Experimental Study and Artificial Intelligence Modeling of Dye Removal in Microfluidic Systems

Abstract

A modeling approach was utilized to achieve efficient operational conditions for Alizarin removal from synthetic wastewater in a T‐type micromixer. Besides experimental work, the neuro‐fuzzy system and artificial neural network techniques were utilized for this purpose. Input parameters were the pH, the initial Alizarin concentration in the feed, the extractant volume percentage in the organic phase, and the fluid flow rate. Based on the obtained results, both models have high precision. However, the accuracy of the neural network for estimating the extraction percentage is higher compared to that of the neuro‐fuzzy model. The optimal values of the operating parameters were determined by the genetic algorithm technique and the extraction percentage value was obtained as about 99.4 %.

Microfluidics: a concise review of the history, principles, design, applications, and future outlook

“…252 In the operation of microfluidic devices, AI and ML can be used to control fluid flow and behaviour within the device, leading to more precise and efficient control. 253,254 Additionally, AI and ML can analyse large datasets of biological data obtained from microfluidic devices, providing new insights into biological systems and developing new diagnostic and therapeutic approaches. 251 However, the integration of AI and ML in microfluidics engineering requires large amounts of high-quality data and interdisciplinary collaborations between microfluidics engineers, computer scientists, and biologists.…”