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作 者:禹新良[1] 柳俊 YU Xin-Liang;LIU Jun(College of Materials and Chemical Engineering,Hunan Institute of Engineering,Xiangtan 411104,China)
机构地区:[1]湖南工程学院材料与化工学院,湖南湘潭411104
出 处:《合成橡胶工业》2022年第5期365-369,共5页China Synthetic Rubber Industry
基 金:湖南省自然科学基金资助项目(12 JJ 6011)。
摘 要:采用支持向量机(SVM)与粒子群寻优算法,建立了丁苯橡胶SBR 1712聚合过程和硫化工艺的模拟模型。结果表明,以SBR 1712产品性能胶乳固含量、门尼黏度和分子量分布系数作为输入,乳液间歇聚合所采用的引发剂、活化剂、链转移剂用量分别作为输出的预测模型,训练集及测试集的决定系数均大于0.8,模型预测值与实验值相吻合。以SBR 1712硫化加工中产品的邵尔A硬度、焦烧时间、最小弹性转矩、最小黏性转矩作为输入变量,建立了以填料炭黑用量作为输出的SVM模型,模型拟合及预测性能效果较好。Prediction models were established for the polymerization and vulcanization process of butadiene-styrene rubber SBR 1712,by applying support vector machine(SVM)and particle swarm optimization algorithms.The results showed that the models with the solid content,Mooney viscosity and molecular weight polydispersity of SBR 1712 as inputs and the amount of initiator,activator and chain transfer agent used in batch emulsion polymerization as outputs produced determination coefficients greater than 0.8 for the training and test sets,and the predicted values from the models were consistent with the experimental values.Another SVM model was developed by using loading amount of furnace carbon black as output parameter and the properties in vulcanization process as input variables,including the shore A hardness,scorch time,minimum elastic torque and minimum viscous torque of SBR 1712.This model had excellent performance in fitting and prediction.
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