Ant colony optimisation and fuzzy system for prediction of computational data of fluid flow in a bubble column reactor

Computational data of a complex problem could be mapped and solved by artificial intelligence (AI) for postprocessing purposes and numerical time-saving. A fuzzy adaptive network as a hybrid AI system has already been used for CFD data machine learning. However, no investigation has been done for the ant colony optimisation (ACO) performance evaluation compared to the adaptive network. As a state-of-the-art method, the ACO and fuzzy system are combined and tuned to estimate the flow in a reactor. The liquid-phase velocity in the y-direction is predicted using a combination of fuzzy system and ACO in a large domain. The findings further indicate that, in addition to the CFD dataset and Navier-Stokes's equations, employing an ant colony optimisation structure as a training technique may give an accurate approximation of two-phase flow. Furthermore, for numerical methods, a continuous link between input and output datasets may be a significant data source for the AI mechanism to train the physical process, incredibly bubbly flow. The ant colony optimisation shows a more accurate prediction than the adaptive network. As far as authors know, there are no such investigations to develop a new AI algorithm and performance analysis for fluid dynamics problems.

Automated summary

This study combines ant colony optimization with fuzzy systems to estimate flow in a reactor, and finds that ant colony optimization provides more accurate predictions of two-phase flow compared to adaptive networks. This research is innovative and important for developing new AI algorithms and performance analysis for fluid dynamics problems.