EuroWire January 2021

Technical Article

Too low impulse times do not cause sufficient curing, while too long times cure a thick layer, which is also not desirable. Higher temperatures give better results, and the optimal impulse times are lower. Figure 6 shows the effect of impulse time and insulation thickness on non-circularity. Higher insulation thicknesses generally lead to larger non- circularity. Optimal conditions These kinds of models are developed with the objective of optimising the process in terms of one or more variables. In this case, the aim is to minimise non-circularity. LUMET systems also have

LUMET system Nonlinear Solutions Oy, Finland

minimum

maximum

answer

Conductor diameter [mm] Insulation thickness [mm] Impulse time [s] Impulse temperature [°C] Second zone temperature [°C] Third zone temperature [°C]

Non-circularity

Minimum

found

▲ ▲ Figure 7 : A typical calculation for determining suitable operating conditions

the facility to calculate suitable operating conditions to achieve a desired result. For example, one calculation is shown in Figure 7 . The conductor diameter is 50mm with an insulation thickness of 30mm. We would like to determine the way to achieve the least non-circularity. The system suggests that it can be done with an impulse time of 38.3 seconds, and temperatures of 575°C, 180°C and 200°C in the three zones. This should result in a non-circularity of about 0.0035. Conclusions Maillefer not only develops and provides the best equipment, it also helps the users derive the most value out of the equipment. With the kind of nonlinear models of this and various other processes, it has the capability to help the production units derive high productivity with high quality. Nonlinear modelling is a powerful tool for materials or product development. Nonlinear models can contain valuable knowledge in a concise and a precise form. Neural networks are very efficient at describing these kinds of relations compared to linear statistical techniques which have commonly been used. It is generally but falsely believed that neural networks require a lot of data. A small number of experiments suffices for the development of nonlinear models, if planned suitably, taking into account that some or all of the variables may have nonlinear effects on the material properties. With appropriate mathematical tools, it becomes possible to determine good or optimal ways to produce the product with desired properties. n [1] Pekka Huotari, “Method and arrangement for cross-linking or vulcanizing an elongate element”, United States Patent Application 20160237226 [2] K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators”, Neural Networks, Vol. 2, (1989) 359-366 [3] A. Bulsari (ed.), Neural Networks for Chemical Engineers, Elsevier, Amsterdam, 1995 [4] A. Bulsari, P. Pitkänen and B. Malm, “Nonlinear modelling paves the way to bespoke polymers”, British Plastics and Rubber (December 2002) 4-5 [5] A. Bulsari, J. Ilomäki, M. Lahtinen and R. Perkiö, “Nonlinear models of mechanical properties reduce rubber recipe development time”, Rubber World, Vol. 252, No. 6 (September 2015) 28-33 [6] A. Bulsari, M. Frankenhaeuser, S. Lindfors and M. Westén, “Nonlinear models help cement process and product development”, Global Cement Magazine (November 2016) 24-28 [7] A. Bulsari, H. Kylmämetsä and K. Juvas, “Nonlinear models of workability and compressive strength help minimise costs”, Concrete Plant International, No. 6 (December 2009) 36-42 [8] A. Bulsari, H. Keife and J. Geluk, “Nonlinear models provide better control of annealed brass strip microstructure”, Advanced Materials and Processes, Vol. 170 (July 2012) 18-20 References

[9] Abhay Bulsari, Ilkka Vuoristo and Ilpo Koppinen, “Nonlinear models tune precipitation hardening”, Advanced Materials and Processes, Vol. 168, No. 5 (May 2010) 31-33 [10] A. Bulsari, K. Lähteenkorva, E. Suokas and M. Huttunen, “Models add efficiency to bioabsorbable implant development”, Medical Design Technology, Vol. 19, No. 2 (March 2015) 26-28 [11] A. Bulsari, et al., “Correlation of in vitro and in vivo dissolution behaviour of stonewools using nonlinear modelling techniques”, Journal of the European Ceramic Society, Vol. 27, No. 2-3 (2007) 1837-1841 [12] Abhay Bulsari, Timo Saarenko, Jaana Valtanen and Laura Kaskinen, “Nonlinear models enhance distillation”, Chemical Processing, Vol. 74, No. 10 (October 2011) 43-46 [13] Abhay Bulsari, Antti Wemberg, Ari Anttila and Ahti Multas, “Nonlinear models: coal combustion efficiency and emissions control”, Power Engineering International, Vol. 17, No. 4 (April 2009) 32-39 [14] A. Bulsari and V.-M. Airaksinen, “Nonlinear models used to address epi layer uniformity”, Solid State Technology, Vol. 47, No. 7 (July 2004) 33-38 [15] Abhay Bulsari, Eero Kiljunen and Mirva Suhonen, “Speeding development of an enantioselective enzymatic process for a pharmaceutical intermediate”, BioProcess International, Vol. 5, No. 8 (September 2007) 52-63 [16] Abhay Bulsari and Mikko Lahti, “Nonlinear Models Guide Secondary Coating of OFCs”, Wire and Cable Technology International, Vol. 29, No. 5 (September 2001) 40-43 [17] Abhay Bulsari, Mikko Lahti and Ståle Ausen, “Nonlinear models help tune the temperature profile in extruder barrels”, Wire and Cable Technology International, Vol. 42, No. 4 (July 2014) 104-107

Nonlinear Solutions Oy Kaivokatu 10A 21, 20520 Turku Finland Tel: +358 2 2154721 www.nonlinear-solutions-oy.com Maillefer Extrusion Oy Ensimmäinen savu, PO Box 176, FI-01511 Vantaa Finland Tel: +358 9 8866 5600 www.maillefer.net

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January 2021