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Lack of robust techniques for optimization of hybrid separation systems is obvious in the literature. A novel hybrid approach for modeling and optimization of a hybrid process consisting of fixed-bed adsorption column (FBAC) and dead-end filtration (DEF) for the removal of methylene blue from water was presented. Artificial neural network (ANN), response surface methodology (RSM) and genetic algorithm (GA) were used for this purpose. ANN was employed to successfully approximate the breakthrough curves. Central composite design was used to investigate the impact of the operating variables, i.e. feed flowrate, initial concentration, adsorption bed length, and filter type on the removal rate as the objective function. RSM variables together with the operation time were considered as input parameters and the normalized concentration was defined as output of the network. A regression model was developed using RSM to correlate the experimental data. The model was then optimized via GA to maximize the removal rate at break time. The optimal removal rate was 3.79 s−1 corresponding to OS-100 filter, 1.12 L/min flowrate, 30 cm bed length and, 40.49 mg/L initial concentration. Sobol sensitivity analysis indicated that flowrate with 43% and filter type with 41% were the most effective variables on the process performance. A combined RSM/ANN/GA approach for the optimization of a hybrid separation process. Image 10396 • A novel combined RSM and GA approach was pioneered for optimization of hybrid separation processes. • Approximation of breakthrough curves was successfully carried out with ANN modeling. • Sobol's analysis allows computing sensitivity of the hybrid separation performance to the operating variables. • Feed flowrate and filter sheet type were the most effective parameters on the system performance. [ABSTRACT FROM AUTHOR]

A novel strategy for optimization of hybrid system of dynamic adsorption/depth filtration. • Fixed bed column/dead-end filtration hybrid separation system was employed for MB removal. • Performance of the hybrid system was optimized using central composite design algorithm. • Feed flowrate was the most affecting variable based on the Pareto sensitivity analysis. • Superiority of the hybrid system performance compared to individual systems at optimum conditions was approved. This work investigates the performance and optimization of a hybrid process consisting of a fixed-bed adsorption (FBA) column and a dead-end filtration (DEF) cell for the removal of methylene blue (MB) from water. Granular activated carbon and cellulose acetate depth filter sheets were used as adsorbent and filter medium, respectively. Effects of four operation parameters (initial concentration, feed flowrate, bed length, and filter type) on the MB removal rate (RR) were investigated using breakthrough data analysis and response surface methodology. This RR (mass of MB removed per second) captures the removal capacity and separation rate, simultaneously. The maximum RR was found theoretically equal to 3.875 s−1 at the optimized conditions of initial concentration =39.86 mg/L, flowrate = 1.15 L/min, bed length =29.95 cm, and filter type = OS100, and validated with the experimental data with 99% agreement. The performance of the hybrid process operating at the optimal operation conditions was found to be superior to those of the individual FBA column and DEF cell. After 100 s from the process commencement, the RR obtained with the hybrid process was about 64% higher than that obtained with the FBA column and 30% higher than that obtained with the DEF cell. [ABSTRACT FROM AUTHOR]