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| 1 | Data-driven intelligent monitoring system for key variables in wastewater treatment process显示文摘In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance. | Honggui Han Shuguang Zhu Junfei Qiao Min Guo | 2018 | Chinese Journal of Chemical Engineering2018，26，10： | 4 |
| 2 | Robust optimal control for anaerobic-anoxic-oxic reactors显示文摘Anaerobic-anoxic-oxic(A_2O) reactors, as the core parts of wastewater treatment process(WWTP), have attracted considerable attention to achieve the reliability of denitrification and dephosphorization. However, it is difficult to realize the optimal operation of A_2O reactors due to the existence of nonlinear dynamics and large uncertainties. To solve this problem, a robust optimal control(ROC) strategy is developed to improve the operation performance of A_2O reactors. First, data-driven systematic evaluation criteria are developed to describe the operational indicators of changeable conditions. Second, a robust optimization algorithm is designed to select the optimal solution. Third, a fuzzy neural network(FNN) is used to track the optimal solution in the control process. Finally, this proposed ROC strategy is applied to the phosphorus removal benchmark simulation model(BSM1-P) and the real A_2O reactors. The results demonstrate that the strategy developed in this paper has great potential for application in real A_2O reactors. | HAN HongGui ZHANG JiaCheng DU ShengLi SUN Hao Yuan QIAO JunFei | 2021 | Science China(Technological Sciences)2021，64，7： | 4 |
| 3 | Dynamic multi-objective differential evolution algorithm based on the information of evolution progress显示文摘The multi-objective differential evolution(MODE)algorithm is an effective method to solve multi-objective optimization problems.However,in the absence of any information of evolution progress,the optimization strategy of the MODE algorithm still appears as an open problem.In this paper,a dynamic multi-objective differential evolution algorithm,based on the information of evolution progress(DMODE-IEP),is developed to improve the optimization performance.The main contributions of DMODE-IEP are as follows.First,the information of evolution progress,using the fitness values,is proposed to describe the evolution progress of MODE.Second,the dynamic adjustment mechanisms of evolution parameter values,mutation strategies and selection parameter value based on the information of evolution progress,are designed to balance the global exploration ability and the local exploitation ability.Third,the convergence of DMODE-IEP is proved using the probability theory.Finally,the testing results on the standard multi-objective optimization problem and the wastewater treatment process verify that the optimization effect of DMODE-IEP algorithm is superior to the other compared state-of-the-art multi-objective optimization algorithms,including the quality of the solutions,and the optimization speed of the algorithm. | HOU Ying WU YiLin LIU Zheng HAN HongGui WANG Pu | 2021 | Science China(Technological Sciences)2021，64，8： | 3 |
| 4 | A data-derived soft-sensor method for monitoring effluent total phosphorus显示文摘The effluent total phosphorus(ETP) is an important parameter to evaluate the performance of wastewater treatment process(WWTP). In this study, a novel method, using a data-derived soft-sensor method, is proposed to obtain the reliable values of ETP online. First, a partial least square(PLS) method is introduced to select the related secondary variables of ETP based on the experimental data. Second, a radial basis function neural network(RBFNN) is developed to identify the relationship between the related secondary variables and ETP. This RBFNN easily optimizes the model parameters to improve the generalization ability of the soft-sensor. Finally, a monitoring system, based on the above PLS and RBFNN, named PLS-RBFNN-based soft-sensor system, is developed and tested in a real WWTP. Experimental results show that the proposed monitoring system can obtain the values of ETP online and own better predicting performance than some existing methods. | Shuguang Zhu Honggui Han Min Guo Junfei Qiao | 2017 | Chinese Journal of Chemical Engineering2017，25，12： | 3 |
| 5 | Adaptive candidate estimation-assisted multi-objective particle swarm optimization显示文摘The selection of global best(Gbest) exerts a high influence on the searching performance of multi-objective particle swarm optimization algorithm(MOPSO). The candidates of MOPSO in external archive are always estimated to select Gbest. However,in most estimation methods, the candidates are considered as the Gbest in a fixed way, which is difficult to adapt to varying evolutionary requirements for balance between convergence and diversity of MOPSO. To deal with this problem, an adaptive candidate estimation-assisted MOPSO(ACE-MOPSO) is proposed in this paper. First, the evolutionary state information,including both the global dominance information and global distribution information of non-dominated solutions, is introduced to describe the evolutionary states to extract the evolutionary requirements. Second, an adaptive candidate estimation method,based on two evaluation distances, is developed to select the excellent leader for balancing convergence and diversity during the dynamic evolutionary process. Third, a leader mutation strategy, using the elite local search(ELS), is devised to select Gbest to improve the searching ability of ACE-MOPSO. Fourth, the convergence analysis is given to prove the theoretical validity of ACE-MOPSO. Finally, this proposed algorithm is compared with popular algorithms on twenty-four benchmark functions. The results demonstrate that ACE-MOPSO has advanced performance in both convergence and diversity. | HAN HongGui ZHANG LinLin HOU Ying QIAO JunFei | 2022 | Science China(Technological Sciences)2022，65，8： | 2 |
| 6 | Mobile phone recognition method based on bilinear convolutional neural network显示文摘Model recognition of second-hand mobile phones has been considered as an essential process to improve the efficiency of phone recycling. However, due to the diversity of mobile phone appearances, it is difficult to realize accurate recognition. To solve this problem, a mobile phone recognition method based on bilinear-convolutional neural network(B-CNN) is proposed in this paper.First, a feature extraction model, based on B-CNN, is designed to adaptively extract local features from the images of secondhand mobile phones. Second, a joint loss function, constructed by center distance and softmax, is developed to reduce the interclass feature distance during the training process. Third, a parameter downscaling method, derived from the kernel discriminant analysis algorithm, is introduced to eliminate redundant features in B-CNN. Finally, the experimental results demonstrate that the B-CNN method can achieve higher accuracy than some existing methods. | HAN HongGui ZHEN Qi YANG HongYan DU YongPing QIAO JunFei | 2021 | Science China(Technological Sciences)2021，64，11： | 2 |
| 7 | A sludge volume index (SVI) model based on the multivariate local quadratic polynomial regression method显示文摘In this study, a multivariate local quadratic polynomial regression(MLQPR) method is proposed to design a model for the sludge volume index(SVI). In MLQPR, a quadratic polynomial regression function is established to describe the relationship between SVI and the relative variables, and the important terms of the quadratic polynomial regression function are determined by the significant test of the corresponding coefficients. Moreover, a local estimation method is introduced to adjust the weights of the quadratic polynomial regression function to improve the model accuracy. Finally, the proposed method is applied to predict the SVI values in a real wastewater treatment process(WWTP). The experimental results demonstrate that the proposed MLQPR method has faster testing speed and more accurate results than some existing methods. | Honggui Han Xiaolong Wu Luming Ge Junfei Qiao | 2018 | Chinese Journal of Chemical Engineering2018，26，5： | 2 |
| 8 | Prediction of activated sludge bulking based on a self-organizing RBF neural network 显示文摘 | Han Honggui Qiao Junfei | 2012 | JournalofProcess Control2012，22，6： | 1 |
| 9 | Nonlinear sys- tems modeling based on self-organizing fuzzy neural network with adaptive computation algorithm显示文摘 | Han Honggui Wu Xiaolong Qiao Junfei | 2014 | IEEE Transactions on Cybernetics2014，44，4： | 1 |
| 10 | Nonlinear model-predictive control for industrial processes: An application to wastewater treatment process显示文摘 | Han Honggui Qiao Junfei | 2014 | IEEE Trans on Industrial Electronics2014，61，4： | 1 |
| 11 | Modelling and identification of nonlinear dynamic systems using a novel self-organizing RBF-based approach显示文摘 | Qiao Junfei Han Honggui | 2012 | Automatica2012，48，8： | 1 |
| 12 | Energy consumption model for wastewater treatment process control显示文摘 | Huang Xiaoqi Han Honggui and Qiao Junfel | 2013 | Water Science & Technology2013，67，3： | 1 |
| 13 | A self-organizing fuzzy neural networkbased on a growing-and-pruning algorithm 显示文摘 | Han Honggui Qiao Junfei | 2010 | IEEE Trans onFuzzy Systems2010，18，6： | 1 |
| 14 | Hierarchical neural network modeling approach to predict sludge volume index of wastewater treatment process 显示文摘 | Han Honggui Qiao Junfei | 2013 | IEEE Transactions on Control Systems Technology2013，21，6： | 1 |
| 15 | Nonlinear model-predictive control for industrial processes:an application to wastewater treatment process显示文摘 | HAN Honggui QIAO Junfei | 2014 | IEEE Transactions on Industrial Electronics2014，61，4： | 1 |
| 16 | Prediction of activated sludge bulking based on a self-organizing RBF neural network显示文摘 | Han Honggui Qiao Junfei | 2012 | Journal of Process Control2012，22，6： | 1 |
| 17 | A self-organizing fuzzyneural network based on a growing-and-pruningalgorithm显示文摘 | HAN Honggui QIAO Junfei | 2010 | IEEE Transactions on Neural Network2010，18，6： | 1 |
| 18 | Reusable electronic products value prediction based on reinforcement learning显示文摘With the appearance of a huge number of reusable electronic products,the precise value evaluation has become an urgent problem to be solved in the recycling process.Traditional methods rely on manual intervention mostly.In order to make the model more suitable for the dynamic updating,this paper proposes the reinforcement learning based electronic products value prediction model which integrates market information to achieve timely and stable prediction results.The basic attributes and depreciation attributes of the product are modeled by two parallel neural networks separately to learn the different effects for prediction.Most importantly,the double deep Q network is adopted to fuse market information by reinforcement learning strategy,and the training on the old product data can be used to predict the following appeared product,which alleviates the cold start problem.Experiments on the real mobile phone recycling platform data verify that the model has achieved higher accuracy and it has a better generalization ability. | DU YongPing JIN XingNan HAN HongGui WANG LuLin | 2022 | Science China(Technological Sciences)2022，65，7： | 1 |
| 19 | An efficent selforganizing RBF neural network for water quality prediction显示文摘 | Han Honggui Chen Qili Qiao Junfei | 2011 | Neural Networks2011，24，7： | 1 |
| 20 | A self-organizing fuzzy neural network based on a growing-and-pruning algorithm显示文摘 | Honggui Han Junfei Qiao | 2010 | IEEE Trans on Fuzzy Systems2010，18，6： | 1 |
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