Optimization of Reaction Selectivity Using CFD-Based Compartmental Modeling and Surrogate-Based Optimization

Abstract

Mixing is considered as a critical process parameter (CPP) during process development due to its significant influence on reaction selectivity and process safety. Nevertheless, mixing issues are difficult to identify and solve owing to their complexity and dependence on knowledge of kinetics and hydrodynamics. In this paper, we proposed an optimization methodology using Computational Fluid Dynamics (CFD) based compartmental modelling to improve mixing and reaction selectivity. More importantly, we have demonstrated that through the implementation of surrogate-based optimization, the proposed methodology can be used as a computationally non-intensive way for rapid process development of reaction unit operations. For illustration purpose, reaction selectivity of a process with Bourne competitive reaction network is discussed. Results demonstrate that we can improve reaction selectivity by dynamically controlling rates and locations of feeding in the reactor. The proposed methodology incorporates mechanistic understanding of the reaction kinetics together with an efficient optimization algorithm to determine the optimal process operation and thus can serve as a tool for quality-by-design (QbD) during product development stage.

Mathematical Modeling of Hydrodynamics in Bioreactor by Means of CFD-Based Compartment Model

“…The CM may be successfully applied for the prediction of hydrodynamics in the analyzed systems [17,18]. The hybrid approach based on the combination of CFD and CM techniques is also discussed in the literature [19,20]. Hristov et al (2004) reported that the CFD-based compartmental model could be used to describe the performance of novel impeller geometry in a triple impeller bioreactor [21].…”

GPdoemd: A Python package for design of experiments for model discrimination

“…Surrogate models are common in applications where the original model does not easily lend itself to optimisation, e.g. Palmer and Realff (2002), Caballero and Grossmann (2008), Fahmi and Cremaschi (2012), Boukouvala et al (2017), Beykal et al (2018), Jones et al (2018), Carpio et al (2018) and Yang et al (2019). Common surrogate models include e.g.…”

Special Issue on “Process Design, Integration, and Intensification”

“…Another work that investigated the improvement on the chemical reaction was reported by Yang et al [5], who proposed an optimisation methodology using Computational Fluid Dynamics (CFD) based compartmental modelling to improve mixing and reaction selectivity. Results demonstrate that reaction selectivity can be improved by controlling rates and feed locations of the reactor.…”