Reaktoro is a computational framework developed in C++ and Python that implements numerical methods for modeling chemically reactive processes governed by either chemical equilibria, chemical kinetics, or both.
The chemical simulator of Reaktoro is based on the Gibbs energy minimization (GEM) methods as well as a revised law of mass action (rLMA) approach that combines the advantages of both GEM and LMA methods. Recently, on-demand machine learning (ODML) of fast and efficient chemical equilibrium calculations was introduced in Reaktoro.
Chemical equilibrium calculations are essential for many chemical reaction modeling problems. Reaktoro’s computational chemical equilibrium capabilities using Gibbs energy minimization algorithms can be applied to solve a variety of modeling problems. However, sometimes the chemical equilibrium model is not sufficient to understand a chemically reactive process. This happens when we need to understand how the composition of the chemical system changes with time as a result of chemical reactions. For this, chemical kinetics is imperative. Reaktoro can perform chemical kinetics calculations combined with chemical equilibrium (i.e., part of the chemical system evolves under kinetics, while the other is continuously in equilibrium at all times). This mode of calculation is particularly useful for simulating chemically reactive systems in which some reactions have rates that are many orders of magnitude higher than others (and thus can be assumed in instantaneous equilibrium at any time). Finally, chemical equilibrium and kinetics calculations are both space independent. If you need to model transport processes (e.g., advection, diffusion) combined with chemical reactive processes, then chemical transport (or reactive transport) simulations are what you need.
Various interactive examples and tutorials can be found by clicking the button below.
On-Demand Machine Learning (ODML) approach
Figure 1: Acceleration of the chemical equilibration calculation in reactive transport modeling of the dolomitization process.
Figure 2: Simulation of (a) dolomitization process and (b)-(c) hydrogen sulfide scavenging in the heterogeneous medium.
Reaktoro would not exist without its past and current supporters and contributors, who supported the project with either financial, scientific, and/or coding input.
Academic and Research Institutions
- Imperial College London, UK (2011-2014), Qatar Carbonates and Carbon Storage Research Centre (QCCSRC)
- Paul Scherrer Institute, Switzerland (2014-2015), Laboratory for Waste Management
Reaktoro is proud of its past and current financial support from industries. We would like to thank:
- Shell (2011-2014, 2019-present), Shell Technology Centre Amsterdam
- Qatar Petroleum and Qatar Science & Technology Park (2011-2014), Qatar Carbonates and Carbon Storage Research Centre (QCCSRC)
REFEREED PUBLICATIONS IN JOURNALS
Leal, A. M. M., Kyas, S., Kulik, D. A., & Saar, M. O. (2020). Accelerating Reactive Transport Modeling: On-Demand Machine Learning Algorithm for Chemical Equilibrium Calculations. Transport in Porous Media, 133(2), 161–204. https://doi.org/10.1007/s11242-020-01412-1
Leal, A. M. M., Kulik, D. A., Smith, W. R., & Saar, M. O. (2017). An overview of computational methods for chemical equilibrium and kinetic calculations for geochemical and reactive transport modeling. Pure and Applied Chemistry, 89(5), 597–643. https://doi.org/10.1515/pac-2016-1107