This study investigates the use of microalgae as a biosorbent to eliminate heavy metals ions from wastewater. The Chlorella kessleri microalgae species was employed to biosorb heavy metals from synthetic wastewater specimens. FTIR, and SEM/XRD analyses were utilized to characterize the microalgal biomass (the adsorbent). The experiments were conducted with several process parameters, including initial solution pH, temperature, and microalgae biomass dose. In order to secure the best experimental conditions, the optimum parameters were estimated using an integrated response surface methodology (RSM), desirability function (DF), and crow search algorithm (CSA) modeling approach. A maximum lead(II) removal efficiency of 99.54% was identified by the RSM–DF platform with the following optimal set of parameters: pH of 6.34, temperature of 27.71 °C, and biomass dosage of 1.5 g L−1. The hybrid RSM–CSA approach provided a globally optimal solution that was similar to the results obtained by the RSM–DF approach. The consistency of the model-predicted optimum conditions was confirmed by conducting experiments under those conditions. It was found that the experimental removal efficiency (97.1%) under optimum conditions was very close (less than a 5% error) to the model-predicted value. The lead(II) biosorption process was better demonstrated by the pseudo-second order kinetic model. Finally, simultaneous removal of metals from wastewater samples containing a mixture of multiple heavy metals was investigated. The removal efficiency of each heavy metal was found to be in the following order: Pb(II) > Co(II) > Cu(II) > Cd(II) > Cr(II).
Settlement simulating in cohesion materials is a crucial issue due to complexity of cohesion soil texture. This research emphasis on the implementation of newly developed machine learning models called hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) algorithm, Ant Colony optimizer (ACO), Differential Evolution (DE), and Genetic Algorithm (GA) as efficient approaches to predict settlement of shallow foundation over cohesion soil properties. The width of footing (B), pressure of footing (qa), geometry of footing (L/B), count of SPT blow (N), and ratio of footing embedment (Df/B) are considered as predictive variables. Nonhomogeneity and inconsistency of employed dataset is a major concern during prediction modeling. Hence, two different modeling scenarios (i) preprocessed dataset (PP) and (ii) nonprocessed (initial) dataset (NP) were inspected. To assess the accuracy of the applied hybrid models and standalone one, multiple statistical metrics were computed and analyzed over the training and testing phases. Results indicated ANFIS-PSO model exhibited an accurate and reliable prediction data intelligent and had the highest predictability performance against all employed models. In addition, results demonstrated that data preprocessing is highly essential to be performed prior to building the predictive models. Overall, ANFIS-PSO model showed a robust machine learning for settlement prediction.
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