Artificial neural network and statistical modelling of biosorptive removal of hexavalent chromium using macroalgal spent biomass

This study focused on the sustainable removal of chromium in its hexavalent form by adsorption using sugarextracted spent marine macroalgal biomass - Ulva prolifera. The adsorption of Cr (VI) from aqueous solutions utilizing macroalgal biomass was studied under varying conditions of pH, adsorbent amount, agitation speed, and time to assess and optimize the process variables by using a statistical method - response surface methodology (RSM) to enhance the adsorption efficiency. The maximum adsorption efficiency of 99.11 +/- 0.23% was obtained using U. prolifera under the optimal conditions: pH: 5.4, adsorbent dosage: 200 mg, agitation speed: 160 rpm, and time: 75 min. Also, a prediction tool - artificial neural network (ANN) model was developed using the RSM experimental data. Eight neurons in the hidden layer yielded the best network topology (4-8-1) with a high correlation coefficient (R-ANN: 0.99219) and low mean squared error (MSEANN: 0.99219). Various performance parameters were compared between RSM and ANN models, which confirmed that the ANN model was better in predicting the response with a high coefficient of determination value (R-ANN(2): 0.9844, RRSM2: 0.9721) , low MSE value (MSEANN: 3.7002, MSERSM: 6.2179). The adsorption data were analyzed by fitting to various equilibrium isotherms. The maximum adsorption capacity was estimated as 6.41 mg/g. Adsorption data was in line with Freundlich isotherm (R-2 = 0.97) that confirmed the multilayer adsorption process. Therefore, the spent U. prolifera biomass can credibly be applied as a low-cost adsorbent for Cr (VI) removal , the adsorption process can be modelled and predicted efficiently using ANN.

Automated summary

This study focused on the sustainable removal of hexavalent chromium using sugarextracted spent marine macroalgal biomass. The adsorption process was studied under different conditions and optimized using statistical methods and artificial neural network models. The results showed that the macroalgal biomass had high adsorption efficiency for Cr(VI) under the optimal conditions, and the adsorption process could be effectively modeled and predicted using ANN.