Monitoring and control of polymer production line based on machine learning

Abstract

The work is devoted to the development of an application for monitoring and controlling the state of equipment (extruder) for the petrochemical industry based on sensor readings using a machine learning model. The statistical relationships of the technological process parameters are analyzed, the most significant parameters influencing the occurrence of failures are determined using SHAP values. The hypotheses regarding the effectiveness of various machine learning algorithms in relation to the real problem of predicting the technical state of the extruder are tested. A gradient boosting model has been developed to predict the probability of extruder shutdown due to the formation of polypropylene agglomerates. The developed application allows interpreting the results of the model, which makes it possible to select the most important process parameters that have the greatest impact on the probability of extruder failure, and also proposing a prototype of an extruder monitoring system based on sensor readings using a machine learning model.

Research and application of adaptive anti-interference based VU-fuzzy-PID algorithm in temperature control system of platic extruder

“…In this paper, as shown in Figure 1，An extruder equipment generally consists of a main machine, auxiliary machine and control system [7]. The main extruder consists of three parts: the extrusion system, the drive system and the heating and cooling sys-tem [8][9][10].…”

Research and Application of Plastic Extruder Temperature System Based on Variable Universe Fuzzy PID Control

“…In this paper, as shown in Figure 1，An extruder equipment generally consists of a main machine, auxiliary machine and control system [7]. The main extruder consists of three parts: the extrusion system, the drive system and the heating and cooling system [8][9][10].…”

“…Cast aluminum heater is selected for the system. (7) Cooling device The common cooling modes of extruder drum mainly include air cooling and water cooling. The air cooling is relatively gentle, homogeneous and the control system is simple, but requires blowers and other equipment, usually used in small and mediumsized plastic extruders.…”

Data-Driven Prediction of Janus/Core–Shell Morphology in Polymer Particles: A Machine-Learning Approach

“…Da Tan and co-workers successfully applied ensemble ML classifiers in combination with XAI analysis to predict a polymer’s MWD diagram, taking into account only the reaction parameters . Also, in industrial processes or production lines, the combined application of Shapley classifiers and ML algorithms has shown improved reliability and interpretability. , Liang et al applied XAI analysis to uncover the factors affecting the ML-assisted discovery of low dielectric-constant polymers …”

“…38 Also, in industrial processes or production lines, the combined application of Shapley classifiers and ML algorithms has shown improved reliability and interpretability. 39,40 Liang et al applied XAI analysis to uncover the factors affecting the ML-assisted discovery of low dielectric-constant polymers. 41 We used ML classification algorithms in conjunction with XAI to increase the precision in predicting the core−shell/ Janus morphology in polymer blend particles.…”