NUMERICAL OPTIMIZATION AND RELATIONSHIPS IN MATHEMATICAL MODELING WITH FUZZY LOGIC

Abstract

Mathematical modeling is essential for representing complex systems in fields such as engineering, economics, and environmental science. Traditional techniques often struggle with the uncertainties and imprecisions of real-world data, making fuzzy logic a valuable framework for nuanced decision-making. This paper emphasizes the importance of numerical approximations in solving mathematical models without analytical solutions, leveraging advanced methods to balance accuracy and computational efficiency. In the agro-industrial sector, drying is a critical process that enhances long-term storage and offers consumers a variety of flavors and textures. Fluidized bed drying is highlighted as an effective method, known for producing uniformly dried products and improving energy efficiency when combined with heat pumps. We present new Python code designed to facilitate numerical approximations in mathematical modeling using fuzzy logic. This software streamlines the modeling process and provides tools for exploring relationships within data. The paper discusses fuzzy logic principles, the significance of numerical approximations, and demonstrates the application of our code in real-world drying processes in agro-industrial contexts.