Electronic noses have been widely used in the food industry to monitor process performance and quality control, but use in wastewater and biosolids treatment has not been fully explored. Therefore, we examined the feasibility of an electronic nose to discriminate between treatment conditions of alkaline stabilized biosolids and compared its performance with quantitative analysis of key odorants. Seven lime treatments (0-30% w/w) were prepared and the resultant off-gas was monitored by GC-MS and by an electronic nose equipped with ten metal oxide sensors. A pattern recognition model was created using linear discriminant analysis (LDA) and principal component analysis (PCA) of the electronic nose data. In general, LDA performed better than PCA. LDA showed clear discrimination when single tests were evaluated, but when the full data set was included, discrimination between treatments was reduced. Frequency of accurate recognition was tested by three algorithms with Euclidan and Mahalanobis performing at 81% accuracy and discriminant function analysis at 70%. Concentrations of target compounds by GC-MS were in agreement with those reported in literature and helped to elucidate the behavior of the pattern recognition via comparison of individual sensor responses to different biosolids treatment conditions. Results indicated that the electronic nose can discriminate between lime percentages, thus providing the opportunity to create classes of under-dosed and over-dosed relative to regulatory requirements. Full scale application will require careful evaluation to maintain accuracy under variable process and environmental conditions.

The combination of enhanced biological phosphorus removal (EBPR) with sidestream struvite precipitation is a synergistic treatment approach for P removal and recovery. However, periodic disruption events in crystallization reactors can cause fine struvite particle washout followed by P solubilization, leading to decreases in EBPR effluent quality, yet modeling tools do not exist to quantify plantwide impacts of struvite loss. To understand the impacts of struvite loss and dissolution on EBPR, this work first quantifies the dissolution rate of field-harvested struvite using the surface area-dependent shrinking object model and presents a novel particle population balance dissolution modeling tool for integration with a plantwide process simulator (SUMO). Analysis of time series P concentration data from dissolution experiments yields a rate constant of 1.14 mm min–1. Simulations of intra-plant phosphorus dynamics indicate that when struvite reactors operate with a high capture efficiency, the majority of influent orthophosphate can be captured through sidestream precipitation. In the event of struvite loss, large particles would not fully dissolve and are removed through sedimentation during primary clarification without significant disruption of EBPR. In contrast, the loss of 200 μm-sized struvite particles can lead to a difference in effluent phosphate of up to 0.8 mg P/L. Our results suggest that surface area-dependent models are essential to quantify the impacts of struvite loss and that reactor design should center on particle retention, rather than conversion of soluble P to struvite.

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