ENHANCING WASTE STABILIZATION POND EFFICIENCY UNDER UNCERTAINTY IN A HILLY RIVERBANK CITY

Abstract

This dissertation compare both deterministic and uncertainty approaches to designing waste stabilization ponds (WSP) for unrestricted crop and also release to open surface water body in a Hilly Riverbank City. Currently, anaerobic and facultative ponds are designed using deterministic and average single values for input parameters, a method that is frequently regarded as unrealistic and overly conservative due to parameter variability. Traditional maturation pond designs, on the other hand, are based on the assumption of completely mixed hydraulic flows and simplistic temperature-dependent fecal coliform removal strategies. Uncertainty design approaches for waste stabilization ponds (WSP) consider a range of input design parameters to address uncertainties. These methods utilize a dispersed hydraulic flow model for predicting fecal coliform removal in facultative and maturation ponds, applying variable fecal coliform removal rates specific to each type of WSP. Unlike deterministic methods that calculate based on average fecal coliform concentrations, Uncertainty techniques use the 95th percentile value of effluent fecal coliform concentrations (<230 FC/100ml) and BOD (<10mg/l) to design maturation ponds. Monte Carlo simulations are employed in these methods to present output designs across statistical percentiles. A Python program has been developed to analyze these results and enhance the design process of WSPs through Monte Carlo simulations. A sensitivity analysis was conducted based on output design data generated by Monte Carlo simulation using the Pearson correlation coefficient (r) to quantify the influence of various critical parameters on the output results for effluent BOD and fecal coliform concentrations. This analysis weights each parameter based on its correlation coefficient, with (r = 1) indicating a strong positive relationship, (r = -1) indicating a strong negative relationship, and (r = 0) indicating no linear correlation. This method aids in determining which parameters have the greatest impact, guiding pond design and management optimization to improve effluent quality levels effectively. The Brute Force method is employed to achieve the effluent standards set by CPHEEO by first fixing specific targets for effluent BOD and FC. Following this, the method optimizes detention time and the area of the pond to determine the most efficient configuration. This optimization process yields the optimal number of ponds are 2 anaerobic ponds,1 facultative pond, and 4 maturation ponds, ensuring to meet the desired effluent quality as per CPHEEO.