ABSTRACT
Background: Data mining [DM] is an approach used in extracting valuable information from environmental processes. This research depicts a DM approach used in extracting some information from influent and effluent wastewater characteristic data of a waste stabilization pond [WSP] in Birjand, a city in Eastern Iran
Methods: Multiple regression [MR] and neural network [NN] models were examined using influent characteristics [pH, Biochemical oxygen demand [BOD[5]], temperature, chemical oxygen demand [COD], total suspended solids [TSS], total dissolved solid [TDS], electrical conductivity [EC] and turbidity] as the regression input vectors. Models were adjusted to input attributes, effluent BOD[5] [BODout] and COD [CODout]. The models performances were estimated by 10-fold external cross-validation. An internal 5-fold cross-validation was also used for the training data set in NN model. The models were compared using regression error characteristic [REC] plot and other statistical measures such as relative absolute error [RAE]. Sensitivity analysis was also applied to extract useful knowledge from NN model
Results: NN models [with RAE = 78.71 +/- 1.16 for BODout and 83.67 +/- 1.35 for CODout] and MR models [with RAE = 84.40% +/- 1.07 for BODout and 88.07 +/- 0.80 for CODout] indicate different performances and the former was better [P < 0.05] for the prediction of both effluent BOD5 and COD parameters. For the prediction of CODout the NN model with hidden layer size [H] = 4 and decay factor = 0.75 +/- 0.03 presented the best predictive results. For BODout the H and decay factor were found to be 4 and 0.73 +/- 0.03, respectively. TDS was found as the most descriptive influent wastewater characteristics for the prediction of the WSP performance. The REC plots confirmed the NN model performance superiority for both BOD and COD effluent prediction
Conclusion: Modeling the performance of WSP systems using NN models along with sensitivity analysis can offer better understanding on exploring the most significant parameters for the prediction of system performance. The findings of this study could build the foundation for prospective work on the characterization of WSP operations and optimization of their performances with a view to conducting statistical approaches
ABSTRACT
Background: Atrazine is one of the most widely used triazine herbicides which has been used for controlling broadleaf and grassy weeds for many years. Its widespread use in water has caused environmental concern because of frequent detection of atrazine in aquatic systems where this herbicide has been spilled. Therefore, the purpose of this study was to determine the herbicide removal efficiency at the optimal conditions
Methods: The effect of different parameters including pH at three different levels [3-11], the initial concentration of atrazine at three different levels [0.1-10 mg/L], and reaction time at five different levels [0-120 min] on the removal of atrazine in the aqueous phase using ultraviolet radiation [1020 microw/cm[2]] was investigated. Finally, the data were analyzed using SPSS software [version 16]
Results: The results demonstrated that atrazine removal rate increased by increasing pH, initial atrazine concentration, and reaction time. The maximum rate of atrazine removal [99.2%] at optimal condition occurred in pH=11, atrazine concentration=10 mg/L at 30 min
Conclusion: According to the findings, it can be concluded that the UV-A process is an effective and commodious method for reducing atrazine in polluted water resources