Optimizing the Methylene Blue Removal from Aqueous Solution Using Pomelo Peel Based Biochar Assisted by RSM and ANN-PSO

Abstract

Biochar is regarded as an ideal adsorbent because of its low cost, high adsorption performance, large porosity and high carbon content, which can effectively adsorb organic pollutants in wastewater. The present study explores the modified pomelo peel based biochar used to remove methylene blue (MB) from the simulated wastewater and its adsorption mechanism. The calcinated biochar prepared by pomelo peel under the three temperatures (450ºC, 500ºC and 550ºC) was characterized using SEM, FI-IR, XRD, N 2 adsorption and TGA. The removal conditions were optimized by response surface methodology (RSM) and artificial neural network-particle swarm optimization (ANN-PSO). The results showed that the bulk structure and surface rougher of the biochar increase with the rising of calcination temperature. The prepared materials contained a large number of active functional groups such as lignin, cellulose and hemicellulose. The specific surface area of the calcinated materials at 450ºC, 500ºC and 550ºC is 2.511, 3.353 and 4.344 m 2 /g, respectively, which were belong to mesoporous materials (2-50 nm). ANN-PSO was more suitable than that of RSM to optimize the removal conditions of MB using the prepared materials. The maximum removal efficiency of MB was reached at 89.72% under temperature = 34.63ºC, reaction time = 80.00 min, initial MB concentration = 169.73 mg/L and initial pH = 7. The actual removal efficiency of verification experiment was 87.96%, and the absolute error was 1.76%. The process of MB adsorbed onto the prepared biochar was well accordant with the pseudo second-order kinetic and Langmuir models. The adsorption process of MB by the biochar was a spontaneous, endothermic and entropy-driven. In summary, it is thus indicated that the modified pomelo peel based biochar can be regenerated within fourth cycle and have application prospect as a useful adsorbent for water treatment.

Application of neural network in metal adsorption using biomaterials (BMs): a review

“…107 The particle swarm optimizer (PSO) has been coupled with ANN to predict the dye and copper removal efficiency using graphene oxide-based nanocomposites and pomelo-peel-based carbonaceous material. 204,205 8. Prospective scope ❖ Based on the reviewed literature on metal ion modelling using ANN, estimating the absorption capability of biomaterials other than polysaccharide-derived materials requires additional research.…”

“…107 The particle swarm optimizer (PSO) has been coupled with ANN to predict the dye and copper removal efficiency using graphene oxide-based nanocomposites and pomelo-peel-based carbonaceous material. 204,205…”

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

“…When the initial concentration was 100 mg L −1 , the bed depth was 13.3 cm, and the flow rate was 5 mL min −1 , the bed capacity reached the maximum. Further, coupling ANN and other algorithms, such as PSO, can further improve the applicability of ANN for optimizing adsorption conditions (Wang et al 2022a).…”

Fabrication of the Ordered Mesoporous nZVI/Zr-Ce-SBA-15 Composites Used for Crystal Violet Removal and Their Optimization Using RSM and ANN–PSO

“…Meanwhile, the trainbr algorithm was used to train the network, and the training network was terminated after the mean square error reached 1 × 10 −5 . Based on the work of Wang et al [35], the parameters of the developed ANN model were as the follows: Epoch (2000), learning rate (0.1), goal (1 × 10 −5 ), and momentum factor (0.9). The experimental results show that the sample training achieved convergence quickly (Figure 10).…”