Browsing by Autor "Jorge Calvimontes"

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    EVALUATION OF LANDFILL BIOGAS POTENTIAL IN BOLIVIA TO PRODUCE ELECTRICITY
    (2017) Juan Pablo Vargas Bautista; Jorge Calvimontes
    The potential to produce electricity using landfill biogas in Bolivia was studied in this article. There important locations (Santa Cruz, La Paz and Cochabamba) and their solid waste disposal characteristics were considered. LandGem first –order degradation model was used to quantify the biogas production from the landfill. Recommend values of k and Lo available in the open literature were used since in Bolivia there are few landfills and no specific data are available (humidity, nutrients, pH, temperature, etc.). An average of 55.2% of the total solid waste in Bolivia is organic waste that can be used in landfills. The results showed that Santa Cruz can produce more landfill biogas than La Paz and Cochabamba. Using the methane obtained from the landfill biogas in reciprocating combustion engines, it was found that Santa Cruz can produce more electricity (265 GWh) than La Paz (175 GWh) and Cochabamba (110.4 GWh). The current electricity price in Bolivia (35 U$D/MWh) was used to evaluate the prefeasibility of the project, representing an average income of 281,061.0 U$D/y for Santa Cruz and 161,000.0 U$D/y for La Paz and Cochabamba, respectively. However, the economic analysis showed that IRR of 6.2% can be achieved for Santa Cruz and 7.9% for La Paz and Cochabamba, respectively, also higher payback periods were obtained (more than 9 years). La Paz and Cochabamba had the higher IRR and less payback period since only one reciprocating engine was chosen to cover the largest period of methane production while Santa Cruz used two. The economic indicators can be improved if international electricity prices are applied. The results presented in this article could provide valuable information to the solid waste management industry, policy makers and investors.
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    Product Demand Forecasting Based on Reservoir Computing
    (Springer International Publishing, 2019) Jorge Calvimontes; Jens Bürger
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    TEMPORAL DICTIONARY LEARNING FOR TIME-SERIES DECOMPOSITION
    (2019) Jens Bürger; Jorge Calvimontes
    Dictionary Learning (DL) is a feature learning method that derives a finite collection of dictionary elements (atoms) from a given dataset. These atoms are small characteristic features representing recurring patterns within the data. A dictionary therefore is a compact representation of complex or large scale datasets. In this paper we investigate DL for temporal signal decomposition and reconstruction. Decomposition is a common method in time-series forecasting to separate a complex composite signal into different frequency components as to reduce forecasting complexity. By representing characteristic features, we consider dictionary elements to function as filters for the decomposition of temporal signals. Rather than simple filters with clearly defined frequency spectra, we hypothesize for dictionaries and the corresponding reconstructions to act as more complex filters. Training different dictionaries then permits to decompose the original signal into different components. This makes it a potential alternative to existing decomposition methods. We apply a known sparse DL algorithm to a wind speed dataset and investigate decomposition quality and filtering characteristics. Reconstruction accuracy serves as a proxy for evaluating the dictionary quality and a coherence analysis is performed to analyze how different dictionary configurations lead to different filtering characteristics. The results of the presented work demonstrate how learned features of different dictionaries represent transfer functions corresponding to frequency components found in the original data. Based on finite sets of atoms, dictionaries provide a deterministic mechanism to decompose a signal into various reconstructions and their respective remainders. These insights have direct application to the investigation and development of advanced signal decomposition and forecasting techniques.