双碳目标下城市固废焚烧过程智能优化控制研究进展及展望

城市固废焚烧是固废污染治理的主要方式,也是可再生能源的来源之一,能为国家生态文明建设和“双碳”战略提供重要支撑。城市固废焚烧过程内嵌了系列物理、化学反应,运行机理复杂、演化机制不清,尤其是我国固废含水率高、热值低、随时空动态波动,难以实施智能优化控制,导致固废燃烧不完全、烟气排放不稳定、能源转化率低、运行成本高等问题时有发生,制约城市固废焚烧行业高质量发展。本文分析了实施城市固废焚烧过程智能优化控制所面临的难点问题,梳理了城市固废焚烧过程智能优化控制关键理论与核心技术的主要进展,凝练出实现高效化和绿色化运行待解决的关键科学问题,探讨了未来研究方向。

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

This paper is concerned with a novel integrated multi-step heuristic dynamic programming(MsHDP)algorithm for solving optimal control problems.It is shown that,initialized by the zero cost function,MsHDP can converge to the optimal solution of the Hamilton-Jacobi-Bellman(HJB)equation.Then,the stability of the system is analyzed using control policies generated by MsHDP.Also,a general stability criterion is designed to determine the admissibility of the current control policy.That is,the criterion is applicable not only to traditional value iteration and policy iteration but also to MsHDP.Further,based on the convergence and the stability criterion,the integrated MsHDP algorithm using immature control policies is developed to accelerate learning efficiency greatly.Besides,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 architecture.Finally,two simulation examples are given to demonstrate that the learning effectiveness of the integrated MsHDP scheme surpasses those of other fixed or integrated methods.

Prediction of NO_(x)concentration using modular long short-term memory neural network for municipal solid waste incineration

Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission controlling.In this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)concentration.First,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex task.Second,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the sub-networks.Third,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application stage.Finally,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI process.And the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

Univariate imputation method for recovering missing data in wastewater treatment process

High-quality data play a paramount role in monitoring,control,and prediction of wastewater treatment process(WWTP)and can effectively ensure the efficient and stable operation of system.Missing values seriously degrade the accuracy,reliability and completeness of the data quality due to network collapses,connection errors and data transformation failures.In these cases,it is infeasible to recover missing data depending on the correlation with other variables.To tackle this issue,a univariate imputation method(UIM)is proposed for WWTP integrating decomposition method and imputation algorithms.First,the seasonal-trend decomposition based on loess method is utilized to decompose the original time series into the seasonal,trend and remainder components to deal with the nonstationary characteristics of WWTP data.Second,the support vector regression is used to approximate the nonlinearity of the trend and remainder components respectively to provide estimates of its missing values.A self-similarity decomposition is conducted to fill the seasonal component based on its periodic pattern.Third,all the imputed results are merged to obtain the imputation result.Finally,six time series of WWTP are used to evaluate the imputation performance of the proposed UIM by comparing with existing seven methods based on two indicators.The experimental results illustrate that the proposed UIM is effective for WWTP time series under different missing ratios.Therefore,the proposed UIM is a promising method to impute WWTP time series.

Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network

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.

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

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.

A sludge volume index (SVI) model based on the multivariate local quadratic polynomial regression method

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.

A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm

Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm(DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly,the information processing strength(IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm(DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIASORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.

A data-derived soft-sensor method for monitoring effluent total phosphorus

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.

Structure Optimization for Echo State Network Based on Contribution

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 performance.First, 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.

Wastewater treatment control method based on a rule adaptive recurrent fuzzy neural network

Purpose-The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment system.Design/methodology/approach-A control strategy based on rule adaptive recurrent neural network(RARFNN)is proposed in this paper to control the dissolved oxygen(DO)concentration and nitrate nitrogen(SNo)concentration.The structure of the RARFNN is self-organized by a rule adaptive algorithm,and the rule adaptive algorithm considers the overall information processing ability of neural network.Furthermore,a stability analysis method is given to prove the convergence of the proposed RARFNN.Findings-By application in the control problem of wastewater treatment process(WWTP),results show that the proposed control method achieves better performance compared to other methods.Originality/value-The proposed on-line modeling and controlling method uses the RARFNN to model and control the dynamic WWTP.The RARFNN can adjust its structure and parameters according to the changes of biochemical reactions and pollutant concentrations.And,the rule adaptive mechanism considers the overall information processing ability judgment of the neural network,which can ensure that the neural network contains the information of the biochemical reactions.