Hydraulic characterization and removal of metals and nutrients in an aerated horizontal subsurface flow "racetrack" wetland treating primary-treated oil industry effluent.

Constructed wetlands (CW) are an attractive technology due to their operational simplicity and low life-cycle cost. It has been applied for refinery effluent treatment but mostly single-stage designs (e.g., vertical or horizontal flow) have been tested. However, to achieve a good treatment efficiency for industrial effluents, different treatment conditions (both aerobic and anaerobic) are needed. This means that hybrid CW systems are typically required with a respectively increased area demand. In addition, a strong aerobic environment that facilitates the formation of iron, manganese, zinc and aluminum precipitates cannot be established with passive wetland systems, while the role of these oxyhydroxide compounds in the further co-precipitation and removal of heavy metals such as copper, nickel, lead, and chromium that can simplify the overall treatment of industrial wastewaters is poorly understood in CW. Therefore, this study tests for the first time an innovative CW design that combines an artificially aerated section with a non-aerated section in a single unit applied for oil refinery wastewater treatment. Four pilot units were tested with different design (i.e., planted/unplanted, aerated/non-aerated) and operational (two different hydraulic loading rates) characteristics to estimate the role of plants and artificial aeration and to identify the optimum design configuration. The pilot units received a primary refinery effluent, i.e., after passing through a dissolved air flotation unit. The first-order removal of heavy metals under aerobic conditions is evaluated, along with the removal of phenols and nutrients. High removal rates for Fe (96-98%), Mn (38-81%), Al (49-73%), and Zn (99-100%) generally as oxyhydroxide precipitates were found, while removal of Cu (61-80%), Ni (70-85%), Pb (96-99%) and Cr (60-92%) under aerobic conditions was also observed, likely through co-precipitation. Complete phenols and ammonia nitrogen removal was also found. The first-order rate coefficient (k) calculated from the collected data demonstrates that the tested CW represents an advanced wetland design reaching higher removal rates at a smaller area demand than the common CW systems.

Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System.

BACKGROUND: Chronic kidney disease (CKD) is a covert disease. Accurate prediction of CKD progression over time is necessary for reducing its costs and mortality rates. The present study proposes an adaptive neurofuzzy inference system (ANFIS) for predicting the renal failure timeframe of CKD based on real clinical data. METHODS: This study used 10-year clinical records of newly diagnosed CKD patients. The threshold value of 15 cc/kg/min/1.73 m(2) of glomerular filtration rate (GFR) was used as the marker of renal failure. A Takagi-Sugeno type ANFIS model was used to predict GFR values. Variables of age, sex, weight, underlying diseases, diastolic blood pressure, creatinine, calcium, phosphorus, uric acid, and GFR were initially selected for the predicting model. RESULTS: Weight, diastolic blood pressure, diabetes mellitus as underlying disease, and current GFR(t) showed significant correlation with GFRs and were selected as the inputs of model. The comparisons of the predicted values with the real data showed that the ANFIS model could accurately estimate GFR variations in all sequential periods (Normalized Mean Absolute Error lower than 5%). CONCLUSIONS: Despite the high uncertainties of human body and dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.