Anchoring Computational Flow Models to Real-World Multiphase Reactors: Toward Ensuring Delivery of Materials and Energy at the Right Time and Place in Reactors

Abstract

Multiphase reactors (MPRs) are crucial in converting raw materials into essential products such as chemicals, polymers, and medicines and contribute immensely to the global economy. MPRs are complex dynamical systems involving chemical reactions and interphase transport processes. State-of-the-art designs of MPRs often struggle to deliver materials and energy precisely at the right time and place in the reactor, leading to unwanted side products and excess energy consumption. This is mainly due to our inability to accurately predict and direct the flow of materials and energy within MPRs. In this Perspective, I propose a novel way of developing high-fidelity models of MPRs by synergistically combining wall pressure fluctuation data acquired from these MPRs with machine learning and physics-based models. This novel approach has the potential to capture multiscale information contained in pressure fluctuations and thereby deliver unprecedented accuracy to MPR models. This will enhance their fidelity and applicability to real-world reactors without needing resolution of micro- and mesoscales or using any ad hoc adjustments. The novel methodology is discussed by considering a case of bubble column reactor as a representative MPR. Evidence available in the published studies that lends support to the key hypothesis underlying the proposed methodology is briefly discussed. Specific suggestions on how to develop and validate the proposed approach are included. The proposed approach will lead to high-fidelity models anchored to real-world reactors via wall pressure fluctuations and thereby facilitate the identification and implementation of optimal strategic interventions to influence the multiphase transport in MPRs. This will ensure precise delivery of materials and energy and thereby eliminate side products and minimize energy consumption. I believe that it will transform the foundations of simulating and intensifying MPRs, leading to significantly better resource utilization and reduced emissions in the future.