Grey-box modelling of a viscose-fibre drying process

Abstract

A dynamic model of a through-air-drying process for viscose staple fibres is presented in this article. In this process fibres formed to a porous web are transported through a convective dryer that consists of numerous rotating drum sieves. Finally, the fibres pass through two remoistening drums. The structure of the model is modular and scalable. On applying spatial discretization the originally partial differential system equations (conservation of mass and energy) turn into a system of ordinary differential equations. Drying rates and heat transfer rates are calculated using phenomenological equations for heat and mass transfer. Kinetics of drying is separated into three phases, where viscose fibres are hygroscopic. The process model is able to simulate transient behaviour of the dryer like changes of the incoming fibre moisture, changes of the drying air temperature and humidity and changes of the thickness of fibre layer on the drums. Stationary validation of the longitudinal fibre moisture distribution along the dryer shows good accordance with measurement data at different operating points, for example, different temperature profiles. Dynamic data like temperature transients are utilized for both model fitting and validation of the dynamic model. For the remoistening process and disturbance behaviour concerning the thickness of the fibre web, black box models have been identified. Results of a successful application of the model in a predictive control algorithm are shown.

Thermal energy savings during cord fiber spinning

“…However, recycling also has some impact on the environment (Sandin & Peters, 2018). It is common practice, not to carry out the production on a single machine, but there are many machines within a production facility (Akhil & Deepthi, 2019) (tens of machines) and in many cases it is necessary to use a suitable mathematical model (C. L. Chen & Chen, 2014, Schuster et al, 2012. In order to achieve good results utilizing the process to get a ber as required, it is necessary to supply a su cient amount of steam at the right temperature, pressure and ow rate (Elustondo et al, 2002, Shuvalov et al, 2017 to each machine.…”

A Systematic Grey-Box Modeling Methodology via Data Reconciliation and SOS Constrained Regression

“…In these cases, it may be sensible to combine what is known with certainty about the model, such as mass or energy balances, with data-driven equations obtained from measurements, representing those parts of the process which are unknown or complex to model. This results in a grey box model being a mixture of first-principles and data-driven equations [25].…”