Nonlinear Reactor Design Optimization With Embedded Microkinetic Model Information - Ghosh, Kanishka; Vernuccio, Sergio; Dowling, Alexander W. (Frontiers in Chemical Engineering, 2022)
Despite the success of multiscale modeling in science and engineering, embedding molecular-level information into nonlinear reactor design and control optimization problems remains challenging. In this work, we propose a computationally tractable scale-bridging approach that incorporates information from multi-product microkinetic (MK) models with thousands of rates and chemical species into nonlinear reactor design optimization problems. We demonstrate reduced-order kinetic (ROK) modeling approaches for catalytic oligomerization in shale gas processing. We assemble a library of six candidate ROK models based on literature. We find that three metrics - quality of fit (e.g., mean squared logarithmic error), thermodynamic consistency (e.g., low conversion of exothermic reactions at high temperatures), and model identifiability - are all necessary to train and select ROK models. Using the four best ROK models, we optimize the temperature profiles in staged reactors to maximize conversions to heavier oligomerization products. We find that optimal temperature profiles are qualitatively consistent when varying the feed space velocity six orders of magnitude corresponding with laboratory-scale to well-scale operation. Moreover, we estimate the covariance matrix for the calibrated parameters in the ROK models and, through first-order uncertainty propagation, find this parametric uncertainty induces less than a 10% variability in the reactor optimization objective function. In contrast, we find the ROK model choice leads to a 22% difference in the objective function values. This result highlights the importance of quantifying model-form uncertainty, in addition to parametric uncertainty, in multi-scale reactor and process design and optimization. Moreover, the fast dynamic optimization solution times suggest the ROK strategy is suitable for incorporating molecular information in sequential modular or equation-oriented process simulation and optimization frameworks.