Paper Technology International 2020 - Journal - Page 79

PAPERTECHNOLOGYINTERNATIONAL
These calculations are now easily done with the nonlinear
models implemented in a LUMET system, resulting in significant
savings in sizing chemicals. Figure 9 shows one such calculation.
The base under consideration is a 180 g/m² liner with a SCT CD
index of 21 J/g and a burst index of 2.5 kN/g. With a given starch,
we have to produce a SCT CD index of at least 26 J/g and a burst
index of at least 3 kN/g, with minimal cost per ton of product. There
is an additional limitation on the wet film. The calculated optimal
conditions are in the last column. A size weight of 2.63 g/m² on
the top side and 1.47 g/m² on the bottom side to be applied at a
temperature of 80°C with a nip load of 360 kN/m should minimise
the raw material cost, according to the calculation.
We could have added a variable SCT CD index increase
divided by the total size weight and maximised that. However,
the calculation of Figure 9 is more realistic. Usually, there are
specifications on SCT CD index and burst index, and it makes sense
to minimise the cost instead of maximising the strength increase for
a given amount of starch.
Figure 9: An optimisation calculation to minimise raw material
cost per ton while keeping SCT CD index above 26 and burst
index around 3 in presence of other constraints.
CONCLUSIONS
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All kinds of processes can be made more efficient.
One can derive a lot more value from a process by tuning it well.
Hard nip sizing is a superior process to film sizing.
The advantage of a good process can be maximised by process optimisation.
Process optimisation needs a good mathematical description of the process.
Nonlinear modelling is an efficient tool to describe various kinds of processes.
Composition variables, process variables and dimension variables affect product properties in a complicated manner, and
people with even decades of experience cannot predict the combined quantitative effects of the relevant variables.
Nonlinear modelling can utilise production data or experimental data.
Nonlinear modelling does not need any significant assumptions.
A good process with optimisation based on nonlinear models is a very powerful combination.
Significant reductions in production costs can be achieved with a modest effort like this.
References
[1] J. Hakola, “Surface sizing with high nip pressure”, presented at the TAPPI conference 19PaperCon,
Indianapolis, USA (May 2019)
[2] E. Pitschmann, “New technology HardNip PM1”, presented at the 28th International Munich Paper Symposium,
Munich, Germany (March 2019)
[3] A. Bulsari (ed.), Neural Networks for Chemical Engineers, Elsevier, Amsterdam, 1995.
[4] K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators”,
Neural Networks, Vol. 2, (1989) 359-366.
[5] A. Bulsari, “Quality of nonlinear modelling in process industries”, Internal Report NLS/1998/2.
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