Established nylon production plants face increased competition from various sources, both foreign and domestic. Product margins are continually minimized due to rising energy costs. Improved modeling, control, and optimization can lead to significant cost savings. A combination of traditional methods and new methods will be presented in this work.

The main goal of this project was to provide industry with an in-line controller for the viscosity build of the melt polymerization of nylon 6. The new controller emulates an in-line viscometer which is part of the primary control loop. This technique was also used to predict product quality of downstream equipment exiting from meltlines of varying length. Two viscometers are used in this nylon production process. Due to a significant time delay between the reactor and the online instrument, model-based inferential control methods are used. Additionally, periodic lab measurements can be used to update the accuracy of the inferential system. In the control system, one viscometer is used to control the viscosity of polymerization exiting the reactor. The other viscometer is used to predict downstream results.

Linear and nonlinear models of various model order were evaluated. These models involve a number of key process variables. Plant data from the in-line viscometer was used in the construction of both models. The Volterra functional format was chosen owing to the ease of manipulation in the optimized selection of variables, time lag and model order. The resulting nonlinear dynamic model was used to improve process efficiency.