Processamento de dados experimentais para estudo de secagem: desenvolvimento de programa em Python e análise de dados da secagem de minério de manganês

Abstract

Drying is an essential unit operation in various industrial processes. For the efficient design of dryers on an industrial scale, bench and pilot-scale studies are fundamental, as they provide specific kinetic information about the products, such as the film coefficients associated with heat and mass transfer phenomena. However, experimental data obtained in drying systems often exhibit noise and uncertainties, frequently resulting from limitations in the methodology used for data collection, such as fluctuations in the electrical network or interference during weighing caused by vibrations and equipment instability. In this context, the objective of this work was to develop a software aimed at processing experimental data from a forced convection oven, focusing on data treatment and the extraction of relevant kinetic information for the study of drying. For testing the program, drying data of manganese ore were applied, and an analysis of the results obtained with the developed tool was also performed. Python routines were created for the processing and analysis of balance data, capable of: generating drying curves; comparing experimental data with theoretical diffusion models and with literature data; applying filters to the data; and storing the results. The program was complemented with textual material and instructional videos to facilitate its use. The test with manganese ore demonstrated the efficiency of the tool in visualizing the drying stages. A critical moisture content value between 0.10 and 0.15 kg water/kg dry solid was identified for 21 mm manganese spheres, dried at 90 °C. The theoretical models of Lewis and Page proved to be suitable for describing the drying rate, showing correlation coefficients greater than 0.98.