Collaborative optimization method for sugarcane juice clarification process based on entropy minimization
A collaborative optimization and minimum entropy technology, applied in instruments, adaptive control, control/regulation systems, etc., can solve the problems affecting sucrose extraction and final product quality, insufficient purity of sucrose clarified juice, etc., to improve extraction and final products. Quality, the effect of improving the purity of clarification
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Embodiment 1
[0030] image 3 A flowchart showing the collaborative optimization method based on the entropy-minimum sugarcane juice clarification process provided by Embodiment 1 of the present invention, such as image 3 As shown, the method acts on the automatic control system of the sugarcane juice clarification process, and the collaborative optimization method includes the following steps.
[0031] Step S1, divide the clarification system according to the basic characteristics of the substances in the clarification system, the factors involved and the relationship between flow changes, and divide the clarification system into a substance subsystem, an energy subsystem and an information subsystem, wherein , the substance subsystem includes: the state data of the processed substance, the energy subsystem includes the state data of energy generated in the current process, and the information subsystem includes the set value of the target control quantity in the clarification process and...
Embodiment 2
[0036] Figure 4 A flowchart showing the collaborative optimization method based on the entropy-minimum sugarcane juice clarification process provided by Embodiment 2 of the present invention, such as Figure 4 shown in Figure 4 neutralize image 3 Steps with the same reference numbers are the same as image 3 The same text descriptions are applicable and will not be repeated here. The collaborative optimization method provided in this embodiment, for image 3 Step S3 in is further defined.
[0037] Step S3 in this embodiment may include the following steps.
[0038] Step S31, initialize parameter values in the objective function.
[0039] Specifically, the parameter values in the objective function may include: setting the evolution algebra of the population, constraining variable ranges, and crossover and mutation parameters.
[0040] Step S32, generating an initial population P(t).
[0041] Specifically, in order to make the initial population evenly distribute...
Embodiment 3
[0049] Figure 5 A flowchart showing the collaborative optimization method based on the entropy-minimum sugarcane juice clarification process provided by Embodiment 3 of the present invention, such as Figure 5 shown in Figure 5 neutralize Figure 4 Steps with the same reference numbers are the same as Figure 4 The same text descriptions are applicable and will not be repeated here. The collaborative optimization method provided in this embodiment, for Figure 4 Step S34 in is further defined.
[0050] Step S34 in this embodiment further includes the following steps.
[0051] In step S341, a selection operation is performed on the gray-coded individuals.
[0052] Specifically, the gamble selection operation is performed on the group, and the probability of each individual being selected is proportional to its fitness.
[0053] Step S342, performing crossover and mutation calculations on the selected individuals.
[0054] Step S343, counting the results of the above c...
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Abstract
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