Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering) - Volume 14, Issue 2, 2021

A critical review of previous studies is presented based on the contextual research background of surfactant flooding in this study. The parameters focused on achieving the analysis include permeability, salinity, temperature, and viscosity from different surfactant flooding operations. The principal theme of this review was to provide the regression analysis technique that may adopt to analyze the collected data and conduct contextual research. The set of analytical discussion is accomplished by extracting and plotting the basic parameters against recovery at Original Oil in Place (OOIP) and tertiary stages. Furthermore, the success rate of such studies is compared to the grounds of oil recovery efficiency at different stages. Moreover, the failure of the surfactant flooding project can also be ensured by the outcomes of this study. It is revealed from this study that the recovery efficiency of surfactant flooding can be obtained maximum at lower permeability ranges, however, other parameters such as salinity and temperature may possess some influence on recovery. In fact, the fluid viscosity of reservoir fluid is inversely rated to recovery. The salinity, temperature and viscosity ranges for efficient surfactant flooding ranges may drop within the range from 1400 to 132606 ppm, 25 to 126 °C, and 1.9 to 150 cP, respectively.

Many studies reported the application of natural coagulants in removing pollutants with the help of the coagulation process, and coagulants showed their efficiency in comparison to the literature available. Yet, the utilization or acceptance of these coagulants in treating industrial wastewater is very low. Thus, there is a need for a strategy for enhancing the potential usage of coagulants for water treatment, presenting prevailing options and efforts for the development of coagulants. The utilization of coagulants can be improved by showing their efficiency in comparison to the advanced treatment technologies available in the present scenario. The coagulation efficiency with natural coagulants can be enhanced by mongrelizing the coagulants with other coagulants, enhancingthe purity during extraction procedures, and coalescence of coagulants. However, the research on natural coagulants isquite encouraging. The perception of sustainable assessment studies revealed that commercialization/acceptance of coagulants for treatment options are hindered by their applicability and feasibility in real-time applications, and utilization of coagulants ignores the socio-economic, ecological, and technical aspects. The present review mainly focuses on the issues related to natural coagulants for clarifying the uncertainties and simultaneously making the water industries to be more sustainable.

Background: There are different types of pollutants in water hence they are constantly in competition for active sites during the adsorption process. A key advantage of competitive adsorption studies is that these inform regarding the adsorbent’s performance in real water treatment applications. Objective: This study aims to investigate the competitive adsorption of Pb(II), Cu(II), Fe(II) and Zn(II) using elephant grass (Pennisetum purpureum) biochar and hybrid biochar from LDPE. Method: The produced biochar was characterised by Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR). The adsorption parameters, equilibrium isotherm modelling and parametric studies were conducted based on data from the batch adsorption experiments. Results: For both adsorbents, the removal efficiency was >99% over the entire investigation for dosage and contact time, suggesting that they are very efficient for removing multiple heavy metals from aqueous media. It was observed that removal efficiency was optimal at 2 g/l dosage and contact time was 20 minutes for both adsorbent types. The Elovich isotherm and the pseudo-second order kinetic models were best-fit for the competitive adsorption process. Conclusion: The study was able to successfully reveal that biomass biochar from elephant grass and hybrid biochar from LDPE can be used as effective adsorbent material for the removal of heavy metals from aqueous media. This study bears a positive implication for environmental protection and solid waste management.

In this study, the Sany-glass test was used to evaluate the performance of a new surfactant prepared from corn oil as a demulsifier for crude oil emulsions. Central composite design (CCD), based on the response surface methodology (RSM), was used to investigate the effect of four variables, including demulsifier dosage, water content, temperature, and pH, on the efficiency of water removal from the emulsion. Besides, analysis of variance was applied to examine the precision of the CCD mathematical model. The results indicate that demulsifier dose and emulsion pH are two significant parameters determining demulsification. The maximum separation efficiency of 96% was attained at an alkaline pH and with 3500 ppm demulsifier. According to the RSM analysis, the optimal values for the input variables were 40% water content, 3500 ppm demulsifier, 60 °C, and pH 8.

One of the promising technologies for isolating carbon dioxide from a mixture of industrial flue gases is the calcium looping process. This process involves a reversible reaction between sorbent Calcium Oxide and Carbon Dioxide. Because sorbent loses its activity after multiple cycles, hydration step was proposed, which is another reversible reaction where deactivated sorbent is treated with steam to form Ca(OH)2, which undergoes the backward reaction to give back the regenerated sorbent. Blamey et al. (2016) developed a shrinking core model based on which, studies were carried out on a small experimental reactor. This paper aims at developing a hybrid model by combining first principles model and an ANN model for improved prediction of the conversion in hydration of calcium looping process and to scale it up for optimal operations. The hybrid model is tested for various combinations of training variables and data sets with respect to temperature and cycle number and it is found that the hybrid model indeed gives better results. The performance prediction of Hybrid modelling is compared to the individual performance prediction of the ANN model and First principles approach.