A predictive system for blast furnaces by integrating a neural network with qualitative analysis

doi:10.1016/S0952-1976(00)00062-2

Engineering Applications of Artificial Intelligence

Volume 14, Issue 1, February 2001, Pages 77-85

https://doi.org/10.1016/S0952-1976(00)00062-2 Get rights and content

Abstract

Silicon content in pig iron has long been used as one of the most important indices to represent the thermal state of a blast furnace. In this paper, a predictive system for blast furnaces by integrating a neural network with qualitative analysis is presented. The qualitative trend of the process in blast furnace is predicted through causal analysis and qualitative reasoning, and the relevant variables as the inputs of a neural network model are determined. Then, a neural network model is constructed and trained with appropriate data. Evaluation of the system is made by comparing the predicted values with observed data (totally 610 heats are included), and the performance of the system is excellent.

Introduction

The iron blast furnace is one of the useful tools for human race. It provides the means by which iron is rapidly and efficiently reduced from ore and virtually it is the basis for all primary steel making. It is a significant item in the economy of any country. Because of the large quantity produced, even small improvements of the process can result in considerable profit. Silicon content in pig iron has long been used as one of the most important indices to represent the thermal state of the blast furnace. The control of silicon content in pig iron at lower level concentration has been regarded as one of the most important operational problems of a blast furnace, due to the following reasons: (1) to improve the quality of product; (2) to meet the need of steelmaking process; (3) to decrease the running cost, etc.

Nowadays, the iron smelting process in a blast furnace is mainly controlled by experienced operators. However, they cannot work forever, and not all operators can become experts. To improve the control performance, we need a process model that could accurately describe stoichiometric and enthalpy balance for the process and be consistent with the observed silicon content in the furnace stack. There are several methods that have been proposed to build the model (Fielden and Wood, 1968; Jin, 1986; Keyser and Van Canwenberghe, 1981; Li et al., 1984; Unbehauen and Diekman, 1982). A blast furnace is a kind of giant reactor in which gas, liquid and solids coexist, and the reaction in a blast furnace is very complicated. Its complexity has prevented it from being controlled optimally. On the other hand, with the success on rule-based expert systems in AI, people expect that the methods of expert operators could be computerized. Some expert systems (Iida et al., 1989; Nakajima, 1987) have been built for increasing the operational effectiveness of a blast furnace. To obtain useful information from a mathematical model and also from the operator's experience, a predictive system based on combining an adaptive predictor and a knowledge base has been developed (Chen, 1993). Generally, knowledge-based systems have been developed solely through the use of rule-based programming, which allow for easy modeling of expert reasoning. However, experience and analyses showed that there were serious limitations to an inference method based on empirical associations between observable findings and diagnostic hypotheses.

In this paper, a predictive system for blast furnaces by integrating a neural network with qualitative analysis is presented, which tries to help push back the bounds on both quantitative methods and qualitative methods alone. Moreover, with the strong expressive capability for incomplete knowledge/information by qualitative reasoning, it may help to make use of the missing normative information. The qualitative trend of the process in blast furnace is predicted through causal analysis and qualitative reasoning, and the relevant variables and model structure are determined. Then, a neural network model is constructed and trained with appropriate data. Evaluation of the system is made by comparing the predicted values with observed data, and the performance of the system is excellent. The paper is organized as follows. The process of a blast furnace, neural networks and qualitative reasoning are introduced briefly in 2 The process of a blast furnace, 3 Neural networks, 4 Qualitative reasoning, respectively. Section 5 describes the structure of the predicting system, which is followed by the qualitative relationship of a blast furnace in Section 6. In Section 7, the evaluation of the system is presented and finally, Section 8 concludes the paper.

Section snippets

The process of a blast furnace

The iron blast furnace is a tall, vertical shaft furnace that employs carbon, mainly in the form of coke to reduce iron from its oxide ores. A schematic view of a typical blast furnace is presented in Fig. 1. Iron ore as metal source, coke as heat energy source and reductant are fed to the top of the blast furnace in charges. These move from the top to the bottom of the furnace in about 6–7 h and are melted during this period. Hot blast is blown in from the tuyeres at the furnace bottom so as to

Neural networks

Artificial neural networks (ANNs) are mathematical models inspired by the organization and functioning of biological neurons. ANNs are composed of a number of very simple processing elements known as neurons. A neuron typically consists of three components: (1) a group of weights, (2) a weighted summer, (3) a nonlinear activation function f(x) (e.g. sigmoidal function). The weights are regression coefficients to be estimated from sample data. The bias term is comparable with the intercept of

Qualitative reasoning

Qualitative reasoning is a relatively new field studied originally from AI research and focuses on using incomplete knowledge. It appears to be an appropriate or even a necessary approach for complex systems (e.g., a blast furnace) where complete numerical information for problem under study is not available at the time of analysis. The basic procedure of qualitative reasoning is to obtain system structure, i.e. components and connections among them that are described by qualitative equations

Structure of the system

With mathematical techniques, we describe dynamic problems as differential equations when the problems are well structured and with precise quantitative information. Solving the equations is trivial by giving initial conditions, parameters and the like. Many real problems, however, are often very complicated and ill-structured, involving numerous factors, large uncertainties, etc. For a complex problem (e.g., a blast furnace), information is very likely to be highly qualitative, at least

Qualitative structure of a blast furnace

In order to reason the behaviors of complex systems qualitatively, it is necessary to build a qualitative model for the system. The causal approach is widely used to model systems qualitatively. Through causality, we can focus on any part or component of a system, without having to deal with the model as a whole. This is useful when trying to understand and explain the behaviors of a system. The causal influences (or causal explanations) between variables are usually represented as an oriented

Empirical results

To empirically implement the system described above, 610 heats from #9 blast furnace in AISC are checked. According to the operating conditions of #9 blast furnace, the following variables are considered to be the input variables of the neural network for predicting silicon content in pig iron:

V₁(t): the quantity of blast
V₂(t): the temperature of blast
V₃(t): the pressure of blast
V₄(t): the quantity of coal powder
V₅(t): the index of ventilating performance
V₆(t): the pressure of top gas
V₇(t): the

Conclusion

In this paper, we present a predictive system for blast furnaces to predict silicon content in pig iron. A blast furnace is a very complex system, in which gas, liquid and solids coexist. For a complex system, information is very likely to be highly qualitative and it involves both qualitative and quantitative aspects. Unlike traditional methods, in this paper, we have designed a predictive system by integrating a neural network with qualitative analysis and expect to make use of both

Acknowledgements

The author thanks the anonymous referees and the editor for their valuable comments and suggestions. This work is supported partly by the National Science Foundation of China, Natural Science Foundation of State Education Commission and National Defense Foundation.

Jian Chen received the B.Sc. degree (1983) in Electrical Engineering, M.Sc. degree (1986), and the Ph.D. degree (1989) both in Systems Engineering from Tsinghua University, China. He is now a Professor and Chairman of Management Science Department, Tsinghua University. He serves as a member of the Administrative Committee of IEEE systems, man and cybernetics society, a member of the Standing Committee of Systems Engineering Society of China. He has over 80 technical publications and has been a

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View full text

A predictive system for blast furnaces by integrating a neural network with qualitative analysis

Abstract

Introduction

Section snippets

The process of a blast furnace

Neural networks

Qualitative reasoning

Structure of the system

Qualitative structure of a blast furnace

Empirical results

Conclusion

Acknowledgements

Artificial Intelligence

Artificial Intelligence

Artificial Intelligence

Predicting system based on combining an adaptive predictor and a knowledge base as applied to a blast furnace

Journal of Forecasting

An improved back propagation algorithm

An accelerated learning algorithm for multilayer perceptronsoptimization layer by layer

IEEE Transactions on Neural Networks

A dynamic digital simulation of a blast furnace

Journal of Iron and Steel Inst.

Reasoning about energy in qualitative simulation

IEEE Transactions on Systems, Man and Cybernetics