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DOI 10.1080/14786451.2023.2244605
A techno-economic assessment for fuel cells hybrid systems in stationary applications
2023-12-14
Fontalvo Morales, Victor Manuel
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Nelson, George J. J.
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Pupo-Roncallo, Oscar
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Sanjuan, Marco E. E.
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Gomez, Humberto A. A.
This study evaluates the techno-economic feasibility of integrating fuel cells into stand-alone power generation systems, combined with different technologies such as diesel generators, wind turbines, and batteries. An optimisation analysis was performed using the Hybrid Optimization Model for Multiple Energy Resources (HOMER) software to minimise energy costs and several system architectures were obtained for different operating scenarios. The performance and cash flow of the selected system were included. The results suggest that the combination of traditional technologies for fuel cell power generation could be feasible for a country like Colombia, depending on economic factors such as the Consumer Price Index (CPI), the discount rate, and the availability of renewable resources. The proposed system achieves a Levelized Cost of Electricity (LCOE) of $0.112/kWh, comparable to state-of-the-art systems in the literature. This research contributes to the global effort of transitioning to alternative energy sources and reducing carbon emissions.
Scopus© Citations 15 5
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DOI 10.1007/s00170-022-08966-y
A machine learning approach for the prediction of melting efficiency in wire arc additive manufacturing
2022-05
Barrionuevo, German O.
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Sequeira-Almeida, Pedro M.
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Rios Contesse, Sergio Eduardo
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Ramos-Grez, Jorge A.
;
Williams, Stewart W.
Wire arc additive manufacturing (WAAM) appears as one of the most promising technologies due to its capacity to process all types of materials used in welding, its high production rate, and capacity to process large geometries of particular interest in the aeronautical industry. Since this technology is still under investigation, it is important to determine the efficiency of the process; in this sense, the melting efficiency stands out not only as a parameter of interest in energy terms but also as a measure of the stability of the process. For calculating melting efficiency, it is necessary to use tailored colorimeters or apply models requiring specific dimensions that involve destructive testing. For this reason, in the development of this work, the melting efficiency is evaluated through machine learning algorithms. Processing parameters such as wire diameter, wire feed speed, travel speed, and net power are used to determine melting efficiency. In addition, a simplified analytical model was developed to compare the results. The average melting efficiency analytically calculated was 44.56 +/- 5.48%, while the predicted value reaches a comparable value of 44.32 +/- 4.79% obtained with the Gaussian process regressor, which shows the highest accuracy. Moreover, the known relationship with travel speed was verified.
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DOI 10.1007/s00170-022-08966-y
A machine learning approach for the prediction of melting efficiency in wire arc additive manufacturing
2022-05
Barrionuevo, German O.
;
Sequeira-Almeida, Pedro M.
;
Rios Contesse, Sergio Eduardo
;
Ramos-Grez, Jorge A.
;
Williams, Stewart W.
Wire arc additive manufacturing (WAAM) appears as one of the most promising technologies due to its capacity to process all types of materials used in welding, its high production rate, and capacity to process large geometries of particular interest in the aeronautical industry. Since this technology is still under investigation, it is important to determine the efficiency of the process; in this sense, the melting efficiency stands out not only as a parameter of interest in energy terms but also as a measure of the stability of the process. For calculating melting efficiency, it is necessary to use tailored colorimeters or apply models requiring specific dimensions that involve destructive testing. For this reason, in the development of this work, the melting efficiency is evaluated through machine learning algorithms. Processing parameters such as wire diameter, wire feed speed, travel speed, and net power are used to determine melting efficiency. In addition, a simplified analytical model was developed to compare the results. The average melting efficiency analytically calculated was 44.56 +/- 5.48%, while the predicted value reaches a comparable value of 44.32 +/- 4.79% obtained with the Gaussian process regressor, which shows the highest accuracy. Moreover, the known relationship with travel speed was verified.
Scopus© Citations 41 3
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DOI 10.3390/machines9100212
Classification of Design Methodologies to Minimize Vibrations in Gears and Bearings in the 21st Century: A Review
2021-10
Tuiran Villalba, Rafae
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Maury Ramirez, Heriberto
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Aguila Estrada, Hector Miguel
Associated vibrations in mechanical power transmission elements such as gears and bearings significantly contribute to the dynamic behavior of the system to which they belong. Most research in this field describes measurements and signal processing methods to determine the effect of some design modifications. However, the number of investigations that try to solve the problem from the design stage is much smaller. The following study presents a review of the trends in the analysis of vibrations of power transmission elements and their relationship to the use of systematic design methodologies in the principal power transmission elements. A revision is shown in design considerations to prevent mechanical vibrations or reduce them to acceptable levels. The effects in terms of improvement in reducing vibrations of the most important works that have taken into account the vibrations in design stages are shown. The results show the importance of considering the mechanical vibrations in the design of power transmission elements. Moreover, the analysis of the state of the art shows the areas to be investigated that contemplate the development or application of formal design methodologies that take into account mechanical vibrations.</p>
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DOI 10.3390/machines9100212
Classification of Design Methodologies to Minimize Vibrations in Gears and Bearings in the 21st Century: A Review
2021-10
Tuiran Villalba, Rafae
;
Maury Ramirez, Heriberto
;
Aguila Estrada, Hector Miguel
Associated vibrations in mechanical power transmission elements such as gears and bearings significantly contribute to the dynamic behavior of the system to which they belong. Most research in this field describes measurements and signal processing methods to determine the effect of some design modifications. However, the number of investigations that try to solve the problem from the design stage is much smaller. The following study presents a review of the trends in the analysis of vibrations of power transmission elements and their relationship to the use of systematic design methodologies in the principal power transmission elements. A revision is shown in design considerations to prevent mechanical vibrations or reduce them to acceptable levels. The effects in terms of improvement in reducing vibrations of the most important works that have taken into account the vibrations in design stages are shown. The results show the importance of considering the mechanical vibrations in the design of power transmission elements. Moreover, the analysis of the state of the art shows the areas to be investigated that contemplate the development or application of formal design methodologies that take into account mechanical vibrations.</p>
Scopus© Citations 10 3
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DOI 10.3390/su12219227
The Analysis Performance of a Grid-Connected 8.2 kWp Photovoltaic System in the Patagonia Region
2020-11
Vidal Gutierrez, Humberto Ricardo
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Rivera, Marco
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Wheeler, Patrick
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Vicencio, Nicolas
Solar PV structures for locations at high latitudes in the Northern and Southern Hemispheres are increasingly in the spotlight. The work reported in this paper analyses the behaviour of a grid-connected 8.2 kWp photovoltaic system to either feed on-site electrical loads (a public institution, Corporacion Nacional Forestal (CONAF), located 5.5 km south of Punta Arenas, lat. 53 degrees S) or to feed into the electrical grid when the photovoltaic system generation is higher than the on-site load demand. The system simulation uses the PVSyst software with Meteonorm derived and measured climate information sets (ambient temperature, solar irradiation and wind speed). The agreement between the simulated and measured energy yield is analysed including the evaluation of the optimal generation energy of the PV array, the energy that is fed into the network, the performance ratio, and the normalised energy generation per installed kWp. The PV system considered in this work generates 7005.3 kWh/year, out of which only 6778 kWh/year are injected into the grid. The measured annual performance ratio is around 89%. The normalised productions of the inverter output or final system yield, i.e., useful energy, is 3.6 kWh/kWp/day. The measured annual average capacity factor obtained from this study is 15.1%. These performance parameters will encourage greater use of photovoltaic technology in the Chilean Patagonia region.
Scopus© Citations 26 1
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DOI 10.1109/icmimt52186.2021.9476168
Comparative Evaluation of Machine Learning Regressors for the Layer Geometry Prediction in Wire arc Additive manufacturing
2021-5-13
Barrionuevo, German Omar
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Rios Contesse, Sergio Eduardo
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Williams, Stewart W.
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Andres Ramos-Grez, Jorge
In this paper, a set of the most employed machine learning (ML) algorithms were trained and tested to assess which ones present the highest accuracy in predicting the layer geometry of the Ti-6Al-4V processed by plasma transfer arc deposition. Wire and arc additive manufacturing brings about the possibility of manufacturing large and robust components based on metal wires. One of the critical aspects to take into account during the manufacturing process is the layer geometry. Bead geometry depends on several processing parameters, e.g., arc voltage, welding current, travel speed, wire feed speed, and gas flow rate. The algorithms that better adjusted the prediction were multilayer perceptron with five hidden layers, linear support vector regression, and boosting regressors, which combine multiple models to reduce overfitting risk.
Scopus© Citations 20 1

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