A model-based expert control strategy using neural networks for the coal blending process in an iron and steel plant

Abstract Two important aspects of the control of the coal blending process in the iron and steel industry are computation of the target percentage of each type of coal to be blended and the blending of the different types in the target percentages. This paper proposes an expert control strategy to compute and track the target percentages accurately. First, neural networks, mathematical models and rule models are constructed based on statistical data and empirical knowledge on the process. Then a methodology is proposed for computing the target percentages that combines the neural networks, mathematical models and rule models and uses forward chaining and model-based reasoning. Finally, the tracking control of the target percentages is carried out by a distributed PI control scheme. The expert control strategy proposed is implemented in an expert control system that contains an expert controller and a distributed controller. The results of actual runs show that the proposed expert control strategy is an effective way to control the coal blending process.

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