ContinuousPlant® Software Suite

Continua Process Systems' unique ContinuousPlant® suite of real-time software and services allows pharmaceutical and biotech manufacturers to deploy advanced continuous manufacturing (CM) processes by integrating in-process Process Analytical Technology (PAT) orchestrations with advanced process control (APC) applications, and materials tracking and traceability solutions. Our integrated CM solutions enable the production of drug products more efficiently, reliably, and of better quality than traditional batch manufacturing methods.

ContinuousPlant® Software Suite

ContinuousPlant® Flexible Batch

Built-In Flexibility Compliant with S88 & S95 Standards

  • ContinuousPlant® Flexible Batch enables a batch management system to be applied to continuous manufacturing. This is the core of the ContinuousPlant® Software Suite, which is built upon object-oriented design principles and established batch industry process control standards such as S88 and S95. Continua Process Systems developed the software to uniquely adapt the S88 batch standard to make continuous manufacturing more flexible.
  • ContinuousPlant® Flexible Batch allows the user to build multiple product recipes and configure multiple continuous manufacturing processes through visually oriented software. The required units are dragged-and-dropped into a recipe window and the recipe parameters are added through drop down menus. Once the recipe is created for the desired product and CM process, it is saved and downloaded. The recipe is then initiated and controlled by an operator through the Emerson DeltaV™ Batch Operator Interface.
  • Multiple recipes can be created by a recipe author to allow the plant to manufacture multiple products using multiple CM processes such as Direct Compression, Dry Granulation, Wet Granulation, and custom processes. ContinuousPlant® Flexible Batch is also ideal for running DOEs.

S88 Enables Flexible Manufacturing for CM

  • Dynamic references are used in lieu of S88 aliases and are driven by the recipe to allow the process to be uniquely configured for each drug product.

Batch Reports - InfoBatch™

  • InfoBatch provides comprehensive reporting by aggregating data from the Materials Traceability and Materials Tracking systems. InfoBatch incorporates ContinuousPlant® batch context based on a specified number of tablets, material quantity or run time. The batch context enables traceability from incoming materials through intermediate process steps to final product containers.
  • Batch reports can include active and excipient ingredient lot information, intermediate process conditions, alarms and events, and final tablet summary data and statistics. Reports can be generated automatically with InfoBatch AutoGen™ or interactively through the InfoBatch Web Server. InfoBatch can also print bar code labels in a configurable format.

ContinuousPlant® Materials Traceability

  • The 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 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® Materials Tracking

Continuous Manufacturing Execution System (C-MES)

  • The Materials Tracking application uses the data from the Materials Traceability to provide several of the functions of a typical batch Manufacturing Execution System (MES). The Materials Tracking application constitutes Continuous Manufacturing Execution System(C-MES). Material is tracked from the time it enters the system through raw material drums to the time it exits the system as packages of tablets. The raw materials drum barcodes are scanned into the Materials Tracking system as soon as they are loaded into the dispensing bay bins. The barcode is stored with the drum weight, material ID, and lot number. This lot and material data is propagated through the system of units via the RTDs.
  • The material compositions and lot numbers of the tablets are stored along with a barcode that is generated for each package of tablets. The barcode is printed on a label that is attached to the final package of tablets including the package weight.

ContinuousPlant® Process Optimizer

  • The 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 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® Process Solver

  • The 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 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® Nonlinear Process Modeler

  • The 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 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 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® 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 Nonlinear Kalman Filter developed by Continua Process Systems 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® Nonlinear Kalman Filter has many applications including chromatography elution endpoint detection, bioreactor glucose control, and API crystallization.

Feeder Algorithm - Kalman Filter

  • Feeder loss-in-weight (LOI) algorithms are known to be problematic. Because the feeder is mounted on top of a weigh scale, the rotation of the feeder shaft and mixer shaft transmits a significant amount of noise to the weigh scale. The weigh scales need to be highly sensitive to pick up the change in weight and accurately predict the mass feed rate out of the hopper. The combination of the sensitive signal and physical vibration creates noise in the weight signal.
  • To dampen the noise, the feeder weight data needs to be filtered, but if the data is over-filtered the mass rate prediction is not representative of the true mass weight. The method in which the data is filtered is also critical to obtain an accurate representative of the mass rate.

ContinuousPlant® Throughput Optimization

  • Continua Process Systems process control engineers and data management solutions architects have the know how 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.

Continua Process Systems Services

Continua Process Systems
Process Automation & Data Management Services

Continua Process Systems 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 Continua Process Systems 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 shows 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.

Continous Manufacturing Partners

Emerson is the leading supplier of automation solutions to the Life Sciences Industry, with automation expertise and technologies to solve your greatest cGMP manufacturing challenges. Emerson has effective technology solutions for improving your real-time product quality, reliability, and operating costs. From design to implementation, and start-up to on-going optimization, rely on Emerson to stay competitive in a global economy.

Emerson’s DeltaV distributed control system (DCS) is an easy-to-use automation system that simplifies operational complexity and lowers project risk. The state-of-the-art suite of products and services increases continuous manufacturing performance with intelligent control that is easy to operate and maintain. The DeltaV DCS adapts to meet your needs, scaling easily without adding complexity.

Founded in 2006, the Center for Structured Organic Particulate Systems (C-SOPS) brings together a cross-disciplinary team of researchers from major universities to work closely with industry leaders and regulatory authorities to improve the way pharmaceuticals, foods and agriculture products are manufactured. C-SOPS focuses on advancing the scientific foundation for the optimal design of SOPS with advanced functionality while developing the methodologies for their active control and manufacturing.

Headquartered at Rutgers University, C-SOPS partners include the New Jersey Institute of Technology, Purdue University, the University of Puerto Rico at Mayaguez, and more than 40 industrial consortium member companies including Continua Process Systems and Emerson.

Integra CMS is the leading provider of comprehensive scientific and technical support to manufacturers who seek to formulate, implement, or optimize continuously manufactured products. Their top network of global universities, industrial partners, and technology suppliers are here to deliver tailored, effective continuous manufacturing solutions. Integra applies a step-by-step approach to help pharmaceutical manufacturers create efficient systems through effective integration of multiple methods and tools to meet key objectives, including maximum process understanding and maximum product quality at minimal cost.

Informetric Systems Inc. develops software applications that enable manufacturers to improve quality and increase productivity. Informetric software provides data acquisition and reporting for critical product release activities in batch and continuous GMP manufacturing facilities.

If you would like to learn more about Continua Process Systems and how our ContinuousPlant® Software Suite of products and services can be applied to your continuous manufacturing process, please contact us to request more information.

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