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Data-driven intelligent monitoring system for key variables in wastewater treatment process

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Chinese Journal of Chemical Engineering 2018年第10期26卷 2093-2101页

作者： Honggui Han Shuguang Zhu junfei qiao Min Guo College of Electronic Information & Control Engineering Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China Engineering Research Center of Digital Community Ministry of Education Beijing 100124 China Beijing Laboratory for Urban Mass Transit Beijing 100124 China

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.

关键词： Data-driven Soft sensor Intelligent monitoring system Data distribution service Wastewater treatment process

Structure Optimization for Echo State Network Based on Contribution

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Tsinghua Science and Technology 2019年第1期24卷 97-105页

作者： Dingyuan Li Fu Liu junfei qiao Rong Li College of Communication Engineering Jilin University Faculty of Information Technology Beijing University of Technology Department of Information Technology Beijing Vocational College of Agriculture

Echo State Network(ESN) is a recurrent neural network with a large, randomly generated recurrent part called the dynamic reservoir. Only the output weights are modified during training. However, proper balancing of the trade-off between the structure and performance for ESN remains a difficult task. In this paper, a structure optimized method for ESN based on contribution is proposed to simplify its network structure and improve its ***, we evaluate the contribution of reservoir neurons. Second, we present a pruning mechanism to remove the unimportant connection weights of reservoir neurons with low contribution. Finally, the new output weights are learned with the pseudo inverse method. The novel optimized ESN, named C-ESN, is tested on a Lorenz chaotic time-series prediction and an actual municipal sewage treatment system. The simulation results show that the C-ESN can have better prediction and generalization performance than ESN.

关键词： neural network structural design time-series prediction

基于三维随机树算法的稠密约束环境铁路线路优化

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铁道科学与工程学报 2024年第8期21卷 3142-3152页

作者：万昕洁蒲浩冉杨李伟胡建平乔俊飞高速铁路建造技术国家工程研究中心湖南长沙410075 中国中铁股份有限公司北京100039 中南大学土木工程学院湖南长沙410075 中铁二院工程集团有限责任公司四川成都610031 中铁工程设计咨询集团有限公司北京100073

铁路选线是一个复杂的工程问题,尤其是当稠密约束选线环境内存在起伏地形高差约束和大量障碍物时,现有计算机辅助线路设计方法往往需要耗费大量的计算时间和资源才能生成优化方案,甚至容易停滞,无法找到可行方案。为解决这一问题,提出... 详细信息

铁路选线是一个复杂的工程问题,尤其是当稠密约束选线环境内存在起伏地形高差约束和大量障碍物时,现有计算机辅助线路设计方法往往需要耗费大量的计算时间和资源才能生成优化方案,甚至容易停滞,无法找到可行方案。为解决这一问题,提出一种三维快速搜索随机树算法以期快速生成满足所有约束的线路方案。首先,为避免随机树搜索陷入局部最优,提出平-纵整合式随机树启发式采样方法,将随机树搜索扩展到三维空间并实现对选线搜索区域的全面探索。其次,为了在线路搜索过程中高效提取相关环境信息,提出多源异构综合地理信息的统一管理方法,针对环境信息特点定制相应的储存策略,将地形、障碍物等环境信息离散到综合地理信息模型中,并在随机树搜索过程中动态处治障碍物约束。随后集成启发式采样方法和约束处治算子,提出随机树进化搜索方法,在随机树拓展过程中高效检索和处治障碍物,快速生成优化路径方案。最后,将此方法应用于一个真实稠密约束线路案例中,实验结果证明,此方法能实现对所有障碍物约束的空间绕避,并能快速产生优化线路方案,相比于人工方案,机选方案的造价降低了4.8%。实验结果表明此方法可以提高线路设计效率,为人工设计提供有价值的参考。

关键词：铁路设计线路优化启发式采样快速搜索随机树约束优化

Adaptive candidate estimation-assisted multi-objective particle swarm optimization

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Science China(Technological Sciences) 2022年第8期65卷 1685-1699页

作者： HAN HongGui ZHANG LinLin HOU Ying qiao junfei Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China

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.

关键词： multi-objective particle swarm optimization evolutionary state information adaptive candidate estimation convergence and diversity convergence analysis

Dissolved oxygen concentration control in wastewater treatment process based on reinforcement learning

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中国科学:技术科学英文版 2023年第9期66卷 2549-2560页

作者： DU ShengLi CHEN PeiXi HAN HongGui qiao junfei School of Artificial Intelligence and Automation Beijing University of TechnologyBeijing 100124China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China

In this article, the dissolved oxygen(DO) concentration control problem in wastewater treatment process(WWTP) is *** existing control strategies that control DO concentration at a fixed value, here we develop a different control *** the proposed control framework, an intelligent control method of DO concentration based on reinforcement learning(RL)algorithm is presented to resolve the DO concentration control problem. By using the deep deterministic policy gradient(DDPG)algorithm, the DO concentration of the fifth tank in the activated sludge reactor can be adjusted dynamically. In addition, by designing two different reward functions and by analysing the relationships among effluent quality, energy consumption, and DO concentration, the target of energy-saving and emission-reducing is achieved. The simulation results indicate that the designed control method can reduce energy consumption while ensuring that the effluent quality meet the specified standards.

关键词： dissolved oxygen concentration wastewater treatment process intelligent control reinforcement learning energy-saving and emission-reducing

Event-triggered-based self-organizing fuzzy neural network control for the municipal solid waste incineration process

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Science China(Technological Sciences) 2023年第4期66卷 1096-1109页

作者： HE HaiJun MENG Xi TANG Jian qiao junfei Faculty of Information Technology Beijing University of TechnologyBejing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Bejing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Bejing 100124China Xi'an Institute of Electromechanical Information Technology Xi'an 710065China

Due to the large uncertainty in the municipal solid waste incineration(MSWI)process,the furnace temperature of the MSWI process is difficult to control and the controller is updated *** improve the accuracy and reduce the number of controller updates,a novel event-triggered control method based correntropy self-organizing TS fuzzy neural network(ETCSTSFNN)is ***,the neurons of the rule layer are grown or pruned adaptively based on activation intensity and control error to meet the dynamic change of the actual operating ***,the performance index is designed based on the correntropy of tracking errors,and the parameters of the controller are adjusted by gradient descent ***,a fixed threshold event-triggered condition is designed to determine whether the current controller is updated or *** stability of the control system is proved based on the Lyapunov stability ***,the furnace temperature control experiments are conducted based on the actual data of a municipal solid waste incineration plant in *** experimental results show that the proposed ET-CSTSFNN controller shows a better control performance,which can reduce the number of the controller update significantly while achieving accurate furnace temperature control compared with other traditional control methods.

关键词： municipal solid waste incineration furnace temperature fuzzy control event-triggered

Event-triggered fuzzy neural multivariable control for a municipal solid waste incineration process

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Science China(Technological Sciences) 2023年第11期66卷 3115-3128页

作者： DING HaiXu qiao junfei HUANG WeiMin YU Tao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China

Because of coupling,nonlinearity,and uncertainty in a municipal solid waste incineration(MSWI)process,a suitable multivariable controller is difficult to establish under strong ***,the problems of reducing mechanical wear and energy consumption in the control process should also be *** solve these problems,an event-triggered fuzzy neural multivariable controller is proposed in this ***,the MSWI object model based on the multiinput multioutput TakagiSugeno fuzzy neural network is established using a data-driven ***,a fuzzy neural multivariable controller is designed to control the furnace temperature and flue gas oxygen content synchronously under external ***,an event-triggered mechanism is constructed to update the control rate online while ensuring control ***,the stability of the proposed control strategy is proven through the LyapunovⅡtheorem to guide its practical ***,the effectiveness of the controller is verified using the real industrial data of an MSWI factory in Beijing,*** experimental results show that the proposed control strategy greatly improves the control efficiency,reduces energy consumption by 66.23%,and improves the multivariable tracking control accuracy.

关键词： municipal solid waste incineration multivariable control event-triggered multiinput multioutput fuzzy neural network

Mobile phone recognition method based on bilinear convolutional neural network

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Science China(Technological Sciences) 2021年第11期64卷 2477-2484页

作者： HAN HongGui ZHEN Qi YANG HongYan DU YongPing qiao junfei Faculty of Information Technology Beijing University of TechnologyBeijing 100124China

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 ***, 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.

关键词： bilinear convolutional neural network low-rank decomposition joint loss fine-grained image recognition

Adaptive Multi-Step Evaluation Design With Stability Guarantee for Discrete-Time Optimal Learning Control

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1797-1809页

作者： Ding Wang Jiangyu Wang Mingming Zhao Peng Xin junfei qiao IEEE Faculty of Information Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China

This paper is concerned with a novel integrated multi-step heuristic dynamic programming(MsHDP)algorithm for solving optimal control *** is shown that,initialized by the zero cost function,MsHDP can converge to the optimal solution of the Hamilton-Jacobi-Bellman(HJB)***,the stability of the system is analyzed using control policies generated by ***,a general stability criterion is designed to determine the admissibility of the current control *** is,the criterion is applicable not only to traditional value iteration and policy iteration but also to ***,based on the convergence and the stability criterion,the integrated MsHDP algorithm using immature control policies is developed to accelerate learning efficiency ***,actor-critic is utilized to implement the integrated MsHDP scheme,where neural networks are used to evaluate and improve the iterative policy as the parameter ***,two simulation examples are given to demonstrate that the learning effectiveness of the integrated MsHDP scheme surpasses those of other fixed or integrated methods.

关键词： Adaptive critic artificial neural networks Hamilton-Jacobi-Bellman(HJB)equation multi-step heuristic dynamic programming multi-step reinforcement learning optimal control