EFFICIENCY OF INTEGRATING ARTIFICIAL INTELLIGENCE TECHNOLOGIES INTO THE EDUCATIONAL PROCESS FOR CHEMICAL ENGINEERING SPECIALTIES
DOI:
https://doi.org/10.56525/jdg2hz48Keywords:
artificial intelligence, chemical engineering, machine learning, digital twins, engineering education, neural networks, technological process modeling, Industry 4.0Abstract
This article investigates the effectiveness of integrating artificial intelligence (AI) technologies into the training process of future engineers in chemical engineering specialties within the framework of the "Industry 4.0" concept. The relevance of the study is driven by the need to apply modern digital tools for modeling and optimizing complex technological processes in chemical production. A comprehensive methodological approach combining chemical engineering and pedagogical monitoring was employed. Experimental studies were conducted on high-performance workstations equipped with Intel Core i7 processors and NVIDIA RTX graphics cards, utilizing the Python programming environment (Pandas, Scikit-learn, TensorFlow) and the DWSIM simulation package.
The research results demonstrated that the use of AI tools increased the average student performance by 22% compared to the control group and brought the accuracy of technological calculations up to 96%. Furthermore, the implementation of automated scripts enabled a five-fold reduction in optimization time. The discussion section analyzes the role of AI in developing "engineering intuition" among students and the necessity of introducing Explainable AI (XAI) methods to address the "black box" issue of models. In conclusion, the proposed educational model is recommended as a foundation for developing a data-driven decision-making culture among future chemical engineers and enhancing their competitiveness in the labor market.
