LAUSR

Abstract Lead pollution in aquatic environment possessed lethal threats to public health. Heavy metal removal using microalgae have gained increasing attention as a novel biosorbent with great economic potential. However, excessive time consumption has been a limiting factor in algal adsorption experiments, while artificial neural network (ANN) prediction models could significantly improve the efficiency for experimental research. In this study, backpropagation (BP) neural network model was established for Pb 2 + removal from water by Scenedesmus obliquus with the 4:9:1 (input layer–hidden layer–output layer) construction. The experimental data of 275 groups derived from orthogonal experiments were obtained for training, validation and test data as input layer. Tangent sigmoid transfer function (tansig) was used in the hidden layer, while linear transfer function was used on the output layer (purelin). The correlation coefficient R2 reached 0.997 for the entire data set after training. Testing model results showed that the correlation between the predicted data and the experimental data was 0.997, with an accuracy rate of 95.4%, coefficient of determination of 0.999, root mean square error of 0.226, relative error of 1.47% and P value of 0.004. The predicted data of BP neural network also successfully applied in the pseudo-second-order kinetic model and Langmuir thermal dynamic model with the relative error less than 5%. Results showed that under pH 6 and algal biomass 2 g/L, Scenedesmus Obliquus achieved highest Pb 2 + removal efficiency. The successful application of BP neural network in Pb 2 + removal by Scenedesmus Obliquus provided an alternative and a more efficient method for microalgal adsorption experimental design and data validation.