Visualization tools for biomass valorization planning and optimization: coupling experiences in Brazil and UK

Abstract

Data mining and visualizing methods are essential for computational decision-making tools, given the large volume of data that is collected, stored and shared by public and private entities. Data-driven models that utilize optimization techniques can be applied for efficient decision-making processes. Systems related to the use of renewable resources, such as biomass, are such an example where geospatial data can be used for better planning and modeling. Biorefinery processes are known to have a large part of their operating costs coming from harvesting, storing and transporting biomass. Therefore, efficient supply chain management plays a key role for biomass valorization projects. Previous work by the Principal Investigator has developed models to optimize decision making in the planning of forestry biorefinery systems. Combining current projects and previous experiences of researchers in Brazil (Prof. Moisés Teles) and the United Kingdom (Prof. Michael Short), this proposal aims to expand the results of the FAPESP IC project currently taking place in Brazil, through a internship in the United Kingdom, in the context of a Research Internship Abroad (BEPE). This collaboration seeks to expand the results of the current IC project (FAPESP IC 2023/05420-0) in two ways: (i) Integrating the computational tools currently under development in Brazil with open source optimization algorithms (Python) developed by the research group in the United Kingdom and (ii) Adding new types of biomass waste to the computational tool, especially from animal origin and food chain trade in the United Kingdom for the production of biogas. In the current FAPESP IC project, map files are obtained in shapefile format. The files are downloaded, converted to json format and the Ramer-Douglas-Peucker algorithm is used to reduce the map resolution and generate lighter files. Using the Python object-oriented programming language, the BiomassMap class was created, whose main objective is to make a biomass distribution map based on a biomass database using the Pandas and Matplotlib libraries. The DynamicUnits and StaticUnits classes were also created to represent points that have an associated magnitude and those that do not, respectively. Using the Pandas library, the data is read and, with the Matplotlib library, these points are drawn on the map created by the BiomassMap class. To make the maps interactive, the Streamlit library was chosen, which allows the creation of data visualization applications on the web using Python. The expected results for the International Internship (BEPE) are: (i) For the FAPESP Project (Bolsa FAPESP IC 2023/05420-0): the current project on biomass data mining and visualization will be improved with the addition of an optimization layer and incorporation of open source programming best practices; (ii) For the United Kingdom: Prof. Michael Short will integrate dynamic map visualization into his current optimization frameworks; (iii) For the Brazilian undergraduate student: he will develop his communication and scientific skills in an international environment, being hosted by a well-recognized university in the United Kingdom, also favoring international insertion and construction of Brazil-United Kingdom collaboration networks in the scope of the FAPESP project under development. (AU)