Predictive Analytics for Optimizing Sewerage Sludge Treatment Efficiency and Resource Recovery in Dhaka City

Abstract

The increasing volume of sewerage sludge generated by rapid urbanization has created significant challenges for wastewater treatment facilities, particularly in densely populated cities such as Dhaka. This study examined the application of predictive analytics for optimizing sewerage sludge treatment efficiency and resource recovery performance in Dhaka City. A quantitative case study design was employed using operational, laboratory, and monitoring records collected from sewerage sludge treatment facilities. The final dataset consisted of 336 valid observations after data quality screening and validation procedures. The analysis incorporated key operational variables, including wastewater inflow, sludge production, total solids, volatile solids, chemical oxygen demand, biochemical oxygen demand, hydraulic retention time, sludge retention time, temperature, pH, and energy utilization. Descriptive statistics, Pearson correlation analysis, multiple regression analysis, time-series evaluation, and machine learning techniques were applied using predictive analytics frameworks to assess treatment performance and recovery outcomes. The findings revealed that the average wastewater inflow was 14,520 m³/day, while average sludge production reached 52.8 tons/day. Methane generation averaged 285.6 m³/day, biogas recovery efficiency reached 71.8%, phosphorus recovery efficiency was 67.4%, nitrogen recovery efficiency was 62.9%, and sludge reduction averaged 54.7%. Correlation analysis demonstrated strong positive relationships between treatment efficiency and hydraulic retention time (r = 0.764), sludge retention time (r = 0.712), and volatile solids concentration (r = 0.648). Multiple regression analysis showed that the selected operational variables explained 78.9% of the variation in treatment efficiency (R² = 0.789), while resource recovery performance achieved an explanatory power of 81.4% (R² = 0.814). Machine learning evaluation indicated that the Random Forest model produced the highest predictive accuracy with an R² value of 0.93, RMSE of 3.21, and prediction accuracy of 94.1%, outperforming Artificial Neural Network, Support Vector Machine, and conventional regression models.  The study concluded that predictive analytics provided a robust quantitative framework for identifying critical operational determinants of treatment efficiency and resource recovery. Hydraulic retention time, sludge retention time, volatile solids concentration, and organic loading rate emerged as the most influential predictors of treatment outcomes. The findings demonstrated that data-driven analytical approaches can effectively support sewerage sludge treatment optimization and resource recovery assessment within urban wastewater management systems.