Portland cement clinker strength prediction method based on DNN neural network

A technology of portland cement and neural network, applied in the field of neural network, can solve the problems of insufficient prediction accuracy in complex scenarios and affect the prediction accuracy, achieve high market returns and environmental returns, simplify the corresponding relationship, and avoid the effects of manual testing cycles

Pending Publication Date: 2021-06-11
湖州槐坎南方水泥有限公司 +1
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  • Abstract
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  • Application Information

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Problems solved by technology

[0003] Traditional regression prediction algorithms such as linear regression, least squares and other algorithms are not accurate enough to predict this complex scene, and it is easy to simplify the highly nonlinear relation

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  • Portland cement clinker strength prediction method based on DNN neural network
  • Portland cement clinker strength prediction method based on DNN neural network
  • Portland cement clinker strength prediction method based on DNN neural network

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Embodiment Construction

[0050] Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0051] Portland cement clinker is made of raw materials mainly containing cao, sio2, A12o3, and Fe2o3, which are ground into fine powder in an appropriate proportion and burned to partially melt to obtain tricalcium silicate, dicalcium silicate, tricalcium aluminate and ferroaluminate Tetracalcium is a hydraulic gelling substance whose main mineral component is. After Portland cement clinker is ground together with an appropriate amount of gypsum, it is made into Portland cement. In the process of Portland cement production, the strength of Portland cement clinker is the foundation of cement production enterprises. If the strength of cement clinker is low, the ...

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Abstract

The invention discloses a Portland cement clinker strength prediction method based on a DNN neural network. The method comprises the following steps: S1, acquiring factors influencing cement clinker strength; S2, neural network model selection: selecting a DNN neural network model; S3, carrying out data dimensionalization processing; and S4, constructing a neural network model which comprises an input layer, a group of hidden layers and an output layer, wherein the input layer is fully connected with the hidden layers, neurons of each layer are only fully connected with neurons of adjacent layers, no connection exists between the neurons in the same layer, no feedback connection exists between the neurons of each layer, the data subjected to dimensionalization processing serves as an input variable, and the intensity value serves as output. The Portland cement clinker strength is effectively predicted in advance, so that an effective support basis is provided for the subsequent improvement of the cement admixture doping amount, the production cost is saved, and the enterprise profit is improved.

Description

technical field [0001] The invention relates to the technical field of neural networks, in particular to a method for predicting the 28-day strength of Portland cement clinker based on a DNN neural network. Background technique [0002] The prediction of the 28-day strength of Portland cement clinker is a complex problem with multivariable, nonlinear, large time-delay and no fixed model. Various minerals in clinker undergo a series of complex chemical reactions in the process of cement hydration and hardening, so the influence of various minerals and their relative content in clinker on the strength of clinker is very complicated, and it is impossible to use a simple functional relationship To represent. The 28-day strength of the clinker in the enterprise needs to wait until the 28-day manual test to obtain it. The cycle is long and the lag time is long, so it is impossible to pre-adjust the amount of mixed materials. Therefore, it is necessary to find the main factors af...

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Application Information

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IPC IPC(8): G16C60/00G16C20/30G16C20/70G06N3/04G06N3/08
CPCG16C60/00G16C20/30G16C20/70G06N3/084G06N3/048G06N3/045
Inventor 林国荣段振洪王真胥坤泉蔡照海段文虎赖德发
Owner 湖州槐坎南方水泥有限公司
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