Patent ID: 9679244
Date: 2017-06-13
CPC Classifications: C04B,G06F,G06N

Claim:
1. A method of predicting quality or manufacturing conditions of cement through use of a neural network including an input layer and an output layer, the input layer being used for inputting an actually measured value of observation data in cement manufacturing, and the output layer being used for outputting an estimated value of evaluation data related to evaluation of the quality or the manufacturing conditions of the cement, the observation data and the evaluation data being used in one of the following combinations: (i) a combination in which the observation data comprises one or more kinds of data selected from data on a clinker raw material, data on burning conditions, data on grinding conditions, and data on clinker, and the evaluation data comprises data on a clinker raw material, data on burning conditions, data on grinding conditions, data on clinker, or data on cement; and (ii) a combination in which the observation data comprises one or more kinds of data selected from data on a clinker raw material, data on burning conditions, data on grinding conditions, data on clinker, and data on cement, and the evaluation data comprises data on physical properties of a cement-containing hydraulic composite, the method comprising the steps of: (A) performing initial setting of a number of times of learning; (B) performing learning of the neural network for the set number of times of learning through use of a plurality of learning data each comprising a combination of an actually measured value of the observation data and an actually measured value of the evaluation data; (C) calculating a mean square error (σ (D) increasing the set number of times of learning to reset the increased set number of times of learning as a new number of times of learning, and performing the steps (B) and (C) again; (E) resetting a number of times of learning obtained by reducing the number of times of learning for which the latest learning of the neural network has been performed as a new number of times of learning; (F) performing the learning of the neural network for the set number of times of learning through use of the plurality of learning data used in the step (B); (G) calculating a mean square error (σ (H) performing the steps (E) to (G) again when the number of times of learning of the neural network in the step (F) performed most recently is more than a preset numerical value, and performing a step (K) when the number of times of learning of the neural network in the step (F) performed most recently is equal to or less than the preset numerical value; (I) calculating a judgment value for analysis degree by the following equation (1), and when the analysis degree determination value is less than a preset value, inputting an actually measured value of the observation data in the cement manufacturing to the input layer and outputting an estimated value of the evaluation data related to the evaluation of the quality or the manufacturing conditions of the cement from the output layer, and when the analysis degree determination value is equal to or more than the preset value, performing the step (K); and (K) initializing learning conditions, and performing the steps (A) to (K) again: (in the equation (1), the mean square error (σdL) of the learning data represents a mean square error (σ