Selectively retaining nutrients in biochar in magnesium added two-zone staged copyrolysis of blue algae and corn gluten wastes.

The disposal of blue algae (BA) and corn gluten (CG) wastes and the simultaneous recovery of abundant phosphorus (P) and nitrogen (N) by pyrolysis to obtain biochars with high fertility is a promising strategy. However, pyrolysis of BA or CG alone by a conventional reactor cannot reach the target. Herein, we propose a novel MgO-enhanced N and P recovery method by designing a two-zone staged pyrolysis reactor to highly efficiently recover N and P with easily available plant forms in BA and CG. The results show that a 94.58% total phosphorus (TP) retention rate was achieved by means of the special two-zone staged pyrolysis method, in which the effective P (Mg2PO4(OH) and R-NH-P) accounted for 52.9% of TP, while the total nitrogen (TN) reached 4.1 wt%. In this process, stable P was formed first at 400 °C to avoid rapid volatilization and then to form hydroxyl P at 800 °C. Meanwhile, Mg-BA char in the lower zone can efficiently absorb N-containing gas generated by the upper CG, forming dispersible N. This work is of great significance for improving the green utilization value of P and N in BA and CG.

A back propagation neural network model for accurately predicting the removal efficiency of ammonia nitrogen in wastewater treatment plants using different biological processes.

Accurately predicting the water quality of treated water from a water treatment plant (WWTP) based on the obtained operating database is of great significance. However, it is difficult for common mechanistic models to work well. In this study, a back propagation artificial neural network (BPANN) model with high accuracy was developed to predict the denitrification efficiency based on a 1-year operating database. Standardized principal component analysis (PCA) methods were used to address the data, and the PCA processed data exhibited the best accuracy. In three WWTPs adopting the anaerobic/anoxic/oxic (A2O) process, the ammonia nitrogen removal efficiency of WWTPs was successfully predicted by using five variables: inlet flow rate, pH value, original ammonia nitrogen concentration, Chemical oxygen demand (COD) concentration, and total phosphorus concentration. Importantly, the obtained BPANN model can be effectively used for other widely used treatment processes, such as oxidation ditch (OD), sequencing batch reactor activated sludge process (SBR), membrane bioreactor (MBR), and cyclic activated sludge technology (CAST), by simply optimizing the training data ratios between 50/50 and 90/10. This is the first trial to set up a universal model for predicting the denitrification efficiency of WWTPs adopting common biological processes. The model could be used to choose the optimum treatment process in the new WWTP design or take action in advance to avoid the risk of excessive emissions when the already built WWTPs are subjected to sudden shocks.