scholarly journals Review of "ML for Surface Complexation Model Development"

2020 ◽  
Author(s):  
M Heinz
Keyword(s):  
Model Development ◽  
Surface Complexation ◽  
Surface Complexation Model

Comparison of a Non Electrostatic Surface Complexation Model and Surface Phase Theories for Prediction of Future Sorption Properties.

2004 ◽  
Vol 824 ◽  
Author(s):  
Allan T. Emrén ◽  
Anna-Maria Jacobsson
Keyword(s):  
Aqueous Solution ◽  
Chemical Potential ◽  
Surface Complexation ◽  
Sorption Properties ◽  
Phase Method ◽  
Detailed Knowledge ◽  
Surface Phase ◽  
Surface Complexation Model ◽  
Advantages And Disadvantages ◽  
The One

AbstractIn performance assessments, sorption of radionuclides dissolved in groundwater is mostly handled by the use of fixed Kd values. It has been well known that this approach is unsatisfying. Only during the last few years, however, tools have become available that make it possible to predict the actual Kd value in an aqueous solution that differs from the one in which the sorption properties were measured.One such approach is surface complexation (SC) that gives a detailed knowledge of the sorption properties. In SC, one tries to find what kinds of sorbed species are available on the surface and the thermodynamics for their formation from species in the bulk aqueous solution. Recently, a different approach, surface phase method (SP), has been developed. In SP, a thin layer including the surface is treated as a separate phase. In the bulk aqueous solution, the surface phase is treated as a virtual component, and from the chemical potential of this component, the sorption properties can be found.In the paper, we compare advantages and disadvantages of the two kinds of models. We also investigate the differences in predicted sorption properties of a number of radionuclides (Co, Np, Th and U). Furthermore, we discuss under which circumstances, one approach or the other is preferable.

Variable charges of a red soil from different depths: Acid-base buffer capacity and surface complexation model

2018 ◽  
Vol 159 ◽  
pp. 107-115 ◽  
Author(s):  
Ying Wang ◽  
Pengfei Cheng ◽  
Fangbai Li ◽  
Tongxu Liu ◽  
Kuan Cheng ◽  
...  
Keyword(s):  
Buffer Capacity ◽  
Surface Complexation ◽  
Red Soil ◽  
Acid Base ◽  
Surface Complexation Model ◽  
Different Depths

Investigation of radionuclide 60Co(II) binding to TiO2 by batch technique, surface complexation model and DFT calculations

2012 ◽  
Vol 55 (9) ◽  
pp. 1752-1759 ◽  
Author(s):  
XueMei Ren ◽  
ShiTong Yang ◽  
XiaoLi Tan ◽  
ChangLun Chen ◽  
XiangKe Wang
Keyword(s):  
Dft Calculations ◽  
Surface Complexation ◽  
Surface Complexation Model ◽  
Batch Technique

scholarly journals A Surface Complexation Model of Alkaline-SmartWater Electrokinetic Interactions in Carbonates

2020 ◽  
Vol 146 ◽  
pp. 02003
Author(s):  
Moataz Abu-Al-Saud ◽  
Amani Al-Ghamdi ◽  
Subhash Ayirala ◽  
Mohammed Al-Otaibi
Keyword(s):  
Experimental Data ◽  
Zeta Potential ◽  
Crude Oil ◽  
Oil Recovery ◽  
Surface Complexation ◽  
Surface Complexation Model ◽  
Zeta Potentials ◽  
Smart Water ◽  
Injection Water ◽  
Carbonate Formations

Understanding the effect of injection water chemistry is becoming crucial, as it has been recently shown to have a major impact on oil recovery processes in carbonate formations. Various studies have concluded that surface charge alteration is the primary mechanism behind the observed change of wettability towards water-wet due to SmartWater injection in carbonates. Therefore, understanding the surface charges at brine/calcite and brine/crude oil interfaces becomes essential to optimize the injection water compositions for enhanced oil recovery (EOR) in carbonate formations. In this work, the physicochemical interactions of different brine recipes with and without alkali in carbonates are evaluated using Surface Complexation Model (SCM). First, the zeta-potential of brine/calcite and brine/crude oil interfaces are determined for Smart Water, NaCl, and Na2SO4 brines at fixed salinity. The high salinity seawater is also included to provide the baseline for comparison. Then, two types of Alkali (NaOH and Na2CO3) are added at 0.1 wt% concentration to the different brine recipes to verify their effects on the computed zeta-potential values in the SCM framework. The SCM results are compared with experimental data of zeta-potentials obtained with calcite in brine and crude oil in brine suspensions using the same brines and the two alkali concentrations. The SCM results follow the same trends observed in experimental data to reasonably match the zeta-potential values at the calcite/brine interface. Generally, the addition of alkaline drives the zeta-potentials towards more negative values. This trend towards negative zeta-potential is confirmed for the Smart Water recipe with the impact being more pronounced for Na2CO3 due to the presence of divalent anion carbonate (CO3)-2. Some discrepancy in the zeta-potential magnitude between the SCM results and experiments is observed at the brine/crude oil interface with the addition of alkali. This discrepancy can be attributed to neglecting the reaction of carboxylic acid groups in the crude oil with strong alkali as NaOH and Na2CO3. The novelty of this work is that it clearly validates the SCM results with experimental zeta-potential data to determine the physicochemical interaction of alkaline chemicals with SmartWater in carbonates. These modeling results provide new insights on defining optimal SmartWater compositions to synergize with alkaline chemicals to further improve oil recovery in carbonate reservoirs.

Comparison of in situ uranium KD values with a laboratory determined surface complexation model

2004 ◽  
Vol 19 (10) ◽  
pp. 1643-1653 ◽  
Author(s):  
Gary P Curtis ◽  
Patricia Fox ◽  
Matthias Kohler ◽  
James A Davis
Keyword(s):  
Surface Complexation ◽  
Surface Complexation Model ◽  
Kd Values
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