[ Objective ] The study aimed at treating wastewater treatment plant （WWTP） effluent by using bio-film reactor with filamentous bamboo as bio-carrier. [ Method] With the aid of a continuous flow reactor, a bio-film ...[ Objective ] The study aimed at treating wastewater treatment plant （WWTP） effluent by using bio-film reactor with filamentous bamboo as bio-carrier. [ Method] With the aid of a continuous flow reactor, a bio-film reactor using filamentous bamboo as bio-carrier was used to treat WWTP effluent with low C/N ratio, and the removal effects of CODc,, TN （total nitrogen）, and NO3--N in the wastewater were analyzed.[ Result ] The average removal rates of CODcr, TN, and NO3- -N reached 47.7%, 23.6% and 34.5% when the C/N ratio of influent was around 2. In addi- tion, a stable bio-film was formed very well in the secondary effluent with low C/N ratio and hardly degradable organic pollutants. The pollutants could be removed effectively because of the excellent surface characteristics and compositions of filamentous bamboo. [ Conclusion] The research provides a new method to treat WWTP effluent with low C/N ratio.展开更多
Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a...Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity.The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.展开更多
基金Supported by the Scientific Research Foundation for Postgraduates of ZhengZhou University (A1003) Open Foundation of Provincial Key Laboratory of Environmental Material and Environmental Engineering (K11027)
文摘[ Objective ] The study aimed at treating wastewater treatment plant （WWTP） effluent by using bio-film reactor with filamentous bamboo as bio-carrier. [ Method] With the aid of a continuous flow reactor, a bio-film reactor using filamentous bamboo as bio-carrier was used to treat WWTP effluent with low C/N ratio, and the removal effects of CODc,, TN （total nitrogen）, and NO3--N in the wastewater were analyzed.[ Result ] The average removal rates of CODcr, TN, and NO3- -N reached 47.7%, 23.6% and 34.5% when the C/N ratio of influent was around 2. In addi- tion, a stable bio-film was formed very well in the secondary effluent with low C/N ratio and hardly degradable organic pollutants. The pollutants could be removed effectively because of the excellent surface characteristics and compositions of filamentous bamboo. [ Conclusion] The research provides a new method to treat WWTP effluent with low C/N ratio.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(61225016)the State Key Program of National Natural Science of China(61533002)
文摘Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity.The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.
