Abstract: A simulation analysis system for dioxin concentration in furnace of municipal solid waste incineration process includes an area division module, the area division module is connected with a numerical simulation module, the numerical simulation module is connected with a single-factor analysis module, the single-factor analysis module includes an orthogonal test analysis module, and the orthogonal test analysis module is connected with a control module; the area division module is used for dividing areas in the incinerator, the numerical simulation module is used for conducting modeling simulation on the divided areas, the single-factor analysis module is used for conducting single-factor analysis according to the output of the numerical simulation module, and the orthogonal test analysis module is used for conducting orthogonal test analysis according to the output of the numerical simulation module.

Abstract: A dynamic modular neural network (DMNN) for NOx emission prediction in MSWI process is provided. First, the input variables are smoothed and normalized. Then, a feature extraction method based on principal component analysis (PCA) was designed to realize the dynamic division of complex conditions, and the prediction task to be processed was decomposed into sub-tasks under different conditions. In addition, aiming each sub-tasks, a long short-term memory (LSTM)-based sub-network is constructed to achieve accurate prediction of NOx emissions under various working conditions. Finally, a cooperative strategy is used to integrate the output of the sub-networks, further improving the accuracy of prediction model. Finally, merits of the proposed DMNN are confirmed on a benchmark and real industrial data of a municipal solid waste incineration (MSWI) process. The problem that the NOx emission of MSWI process is difficult to be accurately predicted due to the sensor limitation is effectively solved.

Abstract: A hardware-in-loop simulation experiment platform of multiple input and multiple output loop control for MSWI process includes a real equipment layer and a virtual object layer, where in the real equipment layer and the virtual object layer realize communication through hard wirings and data acquisition cards, the real equipment layer and virtual object layer realize communication in OPC mode through Ethernet; the real equipment layer comprises monitoring equipment and control equipment, and the virtual object layer comprises an MSWI actuator model, an MSWI instrument device model and an MSWI process object model which are respectively operated in different industrial personal computers. The hardware-in-loop simulation experiment platform of multiple input and multiple output loop control for MSWI process provided by the invention is used for providing a reliable engineering verification environment for MSWI process control.

Abstract: A dynamic multi-objective particle swarm optimization based optimal control method is provided to realize the control of dissolved oxygen (SO) and the nitrate nitrogen (SNO) in wastewater treatment process. In this method, dynamic multi-objective particle swarm optimization was used to optimize the operation objectives of WWTP, and the optimal solutions of SO and SNO can be calculated. Then PID controller was introduced to trace the dynamic optimal solutions of SO and SNO. The results demonstrated that the proposed optimal control strategy can address the dynamic optimal control problem, and guarantee the efficient and stable operation. In addition, this proposed optimal control method in this present invention can guarantee the effluent qualities and reduce the energy consumption.

Abstract: A multi-time scale model predictive control method for wastewater treatment process is designed to control the dissolved oxygen concentration and nitrate nitrogen concentration in different time scales to ensure that the effluent quality meets the standard. In view of the difference of time scales in wastewater treatment process caused by different sampling periods of dissolved oxygen concentration and nitrate nitrogen concentration, prediction models with different time scales are firstly designed to unify the prediction outputs to the fast time scale. Then, the gradient descent algorithm is used to solve the optimal solution with fast time scale to control the wastewater treatment system. It not only conforms to the operation characteristics of wastewater treatment process, but also solves the problem of poor operation performance of multiobjective model predictive control caused by different time scales.

Abstract: A cooperative fuzzy-neural control method is designed in this present invention. Due to the difficulty for cooperatively controlling the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process, a cooperative fuzzy-neural control method is investigated. In this proposed method, firstly, a interval type-2 fuzzy neural network is employed to construct the cooperative fuzzy-neural controller. Secondly, a parameter cooperative strategy is proposed to cooperatively optimize the global and local parameters of the cooperative fuzzy-neural controller to meet the control requirements. This proposed cooperative fuzzy-neural control method can cooperatively control the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process. The results illustrate that the proposed cooperative fuzzy-neural control method can achieve the high control accuracy and guarantee the normal operations of wastewater treatment process under the different operation conditions.

Abstract: An optimal control method for wastewater treatment process (WWTP) based on a self-adjusting multi-task particle swarm optimization (SA-MTPSO) algorithm belongs to the field of WWTP. To balance the relationship between the effluent water quality (EQ) and energy consumption (EC) and achieve optimization online quickly, the invention establishes a data-based multi-task optimization model for WWTP to describe the relationship between the control variables and EQ, EC. Then, the SA-MTPSO algorithm is adopted to solve the optimal set-points of the nitrate nitrogen and dissolved oxygen concentration for WWTP. The PID controller is used to track the optimal set-points, so as to reduce EC while ensuring EQ, and realize the online optimal control of WWTP.

Abstract: An intelligent detection system of effluent total nitrogen (TN) based on fuzzy transfer learning algorithm belongs to the field of intelligent detection technology. To detect the TN concentration, the artificial neural network can be used to model wastewater treatment process due to the nonlinear approximation ability and learning ability. However, wastewater treatment process has the characteristic of time-varying dynamics and external disturbance, artificial neural network prediction method cannot acquire sufficient data to ensure the accuracy of TN prediction, and data loss and data deficiency will make the prediction model invalid. The invention proposed an intelligent detection system of effluent total nitrogen based on fuzzy transfer learning algorithm; the proposed system contains several functional modules, including detection instrument, data acquisition, data storage and TN prediction.

Abstract: In a cooperative optimal control system, firstly, two-level models are established to capture the dynamic features of different time-scale performance indices. Secondly, a data-driven assisted model based cooperative optimization algorithm is developed to optimize the two-level models, so that the optimal set-points of dissolved oxygen and nitrate nitrogen can be acquired. Thirdly, a predictive control strategy is designed to trace the obtained optimal set-points of dissolved oxygen and nitrate nitrogen. This proposed cooperative optimal control system can effectively deal with the difficulties of formulating the dynamic features and acquiring the optimal set-points.

Abstract: Under conventional techniques, wastewater treatment has many problems such as poor production conditions, serious random interference, strong nonlinear behavior, large time-varying, and serious lagging. These problems cause difficulty in detecting wastewater treatment parameters such as biochemical oxygen demand (BOD) values that are used to monitor water quality. To solve problems associated with monitoring BOD values in real-time, the present disclosure utilizes a self-organizing recurrent RBF neural network designed for intelligent detecting of BOD values. Implementations of the present disclosure build a computing model of BOD values based on the self-organizing recurrent RBF neural network to achieve real-time and more accurate detection of the BOD values (e.g., a BOD concentration). The implementations herein quickly and accurately obtain BOD concentrations and improve the quality and efficiency of wastewater treatment.

Abstract: A hierarchical model predictive control (HMPC) method based on fuzzy neural network for wastewater treatment process (WWTP) is designed to realize hierarchical control of dissolved oxygen (DO) concentration and nitrate nitrogen concentration. In view of the difference of time scales in WWTP, it is difficult to accurately control the concentration of DO and nitrate nitrogen. The disclosure establishes a HMPC structure according to different time scales. Then, the concentration of DO and nitrate nitrogen is controlled with different frequencies. It not only conforms to the operation characteristics of WWTP, but also solves the problem of poor operation performance of multivariable model predictive control. The experimental results show that the HMPC method can achieve accurate on-line control of DO concentration and nitrate nitrogen concentration with different time scales.

Abstract: A method for predicting dioxin (DXN) emission concentration based on hybrid integration of random forest (RF) and gradient boosting decision tree (GBDT). A random sampling of a training sample and an input feature is performed on a modeling data with a small sample size and a high-dimensional characteristic to generate a training subset. J RF-based DXN sub-models based on the training subset are established. J×I GBDT-based DXN sub-models are established by performing I iterations on each of the RF-based DXN sub-models. Predicted outputs of the RF-based DXN sub-model and the GBDT-based DXN sub-model are combined by a simple average weighting method to obtain a final output.

Abstract: A total nitrogen intelligent detection system based on multi-objective optimized fuzzy neural network belongs to both the field of environment engineer and control engineer. The total nitrogen in wastewater treatment process is an important index to measure the quality of effluent. However, it is extremely difficult to detect the total nitrogen concentration due to the long detection time and the low prediction accuracy in the wastewater treatment process. To solve the problem, multi-objective optimized fuzzy neural network with global optimization capability may be established to optimize the structure and parameters to solve the problem of the poor generalization ability of fuzzy neural network. The experimental results show that total nitrogen intelligent detection system can automatically collect the variables information of wastewater treatment process and predict total nitrogen concentration.

Abstract: An intelligent warning method based on knowledge-fuzzy learning algorithm is designed for membrane fouling with high accuracy. A multi-step prediction strategy, using the least-squares linear regression model, is developed to predict the characteristic variables of membrane fouling Meanwhile, the knowledge of membrane fouling category, which is extracted from the real wastewater treatment process, can be expressed as the form of fuzzy rules. Moreover, a knowledge-based fuzzy neural network is designed to establish the membrane fouling warning model, thus deal with the problem of difficult warning of membrane fouling. The results reveal that the intelligent warning method can improve the ability to solve the membrane fouling, mitigate the deleterious effect on the process performance and ensure the safety operation of the wastewater treatment process.

Abstract: The wastewater treatment process by using activated sludge process often appear the sludge bulking fault phenomenon. Due to production conditions of wastewater treatment process, the correlation and restriction between variables, the characteristics of nonlinear and time-varying, which lead to hard identification of sludge bulking; Sludge bulking is not easy to detect and the reasons resulting in the sludge bulking are difficult to identify, are current RBF neural network is designed for detecting and identifying the causes of sludge volume index (SVI) in this patent. The method builds soft-computing model of SVI based on recurrent RBF neural network, it has been completed to the real-time prediction of SVI concentration and better accuracy were obtained. Once the fault of sludge bulking is detected, the identifying cause variables (CVI) algorithm can find the cause variables of sludge bulking.

Abstract: Disclosed is a soft measurement method of DXN emission concentration based on multi-source latent feature selective ensemble (SEN) modeling. First, MSWI process data is divided into subsystems of different sources according to industrial processes, and principal component analysis (PCA) is used to separately extract the subsystems' latent features and conduct multi-source latent feature primary selection according to the threshold value of the principal component contribution rate preset by experience.

Abstract: A cooperative fuzzy-neural control method is designed in this present invention. Due to the difficulty for cooperatively controlling the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process, a cooperative fuzzy-neural control method is investigated. In this proposed method, firstly, a interval type-2 fuzzy neural network is employed to construct the cooperative fuzzy-neural controller. Secondly, a parameter cooperative strategy is proposed to cooperatively optimize the global and local parameters of the cooperative fuzzy-neural controller to meet the control requirements. This proposed cooperative fuzzy-neural control method can cooperatively control the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process. The results illustrate that the proposed cooperative fuzzy-neural control method can achieve the high control accuracy and guarantee the normal operations of wastewater treatment process under the different operation conditions.

Abstract: An intelligent identification method of sludge bulking based on type-2 fuzzy-neural-network belongs to the field of intelligent detection technology. The sludge volume index (SVI) in wastewater treatment plant is an important index to measure the sludge bulking of activated sludge process. However, poor production conditions and serious random interference in sewage treatment process are characterized by strong coupling, large time-varying and serious hysteresis, which makes the detection of SVI concentration of sludge volume index extremely difficult. At the same time, there are many types of sludge bulking faults, which are difficult to identify effectively. Due to the sludge volume index (SVI) is unable to online monitoring and the fault type of sludge bulking is difficult to determined, the invention develop soft-computing model based on type-2 fuzzy-neural-network to complete the real-time detection of sludge volume index (SVI).

Abstract: A method for detecting a dioxin emission concentration of a municipal solid waste incineration process based on multi-level feature selection. A grate furnace-based MSWI process is divided into a plurality of sub-processes. A correlation coefficient value, a mutual information value and a comprehensive evaluation value between each of original input features of the sub-processes and the DXN emission concentration are obtained, thereby obtaining first-level features. The first-level features are selected and statistically processed by adopting a GAPLS-based feature selection algorithm and according to redundancy between different features, thereby obtaining second-level features. Third-level features are obtained according to the first-level features and statistical results of the second-level features. A PLS algorithm-based DXN detection model is established based on model prediction performance and the third-level features.

Abstract: A dynamic multi-objective particle swarm optimization based optimal control method is provided to realize the control of dissolved oxygen (SO) and the nitrate nitrogen (SNO) in wastewater treatment process. In this method, dynamic multi-objective particle swarm optimization was used to optimize the operation objectives of WWTP, and the optimal solutions of SO and SNO can be calculated. Then PID controller was introduced to trace the dynamic optimal solutions of SO and SNO. The results demonstrated that the proposed optimal control strategy can address the dynamic optimal control problem, and guarantee the efficient and stable operation. In addition, this proposed optimal control method in this present invention can guarantee the effluent qualities and reduce the energy consumption.