◽  
Advection Diffusion ◽  
Remote Sites ◽  
Different Levels

New approach to the measurements in agro-ecologic micrometeorology is suggested on the bases of renewable solar panels for energy supply to instruments at the remote sites and new turbulent model of the flow of the gases. Analytical dynamic model of the turbulent multi-component flow in the three-layer boundary system is presented. Turbulence is simulated by the non-zero vorticity, but not only. Other mathematical aspects of the turbulence are an introducing new model of the material point and considering a torsion of their trajectories. The generalized advection-diffusion-reaction equation is derived for an arbitrary number of components in the flow. The flows in the layers are objects for matching requirements on the boundaries between the layers. Different types of transport mechanisms are dominant on the different levels of the layers and space scales. The same models of mass and energy transfer are instrumental in simulation rural electrification concepts in general on the bases renewable sources.

China's Energy and Resource Uses: Continuity and Change

1998 ◽  
Vol 156 ◽  
pp. 935-951 ◽  
Author(s):  
Vaclav Smil
Keyword(s):  
20Th Century ◽  
Energy Systems ◽  
Energy Resources ◽  
Complex Energy ◽  
Continuity And Change ◽  
Resource Endowment

Recent writings on China's achievements during the last quarter of the 20th century stress, almost without exception, the enormity of change. But, for both universal and particular reasons, this survey of the country's energy resources and uses will stress continuity as much as change. Taking the inertia of complex energy systems as the key universal given, the most important particular explanation lies in peculiarities of China's resource endowment.

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