Artificial-Intelligence-Assisted Investigation of Quality and Yield of Cumene Production

Abstract

Global demand for cumene is rising day by day due to its broad applications in the production of numerous types of polymers like nylon-6, epoxy resins, and polycarbonates. Efforts to enhance process design and operation efficiency are ongoing. However, addressing process uncertainties remains a significant challenge for stable process industry operations. Artificial neural networks (ANNs) have proven to be powerful tools for modelling and predicting complex chemical processes, offering substantial potential for improving the quality and quantity of cumene production. In the present study, a data-based model was used for the prediction of the molar flow and mole fraction of cumene in the final product stream. A steady-state Aspen plus model was set to a dynamic mode by deliberately introducing ±10% variability in process conditions. This dynamic model served as the foundation for generating a comprehensive dataset. Two ANN models were developed using the dataset for the prediction of the molar flow and mole fraction of cumene. During the rigorous testing phase, the models demonstrated outstanding performance, as evidenced by their correlation coefficient values of 0.99216 and 0.99412 for the molar flow and mole fraction of cumene, respectively. These high correlation coefficients provide compelling evidence of the models’ robust and precise predictive capabilities, highlighting their potential for real-world application. This study paves the way for Al integration in the process industry, making a significant step toward embracing industry 4.0.

Keywords

• digitalization
• machine learning
• quality estimation
• smart manufacturing
• industry 4.0