The ability of the absorbed energy in the flat-plate solar-collector’ tubes for oil-water separation: An experimental-computational approach

The presence of water along with oil creates many technical and economic problems. The accompanying oil will occupy part of the oil pipelines and thus increase the cost of oil transportation. Many salts are soluble in water and cause deposits in pipes and crude oil refineries. As a result, heavy damage is inflicted on transmission facilities and refineries. Therefore, it seems necessary to separate water from the produced crude oil. Thermal and electrical methods and the use of gravity are common methods for separating water from oil. But each of these methods has its own limitations. The use of absorbent materials to separate water from oil has attracted the attention of many researchers. AI modeling is the training and employment of machine learning algorithms for decision-making based on available data. In this paper, new structures are used to separate water from oil. The interactions between the materials have been investigated using molecular dynamics simulation tools. Results showed the highest reduction in gyration radius, the highest absolute adsorption energy, the highest maximum radial distribution function (RDF), and the lowest oscillation of the root mean square deviation (RMSD) chart for TiO2, so it was the best adsorbent for separating water from oil.