Treated Rhizopora mucronata tannin (RMT) as a corrosion inhibitor for carbon steel and copper in oil and gas facilities was investigated. Corrosion rate of carbon-steel and copper in 3wt% NaCl solution by RMT was studied using chemical (weight loss method) and spectroscopic (FTIR) techniques at various temperatures in the ranges of 26-90°C. The weight loss data was compared to the electrochemical by the application of Faraday's law for the conversion of corrosion rate data from one system to another. The inhibitive efficiency of RMT was compared with commercial inhibitor sodium benzotriazole (BTA-S). The best concentration of RMT was 20% (w/v), increase in concentration of RMT decreased the corrosion rate and increased the inhibitive efficiency. Increase in temperature increased the corrosion rate and decreased the inhibitive efficiency but, the rate of corrosion was mild with RMT. The FTIR result shows the presence of hydroxyl group, aromatic group, esters and the substituted benzene group indicating the purity of the tannin. The trend of RMT was similar to that of BTA-S, but its inhibitive efficiency for carbon-steel was poor (6%) compared to RMT (59%). BTA-S was efficient for copper (76%) compared to RMT (74%) at 40% (w/v) and 20% (w/v) concentration respectively. RMT was efficient even at low concentration therefore, the use of RMT as a cost effective and environmentally friendly corrosion inhibiting agent for carbon steel and copper is herein proposed.
Ascorbic acid was used for the first time to synthesize cellulose nanoparticles (CNP) extracted from okra mucilage. The physical properties of the CNP including their size distribution, and crystalline structures were investigated. The rheological properties of the cellulose nanofluid (CNF) were compared with the bulk okra mucilage and commercial polymer xanthan. The interfacial properties of the CNF at the interface of oil-water (O/W) system were investigated at different concentrations and temperatures. The effects of the interaction between the electrolyte and ultrasonic were determined. Core flooding experiment was conducted at reservoir condition to justify the effect of the flow behaviour and disperse phase behaviour of CNF on additional oil recovery. The performance of the CNF was compared to conventional EOR chemical. The combined method of ultrasonic, weak-acid hydrolysis and nanoprecipitation were effective in producing spherical and polygonal nanoparticles with a mean diameter of 100 nm, increased yield of 51% and preserved crystallinity respectively. The zeta potential result shows that the CNF was stable, and the surface charge signifies long term stability of the fluid when injected into oil field reservoirs. The CNF, okra and xanthan exhibited shear-thinning and pseudoplastic behaviour. The IFT decreased with increase in concentration of CNF, electrolyte and temperature. The pressure drop data confirmed the stability of CNF at 120°C and the formation of oil bank was enough to increase the oil recovery by 20%. CNF was found to be very effective in mobilizing residual oil at high-temperature high-pressure (HTHP) reservoir condition. The energy and cost estimations have shown that investing in ultrasonic-assisted weak-acid hydrolysis is easier, cost-effective, and can reduce energy consumption making the method economically advantageous compared to conventional methods.
Ultrafiltration has been proven to be very effective in the treatment of oil-in-water emulsions, since no chemical additives are required. However, ultrafiltration has its limitations, the main limits are concentration polarization resulting to permeate flux decline with time. Adsorption, accumulation of oil and particles on the membrane surface which causes fouling of the membrane. Studies have shown that the ultrasonic is effective in cleaning of fouled membrane and enhancing membrane filtration performance. But the effectiveness also, depends on the selection of appropriate membrane material, membrane geometry, ultrasonic module design, operational and processing condition. In this study, a hollow and flat-sheet polyurethane (PU) membranes synthesized with different additives and solvent were used and their performance evaluated with oil-in-water emulsion. The steady-state permeate flux and the rejection of oil in percentage (%) at two different modes were determined. A dry/wet spinning technique was used to fabricate the flat-sheet and hollow fibre membrane (HFMs) using Polyethersulfone (PES) polymer base, Polyvinylpyrrolidone (PVP) additive and N, N-Dimethylacetamide (DMAc) solvent. Ultrasonic assisted cross-flow ultrafiltration module was built to avoid loss of ultrasonic to the surrounding. The polyurethane (PU) was synthesized by polymerization and sulphonation to have an anionic group (-OH; -COOH; and -SO3H) on the membrane surface. Changes in morphological properties of the membrane had a significant effect on the permeate flow rate and oil removal. Generation of cavitation and Brownian motion by the ultrasonic were the dominant mechanisms responsible for ultrafiltration by cracking the cake layers and reducing concentration polarization at the membrane surface. The percentage of oil after ultrafiltration process with ultrasonic is about 90% compared to 49% without ultrasonic. Ultrasonic is effective in enhancing the membrane permeate flux and controlling membrane fouling.
Ascorbic acid was used for the first time to synthesize crystalline starch nanoparticles (CSNP). The physical properties of the CSNP were investigated. Rheological properties of the crystalline starch nanofluid (CSNF) were compared with native cassava starch (CS) and commercial polymer xanthan. Interfacial properties of the CSNF at the interface of oil and water (O/W) were investigated at different concentrations and temperatures. Wettability alteration efficiency of CSNF on oil-wet sandstone surface was investigated using the sessile drop method. Core flooding experiment was conducted at reservoir conditions. The methods were effective in producing spherical and polygonal nanoparticles with a mean diameter of 100 nm and increased in crystallinity of 7%. Viscosity increased with increase in surface area and temperature of the CSNF compared to a decrease in viscosity as the temperature increases for xanthan. Interfacial tension (IFT) decreased with increase in concentration of CSNF, electrolyte and temperature. The results show that CSNF can change the wettability of sandstone at low concentration, high salinity and elevated temperature. Pressure drops data shows stability of CSNF at 120 °C. The formation of oil bank was enough to increase oil recovery by 23%.
Due to the significant energy and economic losses brought on by the global oil spill, there has been an increased interest in oil-water separation. This study presents strong non-linear machine learning models (support vector regression (SVR) and Gaussian process regression (GPR)) with the Response surface method (RSM) to predict the oil flux and oil-water separation efficiency of wastewater using ceramic membrane technology. For the model development and prediction of oil flux (OF) and oil-water separation efficiency (OSE), oil concentration (mg/L), feed flow rate (mL/min), and pH were considered as input variables. The input variables are combined in three combinations to study the most contributing input features to the models' performance. Mean square error (MSE) and Nash-Sutcliffe coefficient efficiency (NSE) were used to assess the prediction performances of the developed models with the different number of input combinations considered in the study. For the two target variables (OF and OSE), GPR and SVR models were used to separately predict them. For OF, the SVR-2 [Combo-2] model (MSE = 0.9255 and NSE = 2.7976) performed better with higher prediction accuracy compared to GPR-2 [Combo-2] model (MSE = 0.763 and NSE = 6.437). In addition, for OSE, the GPR-3 [Combo-3] model (MSE = 0.995 and NSE = 0.5544) performed slightly better than SVR-3 [Combo-3] model (MSE = 0.992 and NSE = 0.8066). The results showed that the SVR model with the combo-2 and GPR-3 models for OF and OSE variables are the proposed models with the best performance and accuracy. This machine learning study will aid in better evaluating the function of materials such as ceramic in membrane performance features such as oil flux and rejection prediction, separation efficiency, water recovery, membrane fouling, and so on. As for academics and manufacturers, this machine learning (ML) strategy will boost performance and allow a better understanding of system governance.