Abstract-In order to control the drying temperature of the PET resin in the silo of the plastic injection molding

No abstract

No abstract

This article presents a study in process control using the second tuning method Ziegler-Nichols. The second Ziegler-Nichols tuning method determines a gain and a period called critical gain and the critical period, respectively. And through empirical testing, to achieve system stability. The chosen driver for the development of this work was the PID (Proportional -Integral -Derivative). The purpose of this article is the study of the second Ziegler-Nichols tuning method in process control, making the stable control system. The development of this work was performed by MATLAB and Simulink software. The results showed that the research is relevant because the Ziegler and Nichols optimization method is applicable for process control projects using PID controllers..

The industrial production of preforms for the manufacture of PET bottles, during the plastic injection process, is essential to regulate the drying temperature of the PET resin, to control the generation of Acetaldehyde (ACH), which alters the flavor of carbonated or non-carbonated drinks, giving the drink a citrus flavor and putting in doubt the quality of packaged products. In this work, an Artificial Neural Network (ANN) of the Backpropagation type (Cascadeforwardnet) is specified to support the decision-making process in controlling the ideal drying temperature of the PET resin, allowing specialists to make the necessary temperature regulation decisions  for the best performance by decreasing ACH levels. The materials and methods were applied according to the manufacturer's characteristics on the moisture in the PET resin grain, which may contain between 50 ppm and 100 ppm of ACH. Data were collected for the method analysis, according to temperatures and residence times used in the blow injection process in the manufacture of the bottle preform, the generation of ACH from the PET bottle after solid post-condensation stage reached residual ACH levels below (3-4) ppm, according to the desired specification, reaching levels below 1 ppm. The results found through the Computational Intelligence (IC) techniques applied by the ANNs, where they allowed the prediction of the ACH levels generated in the plastic injection process of the bottle packaging preform, allowing an effective management of the parameters of production, assisting in strategic decision making regarding the use of temperature control during the drying process of PET resin.