Guía para el diagnóstico de conjuntos industriales azucareros: el caso del central Toledo, La Habana, Cuba Guía para el diagnóstico de conjuntos industriales azucareros: el caso del central Toledo,
Developing an accurate concentrated solar power (CSP) performance model requires significant effort and time. The power block (PB) is the most complex system, and its modeling is clearly the most complicated and time-demanding part. Nonetheless, PB layouts are quite similar throughout CSP plants, meaning that there are enough historical process data available from commercial plants to use machine learning techniques. These algorithms allowed the development of a very accurate black-box PB model in a very short amount of time. This PB model could be easily integrated as a block into the PM. The machine learning technique selected was SVR (support vector regression). The PB model was trained using a complete year of data from a commercial CSP plant situated in southern Spain. With a very limited set of inputs, the PB model results were very accurate, according to their validation against a new complete year of data. The model not only fit well on an aggregate basis, but also in the transients between operation modes. To validate applicability, the same model methodology is used with a data from a very different CSP Plant, located in the MENA region and with more than double nominal electric power, obtaining an excellent fitting in the validation.
The energy production of concentrated solar power (CSP) plants not only depends on their design, but also of the weather conditions and the way they are operated. A performance model (PM) of a CSP plant is an essential tool to determine production costs, to optimize design and also to supervise the operation of the plant. The challenge is developing a PM that is both easy enough to be useful during the earlier stages of the project, and also useful for supervision of plant operation. This requires one to be able to describe the step between the different modes of operation and to fit the response to transient meteorological phenomena, not so relevant in terms of aggregate values, but crucial for the supervision. The quasi-dynamic performance model (QD-PM) can predict the net energy exported to the grid, as well as all the key operational variables. The QD-PM was implemented using Matlab-Simulink of Mathwoks (MA, USA) with a modular structure. Each module is developed using specific software and a state machine is used to simulate the sequence between the operation modes. The validation of the PM is made using one complete year of commercial operation of a 50 MWe CSP plant situated in Spain. The comparison between the actual data and the results of the model shows an excellent fit, being especially noteworthy as follows the transients between the different CSP operation modes. Then, QD-PM provides an accuracy better than the usual PM, and, almost, as good as that of a fully dynamic model but with a shorter simulation time. But, the main advantage of the QD-PM is that it can be use not only in the feasibility and design stages, but it can be used to supervise the operation of the plant.
BACKGROUND: In Cuba, the first cases of coronavirus 2019 (COVID-19) were confirmed on March 11, 2020, when the World Health Organization (WHO) officially declared the pandemic and the Ministry of Public Health of Cuba (MINSAP) began to execute the COVID-19 Prevention and Control Plan. This plan was prepared two months earlier by MINSAP working together with the National Civil Defense and the government approved it at the end of January. OBJECTIVE: The aim of this research is to assess the effectiveness of the government strategies to deal with COVID-19, by analyzing the role of the different agencies involved in the pandemic management. METHODS: A bibliographical review of the following documents was conducted: information issued by MINSAP and other ministries, archives of the Pedro Kouri Institute (IPK) and Cuban journals regarding the high impact in the field of medicine. The data were processed with different tools (diagrams, bar graphs, analysis and synthesis, etc.) that allowed measuring the effectiveness of the strategies implemented. RESULTS: The government’s strategies focused on: the integration of all state agencies and some private institutions to confront COVID-19; the collaboration between MINSAP specialists, country’s research centers and universities for the creation of vaccines to contain the pandemic; the production of medical equipment and instruments; the design of the organization processes of the services, such as planning techniques and distribution of ambulances, allocation of hospitals and isolation centers for sufferers and direct contacts respectively. CONCLUSION: The analysis carried out showed that the interrelations between the different organizations involved had positive influences on the treatment of the pandemic.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.