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A generalized least square model for the determination of monomer reactivity ratios in free radical copolymerization systems

机译：用于确定自由基共聚体系中单体反应比的广义最小二乘模型

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The reliable estimation of monomer reactivity ratios is a key problem in the development of process models, and, thus, is an important issue in both process design and control. Copolymers of isobutyl methacrylate (i-BMA), and lauryl methacrylate (LMA) were prepared by free radical bulk copolymerization at 70degreesC using 2,2'-azobisisobutyronitrile (AIBN) as initiator. The monomer reactivity ratios as well as the 95% confidence intervals were calculated from Fineman-Ross, Ezrielev-Brokhina-Roskin, Kelen & Tudos, extended Kelen & Tudos and a linear iterative method proposed by Mao and Huglin. The estimation process was performed by Mao and Huglin. The estimation process was performed by applying nonlinear techniques based on Marquardt optimization, "ordinary least square" (OLS) and "generalizaed least square" (GLS) approached and the results were compared with each other. The novel GLS estimator program developed during this study was employed to ascertain the shape of the copolymer composition curve and good agreement between experimental and calculated copolymer composition was obtained. Also, both real and simulated data was presented, which show GLS method is a statistically valid technique for estimating reactivity ratios by accounting response error structure. This new estimation method handles regression models with error terms that are heteroskedastic or serially correlated, or both. [References: 33]
机译：单体反应率的可靠估计是工艺模型开发中的关键问题，因此，在工艺设计和控制中都是重要的问题。甲基丙烯酸异丁酯（i-BMA）和甲基丙烯酸月桂酯（LMA）的共聚物是通过使用2,2'-偶氮二异丁腈（AIBN）作为引发剂在70℃下进行自由基本体共聚制备的。单体反应率以及95％置信区间是根据Fineman-Ross，Ezrielev-Brokhina-Roskin，Kelen＆Tudos，扩展的Kelen＆Tudos以及毛和休林提出的线性迭代方法计算得出的。估计过程由Mao和Huglin进行。通过基于Marquardt优化的非线性技术进行估计过程，采用“普通最小二乘”（OLS）和“广义最小二乘”（GLS）进行比较，并将结果进行比较。在这项研究中开发的新型GLS估计程序用于确定共聚物组成曲线的形状，并在实验和计算得出的共聚物组成之间取得了良好的一致性。此外，还提供了真实数据和模拟数据，这表明GLS方法是一种统计有效的技术，可以通过计算响应误差结构来估计反应率。这种新的估算方法可处理具有异方差或序列相关或两者的误差项的回归模型。 [参考：33]

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