Papers
Plastic Pipes Conference Association # 2016 Berlin
Helmuth Rijnhart
As the cost of raw material is the dominant part of cost price in plastic pipe, pipe producers internally check on scrap rates and overweight to get the lowest cost price.
Scrap rates are mostly defined as start-up scrap. But scrap is also production that is in not in spec, production of pipe that has burning spots, black spots etc. If this is all calculated, scrap rates of 10-15% are not a uncommon.
Overweight is all weight of the pipe that is caused by making pipe heavier than determined by the minimum diameter and the minimum wall thickness. Most pipe producers define as overweight the weight of the pipe bigger than determined by the centre of specification. Doing so, these pipe producers already accept overweight in PVC pipe up to 5% and in PE pipe up to 8,5%. Knowing that the average extrusion line produces per year pipe worth 10 times the investment in that line, the cost of overweight is much more dominant than the depreciation and interest cost on the line.
By applying modern controls, like inline wall thickness scanners with haul-off control, gravimetric control to dose a constant mass flow of raw material to the extruder, and Automatic Thermal Centering on the die head, Overweight can be reduced significantly and labour cost can be reduced. At most of the extrusion lines, devices to reduce scrap rates and overweight have pay back times less than 12 months.
By applying modern control strategies, that fit in the “Industry 4.0” definition, it is possible not only to reduce scrap rates and overweight, but also optimize output and energy consumption. By constantly comparing settings and actual values against a calculated optimum, both operators and management constantly will have access to the best quality information to make sure that best performance in many perspectives is achieved. Using this technology during start and stop of the extrusion process, will help minimizing start and stop scrap rates.
Continuously collecting a wide set of data in the extrusion line makes Big Data Analysis possible, to explore, detect and eliminate root causes of non-optimal performance.
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