An experimental study on on-line optimizing control of free radical bulk polymerization in a rheometer–reactor assembly under conditions of power failure

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• 作者：Jitendra S. Sangwai ; Shrikant A. Bhat ; Deoki N. Saraf ; Santosh K. Gupta
• 关键词：Bulk-free radical polymerization ; On-line optimization ; Polymerization ; Soft sensor ; Trommsdorff effect
• 刊名：Chemical Engineering Science
• 出版年：2007
• 出版时间：May 2007
• 年：2007
• 卷：62
• 期：10
• 页码：2790-2802
• 全文大小：248 K

文摘

An experimental study on the on-line optimizing control of a sample free radical bulk polymerization system, namely, methyl methacrylate (MMA), is carried out in a rheometer–reactor assembly. Two initiator loadings and three cases involving external disturbances (power failure) are studied. The disturbances are assumed to be of two kinds: one that leads to a sudden increase in the temperature of the reaction mass (cooling water pump failure) over the planned temperature history, N68NKN-1&_mathId=mml157&_user=10&_cdi=5229&_rdoc=15&_acct=C000050221&_version=1&_userid=10&md5=821746cc7a1542ce18baedaa30faf6d1"" title=""Click to view the MathML source"">T(t), and one leading to a sudden drop in the temperature (heater failure). The temperature and the viscosity, η, histories are used to describe the ‘state’ (conversion, x_m, and weight-average molecular weight, M_w) of the polymerizing mass. The polymerization is first carried out under an off-line computed optimal temperature history, T_op(t), obtained using the adapted jumping gene version of the elitist genetic algorithm (GA-II-aJG). A planned disturbance is introduced after the start of polymerization and continues for a pre-specified duration. A new optimal temperature history, T_reop(t), is calculated on-line (in about 3 min of real time) using GA-II-aJG. This is implemented as soon as the disturbance is rectified. Experimental values of x_m(t), M_w(t) and η(t) are also measured. These are observed to be in good agreement with model predictions for all the cases. It is found that the information on the viscosity of the reaction mass can be used effectively for on-line optimizing control. This can help ‘save’ the batch (give a product having the desired values of the average molecular weights) optimally, in as short a reaction time as possible. The effect of re-tuning of the model parameters using experimental data on the temperature, T_exp(t), and the viscosity, η(t), is also demonstrated.