Model-based computer-aided framework for design of process monitoring and analysis systems

Singh, Ravendra; Gernaey, Krist V.; Gani, Rafiqul

doi:10.1016/j.compchemeng.2008.06.002

Cited by 72 publications

(30 citation statements)

References 76 publications

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“…8 through a work-flow diagram (adapted from [4]). The designed control system that includes the control variables, actuators, control strategies and controller parameters are the inputs for the methodology [4,6,8].…”

Section: Implementation Of Control Systemmentioning

confidence: 99%

“…The design of a process control system is an iterative procedure that involves the identification of critical controlled variables, coupling of the controlled variables with suitable actuators (manipulated variables), selection of suitable monitoring tools, selection of control strategy followed by tuning of controller parameters, and finally, model-based closed-loop performance assessment [6,8]. The design control system can then be implemented in the manufacturing plant through the control platform (e.g., Delta V, Emerson) and data communication protocol (e.g., OLE Process Control (OPC)) for experiment-based closed-loop performance evaluation [5].…”

Section: Design Of Control System For a Continuous Tablet Manufacturimentioning

confidence: 99%

“…However, better process understanding and controls are vital for minimizing waste due to manufacturing failures and ensure quality product with reduced end product testing [7]. Therefore, a significant opportunity exists for improving product quality and optimizing the production process through the implementation of innovative solutions for online/inline monitoring, analysis and system control [8]. The US Food and Drug Administration (FDA) has supported the application of Process Analytical Technology (PAT) and control systems within manufacturing industries [9].…”

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confidence: 99%

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Advanced Control of Continuous Pharmaceutical Tablet Manufacturing Processes

Singh

Velázquez

Sahay

et al. 2016

Methods in Pharmacology and Toxicology

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A novel manufacturing strategy based on continuous processing, integrated with online/inline monitoring tools, coupled with an advanced automatic feedback control system is highly desired for efficient Quality by Design (QbD)-based manufacturing of the next generation of pharmaceutical products with optimal consumption of time, space and resources. In this work, an advanced hybrid MPC-PID control system as well as a simpler PID controller for a direct compaction continuous tablet manufacturing process has been designed and implemented for a pilot-scale pharmaceutical process. An NIR sensor, an online NIR prediction tool, a PAT data management tool, an OPC communication protocol, a standard control platform and control hardware have been used to close the control loop. A systematic methodology to design and implement the control system has been also proposed. A control framework with features such as the option to run the plant in open-loop as well as in a closed-loop scenario has been developed. Furthermore, within the closed-loop scenario, options for a simpler PID, a dead time compensator (Smith predictor) as well as an advanced model predictive controller have been included. The feature to run the control strategy in simulation mode has been added to the control platform to facilitate virtual control system design and performance evaluation. Two case studies involving a direct compaction continuous tablet manufacturing process have been considered to demonstrate the closed-loop operation. Case Study 1 was completed at Rutgers University and constituted the use of a continuous cylindrical blender with a rotating screw. Case Study 2 was based on a continuous tumble mixer and was completed at the University of Puerto Rico-Mayaguez Campus (UPRM).

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Section: Implementation Of Control Systemmentioning

confidence: 99%

Section: Design Of Control System For a Continuous Tablet Manufacturimentioning

confidence: 99%

mentioning

confidence: 99%

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Advanced Control of Continuous Pharmaceutical Tablet Manufacturing Processes

Singh

Velázquez

Sahay

et al. 2016

Methods in Pharmacology and Toxicology

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“…Such a model therefore incorporates process-relevant input (critical process variables) -output (product concentration and product quality attributes) relationships, which can be used to establish a proper design space. The mechanistic model therefore has great value in planning experiments, or in determining which critical process variables necessitate tighter control [21,22].…”

Section: Potential Of Mvda In Implementing Pat On Biopharmaceutical Pmentioning

confidence: 99%

Process analytical technology (PAT) for biopharmaceuticals

Glassey

Gernaey

Clemens

et al. 2011

Biotechnology Journal

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178

101

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Process analytical technology (PAT), the regulatory initiative for building in quality to pharmaceutical manufacturing, has a great potential for improving biopharmaceutical production.The recommended analytical tools for building in quality, multivariate data analysis, mechanistic modeling, novel models for interpretation of systems biology data and new sensor technologies for cellular states, are instrumental in exploiting this potential. Industrial biopharmaceutical production has gradually become dependent on large-scale processes using sensitive mammalian cell cultures. This further emphasizes the need for improved PAT solutions. We summarize recent progress in this area based on an expert workshop held at the 8 th European Symposium on Biochemical Engineering Sciences (Bologna, 2010), and highlight new opportunities for exploiting PAT when applied in biopharmaceutical production. We conclude with recommendations for advancing PAT applications in the biopharmaceutical industry.

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“…However, so far in model-based PAT system design, it has been assumed that the exact values of the model parameters are known, for example in the nucleation and crystal growth rate expressions [1,2]. These parameters are usually estimated from experimental data, often with considerable measurement errors that also implies a certain error on the estimated parameters.…”

Section: Introductionmentioning

confidence: 99%