Paper Technology International 2020 - Journal - Page 75
PAPERTECHNOLOGYINTERNATIONAL
Nonlinear models guide efficient operation of hard nip sizer to derive more strength from less starch.
H. Vaittinen, A. Räisänen, Valmet Technologies, and A. Bulsari, Nonlinear Solutions Oy
INTRODUCTION:
All kinds of production processes can be made more efficient. When process variables of any process are properly optimised, it
is possible to achieve better quality with lower raw material costs. For example, it is not difficult to derive more strength with less starch
or fibres with hard nip sizing, once quantitative information about the relations between strength and process variables in a mathematical
form is available. These relations are not very simple or linear, which is why conventional linear statistical techniques are not very effective.
However, nonlinear modelling proves once again to be a powerful tool for describing these relations.
Nonlinear empirical and semi-empirical modelling has been used widely in several industrial sectors for process development
as well as materials development. This article describes our experience with nonlinear modelling of hard nip sizing from pilot scale
experimental data, with a short description of the method. The nonlinear models have been implemented in software and are now used to
calculate cost optimal operating conditions such that the final properties of liner board are within desired limits, resulting in more strength
with less starch.
Hard nip sizing
which allows for various kinds of calculations. Developing these
equations is called mathematical modelling, which can be performed
Surface sizing is an essential process in the pulp and
in several different ways.
paper industry for improving the strength properties of base paper
or board. In film sizing, the starch application takes place mostly
on the outer surfaces of the paper or board, and only a minor
portion of the starch penetrates deep inside the structure of the
base paper or board. Hard nip
sizing (Figure 1) overcomes
this weakness by using
pressure, allowing for much
deeper penetration starch
and other sizing chemicals
[1], and thus increasing the
strength of properties more than
conventional surface sizing
processes.
Figure 1: The principle of hard nip
sizing is to use higher nip loads.
Figure 2: Hard nip sizer at the pilot plant of Valmet in Järvenpää.
It also produces better smoothness because hard rolls
work like in calendering. As can be seen in the photograph (Figure
Mathematical modelling
2), hard nip sizer rolls and the loading system are like calenders
Mathematical models can be used instead of experimentation
rather than conventional sizer rolls. In hard nip sizing process, the
if they are reliable enough. Mathematical models also permit the
nip pressure results in an optimal packing of fibres and the sizing
user to carry out various kinds of calculations, like determining
through the z-direction of the web, resulting in bigger increase of
suitable values of variables which will result in desired product
SCT in cross direction and burst strength [2].
quality in an economic way. Mathematical modelling can be
Process development
performed in various ways, and different ways are suitable in
different situations. Mathematical models represent knowledge of
All kinds of production processes can be made more
quantitative effects of relevant variables in a concise and precise
efficient. The aim of process development is often to make
form.
processes more efficient, sometimes by saving production costs
while also improving quality. There are usually a few degrees of
Physical or phenomenological modelling is not particularly
freedom in the process which affect the product properties and may
effective for predicting material properties like strength, thermal
also affect the production rate. One would like to determine the best
conductivity or solubility. Physical modelling usually requires a lot
values of these variables such that the resulting product properties
of assumptions and simplifications. Empirical and semi-empirical
will be within desired limits.
modelling, on the other hand, does not need any major assumptions
or simplifications. Empirical models simply describe the observed
This requires quantitative knowledge of the effects of
behaviour of a system. Empirical modelling is feasible when the
relevant variables in a precise and concise form. In other words, we
relevant variables are measurable.
would need the knowledge of the process in a mathematical form,
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