How CFD can Optimize Process Equipment Cleaning
Proper cleaning and good hygienic maintenance of processing equipment is a prerequisite for the production of safe and high-quality pharmaceutical products. Cleaning validation methods are required with the entire cleaning process needing to be standardized and documented according to the FDA’s cGMP regulations (FDA 21 CFR Part 211.67).
However, as all pharmaceutical manufacturers know, cleaning processing equipment can be a significant challenge – often requiring long cleaning cycles and high water and energy costs.
Factors influencing cleaning efficiency
A number of factors including cleaning methods, soils present, type of manufacturing equipment, surfaces cleaned, choice of cleaning detergent and temperature should all need to be considered when setting up a cleaning procedure. One of the most challenging factors is often found to be the geometry and configuration of the equipment. By nature of its construction, some types of equipment will be more difficult to clean than others.
The efficiency of Cleaning-In-Place (CIP) procedures greatly depends on fluid flow (i.e. the motion of detergent and rinsing water). Thorough understanding of the physical action of fluid flow during cleaning allows for design of optimal CIP procedures, but hidden parts, sharp bends in pipework and blind crevices can all present unique challenges in fully understanding fluid flow.
Cleaning is ultimately related to the energy needed to remove soil from a surface. This energy is in turn influenced by four1 different factors:
detergents (chemistry)
temperature (heat)
cleaning time (contact time between soil and liquid at different concentrations or temperatures) and
mechanical forces (shear stress).
On application of cleaning chemicals and heat, bonds between soil and surface are loosened, while mechanical forces remove soil from the surface. The longer the time of contact between soil, chemicals and heat the weaker the attachment, and thus the lower the forces needed to remove the soil from the surface. Conversely, if the concentration of chemicals, the temperature of the liquid or the mechanical force is too low, the cleaning time must be increased.
The use of Computational Fluid Dynamics
Applying a Computational Fluid Dynamics (CFD) approach for quantitative prediction of cleaning efficiency can be an extremely useful approach. Considering cleaning of closed processing equipment, fluid flow (either directly or indirectly) has an influence on all four of the above factors. The mechanical force is directly connected to the flow by the wall shear stress. In a closed process the only practical method of applying a force is through flowing fluid. Flow parallel to a surface generates a force (a wall shear stress) on the surface or indeed the soil on the surface. Indirectly, fluid flow (i.e. the flow patterns) impacts on the transport of cleaning liquid containing detergent (chemistry) and heat (mass and heat transfer).
In straight pipes fluid is continuously supplied to the edges of the boundary layer mainly due to convection. Through the boundary layer (a thin layer of fluid near the wall), detergent and heat is transported by diffusion driven by a gradient. There will inevitably be a dominant flow on a global level which moves liquid through a particular piece of equipment.
However, some geometrical shapes and configurations can create complex
flow patterns e.g. locally reverse flows and recirculation zones (both static
and dynamic). Areas with static recirculation zones can be considered to be
relatively isolated from the bulk flow. The exchange of heat and detergent
between these zones and the bulk flow is lower than in the bulk flow itself.
Consequently, the contact time between soil and cleaning liquid at the intended
concentration and temperature is reduced (the gradient is smaller than in a
straight pipe). Due to lower temperature and lower degree of detergent renewal, it takes more time to loosen bonds between soil and surface to an extent where
the mechanical force can remove the soil from the surface. In addition, forces
tend to be low in the recirculation zone. Other types of recirculation zone can
be found in some geometrical shapes. These include swirling flows (helical
shaped flow structures). The swirling motion enhances fluid exchange, i.e. the
cleaning efficiency is improved compared to straight pipe flow. An additional
effect of the moving liquid is that the fluid transports detached soil away
from the surface and out of the processing line so reattachment is avoided.
However, the influence of fluid flow on cleaning is not just limited to the mechanical force provided by the flow. Based on knowledge of the combined effect of wall shear stress and mass and heat-transfer (fluid exchange) the influence of fluid flow on cleaning can be predicted. CFD simulations can be used to predict the location of difficult-to-clean areas. Thus it is possible to identify flow patterns that enhance or inhibit good cleaning of surfaces in closed processing equipment.
The CFD approach can be used to predict wall shear stresses and fluid exchange based on predicted velocity fields. In particular, transient simulations (including the evolution of flow over time) of flow in processing equipment with focus on flow in the near-wall region makes it possible to estimate the location of areas where cleaning problems are likely to occur first, second and so on. Furthermore, alternative flow conditions during CIP and the effect on problem areas when incorporating design changes in a component can be compared using CFD simulations.
Conclusion
Harnessing the strength of CFD simulations makes it possible to obtain an insight into the motion of liquid during cleaning of closed processing equipment and thus to optimise cleaning methods with a view to reducing cycle times, water usage and energy costs.
Related Posts
Power Up Your Process Development Knowledge
Receive actionable process development insights and explore cutting edge process development news with great science delivered to your inbox every couple of months.