Right: Dynisco’s LMI5500 Series melt flow indexer uses a unified software platform to simplify

handling and analysis of lab and production data

properties at different process conditions, according to Johannes Lorenz, Sales Manager at Dynisco Europe, in a presentation given at AMI’s Polymer Testing & Analysis Europe conference in September last year. “These data can be used in many ways,” says Lorenz. “Data could provide viscosity as a parameter to be used in the extruder control system — for example to control the dosage of additives or raw materials. They can also be used for in-process quality control instead of, or in addition to, lab tests.” To correlate process and lab

patented slit die approach that is claimed to allow it to provide a more complete picture of melt quality than existing on-line technology using fixed-geometry capillary rheometers. The rheometer works by


Below: Leistritz claims the patented slit die geometry of its Elongation Rhemometer delivers

benefits over capillary types in in-line applications

data, Dynisco’s Internet-of-Things- based platform collects, stores, and visualises data from on-line as well as lab instruments. “Field tests and case studies show that — next to the classical process parameters such as pressure, temperature or motor torque — viscosity or MFR [melt flow rate] can provide a much more sensitive and accurate insight into the process,” Lorenz says. He explains that such measurements have been standard in polymer manufacturing but their use has more recently been increasing in compounding and recycling, especially of polyolefins and polyesters. Dynisco’s ViscoIndicator Online Rheometer, for

example, can be set to calculate either apparent viscosity or MFR. One of the challenges in correlating online and offline measurements is the difference in temperatures of the tests, says Lorenz. However, he says the company’s scientists have been able to calculate an appropriate temperature correlation that gives good

agreement between online and offline MFR measurements.

In-line options First introduced in 2017, Leistritz’s Elongational Rheometer can be used as a standalone instrument or linked to an extruder for on-line or in-line measurement of shear and elongational viscosities along the entire viscosity curve. Developed by Leistritz and the Institute for Polymer Extrusion and Compounding at the Johannes Kepler University in Linz, Austria, the instrument uses a

36 COMPOUNDING WORLD | January 2019

diverting a small amount of melt via a bypass into the slot die, which features a hyperbolic narrowing designed to generate constant elongational flow (said to be a first for an online elongational rheometer). Delivery of the melt to the die is controlled by an internal gear pump, providing full independence from the compounding extruder. After the

measurement has been made the molten material is transferred back into the process, avoiding any waste. The device can be used with materials ranging

from high viscosity pipe grades to low viscosity fibre or injection moulding grades. It provides online measurement of shear viscosity with shear rates in the range 10 to 10,000 s-1

with elongation rates in the range 5 to 75 s-1

and elongational viscosity . During

the continuous measuring process, the operator can query two measured values of shear viscosity and one value of extensional viscosity. Leistritz says the unit can be used to obtain viscosity curves in a very short time through targeted variation of shear and elongational rates. It can also indicate the melt flow index, IV value and melt density and can be used to monitor reactive compounding processes. Goettfert’s on-line rheometer product line

ranges from bypass and side stream instruments to the Dynamic Online Rheometer (DOR), which attaches to the extruder and, because it also connects to a data collection system, can be considered to be “big data friendly,” according to Tim Haake, General Manager of Goettfert. Speak- ing at AMI’s Polymer Testing and Analysis North America conference in 2018, he said: “Connecting instruments via the Internet of Things and starting to collect data now is crucial.” Keeping data relevant by linking rheology data with the lot number and procedure is important for statistical analysis. Haake says that artificial intel- ligence (AI) can analyse unstructured data but, for now at least, keeping lab and process data in sync and understanding where the data comes from is necessary. Big data techniques can be used to recognise patterns, which can then be used to


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