Theoretical Model for the Prediction of Water Flux during the Concentration of an Olive Mill Wastewater Model Solution by Means of Forward Osmosis.

Currently, understanding the dynamics of the interaction between the agents in a process is one of the most important factors regarding its operation and design. Membrane processes for industrial wastewater management are not strangers to this topic. One such example is the concentration of compounds with high added value, such as the phenolic compounds present in olive mill wastewater (OMW).

Comparison of Artificial Intelligence Control Strategies for a Peristaltically Pumped Low-Pressure Driven Membrane Process.

Peristaltic pumping is used in membrane applications where high and sterile sealing is required. However, control is difficult due to the pulsating pump characteristics and the time-varying properties of the system. In this work, three artificial intelligence control strategies (artificial neural networks (ANN), fuzzy logic expert systems, and fuzzy-integrated local models) were used to regulate transmembrane pressure and crossflow velocity in a microfiltration system under high fouling conditions.

Operating Conditions Optimization via the Taguchi Method to Remove Colloidal Substances from Recycled Paper and Cardboard Production Wastewater.

Optimization of the ultrafiltration (UF) process to remove colloidal substances from a paper mill's treated effluent was investigated in this study. The effects of four operating parameters in a UF system (transmembrane pressure (TMP), cross-flow velocity (CFV), temperature and molecular weight cut-off (MWCO)) on the average permeate flux (), organic matter chemical oxygen demand (COD) rejection rate and the cumulative flux decline (SFD), was investigated by robust experimental design using the Taguchi method. Analysis of variance (ANOVA) for an L orthogonal array were used to determine the significance of the individual factors, that is to say, to determine which factor has more and which less influence over the UF response variables.