NITRATE REMOVAL IN WOODCHIP DENITRIFICATION BIOREACTOR – AN APPROACH COMBINING MATHEMATICAL MODELLING AND PI CONTROL

A mathematical model of nitrate removal in woodchip denitrification bioreactor based on field experiment measurements was developed in this study. The approach of solving inverse problem for nonlinear system of differential convection-reaction equations was applied to optimize the efficiency of nitrate removal depending on bioreactor’s length and flow rate. The approach was realized through the developed algorithm containing a nonlocal condition with an incorporated PI controller. This allowed to adjust flow rate for varying inflow nitrate concentrations by using PI controller. The proposed model can serve as a useful tool for bioreactor design. The main outcome of the model is a mathematical relationship intended for bioreactor length selection when nitrate concentration at the inlet and the flow rate are known. Custom software was developed to solve the system of differential equations aiming to ensure the required nitrate removal efficiency.

Performance and microbial community of a novel PVA/iron-carbon (Fe–C) immobilized bioreactor for nitrate removal from groundwater

An efficient immobilized denitrification bioreactor functioning under anaerobic conditions was developed by combining bacterial immobilization technology with iron-carbon (Fe–C) particles. The effects of key factors on nitrate (NO3 −–N) removal efficiency were invested, such as the carbon-nitrogen ratio (C/N), pH and hydraulic retention time (HRT). Experimental results show that 100.00% NO3 −–N removal efficiency and a low level of nitrite (NO2 −–N) accumulation less than 0.05 mg L−1 were obtained under the condition of a C/N ratio of 3, pH 7.0 and HRT of 6 h. Meteorological chromatographic analysis showed that the final product of denitrification was mainly nitrogen (N2). The main component of precipitation formed in the bioreactor was characterized as Fe3O4 by X-ray diffraction. High-throughput sequencing analysis indicated that the dominant bacterial class in the Fe–C bioreactor was Gammaproteobacteria, while the dominant genera were Zoogloea and Azospira, the relative abundances of which were as high as 23.25 and 15.43%, respectively.

A cascade of a denitrification bioreactor and an aerobic biofilm reactor for heavy oil refinery wastewater treatment

The performance of an efficient denitrification bioreactor–aerobic biofilm reactor cascade for heavy oil refinery wastewater treatment was investigated.

Evaluation of the information content of long-term wastewater characteristics data in relation to activated sludge model parameters

A parameter estimation framework was used to evaluate the ability of observed data from a full-scale nitrification–denitrification bioreactor to reduce the uncertainty associated with the bio-kinetic and stoichiometric parameters of an activated sludge model (ASM). Samples collected over a period of 150 days from the effluent as well as from the reactor tanks were used. A hybrid genetic algorithm and Bayesian inference were used to perform deterministic and parameter estimations, respectively. The main goal was to assess the ability of the data to obtain reliable parameter estimates for a modified version of the ASM. The modified ASM model includes methylotrophic processes which play the main role in methanol-fed denitrification. Sensitivity analysis was also used to explain the ability of the data to provide information about each of the parameters. The results showed that the uncertainty in the estimates of the most sensitive parameters (including growth rate, decay rate, and yield coefficients) decreased with respect to the prior information.