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Batching optimization method based on variational Bayesian feedback optimization

A variational Bayesian and optimization method technology is applied in the field of batching optimization based on variational Bayesian feedback optimization, which can solve the problems affecting the batching quality of zinc concentrate and the uncertainty of composition.

Active Publication Date: 2021-05-28
CENT SOUTH UNIV
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Problems solved by technology

[0005] Based on the above problems, the present invention provides a batching optimization method based on variational Bayesian feedback optimization to solve the problem that the uncertain composition of zinc concentrate affects the quality of zinc concentrate batching

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  • Batching optimization method based on variational Bayesian feedback optimization
  • Batching optimization method based on variational Bayesian feedback optimization
  • Batching optimization method based on variational Bayesian feedback optimization

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Embodiment Construction

[0050] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0051] The invention provides a batching optimization method based on variational Bayesian feedback optimization, such as figure 1 As shown in the block diagram, the following steps are included:

[0052] S1. Establish a distribution parameter optimization model according to the test values ​​of the last round of batching process of zinc concentrate;

[0053] The distribution parameter optimization model is:

[0054] z=X1·w+X2·λ+ε (1)

[0055]Among them, z is the test value, including the ratio of M primary feedbacks, and M is the number of ratios of the primary feedback of the test value; X1 is the ratio that obeys the normal distribution, and the dimension is N1, and ...

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Abstract

The invention discloses a variational Bayesian feedback optimization-based batching optimization method. The method comprises the following steps: establishing a distribution parameter optimization model according to a test value of a last round of batching process of zinc concentrate, optimizing parameters of the distribution parameter optimization model through a variational Bayesian method, and substituting the posterior probability distribution obtained through optimization into the distribution parameter optimization model; matching by combining a nonlinear chance constraint programming model, and acting on a batching process; and feeding back a new round of test value to the distribution parameter optimization model. According to a feedback test value, a variational gauss method is adopted to optimize and adjust each component of each ore storage bin, and the problem of uncertainty of zinc concentrate components of each ore storage bin is solved, so that the proportion is optimized, and the batching quality is improved.

Description

technical field [0001] The invention relates to the technical field of smelting, in particular to a batching optimization method based on variational Bayesian feedback optimization. Background technique [0002] Non-ferrous smelting enterprises are flow-type industries with continuous production processes, and their main task is to extract non-ferrous metals from raw materials through complex physical and chemical processes. The zinc hydrometallurgy production process mainly includes five stages of batching, roasting, leaching, purification and electrolysis. The batching process is the pre-process of the roasting process, and the quality of the zinc concentrate after batching is crucial to the subsequent production process. The uncertainty of the main components of zinc concentrate in each mine has become the biggest problem at present, which is mainly caused by the following reasons: 1) There are many kinds of mineral sources and the quality is different. There are more th...

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Application Information

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IPC IPC(8): G06F30/27G06Q10/04G06Q10/06G06Q50/04G06F17/16G06F17/18G06K9/62G06N3/04G06N3/08C22B19/02
CPCG06F30/27G06Q10/04G06Q10/06395G06Q50/04G06F17/16G06F17/18G06N3/084G06N3/086C22B19/02G06N3/044G06F18/24155Y02P90/30
Inventor 李勇刚陈宇孙备阳春华李育东刘卫平黄科科
Owner CENT SOUTH UNIV
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