High throughput multiscale modeling for design of experiments, catalysts, and reactors: Application to hydrogen production from ammonia

Nonlinear Reactor Design Optimization With Embedded Microkinetic Model Information

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 and MK model structure. 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. The ROK models that closely mimic the structure of the MK model offer the best compromise to emulate the product distribution. Using the four best ROK models, we optimize the temperature profiles in staged reactors to maximize conversions to heavier oligomerization products. The optimal temperature starts at 630–900K and monotonically decreases to approximately 560 K in the final stage, depending on the choice of ROK model. For all models, staging increases heavier olefin production by 2.5% and there is minimal benefit to more than four stages. The choice of ROK model, i.e., model-form uncertainty, results in a 22% difference in the objective function, which is twice the impact of parametric uncertainty; we demonstrate sequential eigendecomposition of the Fisher information matrix to identify and fix sloppy model parameters, which allows for more reliable estimation of the covariance of the identifiable calibrated model parameters. First-order uncertainty propagation determines this parametric uncertainty induces less than a 10% variability in the reactor optimization objective function. 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.

CFD Analysis of BED Textural Characteristics on TBR Behavior: Hydrodynamics and Scaling-up

In recent years, CFD has played an important role in the understanding and design of TBR’s. In this work, through CFD with Eulerian approach, a three-phase heterogeneous reactor model was developed, were the accuracy of Interfacial Momentum Exchange Model (IMEM) for the gas-solid interaction, the effect of a more detailed catalytic bed geometry description, and the pellet shape over TBR hydrodynamics of two fluid phases interacting with the solid phase was studied. Then, a second model was developed, where the validated hydrodynamic model was coupled with mass transport for an HDS process of light gasoil. Additionally, in order to insight into the scaling up process of a TBRs, the proposed columns behaviors were compared against literature columns using four different ways, and it was found that the best predictions were obtained when the models’ holdup were equaled to those evaluated in literature columns. Since in reliable literature deviations in pressure drop predictions of around 30% can be found, the model results show significant improvement against literature, achieving 5 times better accuracy in predicting pressure drops, and 50% improvement in holdup prediction; the coupled model reproduces the same conversion values compared with literature data, and predicts conversions with 95% accuracy

Ironmaking with ammonia at low temperature

This paper describes the reduction of hematite with ammonia for ironmaking, in which the effect of temperature on the products was examined. The results showed that the reduction process began at 430 °C during heating, and with an increase in temperature, the reduction mechanism changed apparently from a direct reduction of ammonia (Fe(2)O(3) + 2NH(3) → 2Fe + N(2) + 3H(2)O) to an indirect reduction via the thermal decomposition of ammonia (2NH(3) → N(2) + 3H(2), Fe(2)O(3) + 3H(2) → 2Fe + 3H(2)O) at temperatures over 530 °C. The final product obtained at 600 and 700 °C was pure metallic iron, in contrast with that formed at 450 °C, that is, a mixture of metallic iron and iron nitride. The results suggest the possibility of using ammonia as a reducing agent for carbonless ironmaking, which is operated at a much lower temperature than 900 °C in conventional coal-based ironmaking.