Daicel Corporation

Hidetoshi Kozono

Masahiro Haitsuka

Fumihiro Miyoshi

Hiroshi Okamoto

Shunji Mitsuyoshi

Shuji Shinohara

Marina Takahashi

To accurately obtain operating conditions of a production facility that satisfies desired criteria. A control apparatus includes a process data acquisition unit that reads process data from a storage device that stores the process data obtained from a production facility, and a control unit that obtains an optimum solution for a predetermined adjustment target of the process data and controls the production facility on the basis of the optimum solution. The optimum solution satisfies constraint conditions and optimizes an objective function. The constraint conditions are defined by using at least some of the process data. The objective function is defined by using at least some of the process data. A learned model having learned features of the process data obtained from the production facility is further stored in a storage device on the basis of causality information that defines a combination of first process data and second process data. The first process data is used as an explanatory variable. The second process data is used as a response variable. The constraint conditions include a condition that the second process data or a value corresponding to the second process data is a value calculated by using the first process data and the learned model, and an upper limit or a lower limit for at least some of the process data.

Control apparatus, control method, and program for obtaining operating condition of a production facility that satisfies desired criteria

A regression model reflecting causality between variation of an explanatory variable and variation of a response variable is constructed. A prediction apparatus predicts characteristic values of a product by using process data obtained from a production facility. The prediction apparatus includes a process data acquisition unit that reads the process data from a storage device that stores the process data obtained from the production facility, and a prediction model generation unit that generates a prediction model on the basis of causality information that defines a combination of first process data and second process data or a value corresponding to the second process data. The first process data and the second process data or the value corresponding to the second process data are included in the read process data. The first process data is used as a predetermined explanatory variable. The second process data or the value corresponding to the second process data is used as response variable. The prediction model has learned features of the process data obtained from the production facility. The prediction model generation unit generates the prediction model and determines a positive/negative variation direction of the response variable in accordance with a positive/negative variation direction of the predetermined explanatory variable.

Prediction apparatus, prediction method, and program

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Tokyo, Japan

Hidetoshi Kozono