High-temperature technology survey and comparison among incineration, pyrolysis, and gasification systems for water resource recovery facilities.

Solids from wastewater treatment undergo processing to reduce mass, minimize pathogens, and condition the products for specific end uses. However, costs and contaminant concerns (e.g.

Pyrolysis and gasification at water resource recovery facilities: Status of the industry.

Wastewater treatment generates solids requiring subsequent processing. Costs and contaminant concerns (e.g.

Comparison of random forest and multiple linear regression to model the mass balance of biosolids from a complex biosolids management area.

The use of biosolids as a soil amendment provides an important alternative to disposal and can improve soil health; however, distribution for water resource recovery facilities (WRRFs) in the United States can be challenging due to decreasing cropland, increased precipitation, variable plant operations, and financial constraints. Although statistical modeling is commonly used in the water sector, machine learning is still an emerging tool and can provide insights to optimize operations. Random forest (RF), a machine learning model, and multiple linear regression (MLR) were used in this study to model the mass balance of biosolids from a complex biosolids management area.

Advanced machine learning application for odor and corrosion control at a water resource recovery facility.

The objective of this study was to develop a machine learning (ML) application to determine the optimal dosage of sodium hypochlorite (NaOCl) to curtail corrosion and odor by H S in the headworks of a water resource recovery facility (WRRF) without overly consuming volatile fatty acids (VFAs) that are essential for the enhanced biological phosphorus removal. Given the highly diverse datasets available, three subproblems were formulated, and three cascaded ML modules were developed accordingly. The final ML models, chosen based on performance, were able to predict various targeted variables.