Comparative analysis of wastewater treatment technologies

The aim of this study is to apply the principle of multi-criteria decision-making (MCDM) theories on different types of wastewater treatment technologies. An increase in the production and discharge of wastewater is increasing; therefore wastewater treatment alternatives are needed. With increase in population growth, urbanization and industrialization increasing the amount of pollutants in our environments that leads to more wastewater discharge from both domestic and industries. These wastewaters are produced in large volumes and must be absolutely treated before discharge. Therefore, there is need for wastewater treatment technologies that are cost effective, easy to maintain, low energy use etc. This study will use some criteria on Fuzzy PROMETHEE to analyze the wastewater treatment technologies based on these criteria. The outcome of the decision-making theories in these wastewater technologies will help the concern parties in chosen the best among these technologies and will give an insight to these concerned parties such as engineers, town planners and other government personnel’s in making decisions. The common and most commonly used wastewater treatment technologies were evaluated and are compared based on certain criteria using fuzzy PROMETHEE decision-making theory and Nano-filtration Method is recommended the best followed by Activated Sludge (AS) Method based on this research

Specific Wear Rate Modeling of Polytetraflouroethylene Composites via Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) Tools

Lately, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models have been recognized as potential and good tools for mathematical modeling of complex and nonlinear behavior of specific wear rate (SWR) of composite materials. In this study, modeling and prediction of specific wear rate of polytetraflouroethylene (PTFE) composites using FFNN and ANFIS models were examined. The performances of the models were compared with conventional multilinear regression (MLR) model. To establish the proper choice of input variables, a sensitivity analysis was performed to determine the most influential parameter on the SWR. The modeling and prediction performance results showed that FFNN and ANFIS models outperformed that of the MLR model by 45.36% and 45.80%, respectively. The sensitivity analysis findings revealed that the volume fraction of reinforcement and density of the composites and sliding distance were the most and more influential parameters, respectively. The goodness of fit of the ANN and ANFIS models was further checked using t-test at 5% level of significance and the results proved that ANN and ANFIS models are powerful and efficient tools in dealing with complex and nonlinear behavior of SWR of the PTFE composites