Increased organic waste generation in the residential, industrial, and agricultural sectors results in massive amounts of organic waste that are landfilled and incinerated, thereby contributing to environmental pollution. Opportunities exist to recover valuable resources from organic waste to potentially leverage economic and environmental benefits. One common strategy for managing organic waste is anaerobic digestion (AD). The liquid effluent from AD, called digestate, is a concentrated source of phosphorus and nitrogen. These nutrients can be recovered via struvite precipitation. The overall study goal was to quantify the effectiveness of five statistical and machine learning (ML) models in predicting the percentage of nutrients recovered from digestate derived from different organic waste streams via struvite precipitation. Nine combinations of parameters were developed to quantify the effects of multiple parameters on nutrient recovery efficiency. These five models were multiple linear regression (MLR), polynomial regression (PLR), K-nearest neighbors (KNN), random forest (RF), and eXtreme Gradient Boosting (XGBoost). RF and XGBoost had the best performance in predicting nutrient recovery efficiency among the five developed models. Both models had a regression coefficient (R2) for phosphate and ammonium recoveries above 0.90 and a root mean square error of 2–7.67. The comparison of different combinations indicated that predicting PO43− and NH4+ recoveries (%) was most influenced by the following input variables: pH, Mg:P and N:P molar ratios, mixing speed, reaction temperature, hydraulic retention time, and concentrations of sodium, potassium, calcium, magnesium, ammonium, and phosphate. We concluded that ML models can provide useful nutrient recovery predictions via struvite precipitation. As a result, the operation of resource recovery systems can be optimized using ML models.
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