Estimating groundwater quality parameters through conventional methods is time-consuming through laboratory measurements for megacities. There is a need to develop models that can help decision-makers make policies for sustainable groundwater reserves. The current study compared the efficiency of multivariate linear regressions (MLR) and artificial neural network (ANN) models in the prediction of groundwater parameters for total dissolved solids (TDS) for three sub-divisions in Lahore, Pakistan. The data for this study were collected every quarter of a year for six years. ANN was applied to investigate the feasibility of feedforward, backpropagation neural networks with three training functions T-BR (Bayesian regularization backpropagation), T-LM (Levenberg–Marquardt backpropagation), and T-SCG (scaled conjugate backpropagation). Two activation functions were used to analyze the performance of algorithmic training functions, i.e., Logsig and Tanh. Input parameters of pH, electrical conductivity (EC), calcium (Ca²⁺), magnesium (Mg²⁺), chloride (Cl⁻), and sulfate (SO₄²⁻) was used to predict TDS as an output parameter. The computed values of TDS by ANN and MLR were in close agreement with their respective measured values. Comparative analysis of ANN and MLR showed that TDS root means square error (RMSE) for city sub-division and Pearson’s coefficient of correlation (r) for ANN and MLR were 2.9% and 0.981 and 4.5% and 0.978, respectively. Similarly, for the Farrukhabad sub-division, RMSE and r for ANN were 4.9% and 0.952, while RMSE and r for MLR were 5.5% and 0.941, respectively. For the Shahadra sub-division, RMSE was 10.8%, r was 0.869 for ANN, RMSE was 11.3%, and r was 0.860 for MLR. The results exhibited that the ANN model showed less error in results than MLR. Therefore, ANN can be employed successfully as a groundwater quality prediction tool for TDS assessment.

This paper evaluates the feasibility of bioethanol production from onion waste by Saccharomyces cerevisiae and Pichia pastoris and their novel co-culture through fermentation. The process parameters were optimized for each strain and their combination to observe the synergistic effect of co-fermentation. A dinitro salicylic acid (DNS) test was conducted to study the reducing sugar content of samples at different time intervals. Fourier transform infrared (FTIR) spectroscopic analysis was used to compare results for functional groups of samples before and after fermentation, and gas chromatography with flame ionization detection (GC-FID) analysis was performed to measure the bioethanol concentration obtained at different combinations of pH (5, 5.5, 6), temperature (20 °C, 30 °C, 40 °C), and time (24–110 h). The maximum bioethanol concentration was achieved through a monoculture of Saccharomyces cerevisiae, i.e., 30.56 g/L. The ethanol productivity was determined based on the ethanol concentration and fermentation time ratio. The energy content was determined using the obtained ethanol value and the specific energy content of ethanol, i.e., 30 kJ/g. The productivity and energy of bioethanol obtained at this maximum concentration were 0.355 g/L h and 916.8 kJ/L, respectively, after 86 h of fermentation at 30 °C and pH 5. Pichia pastoris produced a maximum of 21.06 g/L bioethanol concentration with bioethanol productivity and energy of 0.264 g/L h and 631.8 kJ/L, respectively, after 72 h of fermentation at 30 °C and pH 5. The coculture fermentation resulted in 22.72 g/L of bioethanol concentration with bioethanol productivity and energy of 0.264 g/L h and 681.6 kJ/L, respectively, after 86 h of fermentation at 30 °C and pH 5. The results of reducing sugars also supported the same conclusion that monoculture fermentation using Saccharomyces cerevisiae was the most effective for bioethanol production compared to Pichia pastoris and co-culture fermentation.