Method for updating model of waste plastic oil refining combustion optimization
A technology for model updating and combustion optimization, applied in biological neural network models, special data processing applications, instruments, etc., can solve the problems of not being able to use learning results, shortening the computational workload and time of model updating, etc.
Inactive Publication Date: 2013-02-06
浙江宜景环保科技有限公司
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This method overcomes the shortcomings of completely abandoning the existing model in the general model update method and cannot use the learning results of the existing model, makes full use of the learning results of the existing model, and greatly reduces the computational workload and time of model updating
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[0040] A model update method for optimizing the combustion of waste plastics refinery, the specific steps are:
[0041] (1) Establish the prediction error database of the original model. According to the specific combustion conditions and the requirements for model prediction accuracy, set the allowable prediction error limit of the model and , because the detection target of waste plastic refining combustion optimization is the axial temperature distribution of the reactor (the axial temperature distribution of the inner wall of the reactor is a temperature monitoring point, ), so two prediction error limits are set, is the maximum allowable error limit, that is, the axial distribution of the reactor The maximum allowable limit of each point error of the temperature detection points, is the average allowable error, that is, the axial distribution of the reactor The maximum allowable limit of the average error of the temperature detection points. When collecting...
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The invention relates to a method for updating a model of waste plastic oil refining combustion optimization. A bottleneck problem exists in the conventional waste plastic oil refining combustion optimization. The method comprises the following steps of: establishing a novel model by utilizing data which exceeds predicted error limit of the original model; searching the optimal rate coefficient of the novel model and an existing model by utilizing novel combustion data and an optimization algorithm of waste plastic oil refining; and combining the novel model with the existing model together by utilizing the optimal rate coefficient, and predicting and optimizing a novel waste plastic oil refining combustion state to update the model. By the method, the defects that the existing model is completely abandoned and a learning result of the existing model cannot be utilized in a common model updating method are overcome, the learning result of the existing model is fully utilized, and computational workload and time during model updating are greatly shortened.
Description
technical field [0001] The invention belongs to the technical field of information control, relates to a machine learning self-adaptive technology, and in particular relates to a model updating method for optimizing combustion of waste plastic refining. Background technique [0002] The combustion optimization of waste plastic refining is an important technical means to control the pyrolysis reaction and products of waste plastics. Its goal is to obtain the ideal combustion state required by the reactor by adjusting the operating parameters of each burner under certain production conditions and objectives. The temperature distribution of the reactor is good, and the pyrolysis reaction of waste plastics is carried out at an appropriate temperature, so that the benefits of the product can be maximized on the basis of meeting the production requirements. The difference in operating parameters such as air supply and oil supply of each burner in the heating reactor has a direct i...
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IPC IPC(8): G06F19/00G06N3/02
Inventor 吴鹏锋王春林俞天明孔亚广杨成忠
Owner 浙江宜景环保科技有限公司