Evaluation and Prediction on the Effect of Ionic Properties of Solvent Extraction Performance of Oily Sludge Using Machine Learning

Effectiveness of inhibitors to prevent asphaltene aggregation: Insights from atomistic and molecular simulations

The technological landscape for industrial processes handling asphaltene is evolving at a rapid pace due to the increase in the extraction of heavy crude oil. The main underlying challenges in this regard are the flow assurance, the recovery of the spent solvent, and the sophisticated extractor setup required to develop the process to an industrial scale. The number of studies focused on the handling of the asphaltene at the atomic and molecular scales is growing enormously in order to identify new sustainable solvents for the effective extraction of asphaltene from heavy crude oil or oil-bearing sands. This Perspective focuses on the importance of density functional theory and molecular dynamics simulations to explore the broader range of asphaltene inhibitors, e.g., nanoparticles, ionic liquids, and deep eutectic solvents, to prevent asphaltene precipitation. We provide a concise overview of the major accomplishments, analyze the aspects that require attention, and highlight the path-breaking studies having a significant impact on the process of chemical enhanced oil recovery from heavy crude oil reservoirs primarily based on atomistic and molecular simulations.

Defining the optimal conditions using FFNNs and NARX neural networks for modelling the extraction of Sc from aqueous solution by Cryptand-2.2.1 and Cryptand-2.1.1

The main aim of this study is to figure out how well cryptand-2.2.1 (C 2.2.1) and cryptand-2.1.1 (C 2.1.1) macrocyclic compounds (MCs) work as novel extractants for scandium (Sc) by using an artificial neural network (ANN) models in MATLAB software. Moreover, C2.2.1 and C2.1.1 have never been evaluated to recover Sc. The independent variables impacting the extraction process (concentration of MC, concentration of Sc, pH, and time), and a nonlinear autoregressive network with exogenous input (NARX) and feed-forward neural network (FFNN) models were used to estimate their optimum values. The greatest obstacle in the selective recovery process of the REEs is the similarity in their physicochemical properties, specifically their ionic radius. The recovery of Sc from the aqueous solution was experimentally evaluated, then the non-linear relationship between those parameters was predictively modeled using (NARX) and (FFNN). To confirm the extraction and stripping efficiency, an atomic absorption spectrophotometer (AAS) was employed. The results of the extraction investigations show that, for the best conditions of 0.008 mol/L MC concentration, 10 min of contact time, pH 2 of the aqueous solution, and 75 mg/L Sc initial concentration, respectively, the C 2.1.1 and C 2.2.1 extractants may reach 99 % of Sc extraction efficiency. Sc was recovered from a multi-element solution of scandium (Sc), yttrium (Y), and lanthanum (La) under these circumstances. Whereas, at a concentration of 0.3 mol/L of hydrochloric acid, the extraction of Sc was 99 %, as opposed to Y 10 % and La 7 %. The Levenberg-Marquardt training algorithm had the best training performance with an mean-squared-error, MSE, of 5.232x10-6 and 6.1387x10-5 for C 2.2.1 and C 2.1.1 respectively. The optimized FFNN architecture of 4-10-1 was constructed for modeling recovery of Sc. The extraction process was well modeled by the FFNN with an R2 of 0.999 for the two MC, indicating that the observed Sc recovery efficiency consistent with the predicted one.

Lysozyme stability in various deep eutectic solvents using molecular dynamics simulations

The ability of neat deep eutectic solvents (DESs) to influence protein structure and function has gained considerable interest due to the unstable nature of enzymes or therapeutic proteins, which are often exposed to thermal, chemical, or mechanical stresses when handled at an industrial scale. In this study, we simulated a model globular protein, lysozyme, in water and six choline chloride-based DES using molecular dynamics simulations, to investigate the structural changes in various solvent environments, giving insights into the overall stability of lysozyme. Root mean square deviation (RMSD) and root mean square fluctuations (RMSF) of the C-α backbone indicated that most DESs induced a less flexible and rigid lysozyme structure compared to water. The radius of gyration and end-to-end distance calculations pointed towards higher structural compactness in reline and levuline, while the structure of lysozyme considerably expanded in oxaline. Protein-solvent interactions were further analysed by hydrogen bonding interactions and radial distribution functions (RDF), which indicated a higher degree of lysozyme-hydrogen bond donor (HBD) interactions compared to lysozyme-choline hydrogen bonding. Surface area analysis revealed an overall % increase in total positive, negative, donor, and acceptor surface areas in malicine and oxaline compared to water and other DESs, indicating the exposure of a larger number of residues to interactions with the solvent. Reline, levuline, and polyol-based DESs comparatively stabilized lysozyme, even though changes in the secondary/tertiary structures were observed.Communicated by Ramaswamy H. Sarma.

Efficient Treatment of Oily Sludge via Fast Microwave-Assisted Pyrolysis, Followed by Thermal Plasma Vitrification

Oily sludge, as a critical hazardous waste, requires appropriate treatment for resource recovery and harmfulness reduction. Here, fast microwave-assisted pyrolysis (MAP) of oily sludge was conducted for oil removal and fuel production. The results indicated the priority of the fast MAP compared with the MAP under premixing mode, with the oil content in solid residues after pyrolysis reaching below 0.2%. The effects of pyrolysis temperature and time on product distribution and compositions were examined. In addition, pyrolysis kinetics can be well described using the Kissinger-Akahira-Sunose (KAS) and the Flynn-Wall-Ozawa (FWO) methods, with the activation energy being 169.7-319.1 kJ/mol in the feedstock conversional fraction range of 0.2-0.7. Subsequently, the pyrolysis residues were further treated by thermal plasma vitrification to immobilize the existing heavy metals. The amorphous phase and the glassy matrix were formed in the molten slags, resulting in bonding and, hence, immobilization of heavy metals. Operating parameters, including working current and melting time, were optimized to reduce the leaching concentrations of heavy metals, as well as to decrease their volatilization during vitrification.