APPMAR 1.0: A Python application for downloading and analyzing of WAVEWATCH III® wave and wind data

Rivillas-Ospina, Germán; Casas, Diego; Maza-Chamorro, Mauro Antonio; Bolívar, Marianella; Ruiz, Gabriel; Guerrero, Roberto A.; Horrillo-Caraballo, Jose; Guerrero, Milton; Díaz, Karina Elizabeth; Rio, Roberto del; Campos, Erick

doi:10.1016/j.cageo.2022.105098

Cited by 7 publications

(1 citation statement)

References 24 publications

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“…Although limited literature exists on the direct use of Python for drying analysis, Python has been extensively utilized in environmental engineering and modeling, including wastewater treatment processes [5,6] and others like pyrolysis and extraction. The paper provides an outstanding example of interdisciplinary research, combining programming and chemical technology.…”

Section: Introductionmentioning

confidence: 99%

Analyzing and Modeling the Municipal Sewage Sludge Drying Process Using Python

Mihelič,

Klinar,

Rižnar

et al. 2023

Processes

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The programming language Python offers the opportunity to analyze and model the municipal sewage sludge (MSS) drying process in an illustrative chemical engineering practice. The drying process is performed on a flat plate while maintaining a uniform, parallel drying air speed. The Python program helps to analyze the digitalized weight measurements from each sample. The program enables the sorting of input data, determination of the drying critical point, and evaluation of the first and second drying periods. Moreover, the model calculates the fundamental drying parameter and forms a drying master curve to support the transfer to different drying conditions. The basic parameters calculated are mass transfer coefficient, heat transfer coefficient, and diffusion coefficient. The results are consistent with published data for those coefficients over the drying temperature range of 19.4–52.4 °C and relative humidity range of 8.2–33.6%. The findings of this study demonstrate the potential of Python as a powerful tool for analyzing experimental data and modeling chemical processes, which can lead to improved process design, optimization, and control.

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Section: Introductionmentioning

confidence: 99%