Development of power ultrasound‐modified industrial tomato waste as an efficient biosorbent: Characterization, application on synthetic dyes, and optimization using artificial neural networks

Abstract

Power ultrasound (US)-modified tomato waste (pomace) was used to prepare a highly efficient novel biosorbent, which was then used to remove congo red (CR) and methylene blue (MB) dyes from an aqueous solution. Characterization of produced biosorbent using the US was done using Brunauer–Emmett–Teller (BET analysis), X-ray photoelectron spectrometer (XPS), scanning electron microscope (SEM), attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and UV–visible spectrophotometer. Compared with response surface methodology (RSM), the artificial neural network (ANN) model best fitted the experimental data with an R2 value of 0.998. The application of US on raw pomace resulted in increased surface area by 84.25%, an increase in a large number of pores as evident through SEM, conversion of crystalline nature of biosorbent to amorphous nature, and removal of one compound (–COOR) at 288 eV of binding energy for power ultrasound-assisted pomace (UAP) biosorbent. Further, the Langmuir isotherm model described the best adsorption behavior and showed a maximum adsorption capacity of 436.68 and 317.76 mg/g for MB and CR dyes from aqueous solution, respectively. Novelty Impact Statement Tomato waste pomace is widely generated from processing industries, which leads to an environmental burden. Moreover, industrial effluents (synthetic dyes) are contaminants that lead to diseases like cancer in humans and affect the natural and aquatic environment. For the first time, industrial tomato processing waste pomace was used for the preparation of a highly efficient biosorbent by using power ultrasound technology to remove synthetic dyes from an aqueous solution. Ultrasound application resulted in the creation of more sites for adsorption, further, the overall process was low energy and environmentally sustainable for the preparation of biosorbent. The present study highlights an interesting phenomenon and a novel application for the utilization of by-products generated from the tomato industry which was well explained by the artificial neural network.