The Identification of Fouling in Reverse Osmosis in Reverse Osmosis in the Treatment of Water with Petroleum Substances

Water containing petroleum substances is very difficult to clean, and the treatment process usually consists of several stages. Despite the increasing use of integrated membrane processes to purify natural waters, including the removal of organic substances, work is underway on the search for new processes and their optimization. This paper deals with the study of the removal of petroleum hydrocarbons from the benzene, toluene, ethylbenzene, and xylene (BTEX) group by reverse osmosis and optimization. The research was conducted on surface water enriched with a fuel mixture. Then, the reverse osmosis efficiency was modeled using the constant pressure filtration model (Hermia model), which determined the most likely membrane blocking mechanism. When the membrane was operated on surface water enriched with BTEX, the blocking of the membrane was based on the n = 0 cake mechanism. In surface water alone, the highest correlation coefficient was 0.9994 and corresponded to a temporary blocking mechanism (n = 1).

Removal of Cyanotoxins–Microcystins from Water by Filtration through Granulated Composites of Bentonite with Micelles of the Cation Octadecyltrimethyl Ammonium (ODTMA)

Cyanobacteria and their toxins present potential hazards to consumers of water from lakes, reservoirs and rivers; thus, their removal via water treatment is essential. Previously, we demonstrated that nanocomposites of octadecyltrimethyl ammonium (ODTMA) complexed with clay could efficiently remove cyanobacteria and their toxins from laboratory cultures and lake water. In this study, we determined the capacity of ODTMA nanocomposites to remove cyanotoxins, namely microcystins (MCs), from water to below 1 µg/L via filtration. This capacity was 1500 mg MC-LR per Kg of nanocomposite. Similar capacities were estimated for the removal of other MC congeners (MC-WR, MC-3aspWR and MC-YR), whereas substantially lower capacities were recorded for more positively charged MC congeners, such as MC-RR and MC-3aspRR. Filtration results were simulated with a filtration model, which considers convection and adsorption/desorption of one to several toxins. Model calculations for the removal of MC-LR, under a variety of situations, fitted well with all the experimentally measured values and also estimated the co-removal of several MC congeners. In agreement with model predictions, results demonstrated that in the presence of MC-WR, the emerging concentrations of MC-RR congeners eventually exceed their solution values. In conclusion, granulated nanocomposites of ODTMA–bentonite can be applied for the removal of microcystins from drinking water.

Influence of porosity on filtration of biological fluid through a two-layer capillary wall

On the basis of proposed new filtration model the peculiarities of concentration distribution of component carried by two-component biological liquid and fluid velocity in capillary with two-layer porous walls in steady-state mode are studied. Mathematical model takes into account such important phenomena as concentration expansion and viscosity dependence on concentration. The fluid flow corresponds to the Brinkman model. Dimensionless complexes linking characteristic physical scales of different phenomena are highlighted. Influence of model parameters on biological liquid filtration process for capillary wall layers with different porosity is analyzed. The peculiarities of flow regime and distribution of component concentration for different characteristics of internal porous layer (porosity, phase mobility, size) are revealed.

THE PROBLEM OF THE COMBINED USE OF FILTRATION THEORY AND MACHINE LEARNING ELEMENTS FOR SOLVING THE INVERSE PROBLEM OF RESTORING THE HYDRAULIC CONDUCTIVITY OF AN OIL FIELD

This article presents the methodology involving the combined use of machine learning elements and a physically meaningful filtration model. The authors propose using a network of radial basis functions for solving the problem of restoring hydraulic conductivity in the interwell space for an oil field. The advantage of the proposed approach in comparison with classical interpolation methods as applied to the problems of reconstructing the filtration-capacitive properties of the interwell space is shown. The paper considers an algorithm for the interaction of machine learning methods, a filtration model, a mechanism for separating input data, a form of a general objective function, which includes physical and expert constraints. The research was carried out on the example of a symmetrical element of an oil field. The proposed procedure for finding a solution includes solving a direct and an adjoint problem.