超临界二氧化碳在聚合物中的溶解计算模型研究
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摘要
超临界二氧化碳（Supercritical Carbon Dioxide，ScCO2）在聚合物中的溶解性是重要的物理化学属性之一，广泛用作聚合物改性、合成、共混和新型材料制备等领域，主要有实验和模型研究两类。实验研究耗时、费力、开销大，同时也易受实验条件等制约；传统预测模型精度与适应性不高，与实验值偏差也较大。本文作者从粒子群算法（Particle Swarm Optimization，PSO）、聚类方法、人工神经网络（Artificial Neural Network, ANN）和吸附理论、扩散理论与翻埋理论出发，深入研究ScCO2在聚合物中的溶解计算模型。研究主要成果如下：
（1）粒子群改进算法研究。
改进算法引入自适应权值调整策略提高收敛速度和精度，通过混沌理论优化学习因子避免早熟收敛，简称CSAPSO算法。实例表明，CSAPSO算法的收敛速度较快、精度较高、多样性较好。
（2）基于CSAPSO算法的溶解预测模型研究。
将CSAPSO算法与反向传播人工神经网络（Back Propagation，BPANN）结合提出溶解计算模型，称CSAPSO-BPANN模型。BP算法具有较强的局部搜索能力，但全局搜索能力相对薄弱，而CSAPSO算法正好与BP算法互补。模型采用CSAPSO算法对BP ANN的连接权值进行优化得到。实验表明，模型在未训练的聚合物中溶解预测性能较好，具有较好的探索预测能力。
（3）基于聚类方法的溶解预测模型研究。
径向基函数人工神经网络（Radial Basis Function，RBF ANN)的连接权值和基函数中心、扩展常数分别通过CSAPSO算法和聚类方法优化，得到3个统称为CSAPSO-C RBFANN的溶解计算模型。实验表明，聚类方法对基函数中心和扩展常数的优化效果明显，模型在经过训练的聚合物溶解预测中性能较优，具有较好的开发预测能力。
（4）基于扩散理论的溶解预测模型研究。
受溶解与扩散的本质--质量传递和PSO思想的启发，提出基于粒子扩散能和扩散概率的双种群PSO改进算法，得到DCSAPSO-BP ANN溶解计算模型。实验表明，无论是在经过训练还是未经过训练的聚合物溶解预测中，模型都表现优越，稳定性较好。
（5）基于吸附、扩散和翻埋的溶解理论计算模型研究。
将界面吸附、扩散与翻埋理论结合，通过翻埋溶解增长系数来衡量因翻埋而产生的溶解增量随时间的变化关系，提出基于吸附-扩散和吸附-翻埋两个过程的溶解理论计算模型。通过算例表明，模型从理论上描述各实验条件对溶解度的影响，能给挤出加工实验提供合理的实验参考。
（6）挤出实验研究。
在不同温度、压力、螺杆转速和气体进气流量下进行发泡材料的挤出实验，分析各产品的性能与实验参数的相互关系。通过比较在模型预测的理想进气流量下的实验产品性能，表明了模型提供的实验参数是合理的，溶解计算模型对实验有实际的指导意义。
Solubility of supercritical carbon dioxide (ScCO2) in polymers is one of the mostimportant physicochemical properties, which has been used successfully in polymermodification, polymer synthesis, polymer blending, preparation of new material, andso on. Solubility studies consist of experiment and prediction, experimental studiesare difficult to implement under many restricted conditions such as time consuming,strenuosity, and material consuming, while most traditional prediction methods havesome shortcomings such as inaccurate, low adaptability, and the correlation is notgood compared with experimental. In this thesis, study on prediction model of ScCO2solubility in polymers based on particle swarm optimization (PSO), clustering method,artificial neural network (ANN), and the adsorption, diffusion, and burying theory iscarried out. The main work and achievements are as follows:
(1) Study on improved particle swarm optimization algorithm.
The improved PSO algorithm is introduced into the self-adaptive weightadjustment strategy to improve the convergence speed and accuracy, and introducedinto chaos theory to tune the acceleration coefficients and avoid prematureconvergence, hereinafter called CSAPSO algorithm. Examples show that theconvergence speed, accuracy, and diversity of CSAPSO algorithm are better.
(2) Study on solubility prediction model based on CSAPSO algorithm.
A solubility prediction model combined with CSAPSO algorithm and backpropagation ANN, called CSAPSO-BP ANN is proposed. BP algorithm with a strongability to search local optimum suffers weak ability to search global optimum, whileCSAPSO algorithm complements the BP algorithm. The model is developed usingCSAPSO algorithm to optimize the BP ANN connection weights. Examples showthat, the model has excellent prediction capability and better exploration researchcapability in the prediction example of untrained polymers.
(3) Study on solubility prediction model based on clustering method.
Three solubility prediction models, called by a joint name, CSAPSO-C RBFANN, are proposed. The connection weights, function center and spreads of radial basis function ANN are optimized by the CSAPSO algorithm and clustering method.Experiments show that the effect of using clustering method to tune function centerand spreads is obvious, and the model has better prediction performance with gooddevelopment research capability in the prediction example of trained polymers.
(4) Study on solubility prediction model based on diffusion theory.
Inspired by the essence of solubility and diffusion, mass transfer, and PSO, adouble populations PSO algorithm based on the particle diffusion energy anddiffusion probability is developed, then a solubility prediction model, calledDCSAPSO-BP ANN, is proposed. Experiments show that, in the prediction exampleof both trained and untrained polymers, DCSAPSO-BP ANN has superiorperformance and good stability.
(5) Study on solubility theoretical calculation model based on the adsorption,diffusion and burying theory.
Combined with interface adsorption, diffusion and burying theory, a solubilitytheoretical calculation model is developed based on two processes consist ofadsorption-diffusion and adsorption-burying, the relationship between buriedsolublity and time is estimated by a burying solubility growth factor. Experimentshows that the model describes the influence from solubility experiment conditions,and provides a reasonable reference for extrusion processing experiment.
(6) Experimental study on extrusion process.
Foaming materials extrusion process experiment under different temperature,pressure, screw speed and gas inlet flow is carried out, the correlation between theperformance of the products and the experiment parameters is analysed. It shows thatthe experiment parameters provided by prediction models are reasonable bycomparing the performance of the products, the solubility prediction models presentimportant references for practical experiment.
（1）粒子群改进算法研究。
改进算法引入自适应权值调整策略提高收敛速度和精度，通过混沌理论优化学习因子避免早熟收敛，简称CSAPSO算法。实例表明，CSAPSO算法的收敛速度较快、精度较高、多样性较好。
（2）基于CSAPSO算法的溶解预测模型研究。
将CSAPSO算法与反向传播人工神经网络（Back Propagation，BPANN）结合提出溶解计算模型，称CSAPSO-BPANN模型。BP算法具有较强的局部搜索能力，但全局搜索能力相对薄弱，而CSAPSO算法正好与BP算法互补。模型采用CSAPSO算法对BP ANN的连接权值进行优化得到。实验表明，模型在未训练的聚合物中溶解预测性能较好，具有较好的探索预测能力。
（3）基于聚类方法的溶解预测模型研究。
径向基函数人工神经网络（Radial Basis Function，RBF ANN)的连接权值和基函数中心、扩展常数分别通过CSAPSO算法和聚类方法优化，得到3个统称为CSAPSO-C RBFANN的溶解计算模型。实验表明，聚类方法对基函数中心和扩展常数的优化效果明显，模型在经过训练的聚合物溶解预测中性能较优，具有较好的开发预测能力。
（4）基于扩散理论的溶解预测模型研究。
受溶解与扩散的本质--质量传递和PSO思想的启发，提出基于粒子扩散能和扩散概率的双种群PSO改进算法，得到DCSAPSO-BP ANN溶解计算模型。实验表明，无论是在经过训练还是未经过训练的聚合物溶解预测中，模型都表现优越，稳定性较好。
（5）基于吸附、扩散和翻埋的溶解理论计算模型研究。
将界面吸附、扩散与翻埋理论结合，通过翻埋溶解增长系数来衡量因翻埋而产生的溶解增量随时间的变化关系，提出基于吸附-扩散和吸附-翻埋两个过程的溶解理论计算模型。通过算例表明，模型从理论上描述各实验条件对溶解度的影响，能给挤出加工实验提供合理的实验参考。
（6）挤出实验研究。
在不同温度、压力、螺杆转速和气体进气流量下进行发泡材料的挤出实验，分析各产品的性能与实验参数的相互关系。通过比较在模型预测的理想进气流量下的实验产品性能，表明了模型提供的实验参数是合理的，溶解计算模型对实验有实际的指导意义。
Solubility of supercritical carbon dioxide (ScCO2) in polymers is one of the mostimportant physicochemical properties, which has been used successfully in polymermodification, polymer synthesis, polymer blending, preparation of new material, andso on. Solubility studies consist of experiment and prediction, experimental studiesare difficult to implement under many restricted conditions such as time consuming,strenuosity, and material consuming, while most traditional prediction methods havesome shortcomings such as inaccurate, low adaptability, and the correlation is notgood compared with experimental. In this thesis, study on prediction model of ScCO2solubility in polymers based on particle swarm optimization (PSO), clustering method,artificial neural network (ANN), and the adsorption, diffusion, and burying theory iscarried out. The main work and achievements are as follows:
(1) Study on improved particle swarm optimization algorithm.
The improved PSO algorithm is introduced into the self-adaptive weightadjustment strategy to improve the convergence speed and accuracy, and introducedinto chaos theory to tune the acceleration coefficients and avoid prematureconvergence, hereinafter called CSAPSO algorithm. Examples show that theconvergence speed, accuracy, and diversity of CSAPSO algorithm are better.
(2) Study on solubility prediction model based on CSAPSO algorithm.
A solubility prediction model combined with CSAPSO algorithm and backpropagation ANN, called CSAPSO-BP ANN is proposed. BP algorithm with a strongability to search local optimum suffers weak ability to search global optimum, whileCSAPSO algorithm complements the BP algorithm. The model is developed usingCSAPSO algorithm to optimize the BP ANN connection weights. Examples showthat, the model has excellent prediction capability and better exploration researchcapability in the prediction example of untrained polymers.
(3) Study on solubility prediction model based on clustering method.
Three solubility prediction models, called by a joint name, CSAPSO-C RBFANN, are proposed. The connection weights, function center and spreads of radial basis function ANN are optimized by the CSAPSO algorithm and clustering method.Experiments show that the effect of using clustering method to tune function centerand spreads is obvious, and the model has better prediction performance with gooddevelopment research capability in the prediction example of trained polymers.
(4) Study on solubility prediction model based on diffusion theory.
Inspired by the essence of solubility and diffusion, mass transfer, and PSO, adouble populations PSO algorithm based on the particle diffusion energy anddiffusion probability is developed, then a solubility prediction model, calledDCSAPSO-BP ANN, is proposed. Experiments show that, in the prediction exampleof both trained and untrained polymers, DCSAPSO-BP ANN has superiorperformance and good stability.
(5) Study on solubility theoretical calculation model based on the adsorption,diffusion and burying theory.
Combined with interface adsorption, diffusion and burying theory, a solubilitytheoretical calculation model is developed based on two processes consist ofadsorption-diffusion and adsorption-burying, the relationship between buriedsolublity and time is estimated by a burying solubility growth factor. Experimentshows that the model describes the influence from solubility experiment conditions,and provides a reasonable reference for extrusion processing experiment.
(6) Experimental study on extrusion process.
Foaming materials extrusion process experiment under different temperature,pressure, screw speed and gas inlet flow is carried out, the correlation between theperformance of the products and the experiment parameters is analysed. It shows thatthe experiment parameters provided by prediction models are reasonable bycomparing the performance of the products, the solubility prediction models presentimportant references for practical experiment.
引文
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