Reactive electrically conducting membranes for phosphorus recovery from livestock wastewater effluents

Influencing factors for nutrient removal from piggery digestate by coupling microalgae and electric field

Microalgae show great potential for nutrient removal from piggery digestate. However, full-strength piggery digestate have been found to severely inhibit microalgal growth. In this study, microalgae were coupled into the electric field (EF)system to form an electric field-microalgae system (EFMS). The effects of EF characteristics and environmental conditions on the growth of Desmodesmus sp. CHX1 and the removal of nitrogen and phosphorus in EFMS were explored. The results indicated that the optimal EF parameters for forming a fine EFMS were electrode of Zn (anode)/graphite (cathode), electric frequency of three times per day (10 min/time) and voltage of 12 V. The suitable light intensity and microalgae inoculation concentration for the EFMS were 180 μmol photons/(m²·s) and 0.2 g/L, respectively. Ammonium nitrogen and total phosphorus removal efficiencies were 65.38% and 96.16% in the piggery digestate by EFMS under optimal conditions. These results indicate that EFMS is a promising technology for nutrient removal from piggery digestate.

Performance evaluation of whey flux in dead-end and cross-flow modes via convolutional neural networks

Effluent originating from cheese production puts pressure onto environment due to its high organic load. Therefore, the main objective of this work was to compare the influence of different process variables (transmembrane pressure (TMP), Reynolds number and feed pH) on whey protein recovery from synthetic and industrial cheese whey using polyethersulfone (PES 30 kDa) membrane in dead-end and cross-flow modes. Analysis on the fouling mechanistic model indicates that cake layer formation is dominant as compared to other pore blocking phenomena evaluated. Among the input variables, pH of whey protein solution has the biggest influence towards membrane flux and protein rejection performances. At pH 4, electrostatic attraction experienced by whey protein molecules prompted a decline in flux. Cross-flow filtration system exhibited a whey rejection value of 0.97 with an average flux of 69.40 L/m²h and at an experimental condition of 250 kPa and 8 for TMP and pH, respectively. The dynamic behavior of whey effluent flux was modeled using machine learning (ML) tool convolutional neural networks (CNN) and recursive one-step prediction scheme was utilized. Linear and non-linear correlation indicated that CNN model (R² - 0.99) correlated well with the dynamic flux experimental data. PES 30 kDa membrane displayed a total protein rejection coefficient of 0.96 with 55% of water recovery for the industrial cheese whey effluent. Overall, these filtration studies revealed that this dynamic whey flux data studies using the CNN modeling also has a wider scope as it can be applied in sensor tuning to monitor flux online by means of enhancing whey recovery efficiency.