Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Abstract

Graphical abstract

A Laboratory Approach to Measure Carbonate Rocks’ Adsorption Density by Surfactant and Polymer

“…e static experiment indicated that the nanionic surfactant has more adsorption density with respect to the anionic surfactant. e ANN model revealed good agreement with the experimental result, and also, the results showed that adsorption density for both surfactants decreases as temperature increases [42]. Das et al (2020) measured the adsorption density of a nanionic surfactant with two different types of hydrophobic units and hydrophilic polyethoxylate units ranging from 15 to 40 mers on Indiana limestone.…”

“…Ahmed F. Belhaj et al (2021) investigated the adsorption behavior of two chemical surfactant nanionic alkyl polyglucoside (APG) and anionic alkyl ether carboxylate (AEC) on the carbonate surfactant using static adsorption experiments and artificial neural network (ANN) prediction. e static experiment indicated that the nanionic surfactant has more adsorption density with respect to the anionic surfactant.…”

Ultrasensitive Sensing of Volatile Organic Compounds Using a Cu‐Doped SnO2‐NiO p‐n Heterostructure That Shows Significant Raman Enhancement**

“…Since no cross reactions were observed for determination of PYR, 2-NT and EBZA (Figure S8C), these three VOCs were then simultaneously detected using this SERS nose.The SERS intensity ratio (I 1236 /I 1530 ,I 1375 /I 1530 and I 1690 /I 1530 )showed agood linearity with logarithmic VOCconcentration over the range from 10 ppb to 1000 ppm, due to Te mkin adsorption of VOCs on SERS substrates [33] (Figure S8B). Thel imits of detection (LODs) were estimated to be 7.6 AE 0.7, 5.3 AE 0.6, and 4.1 AE 0.5 ppb for PYR, 2-NT and EBZA, respectively, which were 10 times lower than those obtained by GC-MS [12] and fluorescence spectroscopy, [13] 100 times lower than those obtained by electrochemical methods [14] (Table S8).…”

Modeling the Behavior of Amphiphilic Aqueous Solutions

“…The claim that artificial neural networks are useful tools because they can minimize the time of experimental treatment and operating costs can be contrasted in different studies reported in the bibliography. An example of this, is the study carried out by Belhaj et al [37] in which they use artificial neural networks to predict absorption values for alkyl ether carboxylate (AEC) and alkyl polyglucoside (APG). Thus, this book chapter summary the research carried out in our research group to predict density, speed of sound, kinematic viscosity and surface tension of amphiphilic aqueous solutions [13].…”