Simulação de sistema reator-coluna-trocador de calor no DWSIM e estudo de dados Python

Abstract

With the increasing use of operational tools in both academic and professional settings, programming languages and simulation software have become essential for the optimization and improvement of systems, promoting significant advances in research and education. In this context, this study evaluated the effectiveness of the Python programming language in overcoming the limitations of the DWSIM software, which is widely used for chemical process simulations. The research was based on the development of improvements in the handling and analysis of data derived from DWSIM, which faces significant challenges due to the complexity of simulations and the lack of adequate methods for data processing. The main objective was to create a Python code capable of solving evident limitations, such as data handling and pattern recognition. The methodology involved the development of a simulation of a distillation column integrated with a reactor and a heat exchanger. Through this system, a systematic analysis was conducted using Python code. The selection of file types for data export was crucial to structuring the research and developing efficient solutions. The development of the Python code enabled data validation and analysis, as well as the improvement and optimization of simulation processes. Python libraries played a key role in performing complex analyses and generating graphical visualizations of the data, facilitating user interpretation and interaction with the results. The results demonstrated the code’s ability to identify and model patterns in exported data, as well as to ensure the reliability of the obtained data and provide graphical visualizations for interpretation, fostering more active user interaction. Despite anticipated challenges, such as the direct connection between tools due to the data export method, the research achieved its objective, reinforcing the versatility and effectiveness of Python in overcoming DWSIM's limitations. The conclusion highlighted the efficiency in performing and analyzing the system simulation and the positive results achieved throughout the research, as well as opportunities for future improvements. Among these improvements are the systemic integration of the code with DWSIM and the implementation of new libraries to enhance analytical capabilities and data visualization. This study encouraged the use of Python programming language, providing a practical and efficient solution for data analysis and manipulation in industrial chemical process simulations, with applications in both academic and professional settings.