ContinuousPlant^® Software Suite

ContinuousPlant^® RT Materials Traceability Model

• The RT Materials Traceability Model utilizes a statistical approach to trace material from the tablet drum back to the raw material drum by incorporating Residence Time Distribution (RTD) functions. Unlike batch processes where raw material sources are known with certainty, in continuous processes raw material sources must be estimated with a statistical probability.
• Our RTD model is embedded into the process control system software to provide both synchronized real-time material traceability and process control.
• The RT Materials Traceability Model can reduce transient off-spec production that could mean the difference between disposing of a few minutes versus several days of production. It also fulfills the FDA requirement for drug product traceability back to the original raw material sources.
• Based on RTDs, our mechanistic and semi-empirical based model is developed from first principles, then combined and validated with experimental data to accurately predict the states of individual unit operations in the process. Utilization of this model versus empirical methods alone has the advantage of dynamically modifying RTDs based on real-time process parameter data thus enabling automatic control adjustments in response to varying process conditions. The model can also be integrated with Process Analytical Technology (PAT) to accurately associate the time Critical Process Parameters (CPP) are predicted to the time tablets are produced downstream.

ContinuousPlant^® RT Process Optimizer

• The RT Process Optimizer is an embedded advanced process control (APC) algorithm that either maximizes or minimizes a convex or concave parabolic process value in real-time for nonlinear applications. Unlike a linear process controller, there is no predetermined or fixed setpoint for a nonlinear controller. This is because the process is designed to continuously control within its optimal minimum or maximum specified value limits, which are variable.
• The RT Process Optimizer can be used in continuous manufacturing for optimizing blend uniformity of a continuous blender by varying its rotational speed to minimize the Relative Standard Deviation (RSD). The process controller running the RT Process Optimizer algorithm looks at the current RSD, then semi-continuously adjusts the blender speed in real-time to maintain the RSD at its minimum/optimum control point to prevent over-mixing and under-mixing. Each process or process point can have a different nonlinear curve associated with it and a different optimum blender speed. Process conditions can vary even within the same product run. The process controller for the blender must to be able to dynamically adapt to various operating conditions to maintain the minimum RSD.

ContinuousPlant^® RT Process Solver

• The RT Process Solver is an embedded advanced process control (APC) algorithm that continuously solves nonlinear differential equations in real-time using numerical methods for closed loop process control.
• The RT Process Solver can be applied in continuous manufacturing to solve a nonlinear equation for tablet press feed rate to optimize tablet hardness and weight.

ContinuousPlant® RT Nonlinear Process Modeler

• The RT Nonlinear Process Modeler is an embedded advanced process control (APC) algorithm that fits a nonlinear equation to real-time data, solving for multiple coefficients. The result can be used to predict the future trajectory of multiple time variant nonlinear processes such as bioreactors and chromatography systems.
• This technique differs from traditional modeling techniques where the model is developed offline from first principles with model coefficients developed from regression of historical process data. Variations in process data may cause offline models to be inaccurate when run in real-time.
• The RT Nonlinear Process Modeler uses the same model as one developed from first principles, but calculates and recalculates the coefficients dynamically in real-time versus using theoretical coefficients. The algorithm runs continuously producing a new curve with every scan. It predicts a new set of coefficients based upon the new process data inputted from each scan that can be used for model prediction and/or process control. This technique fuses the theoretical model with real-time process data to predict the optimal state.
• Use case examples for the RT Nonlinear Process Modeler include chromatography elution end point prediction and control, the modeling of extrusion and fluid bed processes, bioreactor glucose control, and the optimization of wet granulation processes for incorporating extruder and fluid bed dryers into continuous manufacturing processes.

ContinuousPlant^® RT Nonlinear Kalman Filter

• The Kalman Filter is the de facto standard in avionics and robotics where it is necessary to reduce noise and to improve accuracy. It is particularly useful for applications such as GPS, weather forecasting, and missile guidance where both measurement and model data are available but neither one is sufficient in and of itself. The Kalman Filter algorithm combines measurement data and model prediction to find the statistically optimal estimate of the system state.
• This technique can also be utilized with Process Analytical Technology (PAT) applications in the pharmaceutical and biotech industries where process measurements and predictions are typically noisy and models can be developed.
• The RT Nonlinear Kalman Filter developed by QbD Process Technologies is a novel implementation of the filter that uses an adaptive nonlinear regression technique as the predictive model. This predictive model performs a regression of a nonlinear model in real-time for each new measurement. The model coefficients are updated with the adaptive regression for each iteration of the filter. The process model can be a mechanistic or empirical equation supplied by the user or a standard polynomial, log, or logistic equation built into this product.
• The ContinuousPlant^® RT Nonlinear Kalman Filter has many applications including chromatography elution endpoint detection, bioreactor glucose control, and API crystallization.

ContinuousPlant^® RT Throughput Optimization

• QbD Process Technologies process control engineers and data management solutions architects have the knowhow and technology to develop and implement integrated real-time model predictive control (MPC), in-process PAT software applications, and process data management solutions specifically designed, orchestrated, and tuned to control and maximize plant production flow/feed rate or throughput and product quality within a user defined design space.

QbD Process Technologies
Process Automation & Data Management Services

QbD Process Technologies offers a full range of process automation and data management services to assist with the selection, design and implementation of the solution best suited for your continuous manufacturing process.

We recommend utilizing an integrated project team model to architect, plan and manage the implementation. This approach minimizes your chances of incurring unexpected budget overruns and schedule delays.

At a minimum, project team members should include systems engineers from QbD Process Technologies and the user’s process automation project team lead, process equipment suppliers (i.e. tablet presses, feeders, etc.) and engineering firm. This model emphasizes close collaboration and direct communication between project team members starting with the initial front end engineering and design (FEED) phase through project scoping, implementation, startup and system commissioning.

At the front end, it is essential the project team mutually review the User Requirements Specification (URS) to agree upon the scope of work, the optimal system architecture, control network communication protocols, and process control and data management strategy for monitoring and controlling the critical product quality and process performance parameters defined in the URS.

Our experience has shown that deploying an integrated project team model from project conception to completion enables key project stakeholders to stay focused on assuring the user’s URS is clearly understood and met.