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ANN-based urban and rural mixed garbage aerobic fermentation humic degree prediction method

A technology of aerobic fermentation and prediction method, applied in neural learning methods, chemical process analysis/design, bio-organic part treatment, etc., can solve the difficulty of predicting the humification degree of garbage aerobic fermentation and the inability to predict the aerobic fermentation process of garbage Problems such as humification trends and laws can achieve the effects of improving prediction accuracy, realizing harmlessness, and making up for uncontrollable effects

Pending Publication Date: 2022-05-24
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem of the difficulty in predicting the humification degree of garbage aerobic fermentation due to too many factors of the humification degree in the current humification degree prediction method, thus causing the inability to predict the humification trend of the garbage aerobic fermentation process Therefore, it is difficult to provide data support for urban domestic waste management practice, and a method for predicting the humification degree of aerobic fermentation of urban and rural mixed waste based on ANN is proposed.

Method used

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  • ANN-based urban and rural mixed garbage aerobic fermentation humic degree prediction method
  • ANN-based urban and rural mixed garbage aerobic fermentation humic degree prediction method
  • ANN-based urban and rural mixed garbage aerobic fermentation humic degree prediction method

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specific Embodiment approach 1

[0011] Embodiment 1: An ANN-based method for predicting the degree of humification by aerobic fermentation of urban and rural mixed garbage in this embodiment is as follows: obtaining environmental information of the garbage to be predicted and information on the physical and chemical properties of the garbage itself, and analyzing the environmental information and the physical and chemical properties of the garbage itself. At least one data in the property information is input into the trained optimal BP artificial neural network to obtain the humus production amount of the garbage to be predicted, and the humus degree of the garbage to be predicted is determined according to the humus production amount.

[0012] Input the environmental information of the garbage to be predicted and the number of parameters of the physical and chemical properties of the garbage itself to be set according to experience;

[0013] The environmental information of the garbage to be predicted inclu...

Embodiment

[0036] Embodiment: In order to verify the feasibility of the neural network structure of the present invention, the following experiments are carried out in this embodiment:

[0037] First, based on the same optimization method, comparing the convergence effects of models with different learning rates, when the optimization algorithm is Adam, the optimal learning rate is 0.1, and when the optimization algorithm is SGD, the optimal learning rate is 1×10 -5 . According to this result, different random seeds are used for random initialization. The results show that the use of different random seeds has a certain influence on the learning convergence of the model, and they all show a convergence trend. Compared with seeds 84 and 41, the convergence effect is better. Based on the above analysis, the convergence effects of the two model structures were compared, and the optimal model was obtained, that is, the model structure was 7-14-1, the optimization algorithm was Adam, the rand...

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Abstract

The invention discloses an ANN-based urban and rural mixed garbage aerobic fermentation humic degree prediction method, and relates to the field of biological information prediction. The invention aims to solve the problem of low prediction accuracy caused by too many influence factors in the existing humic degree prediction method. The method comprises the following steps: acquiring environment information of to-be-predicted garbage and physicochemical property information of the garbage, and inputting at least one of the environment information and the physicochemical property information of the garbage into a trained optimal BP (Back Propagation) artificial neural network to obtain humus output of the to-be-predicted garbage, determining the humic degree of the to-be-predicted garbage according to the generation amount of the humus; the environment information of the to-be-predicted garbage comprises the number of days and the temperature; the physicochemical properties of the garbage comprise the carbon nitrogen ratio, the compost type, the compost proportion and the PH value; the BP artificial neural network comprises an input layer, a hidden layer and an output layer. The method is used for predicting the humic degree of aerobic fermentation of the garbage.

Description

technical field [0001] The invention relates to the field of biological information prediction, in particular to an ANN-based method for predicting the degree of humification by aerobic fermentation of urban and rural mixed garbage. Background technique [0002] Aerobic fermentation, as one of the main methods of waste treatment, has the advantages of low cost, high-quality fertilizers, and waste reduction. However, its treatment process is easily affected by factors such as the environment, the physical and chemical properties of the waste itself, and it is difficult to achieve the overall situation. Therefore, the prediction of the degree of humification of garbage has become the focus of research in this field. [0003] Due to the complex environment in which the garbage is located, there are too many factors affecting the degree of humification of the garbage, which makes the aerobic fermentation process difficult to control. Therefore, at present, only the humic acid of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16C20/10G16C20/70G06N3/08
CPCG16C20/10G16C20/70G06N3/084Y02W30/40
Inventor 左薇田禹朱惟琛孙盘飞张军陈志伟寇明月周正明
Owner HARBIN INST OF TECH