This paper presents the genetic-neural network for sinter's burning through point since BTP control is the most important, which is tightly coupled with sinter ore quality. In offline, advanced genetic algorithm (GA) is used to optimize the original connection weights and thresholds, and during online, hybrid neural network (HNN) inherited from the principle of backpropagation is used to train the map parameters and improve the system precision in each sampling period. The results obtained from the actual process demonstrate that the performance and capability of the proposed system are superior.

A building of the genetic-neural network for sinter's burning through point

This work presents adaptive genetic-neural network for sinter's burning through point (BTP), since BTP control is most important, which is tightly coupled with sinter ore quality. In off-line, the adaptive genetic algorithm (AGA) is used to optimize the connection weights and thresholds, and during on-line hybrid neural network (HNN) inherited from the principle of back propagation is used to train the map parameters and improve the system precision in each sampling period. The results obtained from the actual process demonstrate that the performance and capability of the proposed system are superior.

An application of adaptive genetic-neural algorithm to Sinter's BTP process

Abstract Bei stark totzeitbehafteten Prozessen, bei denen die Messung der Qualität online nicht möglich ist, bietet die Vorhersage qualitätsbestimmender Zielgrößen eine geeignete Alternative zur effektiven Verbesserung der Prozessführung. Basis einer solchen Vorhersage ist die Prozessstufen übergreifende, ganzheitliche Modellierung von Einfluss nehmenden Wirkzusammenhängen. Am Beispiel einer Sinteranlage für Eisenerze wird über die Anwendung eines Vorgehensmodells für die Synthese eines globalen Qualitätsmodells berichtet.

Prozessstufen übergreifende Synthese eines Qualitätsmodells für Sinteranlagen von Eisenerzen (Synthesis of Plantwide Quality Prediction Systems for Iron Ore Sintering Plants)

Iron ore sinter machine productivity is one of the most important techno-economic parameters of an integrated steel plant. It depends upon the composition of different constituents of sinter like coke breeze, iron ore fines and flux which are agglomerated to produce sinter for blast furnaces. It is difficult to assess the interdependence of these constituents and their effect on sinter productivity from physical experimentation. In this paper, machine learning and data analytics approach is introduced to predict the sinter machine productivity. Industrial data of sinter machine productivity has been collected from an integrated steel plant. A linear regression model was first developed to predict sinter machine productivity with the composition of constituent materials of the agglomerate as model inputs. Artificial neural network (ANN) model correlating the input parameters with sinter productivity was also developed. It is found that the ANN model is agreeing well with measured sinter machine productivity accurately. Sensitivity analysis was carried out to identify major factors affecting sinter productivity.

Application of Machine Learning Algorithms for Prediction of Sinter Machine Productivity

Based on some features in lead-zinc sintering process (LZSP), such as large time delay and strong non-linearity, an intelligent integrated method for quality prediction based on back-propagation neural network (BPNN) and improved grey system (IGS) is presented. First, the compositions of agglomerate are predicted by BPNN and IGS models. Then, a recursive entropy algorithm for the weighting coefficients is devised from the viewpoint of the information theory and an intelligent integrated prediction model (IIPM) is established. The compositions of sinter agglomerate are predicted by integrating the two prediction models. Application results show that the IIPM has higher prediction precision than that of single model and the proposed intelligent integrated method settles the modeling problem of the quality in the LZSP.

An Intelligent Integrated Method for Quality Prediction in Lead-Zinc Sintering Process

Based on the property of a sintering process, a hybrid fuzzy neural networks (FNN) and genetic algorithm (GA) system is proposed to solve the difficult and challenging problem of constructing a system model from the given input and output data to predict the quality of chemical components of the finished sinter mineral. A dual input fuzzy neural networks (DIFNN) system is proposed to represent the fuzzy model and realize fuzzy inference. The learning process of DIFNN is divided into off-line and online learning. In off-line learning, a GA is used to train the DIFNN and construct a system model based on the training data. During online operation, the algorithm inherited from the principle of backpropagation is used to adjust the network parameters and improve the system precision in each sampling period. The process of constructing a system model is introduced in detail. The results obtained from actual prediction demonstrate the performance and capability of the proposed system.

Fuzzy-neural-network-based quality prediction system for sintering process

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Fuzzy-neural-network-based quality prediction system for sintering process