Javier Vera-Sorroche

Monitoring and modelling of the energy consumption in polymer extrusion

Extrusion is one of the fundamental methods in polymer processing and is involved in the production of various commodities in diverse industrial sectors. Being an energy intensive production method, process energy efficiency is one of the major concerns and the selection of the most energy efficient processing conditions is a key to reduce operating expenses. Usually, extruders consume energy through the drive motor, barrel heaters, cooling fans, cooling water pumps, gear pumps, etc. Typically the drive motor is the dominant energy consuming device in a polymer processing extruder while barrel/die heaters are responsible for the second largest energy demand. This study was focused on investigating the total energy demand of an extrusion plant while identifying ways to optimise the energy consumption. Total energy consumption of a single screw extruder was measured over different processing conditions and then modelled as a function of a number of major process variables. The results show that the extruder energy demand is heavily coupled with machine, material and process parameters. Also, the proposed models show excellent agreement with the experimental measurements and hence these should be useful in optimising process energy efficiency.

Low-cost process monitoring for polymer extrusion

Polymer extrusion is regarded as an energy-intensive production process, and the real-time monitoring of both energy consumption and melt quality has become necessary to meet new carbon regulations and survive in the highly competitive plastics market. The use of a power meter is a simple and easy way to monitor energy, but the cost can sometimes be high. On the other hand, viscosity is regarded as one of the key indicators of melt quality in the polymer extrusion process. Unfortunately, viscosity cannot be measured directly using current sensory technology. The employment of on-line, in-line or off-line rheometers is sometimes useful, but these instruments either involve signal delay or cause flow restrictions to the extrusion process, which is obviously not suitable for real-time monitoring and control in practice. In this paper, simple and accurate real-time energy monitoring methods are developed. This is achieved by looking inside the controller, and using control variables to calculate the power consumption. For viscosity monitoring, a ‘soft-sensor’ approach based on an RBF neural network model is developed. The model is obtained through a two-stage selection and differential evolution, enabling compact and accurate solutions for viscosity monitoring. The proposed monitoring methods were tested and validated on a Killion KTS-100 extruder, and the experimental results show high accuracy compared with traditional monitoring approaches.