Comparing Modeling Tools Visual MINTEQ and OLI Studio to Evaluate Scaling Tendency of Aerated Anaerobically Digested Solids: A Pilot Study

Common phosphate minerals in wastewater treatment plants such as struvite (MAP, MgNH4PO4*6H2O), calcium phosphates such as amorphous calcium phosphate (ACP, Ca3(PO4)2), and iron salt precipitates such as vivianite (Fe3(PO4)2*8H2O) have been recognized as a maintenance and operational nuisance for their tendency to scale and deposit within anaerobic digesters, in pipe bends, and on dewatering equipment downstream of anaerobic digesters. Precipitation of these minerals occur if the concentrations of the ionic constituents exceed the solubility product (Ksp) of the solid which is measured as scaling tendency (ST) or scaling index (SI). The solubility of these precipitates is dependent on factors such as pH, temperature, other competing ions, and nucleation sites. Chemical thermodynamic modeling could be used in evaluating the parameters that contribute to scaling and, thereby, safeguard process equipment from scaling-induced damage, improve treatment efficiency, and regulate phosphorus sequestration to produce nutrient rich biosolids. In this study two commercially available models were evaluated: (i) Visual MINTEQ Version 4.0 and (ii) OLI Studio. The model inputs were gathered at a pilot-scale experimental setup at Hampton Roads Sanitation District's (HRSD) Atlantic Treatment Plant (ATP), Virginia Beach, VA. The pilot evaluates the impact of aeration, mixing, and chemical addition of thermally hydrolyzed pretreated, anaerobically digested solids on scaling tendency. Critical parameters such as pH, temperature, alkalinity, orthophosphate (OP), ammonia (NH3), and major cations and anions concentrations were measured as model inputs. The solids formed during these evaluations were characterized by X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM)/Energy Dispersive X-Ray Spectroscopy (EDS), and Raman Spectroscopy analysis to validate prediction by the two models. The pilot set up at ATP consists of four tanks (Figure 1). Each tank is 11 feet tall and can hold up to about 63 gallons of digestate. The tanks are operated as daily batch fed continuously stirred tank reactors maintained by pump recirculation at a 3-day solids retention time (SRT). The tanks are aerated with fine bubble diffuser membranes which can be operated at constant or intermittent air flow rates, via dissolved oxygen (DO) or pH set points. Chemical injection lines will be added to measure the effects of calcium hydroxide [Ca(OH)2] and magnesium hydroxide [Mg(OH)2] at varying dosages. The modelling process was based on the pilot influent solution compositions depicted in Table 1 of anaerobic digestate. In both modeling programs, anaerobically digested solids at a temperature of 30 degrees C, a pH of 7.4, and initial parameters from Table 1 were analyzed to determine the pilot influent potential precipitate formation. Figure 2 represents the fractions of solids predicted to form at equilibrium in Visual MINTEQ and Figure 3 displays similar results produced from OLI, both in mg/L. The two model predictions are closely related, listing siderite, struvite, and calcite as the top three controlling precipitates, respectively. This is a prediction of what is forming currently in the digesters, prior to manipulation in the pilot from aeration or chemical addition. A pH sweep from 7 to 8.5 as well as a temperature sweep from 20 degrees C to 40 degrees C were also analyzed in both modeling programs to understand the effects of aeration within the pilot on pH, biological activity, and how the solubility of scaling precipitates were affected. These results, as well as future work that includes modeling the effects of chemical addition in the pilot, will be presented. Precipitate Characterization was conducted in a contract laboratory for XRD, SEM/EDS, and Raman Spectroscopy to determine precipitate identification. The major peaks in XRD patterns produced likely matched with struvite. Further testing will be completed and presented to validate the solids predicted to form by the models. The presentation will showcase the outcomes derived from each model, offering insights into their respective abilities to predict the potential precipitation of controlling solids. The precipitate characterization analysis will also be compared to determine the validity of results and to further optimize both modeling software.