Kinetics and neuro-fuzzy soft computing modelling of river turbid water coag-flocculation using mango (Mangifera indica) kernel coagulant

Magnetic Field Effect on Coagulation Treatment of Wastewater Using Magnetite Rice Starch and Aluminium Sulfate

The use of synthetic coagulants to reduce suspended particles from drinkable water and wastewater is prompting new issues because it poses many health and environmental risks. Hence, improving the coagulation process using sophisticated nanotechnology with a magnetic field (MF) for quick recoverability emerges as being useful. In this study, the effects of magnetite rice starch (MS) and aluminum sulfate (alum) were investigated at a constant dose (3 g or 3000 mg/L) using a Jar test (six beakers) as potential low-cost coagulants for industrial wastewater treatment. At a high magnification of 1000× and a surface pore size of 298 µm, scanning electron microscopy and energy dispersive X-ray (SEM/EDX) analyses were utilized to elucidate the morphology of the coagulants. Coagulation was performed at 150 rpm (quick mixing) for 2 min, and 30 rpm (slow mixing) for 15 min. Thereafter, samples were allowed to settle (10-60 min) with and without MF. The findings showed more than 65% contaminants removal (turbidity and TSS) and 30% chemical oxygen demand (COD) removal using alum while MS showed 80% contaminants removal (turbidity and TSS) and 50% COD removal. MS showed an increase of more than 3% in contaminants removal (COD, turbidity, and TSS) when exposed to MF. As a result, the use of MS together with MF in water and wastewater treatment is anticipated as an environmentally benign and effective coagulant.

Black-box modelling, bi-objective optimization and ASPEN batch simulation of phenolic compound extraction from Nauclea latifolia root

Nauclea latifolia root (NLR) extract is one of phytochemicals used to treat various ailments in most of developing countries. This investigation focuses on modelling, optimization and computer-aided simulation of phenolic solid-liquid extraction from NLR. The extraction experiments were conducted at extraction temperature (ET: 33.79–76.21 °C), process time (PT: 2.79–4.21 h) and solid-liquid ratio (SLC: 0.007929–0.018355 g/ml). Regression models (RM) were developed, using Response Surface Methodology (RSM) in Design Expert software, for predicting and optimizing total phenolic content (TPC) and total flavonoid content (TFC) and also compared with adaptive neuro-fuzzy inference system (ANFIS) modelling in Matlab environment. Aspen Batch Process Developer (ABPD) V10 was used to simulate phenolic extract production and perform material balance of the process. Both Coefficients of determination (R²) of RSM (TFC: 0.9996, TPC: 0.9932) and ANFIS models (TFC: 0.99998, TPC: 0.9982) were compared and predicted satisfactorily. Optimization results show: ET (2.79 h), PT (38.8 °C), SLC (0.0198 g/ml), TFC (25.92 25.92 μg RE/g) and TPC (8.47 mg GAE/g). The phenolic extraction base case simulation results gave batch throughput, annual throughput, number of batches per year 0.0089 g/batch, 0.139 g/year and 1019 batches, respectively. The ABPD predicted TPC and experimental TPC results were compared and gave mean relative deviation error of 3.75%. Thus, ABPD simulation model is reasonably reliable for the scale-up design engineering of the phenolic extract production from NLR.