Browsing by Author "Starke, Allan R."

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    A detailed multi-component heat configuration assessment for complex industrial plants through Monte Carlo simulations: a case study for the cement industry
    (2025) Wolde Ponce, Ian; Starke, Allan R.; da Silva, Alexandre K.; Cardemil, José M.
    The decarbonization of industrial plants involves the integration of cleaner and more efficient energy processes, which might include electrification, renewable energy sources, waste heat recovery, and thermal energy storage. The technical viability of each assisting technology is usually assessed through direct simulations of the integrated system, which makes evaluation often difficult. This study proposes a methodology for estimating the heat demands of different configurations of a generic cement plant, aiming to assess the fuel consumption for the several integration cases considered. The waste heat and the mass flow rate of the internal streams are considered variable parameters, which lead to 32 distinct integration cases and 16,000 plant simulations. The operating conditions are generated through a Monte Carlo approach, ensuring the probability distribution of the results. The waste heat measures increase the plant’s heat demand and hinder its efficiency. A linear regression for fuel heat demand shows results ranging from 113.72MW to 492.62MW
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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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    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