Iranian Journal of Chemical Engineering (IJChE)

Abstract Oily wastewaters and Oil–in-water emulsions are two of the major pollutants of the environment. Ultrafiltration (UF) membranes play an important role in the treatment and reuse of oily wastewaters. Fouling of UF membranes is typically caused by inorganic and organic materials present in wastewaters that adhere to the surface and pores of the membrane and result in the deterioration of performance with a consequent increase in energy costs and membrane replacement. In the experiments, polyacrylonitrile (PAN) and outlet wastewater of the API (American Petroleum Institute) separator unit of Tehran refinery as membrane and feed were used, respectively. Fouling and cleaning experiments were performed with oily wastewater and selected cleaning agents using a laboratory scale cross flow test unit. The results showed that metal chelating agent (ethylene diamine tetra acetic acid disodium salt-2-hydrate (EDTA)) and an anionic surfactant (sodium dodecyl sulfate (SDS)) were able to clean the fouled UF membrane effectively by optimizing chemical (pH) and physical (cleaning time, cross flow velocity (CFV) and temperature) conditions during cleaning. Flux recovery and resistance removal were found to improve with increasing CFV, temperature, pH, cleaning time and concentration of the cleaning chemicals. In this paper, the cleaning mechanism is also investigated.

Abstract Prediction of the dynamic crossflow ultrafiltration rate of a protein solution such as milk poses a complex non-linear problem as the filtration rate has a strong dependence on both the solution physicochemical conditions and the operating conditions. As a result, the development of general physics-based models has proved extremely challenging. In this study an alternative dynamic neuro-fuzzy model for milk ultrafiltration that describes the variation in dynamic permeate flux decline with temperature, transmembrane pressure (TMP), fat percentage, pH and molecular weight cut off (MWCO) has been developed with the experimental data of the pilot spiral wound membrane test rig. By increasing the temperature, TMP, and pH the permeate flux is increased, and by increasing fat concentration the permeate flux is decreased. The MWCO variation indicates a paradoxical permeate flux. Additionally, a hybrid physical model for dynamic prediction of total resistance in the milk ultrafiltration by combination of two neuro-fuzzy (ANFIS) models and a physical model (BLA model) is developed. By increasing the TMP and fat concentration, the total resistance is increased. But by increasing the pH and temperature, the total resistance is decreased. Also, MWCO variation indicates a paradoxical total resistance value.