Amrita Malik

Modeling the performance of "up-flow anaerobic sludge blanket" reactor based wastewater treatment plant using linear and nonlinear approaches - a case study.

The paper describes linear and nonlinear modeling of the wastewater data for the performance evaluation of an up-flow anaerobic sludge blanket (UASB) reactor based wastewater treatment plant (WWTP). Partial least squares regression (PLSR), multivariate polynomial regression (MPR) and artificial neural networks (ANNs) modeling methods were applied to predict the levels of biochemical oxygen demand (BOD) and chemical oxygen demand (COD) in the UASB reactor effluents using four input variables measured weekly in the influent wastewater during the peak (morning and evening) and non-peak (noon) hours over a period of 48 weeks. The performance of the models was assessed through the root mean squared error (RMSE), relative error of prediction in percentage (REP), the bias, the standard error of prediction (SEP), the coefficient of determination (R(2)), the Nash-Sutcliffe coefficient of efficiency (E(f)), and the accuracy factor (A(f)), computed from the measured and model predicted values of the dependent variables (BOD, COD) in the WWTP effluents. Goodness of the model fit to the data was also evaluated through the relationship between the residuals and the model predicted values of BOD and COD. Although, the model predicted values of BOD and COD by all the three modeling approaches (PLSR, MPR, ANN) were in good agreement with their respective measured values in the WWTP effluents, the nonlinear models (MPR, ANNs) performed relatively better than the linear ones. These models can be used as a tool for the performance evaluation of the WWTPs.

Iron-induced oxidative stress in a macrophyte: A chemometric approach

Iron-induced oxidative stress in plants of Bacopa monnieri L., a macrophyte with medicinal value, was investigated using the chemometric approach. Cluster analysis (CA) rendered two distinct clusters of roots and shoots. Discriminant analysis (DA) identified discriminating variables (NP-SH and APX) between the root and shoot tissues. Principal component analysis (PCA) results suggested that protein, superoxide dismutase (SOD), ascorbic acid, proline, and Fe uptake are dominant in root tissues, whereas malondialdehyde (MDA), guaiacol peroxidase (POD), cysteine, and non-protein thiol (NP-SH) in shoot of the stress plant. Discriminant partial-least squares (DPLS) results further confirmed that SOD and ascorbic acid contents dominated in root tissues, while NP-SH, cysteine, POD, ascorbate peroxidase (APX), and MDA in shoot. MDA and NP-SH were identified as most pronounced variables in plant during the highest exposure time. The chemometric approach allowed for the interpretation of the induced biochemical changes in plant tissues exposed to iron.

Chemometrics assisted spectrophotometric determination of pyridine in water and wastewater

The paper reports a direct method for the determination of pyridine in water and wastewater samples based on ultraviolet spectrophotometric measurements using multi-way modeling techniques. Parallel factor analysis (PARAFAC) and multi-way partial least squares (N-PLS) regression methods were employed for the decomposition of spectra and quantification of pyridine. The study was carried out in the pH range of 1.0-12.0 and concentration range of 0.67-51.7 microgmL(-1) of pyridine. Both the three-way PARAFAC and tri-PLS1 models successfully predicted the concentration of pyridine in synthetic (spiked) river water and field wastewater samples. The mean recovery obtained from PARAFAC regression model were 97.39% for the spiked and 99.84% for the field wastewater samples, respectively. The sensitivity and precision of the method for pyridine determination were 0.58% and 5.95%, respectively. The N-PLS regression model yielded mean recoveries of 99.29% and 100.18% for the spiked and field wastewater samples, respectively. The prediction accuracy of the methods was evaluated through the root mean square error of prediction (RMSEP). For PARAFAC, it was 0.65 and 0.82 microgmL(-1) for spiked river water and field wastewater samples, respectively, while for N-PLS, it was 0.25 and 0.37 microgmL(-1), respectively. Both the PARAFAC and N-PLS methods, thus, yielded satisfactory results for the prediction of pyridine concentration in water and wastewater samples.