Optimization of Process Parameters for Remediation Performance on Mass Removal of Tetrachloroethylene Source Zones by using Taguchi Design of Experiment and Artificial Neural Network

This study tested the remediation performance of various conditions for pool-dominated tetrachloroethylene resided in the soil at three different levels of type of flushing agent, flushing agent concentration and flushing rate. The experimental data obtained according to the Taguchi orthogonal array L9 were analysed using the Minitab 17 program. Results of the experimental design analysis indicated that the most effective factor is the flushing agent type, followed by flushing agent concentration and flushing rate, respectively. All the parameters used in the system have a significant effect on the experimental results, and the Taguchi method can explain 97.94% of the experimental data. As a result of the ANN analysis, the R2 values for training, testing and validation were calculated as 0.99942, 0.99795 and 0.99964, respectively. This situation shows the importance of experimental design and selection of appropriate conditions, which is very important for rapid and cost-effective remediation of contaminated sites.