Through a glass, darkly: Kinetics and reactors for complex mixtures syncrude innovation award lecture

Abstract

The concepts of chemical reaction engineering are powerful, because the most basic design equations are applicable to a wide range of physical phenomena. For example, the applicability of the ideal continuous-flow stirred tank reactor goes far beyond chemical reactors to include biological, medical and environmental processes. The difficulty in analyzing these processes is not in formulating an appropriate reactor equation, but in modeling the chemical kinetics. The challenge is to define kinetic rate equations that allow for the mixtures of reactants and mixtures of catalysts, especially given incomplete information. Examples drawn from biology and medicine illustrate reaction kinetics that are complex due to the nature of the catalyst.In contrast, processes for upgrading of bitumen to more valuable products exhibit ill-defined reaction chemistry and mixtures of thousands of reactants and products. The need to define kinetics for such mixtures has given rise to several distinct approaches, including empirical rate equations, simplification to model reactions, lumped kinetics and Monte Carlo simulation. A summary of these methods shows that the key element for successful kinetic modeling is creative definition of a model, followed by vigorous testing of the model to determine its ability to predict performance. chemical and chemical processes illustrate that the practice of kinetics inevitably involves dealing with complex mixtures, complex catalysts, or both simultaneously. The art in the practice of chemical kinetics lies in balancing the need for fundamental understanding of the variables that influence the reactions, on the one hand, with the need to get the job done to support the overall work of development of chemical processes. Objective of chemical reaction engineeringChemical kinetics is only one component of the larger field of chemical reaction engineering, which seeks to design reactors for safe and efficient manufacture of products of desired purity. Large-scale manufacturing favors continuous or semi-continuous operation over batch processes; therefore, the core intellectual discipline of chemical reaction engineering is the analysis of chemical reactions in open systems, where mass enters and leaves the reactor vessel simultaneous to the chemical transformation. This discipline is unique to the education and practice of chemical engineers, who can also use this approach to make a unique contribution wherever reactions occur in open systems, ranging from medicine and biology to the environment.

Kinetic Modeling of Arab Light Vacuum Residue Upgrading by Aquaprocessing at High Space Velocities

“…Having a complete kinetic description of all components participating in a reaction complex is involved and impractical. Combining the oil components into predefined lumps, depending on type or boiling range, seeks to ease the prediction of the kinetic information . Therefore, the lumping approach is applied in this work to represent complex reaction mechanisms with simplified reactions system.…”

“…Combining the oil components into predefined lumps, depending on type or boiling range, seeks to ease the prediction of the kinetic information. 13 Therefore, the lumping approach is applied in this work to represent complex reaction mechanisms with simplified reactions system. However, there exist limitations and drawbacks associated with using the lumping approach for kinetic modeling.…”

Selection of simplified models: I. Analysis of model‐selection criteria using mean‐squared error

“…In many modelling situations in science and engineering, modellers have sufficient scientific knowledge to derive complex phenomenological models. However, it is often too difficult or costly to obtain enough good data to reliably estimate all of the unknown model parameters (e.g., Perregaard, 1993; Gray, 1997; Bagajewicz and Cabrera, 2003; Yoshida et al, 2003; Lv et al, 2004; Maria, 2004, 2006; Mchaweh et al, 2004; Chang et al, 2005; Marquardt, 2005; Romdhane and Tizaoui, 2005; Wang et al, 2007). For complex models with many parameters and limited data, the resulting parameter estimates and model predictions may exhibit high variability.…”

“…Inevitably, modelling leads to the situation where some parameters and terms that would appear in the “true” model are missing, either by mistake or by design. There are many reasons to choose a simplified model (SM) with fewer parameters and terms than the truly structured complex model (Neto and Cotta, 1992; Talukdar and Basu, 1995; Gray, 1997; Zhang, 1997; Ismail, 2004; Jaree et al, 2004; Forney et al, 2005; Marquardt, 2005). The practical advantages of a parsimonious model often overshadow concerns over the correctness of the model structure.…”