Toshihiko Watanabe

A new mill-setup system for hot strip rolling mill that integrates a process model and expertise

This paper describes a new mill-setup system for a hot strip rolling mill in the iron and steel making industry, that integrates mathematical models and operator expertise. Operator expertise is expressed in terms of roll gap differences between neighbouring rolling stands to avoid excessive rolling loads that may lead to shape defects in the rolled material. The problem is formulated as that of minimizing deviation between the know-how-based roll gap difference pattern and the actual one. Applying the system to an actual plant, it was found that operator intervention was reduced by 80%.

A case-based approach for modeling nonlinear systems

Abstract We propose a case-based approach for modeling nonlinear systems. Our method is a model-free approach which does not depend on any specific model structure. Given an input to a system, our method predicts the output by interpolating the outputs in the “similar” cases whose inputs are close to the given input. The similarity degree and coefficients used in interpolation are determined from the data in a context-dependent manner. We also propose a method to extract cases from a database to meet the specification on the prediction accuracy. Finally, applications to nonlinear dynamical systems are discussed.

Application of expert system for quality and process design of steel plates

In order to automate the quality and process design of steel plates, an expert system based on the case-based reasoning system has been developed. The case-based reasoning is a plausible approach for design problems, as knowledge in such problems is almost always fragmentary. However, it is also true that raw cases contain mistakes. In this system, model cases are extracted from raw cases, generalized somewhat, and then referred to as good designs. Using model cases is also effective to shorten inference time. This system shortened design work time by one-eighth and contributed to the standardization and inheritance of skilled design techniques.<<ETX>>

An adaptive fuzzy modeling for continuous galvanizing line

This paper describes an adaptive fuzzy modeling method and its successful application to the feedforward control for a continuous galvanizing line (CGL) in an iron and steel making plant. In CGL, due to the time delay that exists in the process, the feedforward control is indispensable for the accurate control of the solidified zinc of a galvanized steel strip. For this feedforward control to be successful, a model having the following two properties is required: 1) the model should be able to describe the nonlinear characteristics of the process; and 2) the model should have an adaptive capability to maintain accuracy in the long run. In this paper, we present an adaptive fuzzy modeling method that satisfies the above two criteria. The usefulness of our method is demonstrated using experimental results.

Intelligent lay-out design model for sand and aggregate plant

An intelligent layout design model for a sand and aggregate plant is studied. In order to produce an efficient and valid layout design plan, it is required to consider various factors such as constraints, costs, and user demands. Consequently, this work is difficult and time-consuming even for design specialists. In order to standardize and shorten design time, the layout design model is constructed with a multi-agent structure based on interactive nonlinear optimization. Interactive constraining techniques to formulate the plant layout effectively and rapidly, are also studied. Some functions of the support system are introduced.