Modeling of two-stage anaerobic onsite wastewater sanitation system to predict effluent soluble chemical oxygen demand through machine learning.

The present research aims to predict effluent soluble chemical oxygen demand (SCOD) in anaerobic digestion (AD) process using machine-learning based approach. Anaerobic digestion is a highly sensitive process and depends upon several environmental and operational factors, such as temperature, flow, and load. Therefore, predicting output characteristics using modeling is important not only for process monitoring and control, but also to reduce the operating cost of the treatment plant.

Reduced sludge growth at high bulk liquor dissolved oxygen induced by increased secondary cell maintenance.

Sludge reduction by physico-chemical methods results in the buildup of chemicals, which may require further treatment. Owing these reasons various biologically sustainable methods of sludge reduction including the application of high oxygenation have been successfully tested. Experiments on actual sewage in two lab-scale sequencing batch reactors (SBRs) were conducted under normal (1.

Modified septic tank-anaerobic filter unit as a two-stage onsite domestic wastewater treatment system.

This study demonstrates the performance evaluation of a uniquely designed two-stage system for onsite treatment of domestic wastewater. The system consisted of two upflow anaerobic bioreactors, a modified septic tank followed by an upflow anaerobic filter, accommodated within a single cylindrical unit. The system was started up without inoculation at 24 h hydraulic retention time (HRT).