CN-121998323-A - Multi-source emission and quality collaborative optimization method for mixing and injection molding integrated production line

CN121998323ACN 121998323 ACN121998323 ACN 121998323ACN-121998323-A

Abstract

The invention provides a multi-source emission and quality collaborative optimization method of a mixing injection molding integrated production line. The method is particularly aimed at a mixing and injection molding integrated production line, and aims to synergistically optimize the multi-source environment emission and the product quality. The quantitative model and the multi-objective optimization solution are established, so that the process parameter optimization of emission reduction and quality guarantee objectives is realized. The reduction of carbon emission, VOCs emission and PM2.5 emission is completed on the premise of ensuring the qualification rate or quality requirement of products by adjusting key process parameters of a production line. The invention provides a decision support method close to the actual production condition of the production line for reducing the comprehensive environmental emission of the mixing injection molding production line on the premise of ensuring the product quality through a systematic modeling and optimizing framework.

Inventors

FENG JIA
HE SHUANGYU
Zhang Qiongzhi
DING QIYU
ZHU LINQUAN

Assignees

重庆大学

Dates

Publication Date: 20260508
Application Date: 20260115

Claims (9)

1. The multi-source emission and quality collaborative optimization method of the mixing and injection molding integrated production line is characterized by comprising the following steps of: S1) establishing an emission quantitative calculation function and an optimization objective function, respectively establishing a carbon emission calculation function, a VOCs emission calculation function and a particulate matter emission calculation function aiming at a mixing injection molding integrated production line; s2) determining decision variables and constraint conditions, selecting process parameters of production line adjustment as decision variables, determining a feasible region range of the decision variables according to equipment working conditions, and taking the feasible region range as the constraint conditions of an optimization process; s3) establishing a functional relation between the technological parameters and the product percent of pass as a product percent of pass prediction function; s4) performing multi-objective optimization solution, namely taking minimized carbon emission, minimized VOCs emission and minimized particulate matter emission as optimization targets, and performing optimization calculation by utilizing a multi-objective optimization algorithm based on a feasible region range of decision variables to obtain an initial Pareto optimal solution set containing a plurality of groups of process parameter combinations; S5) substituting each group of process parameters in the initial Pareto optimal solution set into a product percent of pass prediction function to calculate a corresponding prediction percent of pass; s6) outputting the screened optimal technological parameter scheme for production line regulation and control.
2. The multi-source emission and quality collaborative optimization method of a mixing and injection molding integrated production line according to claim 1 is characterized in that in step S1), based on energy consumption monitoring and carbon flow analysis, the total carbon emission rate D carbon is calculated by adopting the following model: D carbon =E total ×EF grid wherein EF grid is a power grid carbon emission factor, E total is total energy consumption in the mixing injection molding process, and E total is formed by superposition of energy consumption of each process step: E total =E comp +E inj +E hold +E cool +E base Wherein E comp is mixing plasticizing energy consumption, Einj is injection energy consumption, E hold is pressure maintaining energy consumption, E cool is cooling energy consumption, and E base is base energy consumption.
3. The multi-source emission and quality collaborative optimization method of a mixing and injection molding integrated production line according to claim 2, characterized in that the calculation formula of the energy consumption of each process step is as follows: Mixing and plasticizing energy consumption: Injection energy consumption: pressure maintenance energy consumption: Where n screw is the screw speed, V inj is the injection speed, P hold is the dwell pressure, t comp 、t inj 、t hold is the plasticizing, injection, dwell time, A screw is the screw cross-sectional area, η motor and η system are the motor and system efficiency, respectively.
4. The multi-source emission and quality collaborative optimization method of a mixing and injection molding integrated production line according to claim 1 is characterized in that in step S1), a VOCs emission calculation experience-mechanism model related to a melt temperature is established based on an Arrhenius equation: Wherein D VOCs is the emission rate of VOCs, k VOCs is the specific coefficient of the material, A is the factor before referring to Ea, ea is the apparent activation energy, R is the ideal gas constant, T melt is the absolute temperature of the melt, and T exposure is the accumulation time of the material in the molten state.
5. The multi-source emission and quality collaborative optimization method of a mixing and injection molding integrated production line according to claim 1 is characterized in that in step S1), a particulate matter emission calculation model related to mechanical motion parameters is established: Wherein, D PM2.5 is PM2.5 emission rate, k PM is basic emission coefficient, N ref and V ref are reference rotation speed and reference speed respectively, alpha and beta are empirical indexes, N cycle is production cycle number, N screw is screw rotation speed, and V inj is injection speed.
6. The multi-source emission and quality collaborative optimization method of a mixing and injection molding integrated production line according to claim 1, wherein in step S2), the decision variables are defined as vectors x= [ T, V, P, N ], and the constraint conditions include upper and lower limit ranges of the decision variables: Wherein T is the melt temperature, v is the injection speed, P is the holding pressure, and n is the screw rotation speed.
7. The multi-source emission and quality collaborative optimization method of a mixing and injection molding integrated production line according to claim 1, characterized in that in step S3), a qualification rate function is used as one of optimization objective functions to construct an objective function; in the formula, Representing the carbon emission rate of a unit or unit batch of products; representing the VOCs emission rate of a unit or unit batch of products; representing the particulate matter emission rate of a unit or unit batch of products; Representing the yield multiplied by minus one.
8. The method for optimizing multi-source emission and quality cooperation of a mixing and injection molding integrated production line according to claim 1, wherein in step S3), the establishment of the product percent of pass prediction function specifically comprises: Collecting a plurality of groups of process parameters and corresponding actual qualification rate observation value data thereof, and constructing a multiple linear regression model based on a least square method principle: Wherein Q is the product percent of pass, N is the screw speed, T is the melt temperature, V is the injection speed, P is the holding pressure, and beta 0 ～β 4 is the regression coefficient.
9. The multi-source emission and quality collaborative optimization method of a mixing and injection molding integrated production line according to claim 1, wherein in the step S4), the multi-objective optimization algorithm adopts a non-dominant ordering genetic algorithm with elite strategy, and the step S4) specifically comprises the following substeps: S4.1) randomly generating an initial parent population in a feasible domain range of the decision variable; S4.2) calculating three objective function values of each individual in the population by using the carbon emission, VOCs emission and particulate matter emission calculation functions established in the S1); s4.3) performing non-dominant sorting and crowding degree calculation on the population, and generating a child population through selection, crossing and mutation operations; S4.4) merging the parent population and the offspring population, and screening out a new generation population according to the non-dominant grade and the crowding degree; s4.5) repeating the evaluation and evolution operation until the preset maximum iteration number is reached, and outputting a Pareto optimal solution set.

Description

Multi-source emission and quality collaborative optimization method for mixing and injection molding integrated production line Technical Field The invention relates to the technical field of green intelligent manufacturing, in particular to a multi-source emission and quality collaborative optimization method of a mixing injection molding integrated production line. Background The mixing and injection molding integrated technology is one of the core processes in the field of modern high polymer material processing and molding, and realizes the one-step high-efficiency manufacturing of materials by integrating mixing and granulating and injection molding processes. At present, in order to improve the production benefit, the industry generally adopts a mode of adjusting process parameters to intervene in the production process. The existing optimization technology mainly focuses on traditional production indexes, and focuses on improving the yield of products, reducing the direct operation energy consumption of equipment or controlling single production cost. In terms of process control and prediction, some of the prior art began to introduce data-driven methods, such as using algorithms such as neural networks to build a mapping model between process parameters and product quality to aid in production decisions. Further, improvements to production lines have focused on upgrades of hardware devices or on shop-level macro management systems. However, with the increasing demand for green intelligent manufacturing, the above-mentioned prior art reveals the following obvious drawbacks and disadvantages in practical applications: A. Existing optimization methods tend to be one-to-one, focusing mainly on single or few traditional objectives. In actual production, the mixing injection molding process not only produces carbon emissions, but also produces Volatile Organic Compounds (VOCs) and particulate matter due to thermal degradation of the polymer and volatilization of the additives. The prior art fails to put the three multi-source environmental emission indexes and the product qualification rate into the same optimization frame for consideration, so that the emission of other pollutants is often ignored when high quality or low energy consumption is pursued, and the minimization of the comprehensive environmental load is difficult to realize on the premise of ensuring the product quality. B. The neural network equal quality prediction model widely used at present belongs to a black box model, and although the prediction precision is improved to a certain extent, the internal mechanism is opaque, the interpretation is poor, and the calculation process is complex. Such defects result in such models being difficult to translate into explicit mathematical functions that are well-structured, computationally efficient, and cannot be directly and quickly invoked by multi-objective optimization algorithms as deterministic constraints. This greatly limits the real-time response capability and computational efficiency of the optimization algorithm in real-time engineering. C. The improvement of the prior art is at the level of local improvement of hardware equipment or a general management system, and the combination of software and hardware is not tight enough. Aiming at a complex system with multivariable and strong coupling characteristics, such as a mixing injection molding integrated production line, the prior art lacks a systematic software method capable of deeply fusing a mechanism model, data mining and a multi-objective optimization algorithm. This results in insufficient exploitation of the green production potential of the production line under dynamic conditions, and difficulty in automatically searching out the optimal process parameter combination which can meet the quality constraint and also meet the multi-dimensional environmental protection requirement in a complex process window. Therefore, the development of the multi-source emission and quality collaborative optimization method of the mixing and injection molding integrated production line has great significance. Disclosure of Invention The invention aims to provide a multi-source emission and quality collaborative optimization method of a mixing and injection molding integrated production line, which aims to solve the problems in the prior art. The technical scheme adopted for realizing the purpose of the invention is that the multi-source emission and quality collaborative optimization method of the mixing injection molding integrated production line comprises the following steps: S1) establishing an emission quantitative calculation function and an optimization objective function. And respectively establishing a carbon emission calculation function, a VOCs emission calculation function and a particulate matter emission calculation function aiming at the mixing and injection molding integrated production line. And constructing an optimized object