Thermal and Electrical Parameter Identification of a Proton Exchange Membrane Fuel Cell Using Genetic Algorithm

[EN] Proton Exchange Membrane Fuel Cell (PEMFC) fuel cells is a technology successfully used in the production of energy from hydrogen, allowing the use of hydrogen as an energy vector. It is scalable for stationary and mobile applications. However, the technology demands more research. An important research topic is fault diagnosis and condition monitoring to improve the life and the efficiency and to reduce the operation costs of PEMFC devices. Consequently, there is a need of physical models that allow deep analysis. These models must be accurate enough to represent the PEMFC behavior and to allow the identification of different internal signals of a PEM fuel cell. This work presents a PEM fuel cell model that uses the output temperature in a closed loop, so it can represent the thermal and the electrical behavior. The model is used to represent a Nexa Ballard 1.2 kW fuel cell; therefore, it is necessary to fit the coefficients to represent the real behavior. Five optimization algorithms were tested to fit the model, three of them taken from literature and two proposed in this work. Finally, the model with the identified parameters was validated with real data.This research was funded by COLCIENCIAS (Administrative department of science, technology and innovation of Colombia) scholarship program PDBCEx, COLDOC 586, and the support provided by the Corporacion Universitaria Comfacauca, Popayan-ColombiaAriza-Chacón, HE.; Correcher Salvador, A.; Sánchez-Diaz, C.; Pérez-Navarro, Á.; García Moreno, E. (2018). Thermal and Electrical Parameter Identification of a Proton Exchange Membrane Fuel Cell Using Genetic Algorithm. Energies. 11(8):1-15. https://doi.org/10.3390/en11082099S115118Mehta, V., & Cooper, J. S. (2003). Review and analysis of PEM fuel cell design and manufacturing. Journal of Power Sources, 114(1), 32-53. doi:10.1016/s0378-7753(02)00542-6Wang, Y., Chen, K. S., Mishler, J., Cho, S. C., & Adroher, X. C. (2011). A review of polymer electrolyte membrane fuel cells: Technology, applications, and needs on fundamental research. Applied Energy, 88(4), 981-1007. doi:10.1016/j.apenergy.2010.09.030Amphlett, J. C., Baumert, R. M., Mann, R. F., Peppley, B. A., Roberge, P. R., & Harris, T. J. (1995). Performance Modeling of the Ballard Mark IV Solid Polymer Electrolyte Fuel Cell: I . Mechanistic Model Development. Journal of The Electrochemical Society, 142(1), 1-8. doi:10.1149/1.2043866Tao, S., Si-jia, Y., Guang-yi, C., & Xin-jian, Z. (2005). Modelling and control PEMFC using fuzzy neural networks. Journal of Zhejiang University-SCIENCE A, 6(10), 1084-1089. doi:10.1631/jzus.2005.a1084Amphlett, J. C., Mann, R. F., Peppley, B. A., Roberge, P. R., & Rodrigues, A. (1996). A model predicting transient responses of proton exchange membrane fuel cells. Journal of Power Sources, 61(1-2), 183-188. doi:10.1016/s0378-7753(96)02360-9Mo, Z.-J., Zhu, X.-J., Wei, L.-Y., & Cao, G.-Y. (2006). Parameter optimization for a PEMFC model with a hybrid genetic algorithm. International Journal of Energy Research, 30(8), 585-597. doi:10.1002/er.1170YE, M., WANG, X., & XU, Y. (2009). Parameter identification for proton exchange membrane fuel cell model using particle swarm optimization. International Journal of Hydrogen Energy, 34(2), 981-989. doi:10.1016/j.ijhydene.2008.11.026Askarzadeh, A., & Rezazadeh, A. (2011). A grouping-based global harmony search algorithm for modeling of proton exchange membrane fuel cell. International Journal of Hydrogen Energy, 36(8), 5047-5053. doi:10.1016/j.ijhydene.2011.01.070El-Fergany, A. A. (2018). Electrical characterisation of proton exchange membrane fuel cells stack using grasshopper optimiser. IET Renewable Power Generation, 12(1), 9-17. doi:10.1049/iet-rpg.2017.0232Li, Q., Chen, W., Wang, Y., Liu, S., & Jia, J. (2011). Parameter Identification for PEM Fuel-Cell Mechanism Model Based on Effective Informed Adaptive Particle Swarm Optimization. IEEE Transactions on Industrial Electronics, 58(6), 2410-2419. doi:10.1109/tie.2010.2060456Ali, M., El-Hameed, M. A., & Farahat, M. A. (2017). Effective parameters’ identification for polymer electrolyte membrane fuel cell models using grey wolf optimizer. Renewable Energy, 111, 455-462. doi:10.1016/j.renene.2017.04.036Sun, Z., Wang, N., Bi, Y., & Srinivasan, D. (2015). Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm. Energy, 90, 1334-1341. doi:10.1016/j.energy.2015.06.081Gong, W., Yan, X., Liu, X., & Cai, Z. (2015). Parameter extraction of different fuel cell models with transferred adaptive differential evolution. Energy, 86, 139-151. doi:10.1016/j.energy.2015.03.117Turgut, O. E., & Coban, M. T. (2016). Optimal proton exchange membrane fuel cell modelling based on hybrid Teaching Learning Based Optimization – Differential Evolution algorithm. Ain Shams Engineering Journal, 7(1), 347-360. doi:10.1016/j.asej.2015.05.003Al-Othman, A. K., Ahmed, N. A., Al-Fares, F. S., & AlSharidah, M. E. (2015). Parameter Identification of PEM Fuel Cell Using Quantum-Based Optimization Method. Arabian Journal for Science and Engineering, 40(9), 2619-2628. doi:10.1007/s13369-015-1711-0Methekar, R. N., Prasad, V., & Gudi, R. D. (2007). Dynamic analysis and linear control strategies for proton exchange membrane fuel cell using a distributed parameter model. Journal of Power Sources, 165(1), 152-170. doi:10.1016/j.jpowsour.2006.11.047KUNUSCH, C., HUSAR, A., PULESTON, P., MAYOSKY, M., & MORE, J. (2008). Linear identification and model adjustment of a PEM fuel cell stack. International Journal of Hydrogen Energy, 33(13), 3581-3587. doi:10.1016/j.ijhydene.2008.04.052Li, C.-H., Zhu, X.-J., Cao, G.-Y., Sui, S., & Hu, M.-R. (2008). Identification of the Hammerstein model of a PEMFC stack based on least squares support vector machines. Journal of Power Sources, 175(1), 303-316. doi:10.1016/j.jpowsour.2007.09.049Fontes, G., Turpin, C., & Astier, S. (2010). A Large-Signal and Dynamic Circuit Model of a $\hbox{H}_{2}/\hbox{O}_{2}$ PEM Fuel Cell: Description, Parameter Identification, and Exploitation. IEEE Transactions on Industrial Electronics, 57(6), 1874-1881. doi:10.1109/tie.2010.2044731Cheng, S.-J., & Liu, J.-J. (2015). Nonlinear modeling and identification of proton exchange membrane fuel cell (PEMFC). International Journal of Hydrogen Energy, 40(30), 9452-9461. doi:10.1016/j.ijhydene.2015.05.109Buchholz, M., & Krebs, V. (2007). Dynamic Modelling of a Polymer Electrolyte Membrane Fuel Cell Stack by Nonlinear System Identification. Fuel Cells, 7(5), 392-401. doi:10.1002/fuce.200700013Meiler, M., Schmid, O., Schudy, M., & Hofer, E. P. (2008). Dynamic fuel cell stack model for real-time simulation based on system identification. Journal of Power Sources, 176(2), 523-528. doi:10.1016/j.jpowsour.2007.08.051Wang, C., Nehrir, M. H., & Shaw, S. R. (2005). Dynamic Models and Model Validation for PEM Fuel Cells Using Electrical Circuits. IEEE Transactions on Energy Conversion, 20(2), 442-451. doi:10.1109/tec.2004.842357Restrepo, C., Konjedic, T., Garces, A., Calvente, J., & Giral, R. (2015). Identification of a Proton-Exchange Membrane Fuel Cell’s Model Parameters by Means of an Evolution Strategy. IEEE Transactions on Industrial Informatics, 11(2), 548-559. doi:10.1109/tii.2014.2317982Salim, R., Nabag, M., Noura, H., & Fardoun, A. (2015). The parameter identification of the Nexa 1.2 kW PEMFC’s model using particle swarm optimization. Renewable Energy, 82, 26-34. doi:10.1016/j.renene.2014.10.012Pérez-Navarro, A., Alfonso, D., Ariza, H. E., Cárcel, J., Correcher, A., Escrivá-Escrivá, G., … Vargas, C. (2016). Experimental verification of hybrid renewable systems as feasible energy sources. Renewable Energy, 86, 384-391. doi:10.1016/j.renene.2015.08.03

MPC for optimal dispatch of an AC-linked hybrid PV/wind/biomass/H2 system incorporating demand response

[EN] A Model Predictive Control (MPC) strategy based on the Evolutionary Algorithms (EA) is proposed for the optimal dispatch of renewable generation units and demand response in a grid-tied hybrid system. The generating system is based on the experimental setup installed in a Distributed Energy Resources Laboratory (LabDER), which includes an AC micro-grid with small scale PV/Wind/Biomass systems. Energy storage is by lead-acid batteries and an H2 system (electrolyzer, H2 cylinders and Fuel Cell). The energy demand is residential in nature, consisting of a base load plus others that can be disconnected or moved to other times of the day within a demand response program. Based on the experimental data from each of the LabDER renewable generation and storage systems, a micro-grid operating model was developed in MATLAB(C) to simulate energy flows and their interaction with the grid. The proposed optimization algorithm seeks the minimum hourly cost of the energy consumed by the demand and the maximum use of renewable resources, using the minimum computational resources. The simulation results of the experimental micro-grid are given with seasonal data and the benefits of using the algorithm are pointed out.Acevedo-Arenas, CY.; Correcher Salvador, A.; Sánchez-Diaz, C.; Ariza-Chacón, HE.; Alfonso-Solar, D.; Vargas-Salgado, C.; Petit-Suarez, JF. (2019). MPC for optimal dispatch of an AC-linked hybrid PV/wind/biomass/H2 system incorporating demand response. Energy Conversion and Management. 186:241-257. https://doi.org/10.1016/j.enconman.2019.02.044S24125718

Características actuales y factores de riesgo de mortalidad en choque cardiogénico por infarto de miocardio en un hospital latinoamericano

Objective. To know the clinical characteristics and determine the related factors to higher in-hospital mortality in patients with cardiogenic shock (CS) due to myocardial infarction in a Peruvian reference hospital. Materials and methods. We conducted a prospective single-center cohort study, to evaluate the clinical characteristics, treatment, and complications of patients with CS due to myocardial infarction from March 2019 to August 2020 at the Instituto Nacional Cardiovascular INCOR. Factors related to higher in-hospital mortality and during follow-up were evaluated. Also, the IABP shock II score was applied to stratify the cohort. Results. Forty patients were included in the study, 75% of cases were due to left ventricular dysfunction, most of the men and with a median age of 75 (69-82) years. Fifty percent of cases presented CS after admission to the emergency room. Patients stratified by the IABP shock II score as low, intermediate, and high risk, had in-hospital mortality of 37.5%, 71.4%, and 91.6% respectively. In a hospital, mortality was 70%, higher in women, in those over 75 years old, and in those who developed CS during their hospitalization. Serum lactate > 4 mmol/L in univariate analysis was associated with higher mortality risk (HR: 2.8; IC:1.6-3.6, p=0.009). Survival to the end of the study was 12.8%. Conclusions. CS due to myocardial infarction is a clinical entity with high mortality in spite of revascularization and the available treatment in our reality. The highest mortality predictor was the serum lactate at admission >4 mmol/L. The IABP shock II score showed to be an accurate parameter to stratify the death risk in our population.Objetivo. Conocer las características clínicas y determinar los factores relacionados a mayor mortalidad en pacientes con choque cardiogénico (CC) por infarto de miocardio en un hospital de referencia peruano. Materiales y métodos. Cohorte única prospectiva donde se evaluó la presentación, tratamiento y complicaciones de pacientes con CC por infarto de miocardio atendidos entre marzo 2019 a agosto 2020 en el Instituto Nacional Cardiovascular - INCOR. Se evaluaron los factores relacionados con mayor mortalidad hospitalaria y en el seguimiento, además del uso del score IABP shock II en la población de estudio. Resultados. Cuarenta pacientes fueron incluidos, el 75% con CC por disfunción ventricular izquierda, la mayoría varones con edad de 75 (69-82) años. Un 50% de casos presentaron CC luego del ingreso a emergencia. Los pacientes estratificados mediante el score IABP shock II como bajo, intermedio y alto riesgo, tuvieron una mortalidad intrahospitalaria de 37,5; 71;4 y 91,6% respectivamente. La mortalidad intrahospitalaria fue 70%, mayor en mujeres, mayores de 75 años y en los que desarrollaron el CC durante la hospitalización. En el análisis univariado, el lactato sérico > 4 mmol/L al ingreso se relacionó con mayor mortalidad (HR:2,8; IC:1,6-3,6, p=0,009). La sobrevida hasta el término del estudio fue de 12,8%. Conclusiones. EL CC por infarto de miocardio representa una entidad clínica de elevada mortalidad a pesar de la revascularización y el tratamiento disponible en nuestra realidad. El mayor predictor de mortalidad fue el valor de lactato sérico mayor a 4 mmol/L al ingreso. El score IABP shock II demostró ser un buen parámetro para estratificar el riesgo de muerte en nuestra población

Breve historia de la cardiología y la cirugía cardiovascular en el Perú

As we commemorate this year the Bicentennial of the Independence of Peru, we cannot fail to mention aspects of the history of cardiology and cardiovascular surgery in the country, and the contributions that these specialties have made to society.Al conmemorar este año el Bicentenario de la Independencia del Peru, no podemos dejar de mencionar aspectos de la historia de la cardiología y cirugía cardiovascular en el país y los aportes que estas especialidades han realizado a la sociedad

Experimental verification of hybrid renewable systems as feasible energy sources

[EN] Renewable energies are a central element in the search for energy sustainability, so they are becoming a substantial component of the energy scenario of every country, both as systems connected to the grid or in stand-alone applications. Feasibility of these renewable energy systems could be necessary not only in their application in isolated areas, but also in systems connected to the grid, in this last case when their contribution reaches a substantial fraction of the total electricity demand. To overcome this reliability problem, hybrid renewable systems could become essential and activities to optimize their design should be addressed, both in the simulation and in the experimental areas. In this paper, a laboratory to simulate and verify the reliability of hybrid renewable systems is presented and its application to the feasibility analysis of multicomponent systems including photovoltaic panels, wind generator and biomass gasification plant, plus energy storage in a battery bank, are described.Pérez-Navarro, Á.; Alfonso-Solar, D.; Ariza-Chacón, HE.; Cárcel Carrasco, FJ.; Correcher Salvador, A.; Escrivá-Escrivá, G.; Hurtado, E.... (2016). Experimental verification of hybrid renewable systems as feasible energy sources. Renewable Energy. 86(2):384-391. doi:10.1016/j.renene.2015.08.030S38439186

Associated factors for mortality in a COVID-19 colombian cohort : is the third wave relevant when Mu variant was predominant epidemiologically?

Q1Q1Pacientes con COVID-19Objectives: To evaluate the association between Colombia's third wave when the Mu variant was predominant epidemiologically (until 75%) in Colombia and COVID-19 all-cause in-hospital mortality. Methods: In this retrospective cohort, we included hospitalized patients ≥18 years with SARS-CoV-2 infection between March 2020 to September 2021 in ten hospitals from three cities in Colombia. Description analysis, survival, and multivariate Cox regression analyses were performed to evaluate the association between the third epidemic wave and in-hospital mortality. Results: A total of 25,371 patients were included. The age-stratified time-to-mortality curves showed differences according to epidemic waves in patients ≥75 years (log-rank test p = 0.012). In the multivariate Cox analysis, the third wave was not associated with increased mortality relative to the first wave (aHR 0.95; 95%CI 0.84–1.08), but there was an interaction between age ≥75 years and the third wave finding a lower HR for mortality (aHR 0.56, 95%CI 0.36–0.86). Conclusions: We did not find an increase in in-hospital mortality during the third epidemic wave in which the Mu variant was predominant in Colombia. The reduced hazard in mortality in patients ≥75 years hospitalized in the third wave could be explained by the high coverage of SARS-CoV-2 vaccination in this population and patients with underlying conditions.https://orcid.org/0000-0003-1833-1599https://orcid.org/0000-0001-5363-5729https://orcid.org/0000-0001-6964-2229https://orcid.org/0000-0003-3975-2835https://orcid.org/0000-0001-9441-4375Revista Internacional - IndexadaA1N

Historia y Derecho: Homenaje a Carlos Ramos Núñez

It is a work with investigations on the history of Law in homage to the jurist Carlos Ramos Núñez.Es una obra con investigaciones sobre historia del Derecho en homenaje al jurista Carlos Ramos Núñez

Evolving trends in the management of acute appendicitis during COVID-19 waves. The ACIE appy II study

Background: In 2020, ACIE Appy study showed that COVID-19 pandemic heavily affected the management of patients with acute appendicitis (AA) worldwide, with an increased rate of non-operative management (NOM) strategies and a trend toward open surgery due to concern of virus transmission by laparoscopy and controversial recommendations on this issue. The aim of this study was to survey again the same group of surgeons to assess if any difference in management attitudes of AA had occurred in the later stages of the outbreak. Methods: From August 15 to September 30, 2021, an online questionnaire was sent to all 709 participants of the ACIE Appy study. The questionnaire included questions on personal protective equipment (PPE), local policies and screening for SARS-CoV-2 infection, NOM, surgical approach and disease presentations in 2021. The results were compared with the results from the previous study. Results: A total of 476 answers were collected (response rate 67.1%). Screening policies were significatively improved with most patients screened regardless of symptoms (89.5% vs. 37.4%) with PCR and antigenic test as the preferred test (74.1% vs. 26.3%). More patients tested positive before surgery and commercial systems were the preferred ones to filter smoke plumes during laparoscopy. Laparoscopic appendicectomy was the first option in the treatment of AA, with a declined use of NOM. Conclusion: Management of AA has improved in the last waves of pandemic. Increased evidence regarding SARS-COV-2 infection along with a timely healthcare systems response has been translated into tailored attitudes and a better care for patients with AA worldwide

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives