(185a) Predicting Screw Feeder Flow Rates from Powder Properties and Operating Conditions

A well-known bottleneck in drug product development is the dependence on experimentally fit semi-empirical flowsheet models to design continuous manufacturing processes. The creation of predictive flowsheet models that account for powder properties and operating conditions effects on powder flow loosens this bottleneck by enabling engineers to begin design upon characterization and inform experimentation, ultimately speeding up a product’s time-to-market. Such processes begin with identifying several screw feeders which are responsible for controlling the mass flow rates of active pharmaceutical ingredients and excipients across the entire process. Prior work has shown screw feeder mass flow rates are related to both the feeder configuration [1]–[3] and a powder’s characterized properties [4]–[8], but do not provide a framework for predictively simulating a screw feeder’s dynamics. Our paper addresses this process development gap by proposing a predictive, high-fidelity flowsheet model that can provide insight into how powder characteristics, feeder configuration, and operating state influence a screw feeder’s dynamic behavior.

Our predictive feeder model is built from three components: a simple volumetric displacement screw feeder flowsheet model, a statistical autoregressive moving average model (ARMA), and a predictive projection to latent spaces (PLS) deterministic model. First, the hybrid deterministic displacement and stochastic ARMA model, as proposed by Johnson et al. [9], were fit to experimental runs of five powders with varying property values and feeding behaviors using both Coperion K-tron KT20 and MT12 feeders with twin concave screws operated at many different volumetric and gravimetric setpoints. This yielded sets of deterministic and stochastic deviation model parameters for each powder-feeder-setpoint operating configuration. Next, a PLS model was built that maps a database of powder properties [10], an abstraction of feeder geometries, and operating state to their experimentally estimated deterministic and stochastic parameters, extending the work of Bostijn et al. [8]. Constraints were integrated into the PLS model as needed to ensure deterministic parameters are physically possible and stochastic ARMA parameters meet theoretical requirements for stability and invertibility. The PLS model scores and loadings are presented, providing insight into how different powder properties influence the minute-by-minute and second-by-second dynamic feeding behavior. Last, the predictive capabilities of our PLS model are demonstrated and evaluated by simulating the feeding behavior of out of sample powders and conditions.

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