[en] A research tool for modeling the reactive flow and transport of groundwater contaminants in multiple dimensions is presented. Arbitrarily complex coupled kinetic-equilibrium heterogeneous reaction networks, automatic code generation, transfer-function based solutions, parameter estimation, high-resolution methods for advection, and robust solvers for the mixed kinetic-equilibrium chemistry are some of the features of reactive flow and transport (RAFT) that make it a versatile research tool in the modeling of a wide variety of laboratory and field experiments. The treatment of reactions is quite general so that RAFT can be used to model biological, adsorption/desorption, complexation, and mineral dissolution/precipitation reactions among others. The integrated framework involving automated code generation and parameter estimation allows for the development, characterization, and evaluation of mechanistic process models. The model is described and used to solve a problem in competitive adsorption that illustrates some of these features. The model is also used to study the development of an in situ Fe(II)-zone by encouraging the growth of an iron-reducing bacterium with lactate as the electron donor. Such redox barriers are effective in sequestering groundwater contaminants such as chromate and TCE

[en] Harmful contaminants such as Cr(VI) and TCE can be removed from groundwater by reactions with chemically reduced subsurface sediments. This paper studies the optimal selection of the number of wells, the injection rate, and the number of regenerations of a large-scale Fe(II) barrier for Cr(VI) remediation at Hanford, WA. The process model consists of two parts: (a) the creation of the Fe(II) barrier by the injection of a dithionite reagent and (b) the reoxidation of the barrier by Cr(VI) and oxygen in the invading groundwater. The solution to the process model is used to develop the total cost as a function of the design variables. This cost model is applied to the Cr(VI) contamination at Hanford to obtain the optimal design configuration and its sensitivity to cost and process uncertainties