Risk Mitigation in Process Development

Session Chair:

• Jorge Jimenez, SABIC
• Abhishek Pednekar, Honeywell

Session Description:

Reducing the time span for technology development while avoiding scale-up/implementation surprises is a key challenge faced by the R&D community. It requires project teams to be more effective at identifying and assessing risks at every stage of process development, and to better understand how uncertainties in data, physical properties, thermodynamic models, unit operation models, operating conditions, etc., propagate & impact critical process variables. The talks in this session illustrate risk mitigation tools/strategies for development of robust technologies that deliver required product quality and stable operation under conditions of uncertainty.

Schedule:

Abstracts:

Process Revitalization

Greg Hemmer, SABIC

SABIC’s Specialties Business constantly strives to engineer and commercialize new, world-class products that address solution for the marketplace.  This often requires that one-of-a-kind processes are re-vitalized to achieve increases in capacity or reductions in operating cost. 

This paper will present a process that the authors employed to identify creative R&D opportunities for optimization and modernization of a 20+ year old commercialized process.  The end purpose is to revitalize the design of the process to deliver reductions in capital investment and operating costs via process simplification and intensification. 

The concepts employed, examples of opportunities identified and their potential benefits and risk mitigation strategies will be presented.

Risk and Uncertainty Analysis of Error Propagation in Process Design and Simulation—Approaches and Modeling Results

Victor Vasquez, University of Nevada, Reno

Risk and uncertainty analysis of error propagation in process design and simulation play an important role in the determination of error bounds of model predictions, design of safety factors, and risk estimation among other. Chemical process design and simulation rely heavily on computer-aided methodologies and thermophysical properties. The latter are commonly estimated through the use of empirical models, correlations, and theoretical models whose parameters are usually estimated from experimental data. In this presentation, we discuss how the issues of random and systematic errors in thermodynamic models affect their predictions and the impact of these on design and simulation with the analysis of a variety of situations and examples. Using Monte Carlo-based methods, we show that there is significant uncertainty in the design and operations of chemical processes when one considers the seemingly small uncertainty in data for vapor-liquid and liquid-liquid equilibria. A common approach in the chemical processing industries is to apply safety factors in design to account for the effects of uncertainties. However, Monte Carlo uncertainty analysis can be used to quantify the uncertainty and lead to rationalization of the safety factor approach. For example, sensitivity analysis of binary interaction parameters typical of models such as NRTL and UNIQUAC show that small variations in these translate into significant differences in performance evaluation of unit operations such as distillation and liquid-liquid extraction. The development of thermodynamic models from experimental data requires the regression of model parameters, which we use as input to the Monte Carlo simulations. However, the classical application of maximum likelihood methods for regression assumes that the model is inherently more accurate than the data. For phase equilibrium data, often the inverse is true. We show, with examples, the importance of separating the effects of different types of uncertainty such as those caused by model uncertainty and those by experimental uncertainty. Monte Carlo methods show to be a valuable tool in developing strategies to make this separation. If time allows, we will briefly discuss current initiatives in our research group towards developing methods for real-time risk assessment of patient safety in clinical processes using machine learning methods and big data; ideas that can be easily extended to many operations of the CPI.

Quality Risk Assessment in Late Phase Drug Substance Process Development

Vidya Iyer, Bristol-Myers Squibb

 This presentation outlines the process and tools that Bristol-Myers Squibb uses to conduct Quality Risk Assessment (QRA), to achieve consistent drug substance quality and production process performance. The QRA is carried out at multiple stages during the drug substance development lifecycle, with the knowledge requirements and risk mitigation plans increasing with each subsequent iteration. During this process, the quality attribute(s) (QAs) of the drug substance are established, and risk based linkages from the process raw materials and production procedures to these QAs are established. From this analysis, high risk items are identified to focus further development work, improve scientific understanding of the process and/or develop additional process controls. A Failure Modes and Effects Analysis (FMEA) methodology is employed that identifies failure modes (for the QAs) and potential causes (variability in process parameters, chemical equivalents, etc.). The impact of the failure mode (severity) is determined, together with the probability and detection of its occurrence. Understanding failure modes leads to the development of a control strategy designed to ensure the proposed acceptance criteria are consistently met. Ultimately, this risk assessment process establishes a thorough understanding of the process variables and their collective impact to the drug substance QA, thereby effectively managing risk to quality.