Treatment of Aniline-Contaminated Wastewater by Sequencing Biofilm Batch Reactor System Under Micro-Aerobic Condition

Sequencing biofilm batch reactor(SBBR) under micro-aerobic condition was applied to the treatment of aniline-contaminated wastewater in this study.Hydraulic retention time(HRT) of 12—36 h and dissolved oxygen(DO) concentration of 0.1—0.5 mg/L were selected as the operating variables to model,analyze and optimize the process.Five dependent parameters,aniline(AN),chemical oxygen demand(COD),ammonium,total nitrogen(TN) and total phosphorus(TP) removal efficiencies as the process responses,were studied.From the results,increase in DO concentration could promote the AN,COD and ammonium removal;increase in HRT could also lead to increase of the AN and ammonium removal,but might decrease COD removal due to endogenous respiration and soluble microbial products.In the SBBR system,24 h for HRT and 0.5 mg/L for DO concentration were chosen as the optimum operating condition.The actual removal efficiencies of COD,AN and ammonium under the optimum operating condition were 98.37%,100%and 89.29%,respectively.The experimental findings were in close agreement with the model prediction.The presence of glucose could promote bacterial growth and has positive influence on AN degradation and ammonium removal.

Forecasting of dissolved oxygen in the Guanting reservoir using an optimized NGBM(1,1) model

An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guantlng reservoir inlet and outlet （China）. The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What＇s more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting.