Application of multi-gene genetic programming technique for modeling and optimization of phycoremediation of Cr(VI) from wastewater

Abstract

Background Removal of Cr(VI) from wastewater is essential as it is potentially toxic and carcinogenic in nature. Bioremediation of heavy metals using microalgae is a novel technique and has several advantages such as microalgae remove metals in an environmentally friendly and economic manner. The present study deals with modeling and optimization of the phycoremediation of Cr(VI) from synthetic wastewater. The initial concentration of Cr(VI), initial pH, and inoculum size were considered as input factors, and the percentage removal of Cr(VI) was chosen as a response. Results An accurate data-driven genetic programming model was developed with the experimental data of other scientists to find a relation between the percentage removal of Cr(VI) and all input parameters. To maximize the removal of Cr(VI), the grey wolf optimization technique was applied to determine the optimal values of input parameters. Conclusion These optimum input parameters are difficult to get through experimentation using the trial-and-error method. The established modelling and optimization technique is generic and can be applied to any other experimental study. Graphical Abstract

Multi-stage manufacturing process parameter optimization method based on improved marine predator algorithm

“…MGGP employs symbolic regression to search for possible combinations of input variables, constants, and function relationships in the provided dataset until the model that best fits the dataset is achieved [13,14]. MGGP combines the capabilities of classical linear regression with capturing nonlinear behaviors to discover some hidden mathematical expressions or functions to best fit the given dataset and automatically perform the selection of the most suitable control parameters (i.e., feature selection).…”