Browsing by Author "Starke, Allan R."

Now showing 1 - 7 of 7
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    A thermo-economical assessment of solar-based low-grade heat applied to the meat and dairy industries in Brazil
    (2024) Lemos, Leonardo F. L.; Starke, Allan R.; Cardemil, Jose M.; da Silva, Alexandre K.
    Solar heating for industrial processes (SHIP) is a promising alternative for heat generation worldwide, especially in industries where low-temperature heat is required. However, despite the large importance of the food industry in Brazil's gross domestic product, SHIP technology is still incipient. Among the reasons, one can mention high installation costs and the fact that many industries in Brazil already use affordable biomass as fuel for heat generation. Therefore, this work carries out a nationwide study of the technical and economic applicability of SHIP for hot water production in the Brazilian food industry, assessing the influence of several variables on SHIP systems profitability, such as the location of the food processing plant, the amount of heat it consumes, the size of the SHIP system installed for this plant, the costs of the solar heating system and of the replaced fuel. Results show that SHIP can be a profitable alternative to natural gas in any part of Brazil but can only compete with firewood in very specific locations, at very specific conditions. For instance, costs reductions around 20% for small SHIP systems allow them to compete with firewood for heat generation, while larger reductions (i.e., similar to 40%) would be beneficial for larger SHIP systems even when firewood costs are below 16 USD/MWh.
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    Assessing the performance of hybrid CSP. + PV. plants in northern Chile
    (2016) Starke, Allan R.; Cardemil Iglesias, José Miguel; Escobar Moragas, Rodrigo; Colle, Sergio
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    Assessing the performance of novel molten salt mixtures on CSP applications
    (2024) Starke, Allan R.; Cardemil Iglesias, José Miguel; Bonini, Vinicius R.B.; Escobar Moragas, Rodrigo; Castro Quijada, Matías Daniel; Videla Leiva, Álvaro Rodrigo
    The use of molten salt mixtures as a storage medium in Concentrating Solar Power (CSP) plants has been shown to have a significant impact on increasing the reliability of CSP plants and reducing the levelized cost of energy. In this context, the present work presents the implementation of a detailed simulation procedure that contemplates different design considerations for the Rankine cycle to maximize its efficiency according to the temperature constraints established by utilizing different molten salt mixtures. To achieve this, three commercial CSP plant configurations were considered: an indirect coupling using parabolic trough collectors (PTC) with thermal oil as field heat transfer fluid (HTF) and molten salts as the storage medium, a direct coupling of PTC using molten salt as HTF and storage medium, and a central receiver plant (solar tower). The analysis considers the integration strategy between the solar system and the thermal storage, where all of those configurations considered the integration of the two-tank (hot/cold) approach. The analysis enables the development of an accurate estimation of the economic performance of changing the HTF in CSP plants, as well as the assessment of the parasitic consumption due to freezing protection systems, the effect of increasing the current field temperature, and the effect on the plant’s capacity factor. The results show that the improvement in conversion efficiencies associated with salt mixtures operating at higher temperatures induces a higher electricity generation; however, such improvement is not compensated by the material change costs within the specified considerations.
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    Enhancing the estimation of direct normal irradiance for six climate zones through machine learning models
    (2024) Rodríguez, Eduardo; López Droguett, Enrique; Cardemil Iglesias, José Miguel; Starke, Allan R.; Cornejo-Ponce, Lorena
    The evaluation of solar radiation is essential for large-scale solar energy systems, as assessing economic feasibility early on depends on accurate solar radiation data. Accurate sensors are needed to characterize the solar resource. Due to a scarcity of solar radiation data, numerical models are commonly used to estimate solar radiation components using meteorological variables that are simple or cheap to measure. In recent years, the use of machine learning (ML) algorithms has gained significant popularity in the estimation of solar radiation components. In this study it is proposed a post-processing approach using the separation model outcomes as input variables to estimate the diffuse fraction. Three ML models are employed (XGBoost, Random Forest, and Multilayer Perceptron) to boost the accuracy in terms of three statistical indicators: nRMSE, nMBE, and . The employed technique takes a distinctive approach by using reference stations to train the machine learning models and, afterward, make the assessment at the site under study. The results show an improvement in terms of precision of individual separation model outcomes. Thus, the proposed methodology may serve as a reliable approach for estimating solar radiation components in cases where historical data for a specific place of interest is not accessible.
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    Multi-objective optimization of a solar-assisted heat pump for swimming pool heating using genetic algorithm
    (2018) Starke, Allan R.; Cardemil Iglesias, José Miguel; Colle, Sergio
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    Multi-objective optimization of hybrid CSP+PV system using genetic algorithm
    (2018) Starke, Allan R.; Cardemil Iglesias, José Miguel; Escobar Moragas, Rodrigo; Colle, Sergio
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    Thermal analysis of solar-assisted heat pumps for swimming pool heating
    (2017) Starke, Allan R.; Cardemil Iglesias, José Miguel; Escobar Moragas, Rodrigo; Colle, Sergio