Omics-to-Reactive-Transport (ORT): A workflow linking genome-scale metabolic models with reactive transport codes

Abstract. Many types of geologic subsurface utilisation are associated with fluid and heat flow as well as simultaneously occurring chemical reactions. For that reason, reactive transport models are required to understand and reproduce the governing processes. In this regard, reactive transport codes must be highly flexible to cover a wide range of applications, while being applicable by users without extensive programming skills at the same time. In this context, we present an extension of the Open Source and Open Access TRANSPORT Simulation Environment, which has been coupled with the geochemical reaction module PHREEQC, and thus provides multiple new features that make it applicable to complex reactive transport problems in various geoscientific fields. Code readability is ensured by the applied high-level programming language Python which is relatively easy to learn compared to low-level programming languages such as C, C++ and FORTRAN. Thus, also users with limited software development knowledge can benefit from the presented simulation environment due to the low entry-level programming skill requirements. In the present study, common geochemical benchmarks are used to verify the numerical code implementation. Currently, the coupled simulator can be used to investigate 3D single-phase fluid and heat flow as well as multicomponent solute transport in porous media. In addition to that, a wide range of equilibrium and nonequilibrium reactions can be considered. Chemical feedback on fluid flow is provided by adapting porosity and permeability of the porous media as well as fluid properties. Thereby, users are in full control of the underlying functions in terms of fluid and rock equations of state, coupled geochemical modules used for reactive transport, dynamic boundary conditions and mass balance calculations. Both, the solution of the system of partial differential equations and the PHREEQC module, can be easily parallelised to increase computational efficiency. The benchmarks used in the present study include density-driven flow as well as advective, diffusive and dispersive reactive transport of solutes. Furthermore, porosity and permeability changes caused by kinetically controlled dissolution-precipitation reactions are considered to verify the main features of our reactive transport code. In future, the code implementation can be used to quantify processes encountered in different types of subsurface utilisation, such as water resource management as well as geothermal energy production, as well as geological energy, CO2 and nuclear waste storage.

Reactive transport modeling involves solving several nonlinear coupled phenomena, among them, the flow of fluid phases, the transport of chemical species and energy, and chemical reactions. There are different ways to consider this coupling that might be more or less suitable depending on the nature of the problem to be solved. In this paper we acknowledge the importance of flexibility on reactive transport codes and how object-oriented programming can facilitate this feature. We present PROOST, an object-oriented code that allows solving reactive transport problems considering different coupling approaches. The code main classes and their interactions are presented. PROOST performance is illustrated by the resolution of a multiphase reactive transport problem where geochemistry affects hydrodynamic processes.

Dissolution–precipitation processes in tank experiments for testing numerical models for reactive transport calculations: Experiments and modelling

<p>The formation of alkaline magmas observed worldwide requires that low degree-melts, potentially formed in the asthenosphere, were able to cross the overlying lithosphere. Fracturing in the upper, brittle part of the lithosphere may help to extract this melt to the surface. However, the mechanism of extraction in the lower, ductile part of the lithosphere is still contentious. Metasomatic enrichment of the lithospheric mantle demonstrates that such low-degree melts interact with the lithosphere, but the physical aspect of this process remains unclear. The aim of this study is to better understand the percolation of magma in a porous viscous medium at pressure (P) and temperature (T) conditions relevant for the base of the lithosphere. We study such melt percolation numerically with a Thermo-Hydro-Chemical model of reactive transport coupled with thermodynamic data obtained via Gibbs energy minimisation. We perform Gibbs energy minimisation with Matlab using the linprog algorithm. We start with a simple ternary system of Forsterite/Fayalite/Enstatite solids and melts. All variables are a function of T, P and composition of the system (C), and are computed in both the Gibbs energy minimisation and in the reactive transport code, and can therefore vary freely.</p>

Although multicomponent reactive transport modeling is gaining wider application in various geoscience fields, it continues to present significant mathematical and computational challenges. There is a need to solve and compare the solutions to complex benchmark problems, using a variety of codes, because such intercomparisons can reveal promising numerical solution approaches and increase confidence in the application of reactive transport codes. In this contribution, the results and performance of five current reactive transport codes are compared for the 1D and 2D subproblems of the so-called easy test case of the MoMaS benchmark (Carrayrou et al., Comput Geosci, 2009, this issue). This benchmark presents a simple fictitious reactive transport problem that highlights the main numerical difficulties encountered in real reactive transport problems. As a group, the codes include iterative and noniterative operator splitting and global implicit solution approaches. The 1D easy advective and 1D easy diffusive scenarios were solved using all codes, and, in general, there was a good agreement, with solution discrepancies limited to regions with rapid concentration changes. Computational demands were typically consistent with what was expected for the various solution approaches. The differences between solutions given by the three codes solving the 2D problem are more important. The very high computing effort required by the 2D problem illustrates the importance of parallel computations. The most important outcome of the benchmark exercise is that all codes are able to generate comparable results for problems of significant complexity and computational difficulty.

The use of the subsurface for CO2 storage, geothermal energy generation, and nuclear waste disposal will greatly increase the interaction between clay(stone) and concrete. The development of models describing the mineralogical transformations at this interface is complicated, because contrasting geochemical conditions (Eh, pH, solution composition, etc.) induce steep concentration gradients and a high mineral reactivity. Due to the complexity of the problem, analytical solutions are not available to verify code accuracy, rendering code intercomparisons as the most efficient method for assessing code capabilities and for building confidence in the used model. A benchmark problem was established for tackling this issue. We summarize three scenarios with increasing geochemical complexity in this paper. The processes considered in the simulations are diffusion-controlled transport in saturated media under isothermal conditions, cation exchange reactions, and both local equilibrium and kinetically controlled mineral dissolution-precipitation reactions. No update of the pore diffusion coefficient as a function of porosity changes was considered. Seven international teams participated in this benchmarking exercise. The reactive transport codes used (TOUGHREACT, PHREEQC, with two different ways of handling transport, CRUNCH, HYTEC, ORCHESTRA, MIN3P-THCm) gave very similar patterns in terms of predicted solute concentrations and mineral distributions. Some differences linked to the considered activity models were observed, but they do not bias the general system evolution. The benchmarking exercise thus demonstrates that a reactive transport modelling specification for long-term performance assessment can be consistently addressed by multiple simulators.

Abstract. We explore coupling to a configurable subsurface reactive transport code as a flexible and extensible approach to biogeochemistry in land surface models. A reaction network with the Community Land Model carbon–nitrogen (CLM-CN) decomposition, nitrification, denitrification, and plant uptake is used as an example. We implement the reactions in the open-source PFLOTRAN (massively parallel subsurface flow and reactive transport) code and couple it with the CLM. To make the rate formulae designed for use in explicit time stepping in CLMs compatible with the implicit time stepping used in PFLOTRAN, the Monod substrate rate-limiting function with a residual concentration is used to represent the limitation of nitrogen availability on plant uptake and immobilization. We demonstrate that CLM–PFLOTRAN predictions (without invoking PFLOTRAN transport) are consistent with CLM4.5 for Arctic, temperate, and tropical sites.Switching from explicit to implicit method increases rigor but introduces numerical challenges. Care needs to be taken to use scaling, clipping, or log transformation to avoid negative concentrations during the Newton iterations. With a tight relative update tolerance (STOL) to avoid false convergence, an accurate solution can be achieved with about 50 % more computing time than CLM in point mode site simulations using either the scaling or clipping methods. The log transformation method takes 60–100 % more computing time than CLM. The computing time increases slightly for clipping and scaling; it increases substantially for log transformation for half saturation decrease from 10−3 to 10−9 mol m−3, which normally results in decreasing nitrogen concentrations. The frequent occurrence of very low concentrations (e.g. below nanomolar) can increase the computing time for clipping or scaling by about 20 %, double for log transformation. Overall, the log transformation method is accurate and robust, and the clipping and scaling methods are efficient. When the reaction network is highly nonlinear or the half saturation or residual concentration is very low, the allowable time-step cuts may need to be increased for robustness for the log transformation method, or STOL may need to be tightened for the clipping and scaling methods to avoid false convergence.As some biogeochemical processes (e.g., methane and nitrous oxide reactions) involve very low half saturation and thresholds, this work provides insights for addressing nonphysical negativity issues and facilitates the representation of a mechanistic biogeochemical description in Earth system models to reduce climate prediction uncertainty.

Complex cementitious matrices undergo weathering with environmental exchange and can release metallic pollutants during alteration. The molecular mechanisms responsible for metal release are difficult to identify, though this is necessary if such processes are to be controlled. The present study determines and models the molecular mechanisms of Pb release during Portland cement leaching. As Pb release is strongly related to its speciation (i.e. atomic environment and the nature of bearing phases), the first objective of the present study was to investigate the evolution of Pb retention sites together with the evolution of the cement mineralogy during leaching. Complementary and efficient investigation tools were used, namely X-ray diffraction, micro-X-ray fluorescence and X-ray absorption fine structures. The second objective was to reproduce our results with a reactive transport code (CHESS/HYTEC) in order to test the proposed speciation model of Pb. Combined results indicate that in both the unaltered core and the altered layer of the leached cement, Pb(II) would be retained through C–S–H ’nano-structure', probably linked to a Q1 or Q2P silicate tetrahedra. Moreover in the altered layer, the presence of Fe atoms in the atomic environment of Pb is highly probable. Unfortunately little is known about Fe phases in cement, which makes the interpretation difficult. Can Fe-substituted hydrogranet (C3AH6) be responsible for Pb retention? Modelling results were consistent with Pb retention through C–S–H in layers and also in an additional, possibly Fe-containing, Pb-retention phase in the altered layer.

One of the challenging problems in mathematical geosciences is the determination of analytical solutions of nonlinear partial differential equations describing transport processes in porous media. We are interested in diffusive transport coupled with precipitation‐dissolution reactions. Several numerical computer codes that simulate such systems have been developed. Analytical solutions, if they exist, represent an important tool for verification of numerical solutions. We present a methodology for deriving such analytical solutions that are exact and explicit in space and time variables. They describe transport of several aqueous species coupled to precipitation and dissolution of a single mineral in one, two, and three dimensions. As an application, we consider explicit analytical solutions for systems containing one or two solute species that describe the evolution of solutes and solid concentrations as well as porosity. We use one of the proposed analytical solutions to test numerical solutions obtained from two conceptually different reactive transport codes. Both numerical implementations could be verified with the help of the analytical solutions and show good agreement in terms of spatial and temporal evolution of concentrations and porosities.

Sodium diffusion in heterogeneous porous media: Connecting laboratory experiments and simulations

pH-dependent control of feldspar dissolution rate by altered surface layers

Computational codes are necessary tools for geochemical modeling of the alteration of minerals due to their ability to handle key mechanisms, such as dissolution, precipitation, diffusion, and convection at many temporal and spatial resolutions. Modeling glass corrosion specifically requires a description of the amorphous layer that forms on the surface of the glass and its effect on glass alteration kinetics. The objective of the GRAAL model (glass reactivity in allowance of the alteration layer) is both to provide a simple implementation of the passivation process in a reactive transport code and to provide data relative to the composition and the solubility of the amorphous layer. The size and properties of the protective amorphous layer drives the glass alteration rate, with regard to passivation; the greater the quantity of the protective amorphous layer, the lower the dissolution rate of the primary mineral. Here, concepts, equations, and implementation of GRAAL are reported. Simple glass alteration experiments are used to apply the model and measure parameters. The International Simple Glass used for nuclear glass long-term behavior studies is at the center of the glass compositions studied.

The DOE SciDAC program funded a team that developed PFLOTRAN, the next-generation (peta-scale) massively parallel, multiphase, multicomponent reactive flow and transport code. These codes are required to improve understanding and risk management of subsurface contaminant migration and geological sequestration of carbon dioxide. The important fate and transport processes occurring in the subsurface span a wide range of spatial and temporal scales, and involve nonlinear interactions among many different chemical constituents. Due to the complexity of this problem, modeling subsurface processes normally requires simplifying assumptions. However, tools of advanced scientific computing that have been used in other areas such as energy and materials research can also help address challenging problems in the environmental and geoscience fields. The overall project was led by Los Alamos National Laboratory and included Argonne, Oak Ridge and Pacific Northwest National Laboratories, in addition to the University of Illinois. This report summarizes the results of the research done at the University of Illinois, which focused on improvements to the underlying physical and computational modeling of certain transport and mixing processes.

PFLOTRAN, a next-generation reactive flow and transport code for modeling subsurface processes, has been designed from the ground up to run efficiently on machines ranging from leadership-class supercomputers to laptops. Based on an object-oriented design, the code is easily extensible to incorporate additional processes. It can interface seamlessly with Fortran 9X, C and C++ codes. Domain decomposition parallelism is employed, with the PETSc parallel framework used to manage parallel solvers, data structures and communication. Features of the code include a modular input file, implementation of high-performance I/O using parallel HDF5, ability to perform multiple realization simulations with multiple processors per realization in a seamless manner, and multiple modes for multiphase flow and multicomponent geochemical transport. Chemical reactions currently implemented in the code include homogeneous aqueous complexing reactions and heterogeneous mineral precipitation/dissolution, ion exchange, surface complexation and a multirate kinetic sorption model. PFLOTRAN has demonstrated petascale performance using 2{sup 17} processor cores with over 2 billion degrees of freedom. Accomplishments achieved to date include applications to the Hanford 300 Area and modeling CO{sub 2} sequestration in deep geologic formations.

Genome-scale metabolic models (GEMs) are widely used in systems biology to investigate metabolism and predict perturbation responses. Automatic GEM reconstruction tools generate GEMs with different properties and predictive capacities for the same organism. Since different models can excel at different tasks, combining them can increase metabolic network certainty and enhance model performance. Here, we introduce GEMsembler, a Python package designed to compare cross-tool GEMs, track the origin of model features, and build consensus models containing any subset of the input models. GEMsembler provides comprehensive analysis functionality, including identification and visualization of biosynthesis pathways, growth assessment, and an agreement-based curation workflow. GEMsembler-curated consensus models built from four Lactiplantibacillus plantarum and Escherichia coli automatically reconstructed models outperform the gold-standard models in auxotrophy and gene essentiality predictions. Optimizing gene-protein-reaction (GPR) combinations from consensus models improves gene essentiality predictions, even in the manually curated gold-standard models. GEMsembler explains model performance by highlighting relevant metabolic pathways and GPR alternatives, informing experiments to resolve model uncertainty. Thus, GEMsembler facilitates building more accurate and biologically informed metabolic models for systems biology applications.IMPORTANCEGenome-scale metabolic models (GEMs) capture our knowledge of cellular metabolism as encoded in the genome, enabling us to describe and predict how cells function under different conditions. While several automated tools can generate these models directly from genome data, the resulting models often contain gaps and uncertainties, highlighting areas where our metabolic knowledge is incomplete. Here, we introduce a new tool called GEMsembler, which integrates GEMs constructed by different methods, evaluate model uncertainty, and build consensus models, harnessing the unique features of each approach. These consensus models more accurately reflect experimentally observed metabolic traits, such as nutrient requirements and condition-specific gene essentiality. GEMsembler facilitates comprehensive analysis of model structure and function, helping to pinpoint knowledge gaps and prioritize experiments to address them. By synthesizing information from diverse sources, GEMsembler accelerates the development of more reliable and biologically meaningful models, advancing research in metabolic engineering, pathogen biology, and microbial community studies.