5 Development of a Dynamic-Physical Process Model for Sieving
155
mass and energy balance without accumulations. In contrast, dynamic modeling can
take into account accumulations and unbalanced states to cover a variety of additional
issues such as oscillations, batch or semi-batch processes, load changes, and startup
and shutdown procedures [95]. In addition to the direct dynamic behavior in a process
unit, dynamic effects can also be attributed to changes in upstream process steps and,
in addition, influence subsequent downstream processes. To avoid interruptions or
mechanical failures in industrial processes, the understanding of transient processes
and the development of transient process models is essential [97]. To study the
dynamic behavior of entire solids process chains without extensive experimental
investigations, these models need to be linked by a robust and efficient framework
for dynamic process simulation [98]. It should be noted that due to the complexity
and disparity of the solid phase with sets of distributed parameters that may even
be interdependent [99], such a framework for solids processes needs to meet very
different requirements than a similar framework for fluid processes [100]. Therefore,
most of the current dynamic simulation tools are designed for liquid processes with
some limited extensions for the solid phase. In this context, a new framework for
dynamic simulations of solid processes (Dyssol) was developed as part of the DFG
priority program SPP 1679. Further information on the Dyssol framework can be
found in the work by Skorych et al. [95], in which this novel tool for dynamic
flowsheet simulations of solids processes is introduced, and of course throughout
this book.
3.2 Phenomenological Process Models for Screening
In order to analyze entire process chains of interconnected solids processes, the
understanding of the individual processes and their modeling as process models is
unavoidable. Usually, these models are often partially empirical, their parameters
have to be adjusted to measured data to obtain reliable results, and therefore they
depend on material properties and device geometries [101, 102]. In particular, a
predictive process model for screening that includes consideration of its inherent
transient nature as well as directly induced or resulting dynamic effects would be
important for industrial process design, monitoring, and optimization [97]. To represent the particle size separation during a screening process without extensive experiments, several phenomenological screening process models are available which are
discussed in the following.
3.2.1 Steady State Separation Curve Screening Models
One way of obtaining a process model for screening is the utilization of a separation
curve that is limited to the steady state. For known fractional mass flow rates in
the feed and in the overflow, which are obtained from experiments or simulations,
overflow separation curves can be derived as
155
mass and energy balance without accumulations. In contrast, dynamic modeling can
take into account accumulations and unbalanced states to cover a variety of additional
issues such as oscillations, batch or semi-batch processes, load changes, and startup
and shutdown procedures [95]. In addition to the direct dynamic behavior in a process
unit, dynamic effects can also be attributed to changes in upstream process steps and,
in addition, influence subsequent downstream processes. To avoid interruptions or
mechanical failures in industrial processes, the understanding of transient processes
and the development of transient process models is essential [97]. To study the
dynamic behavior of entire solids process chains without extensive experimental
investigations, these models need to be linked by a robust and efficient framework
for dynamic process simulation [98]. It should be noted that due to the complexity
and disparity of the solid phase with sets of distributed parameters that may even
be interdependent [99], such a framework for solids processes needs to meet very
different requirements than a similar framework for fluid processes [100]. Therefore,
most of the current dynamic simulation tools are designed for liquid processes with
some limited extensions for the solid phase. In this context, a new framework for
dynamic simulations of solid processes (Dyssol) was developed as part of the DFG
priority program SPP 1679. Further information on the Dyssol framework can be
found in the work by Skorych et al. [95], in which this novel tool for dynamic
flowsheet simulations of solids processes is introduced, and of course throughout
this book.
3.2 Phenomenological Process Models for Screening
In order to analyze entire process chains of interconnected solids processes, the
understanding of the individual processes and their modeling as process models is
unavoidable. Usually, these models are often partially empirical, their parameters
have to be adjusted to measured data to obtain reliable results, and therefore they
depend on material properties and device geometries [101, 102]. In particular, a
predictive process model for screening that includes consideration of its inherent
transient nature as well as directly induced or resulting dynamic effects would be
important for industrial process design, monitoring, and optimization [97]. To represent the particle size separation during a screening process without extensive experiments, several phenomenological screening process models are available which are
discussed in the following.
3.2.1 Steady State Separation Curve Screening Models
One way of obtaining a process model for screening is the utilization of a separation
curve that is limited to the steady state. For known fractional mass flow rates in
the feed and in the overflow, which are obtained from experiments or simulations,
overflow separation curves can be derived as