Extraction of Nano-Chitosan from waste white shrimp shells for the removal of phenol from refinery wastewater: Optimization of chitosan synthesis and phenol adsorption

Abstract

The waste generated from the production and processing of aquatic and petroleum resources constitutes a major source of pollution in our environment. However, this research work aims at the optimisation of both phenol adsorption and low molecular weight nano–chitosan synthesis from waste white shrimp shells. The optimization of nano–chitosan from waste white shrimp shells was carried out. Design of Experiment (DOE) technique was considered to prepare an experimental matrix using the central composite design (CCD) approach. Response surface methodology (RSM) was applied to optimize the process parameters to achieve model-validated degree of demineralization, deproteination, deacetylation of chitin, and nano-chitosan size efficiency. Results revealed a degree of demineralization, deproteination of chitin and deacetylation of chitosan were 99.57%, 96.4%, and 91.20% respectively. The molecular weight of chitosan was 21374 Da, which indicates that the chitosan obtained was low molecular weight and has the potential for various technological usage. Analysis of the synthesized nano–chitosan displays a size of 84.36 nm through Dynamic Light Scattering (DLS) with more surface areas for phenol removal and adsorption processes. Fourier transform infrared spectrometer (FT-IR) and High-resolution Scanning electron microscopy (SEM) were used for characterization. The concentrations of refinery wastewater before and after treatment were carried out with the aid of a Double beam UV – spectrophotometer and obtained values were 7.18 and 0.033 mg/l respectively. The removal efficiency of phenol was obtained from RSM and model validation values for both experimental and predicted values of 97.22 % and 97.61% respectively, at factors of 3.86 g, 59.20 oC, and 98 mins. The obtained results agree with the statistical model, confirming that RSM can be used effectively to optimize process parameters.