A prediction method for sugarcane crushing process based on deep feature recognition

A prediction method and deep feature technology, applied in prediction, instruments, biological models, etc., can solve problems such as hysteresis and difficulty in integrating multiple models, and achieve the effect of improving prediction accuracy and model fitting effect

Active Publication Date: 2022-03-11
GUANGXI UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to provide a method for predicting the sugarcane crushing process based on deep feature recognition, thereby overcoming the shortcomings of the existing sugarcane crushing process analysis system that is hysteresis and difficult to integrate multiple models for prediction

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  • A prediction method for sugarcane crushing process based on deep feature recognition
  • A prediction method for sugarcane crushing process based on deep feature recognition
  • A prediction method for sugarcane crushing process based on deep feature recognition

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Embodiment Construction

[0035] The specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.

[0036] Unless expressly stated otherwise, throughout the specification and claims, the term "comprise" or variations thereof such as "includes" or "includes" and the like will be understood to include the stated elements or constituents, and not Other elements or other components are not excluded.

[0037] Figure 1 to Figure 5 A schematic diagram of a sugarcane crushing process prediction method based on deep feature recognition according to a preferred embodiment of the present invention is shown, and the sugarcane crushing process prediction method based on deep feature recognition includes the following steps:

[0038] Step 1: collect the real-time data of the sugarcane crushing process through the DCS system on t...

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Abstract

The invention discloses a sugarcane pressing process prediction method based on deep feature recognition, which comprises the following steps: 1. collecting several sets of original data; 2. removing abnormal data and standardizing the original data collected in step 1 to Obtain standardized data; 3. Multi-level screening is performed on the standardized data obtained in step 2 to obtain feature vectors with high correlation with energy consumption and extraction rate and low redundancy; 4. Use the mixed chicken flock algorithm to obtain from step 3 Search the effect of different feature combinations and model parameters on a single data-driven model in the eigenvector candidate set, and obtain the parameter variables, energy consumption and extraction rate under the optimal performance of a single model; 5. Establish the first layer of deterministic prediction output; 6. , establish a multi-model combination model, and realize the deterministic and probabilistic prediction of extraction rate and energy consumption. The invention greatly improves the model fitting effect and prediction accuracy, and solves the problems that these indexes are difficult to measure online.

Description

technical field [0001] The invention relates to the technical field of sugarcane crushing process design optimization, in particular to a sugarcane crushing process prediction method based on deep feature recognition. Background technique [0002] Extraction of sugarcane juice is the first link of sugar production. The extraction rate of pressing and production energy consumption are two important indicators of this section. Whether they meet the standards will affect the smooth operation and economic benefits of the entire sugar production. Due to technical limitations, these indicators are currently calculated through offline laboratory experiments. This method has a lag, which makes it impossible to quickly adjust the system indicators in time. Therefore, being able to monitor these indicators in real time has positive significance for guiding the optimal control of the process operation. [0003] With the development of artificial intelligence technology, the "black box...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q50/04G06N3/00
CPCG06Q10/04G06Q10/0639G06Q10/0633G06Q50/04G06N3/006Y02P90/30
Inventor 蒙艳玫陈劼柳宏耀邱敏敏韦锦陆冠成董振李正源胡松杰吴雪张月李济钦
Owner GUANGXI UNIV
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