Model prediction of coagulation by magnetised rice starch for wastewater treatment using response surface methodology (RSM) with artificial neural network (ANN)

An investigation into the performance and perikinetics of Brassica nigra meal in the treatment of real vegetable oil refinery condensate effluent

In this study, the treatment of vegetable oil refinery plant condensate effluent (VORCE) having high total suspended solids (TSS) and chemical oxygen demand (COD) generated from acid oil unit was focused. The utilization of waste Brassica nigra meal (BNM) as protein flocculant in treating VORCE was explored. The B. nigra meal flocculant (BNMF) exhibited a crystalline nature, with the presence of amino and carboxyl functional groups, rendering it highly efficient (89.69% efficiency) in floc formation. Zeta potential and particle size (-5.6 mV and 240.68 nm, respectively) indicate BNMF's effectiveness in initiating floc formation. The interactive effects of pH, dosage, settling time on COD, and TSS removal were investigated using the Box-Behnken design. At an optimal pH of 6.9 and BNMF dosage of 0.77 g/L, a maximum removal of 85.38% COD and 72.56% TSS was obtained. The perikinetic theory for the coagulation-flocculation followed a second-order rate reaction with high Kc (0.0001 L/mg min), low settling time (37.04 min), and high collision efficiency (2.703 × 1017), indicating the model's significance in achieving maximum COD and TSS removal. These findings highlight the potential use of BNMF in the treatment of VORCE, leading to circular economy by valorizing waste from mustard oil extraction and zero discharge. PRACTITIONER POINTS: Valorization of waste Brassica nigra meal (BNM) as a potent protein flocculant Optimization for vegetable oil refinery condensate effluent (VORCE) treatment was done. Interactive effects of the process parameters were analyzed using Design expert. Perikinetic theory for VORCE treatment follows second-order reaction rate with high Kc.

Synthesis of Natural-Inspired Materials by Irradiation: Data Mining from the Perspective of Their Functional Properties in Wastewater Treatment

The present study is focused on assessing the interrelation of variables involved in the synthesis of natural-inspired copolymers by electron beam grafting while taking the functionality of the resulting materials into account. In this respect, copolymers of starch-graft-polyacrylamide (St-g-PAM) were synthesized by irradiation, and their flocculation efficiency regarding the total suspended solids (TSS), chemical oxygen demand (COD), and fatty matters (FM) was tested in coagulation-flocculation experiments at laboratory scale on wastewater from the oil industry. Data mining involved approaches related to the association (correlation and dimensionality reduction with principal component analysis (PCA)), clustering by agglomerative hierarchical clustering (AHC), classifying by classification and regression tree (CART), and prediction (decision tree prediction, multiple linear regression (MLR), and principal component regression (PCR)) of treatments applied with the variation of the monomer concentration, irradiation dose, and dose rate. The relationship mining proved that the level of COD was significantly affected by the irradiation dose and monomer concentration, and FM was mainly affected by the dose rate (significance level = 0.05). TSS showed the highest negative correlation with the tested variables. Moreover, the consequences of MLR demonstrated an acceptable accuracy (mean absolute percentage error < 5%) for COD and FM; meanwhile, linear modeling together with the consequences of PCA in the structure of PCR could help to simplify and improve the prediction accuracy of equations.

Deep Study on Fouling Modelling of Ultrafiltration Membranes Used for OMW Treatment: Comparison Between Semi-empirical Models, Response Surface, and Artificial Neural Networks

Olive oil production generates a large amount of wastewater called olive mill wastewater. This paper presents the study of the effect of transmembrane pressure and cross flow velocity on the decrease in permeate flux of different ultrafiltration membranes (material and pore size) when treating a two-phase olive mill wastewater (olive oil washing wastewater). Both semi-empirical models (Hermia models adapted to tangential filtration, combined model, and series resistance model), as well as statistical and machine learning methods (response surface methodology and artificial neural networks), were studied. Regarding the Hermia model, despite the good fit, the main drawback is that it does not consider the possibility that these mechanisms occur simultaneously in the same process. According to the accuracy of the fit of the models, in terms of R2 and SD, both the series resistance model and the combined model were able to represent the experimental data well. This indicates that both cake layer formation and pore blockage contributed to membrane fouling. The inorganic membranes showed a greater tendency to irreversible fouling, with higher values of the Ra/RT (adsorption/total resistance) ratio. Response surface methodology ANOVA showed that both cross flow velocity and transmembrane pressure are significant variables with respect to permeate flux for all membranes studied. Regarding artificial neural networks, the tansig function presented better results than the selu function, all presenting high R2, ranging from 0.96 to 0.99. However, the comparison of all the analyzed models showed that depending on the membrane, one model fits better than the others. Finally, through this work, it was possible to provide a better understanding of the data modelling of different ultrafiltration membranes used for the treatment of olive mill wastewater.

Effect of Magnetized Coagulants on Wastewater Treatment: Rice Starch and Chitosan Ratios Evaluation

Coagulation with synthetic chemicals has been used to treat a wide range of industrial effluents. Herein, the unique characteristics of industrial effluents being detrimental to the environment warrants urgent resource-efficient and eco-friendly solutions. Therefore, the study investigated the use of two magnetized coagulants (chitosan magnetite (CF) and rice starch magnetite (RF)), prepared via co-precipitation in three different ratios (1:2, 1:1 and 2:1) of natural coagulants (chitosan or rice starch) and magnetite nanoparticles (F) as alternative coagulants to alum for the treatment of wastewater. A Brunauer–Emmett–Teller (BET) analyzer, an X-ray diffraction (XRD) analyzer, and energy-dispersive X-ray (EDX) spectroscopy were used to characterize the surface area, crystal structure, and elemental composition of the coagulants. The influences of settling time (10–60 min) on the reduction of turbidity, color, phosphate, and absorbance were studied. This was carried out with a jar test coupled with six beakers operated under coagulation conditions of rapid stirring (150 rpm) and gentle stirring (30 rpm). Wastewater with an initial concentration of 45.6 NTU turbidity, 315 Pt. Co color, 1.18 mg/L phosphate, 352 mg/L chemical oxygen demand (COD), and 73.4% absorbance was used. The RF with a ratio of 1:1 was found to be the best magnetized coagulant with over 80% contaminant removal and 90% absorbance. The treatability performance of RF (1:1) has clearly demonstrated that it is feasible for wastewater treatment.