In wastewater facilities, struvite (MgNH4PO4·6H2O) precipitation and subsequent accumulation within sludge processing can be an expensive nuisance or a pathway to orthophosphate reclamation and beneficial reuse. ...In wastewater facilities, struvite (MgNH4PO4·6H2O) precipitation and subsequent accumulation within sludge processing can be an expensive nuisance or a pathway to orthophosphate reclamation and beneficial reuse. Predictive solubility models developed in the past have been computationally intensive, highly conservative, and have employed uncertain equilibrium constants for the evaluation of solution saturation. The StrPI (Struvite Precipitation Index) developed in this study is a new, computationally light framework for predicting struvite precipitation based on saturation pH. The model permits process-specific calibration (i.e. StrPI plus a correction pH) to deal with the highly variable characteristics of wastewater streams and to eliminate the pH-independent overprediction inherent in existing solubility models. Verification of this model was performed across a range of waste compositions, ionic strengths, and root-mean-square velocity gradients using data from both synthetic laboratory experiments and field tests. The StrPI framework was found to be an effective and uncomplicated predictor of struvite precipitation in both environments.展开更多
文摘In wastewater facilities, struvite (MgNH4PO4·6H2O) precipitation and subsequent accumulation within sludge processing can be an expensive nuisance or a pathway to orthophosphate reclamation and beneficial reuse. Predictive solubility models developed in the past have been computationally intensive, highly conservative, and have employed uncertain equilibrium constants for the evaluation of solution saturation. The StrPI (Struvite Precipitation Index) developed in this study is a new, computationally light framework for predicting struvite precipitation based on saturation pH. The model permits process-specific calibration (i.e. StrPI plus a correction pH) to deal with the highly variable characteristics of wastewater streams and to eliminate the pH-independent overprediction inherent in existing solubility models. Verification of this model was performed across a range of waste compositions, ionic strengths, and root-mean-square velocity gradients using data from both synthetic laboratory experiments and field tests. The StrPI framework was found to be an effective and uncomplicated predictor of struvite precipitation in both environments.
