Xuelei Wang

Modeling of integrated processes for coking flue gas desulfurization and denitrification based on RBFNN

This paper proposes an efficient modeling method based on the history running data of a coking chemical company flue gas desulfurization and denitrification integration device: construct data set according to the technology principle and corresponding data preprocessing method; make division of working conditions and reduce the sample set by means of K-Means clustering method; realize static modeling for each of the conditions based on RBF neural network. The simulation results show the effectiveness of the method and the artificial neural network model.

Integrated modeling of coking flue gas indices based on mechanism model and improved neural network

Focusing on the first domestic coking flue gas desulfurization and denitration integrated unit in China, the current condition of inlet flue gas indices cannot be determined timely owing to the large detection lag and complex upstream coking process, which is extremely unfavorable for the optimal control of desulfurization and denitration process. In order to solve this problem, an intelligent integrated modeling method of flue gas SO2 concentration, O2 content and NOx concentration is proposed. Firstly, the gas flow diagram in combustion process is built, the mechanism models of SO2, NOx concentration and O2 content are established according to the principle of material balance and reaction kinetics, respectively. Then the RBF neural network is adopted to compensate the prediction error, an improved training algorithm combining optimal stopping principle and dual momentum adaptive learning rate is proposed to improve the training speed and generalization ability of neural network. Based on the practical data of two 55-hole and 6-meter top charging coke ovens in the coking group, the effectiveness and superiority of proposed model and method are verified by simulation via comparison of various methods.

Fault Diagnosis and Knowledge Extraction Using Fast Logical Analysis of Data with Multiple Rules Discovery Ability

Data-based explanatory fault diagnosis methods are of great practical significance to modern industrial systems due to their clear elaborations of the cause and effect relationship. Based on Boolean logic, logical analysis of data (LAD) can discover discriminative if-then rules and use them to diagnose faults. However, traditional LAD algorithm has a defect of time-consuming computation and extracts only the least number of rules, which is not applicable for high-dimensional large data set and for fault that has more than one independent causes. In this paper, a novel fast LAD with multiple rules discovery ability is proposed. The fast data binarization step reduces the dimensionality of the input Boolean vector and the multiple independent rules are searched using modified mixed integer linear programming (MILP). A Case Study on Tennessee Eastman Process (TEP) reveals the superior performance of the proposed method in reducing computation time, extracting more rules and improving classification accuracy.

Intelligent integrated coking flue gas indices prediction

Focus on the first China domestic coking flue gas desulfurization and denitriation integrated device, in order to solve the problem that the entrance parameters fluctuate and a detection lag exists due to the upstream coking workshop, which is extremely unfavorable to the optimal control of desulfurization and denitriation process. An intelligent integrated prediction model of flue gas SO2 concentration, O2 content and NOx concentration was proposed: the mechanism models of SO2, NOx concentration and O2 content were established according to the principle of material balance and reaction kinetics, respectively. For the prediction error, raw data was pretreated and the auxiliary variables were determined by principal component analysis, in order to improve the training speed and generalization ability of neural network, an improved RBFNN combining optimal stopping principle and dual momentum adaptive learning rate was proposed and used to compensate the error. Based on the practical data of two 55-hole and 6-meter top charging coke ovens in the coking group, the effectiveness and superiority of proposed model and method were verified by simulation via comparison of various models.

Optimal Setting for Coking Flue Gas Denitriation Process Indices Based on PCR-Multi-Case Fusion

In the operation process of the flue gas desulfurization and denitriation, the concentration of ozone and denitriation solution are the most important operating parameters. The two parameters are set manually at the present, where exist great subjectivity and randomness, causing great waste of energy. However, the complex mechanism, serious nonlinear and interference of the process make it difficult to establish precise mathematical model to calculated set-points. In order to solve this problem, a method based on case based reasoning is proposed to optimize the set-points of coking gas denitriation process. The case construction, case retrieval, case reuse, case modification and storage are discussed in detail, meanwhile, noticed the defect that the single feature description of current working condition may result in biased solutions of traditional case reuse, a case reuse method based on principal component regression-multiple case fusion is proposed. This approach has been simulated and applied in a coking plant with its effectiveness and superiority actually proved.

Introduction of Advanced Control Strategy for Coking Flue Gas Processing

Advanced Process Control(also known as advanced control) has been considerable development since the 1970s. Firstly, the advanced control and its applications in the typical industrial process, especially the industrial flue gas processing procedure are reviewed in this paper, for the first domestic coking flue gas desulfurization and denitrification device, we make an in-depth analysis of process characteristics, influencing factors and control difficulties through the introduction of the process principle and technics, based on the control objectives, we put forward the research idea and prospect for future work.