Nutrients elimination from meat processing wastewater using Scenedesmus sp.; optimizations; artificial neural network and kinetics models
Nur Atikah Ahmad Latiffi, Radin Maya Saphira Radin Mohamed, Adel Al‐Gheethi, R.M. Tajuddin, Muhanna Mohammed Al-shaibani, Dai‐Viet N. Vo, Parveen Fatemeh Rupani
Abstract
The potential of an algae-based system as an environmentally friendly and low-cost water treatment method to eliminate contaminants from water bodies has been considered. The purpose of this research was to see how effective Scenedesmus sp is in eliminating nutrients from meat processing wastewater (MPWW) throughout the phycoremediation process. Response surface methodology (RSM) and an artificial neural network (ANN) model were applied to improve the inactivation process as a function of cell concentrations (3–7 log 10 CFU/mL) and time (1–13 days). At 103 to 107 cell/mL of Scenedesmus sp., phycoremediation was carried out at atmospheric temperature (28 ± 2 °C, ±2500lux for 12:12 h of light/dark and pH 8). The findings documented 73.76% as the highest removal efficacy of total nitrogen (TN) and 77.85% of total phosphorus (TP), 75.40% of ammonia nitrogen (NH4-H), 77.88% of orthophosphate (PO43−), and 64.97% of chemical oxygen demand (COD). The ANN revealed that both factors contribute significantly to the nutrient removal process. The batch kinetic coefficients of NH4-H removal were Km=40.10 mg/L and k=1.43 mg mg −1Chl a d−1. Meanwhile, for PO43−, 1.07 mg mg −1Chl a d−1, as well as 42.80 mg/L, were obtained. The NH4-N yield coefficient of NH4-N was Yn=0.0192 mg Chl a mg −1 while PO43− was equal to Yp=0.0409 mg Chl a mg −1. These findings indicated successful use of Scenedesmus sp. for efficient pollutant removal from meat processing wastewater plants.