Parametric analysis of lignocellulosic ultrafiltration in lab scale cross flow module using pore blocking and artificial neural network model.

In this study, fouling mechanism and modelling analysis of synthetic lignocellulose biomass and agricultural palm oil effluent was studied using polyethersulfone (PES) ultrafiltration (UF) 10 kDa membrane. The impact of process variables (transmembrane pressure (TMP), pH and concentration of feed solution) on lignocellulosic flux was analysed using pore blocking model. The feasible approaches on utilising deep learning artificial neural network (ANN) to predict smaller flux datasets are studied. Among the input variables, pH of lignin feed solution has significant control towards flux and lignin rejection coefficient for both lignin and lignocellulosic solution. Alteration in the structure of lignin at different pH conditions contributed in the improvement of lignin rejection coefficient to 0.98 at the feed pH of 9. A maximum steady state flux of 52.03 L/m2h was observed at the lower lignin concentration (0.25 g/L), TMP of 200 kPa and feed pH of 3. At high TMP and concentration, lignin rejection decreased due to enhancement of feed concentration on membrane surface. The mechanistic model exhibited that cake layer phenomena was dominant in both lignin and lignocellulosic solution. The proposed ANN model showed good correlation (R2-1.00) with experimental non-linear flux dynamic data of both lignin and synthetic lignocellulosic solution. In ANN analysis, activation function, algorithm and neuron effect have significant effect in design of accurate model for prediction of small flux datasets. Aerobically-treated palm oil mill filtration analysis also showed that cake layer phenomenon was dominant. A water recovery of 82 % was achieved even at low TMP under short durations.

ZrO2-TiO2 Incorporated PVDF Dual-Layer Hollow Fiber Membrane for Oily Wastewater Treatment: Effect of Air Gap.

Dual-layer hollow fiber (DLHF) nanocomposite membrane prepared by co-extrusion technique allows a uniform distribution of nanoparticles within the membrane outer layer to enhance the membrane performance. The effects of spinning parameters especially the air gap on the physico-chemical properties of ZrO2-TiO2 nanoparticles incorporated PVDF DLHF membranes for oily wastewater treatment have been investigated in this study. The zeta potential of the nanoparticles was measured to be around -16.5 mV. FESEM-EDX verified the uniform distribution of Ti, Zr, and O elements throughout the nanoparticle sample and the TEM images showed an average nanoparticles grain size of ~12 nm. Meanwhile, the size distribution intensity was around 716 nm. A lower air gap was found to suppress the macrovoid growth which resulted in the formation of thin outer layer incorporated with nanoparticles. The improvement in the separation performance of PVDF DLHF membranes embedded with ZrO2-TiO2 nanoparticles by about 5.7% in comparison to the neat membrane disclosed that the incorporation of ZrO2-TiO2 nanoparticles make them potentially useful for oily wastewater treatment.