DeMaco 2VSM Pasta Extruder with Electronic Data Collection System

“Extruder rpms, flow rate, temperature, and pressure readings feed into the ‘black box’ that is attached to the computer. The computer reads the data and puts it onto the screen (shown above),” explains Brian Harris, project manager at Standard Industries, Fargo.

100 kg/hour.  Scalable to commercial production. Second feeder for addition of functional ingredients.

Electronic Data Collection System

The electronic data collection system in NCI’s Demaco pasta press greatly enhances the education of participants in the pasta and noodles courses and assists contract processors in achieving their goals, according to Brian Sorenson, NCI Director.

“Electronic monitoring devices give us an enhanced picture of the processing that we are doing,” explains Sorenson. “We can capture information such as the temperature of flour or semolina that we are adding, and the water temperature as it is added to the mixer. We also have temperature sensors for dough temperature, mixer vacuum, die pressure and extruded product temperature.”

Brian Harris, project manager at Standard Industries, Fargo, ND, says, “We adapted a program made by General Electric called ‘Simplicity,’ which is designed for plant control. NCI’s extruder had many sensors that were reading data, but no central place where you could read it all. With this setup, the participants can see on-screen every point of what is happening in the extruder.”

A ‘black box’ is an important part of this operation. Sensors throughout the extruder record critical activities and feed information back into the black box. “Extruder rpms, dry flow rate, temperature, pressure, all feed into the black box that is attached to the computer. The computer reads the data and puts it onto the screen,” explains Harris.

The computer screen displays all the important processing conditions in real time. At the same time, the black box records how the extruder operates so that the processing parameters can be correlated to the quality of the product.

“Any time you are running an experimental pasta product, you need to make adjustments because it will run differently than standard durum semolina,” says Sorenson. “Even when you are comparing sources of semolina or different varieties of durum, you will have different processing requirements. By having all of this information, you can make certain adjustments while you are processing to optimize conditions.”

“The more information that we can collect during processing, the better we can help people scale the results up to large-scale pasta processing. Not only will we have the information on the look and the quality of the pasta desired, but also we can provide additional practical information that will help companies make decisions. I think this system is a great tool as we continue to expand our processing capabilities at Northern Crops Institute,” concludes Sorenson.