ARABI, SARA

Arabi, Sara

Sara has 20 years of experience in environmental consulting, research, and process engineering for water/wastewater treatment. She has a PhD in Chemical Engineering from Western University in Canada and is currently a wastewater practice leader at Stantec. Her primary background and area of expertise is wastewater treatment plant modeling, design and optimization for wastewater treatment facilities across North America for municipal and industrial applications. She is been involved in upgrades/expansions, retrofit analyses, and capacity assessments for Biological Nutrient Removal (BNR) plants, membrane-based treatment processes, and nutrient recovery applications. She has published her work in several peer reviewed journals--specifically in the wastewater treatment and solid waste practice areas--and presented at conferences around the world.

Titles from this speaker

Description: Brushite Recovery from Autothermal Thermophilic Aerobic Digestion (ATAD) Sludge to...

Brushite Recovery from Autothermal Thermophilic Aerobic Digestion (ATAD) Sludge to Improve Dewatering Characteristics

Abstract

The dewatering challenges and large chemical costs associated with dewatering sludges from Autothermal thermophilic aerobic digestion (ATAD) has limited the use of this technology. A small Water Resource Recovery Facility (WRRF) in Colorado spends over $500,000 per year on dewatering ATAD sludge. A new approach is proposed for recovery of phosphorus (P) from ATAD sludge with brushite recovery which results in improvements in dewatering characteristics. Removal of Ortho-P and reduction in Extracellular Polymeric Substances (EPS) with acidification, coupled with Ortho-P removal improves dewatering of ATAD sludge. Lab testing, process analysis, and modeling was conducted for P recovery with CalPrexTM. The indicated low quantity of chemicals required for phosphorus recovery. Lab testing with ATAD sludge showed 60% less polymer requirement for dewatering and 98% soluble phosphorus removal. The economic analysis comparing the current practice with the proposed P recovery (with brushite) showed that this new approach is capable of reducing the WRRF Operation & Maintenance (O&M) cost.

A new approach is proposed for recovery of phosphorus from ATAD sludge with brushite recovery which results in improvements in dewatering characteristics. Removal of Ortho-P with acidification, coupled with CalPrexTM showed promising results in improving dewatering of ATAD sludge. Lab testing, process analysis, and modeling was conducted for which indicated low quantity of chemicals required for phosphorus recovery. Lab testing with ATAD sludge showed 60% less polymer requirement for dewatering and 98% soluble phosphorus removal. The economic analysis comparing the current practice with the proposed P recovery showed that this approach is capable of reducing the WRRF O&M cost.

SpeakerArabi, Sara

Presentation time

16:00:00

16:15:00

Session time

16:00:00

17:30:00

SessionTechnology Advancement in Sidestream Phosphorus Recovery

Session number113

TopicBiosolids and Residuals, Facility Operations and Maintenance, Nutrients

Author(s)

Sara Arabi

Author(s)S. Arabi1; T. Gulliver1; C. Bye2; M.E. Tabanpour3; J. Wippo4;

Author affiliation(s)HDR, Chicago, IL 1EnviroSim, Hamilton, ON, CA 2Centrisys/CNP, Kenosha, WI 3Thermal Process Systems, Crown Point, IN 4

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2021

DOI10.2175/193864718825158020

Volume / Issue

Content sourceWEFTEC

Word count14

Description: Comparison of Struvite and Brushite Recovery: Model Based Technical and Economic...

Comparison of Struvite and Brushite Recovery: Model Based Technical and Economic Evaluation and Sensitivity Analysis

Abstract

In this paper, process modeling was used to compare struvite and brushite based phosphorus (P) recovery alternatives with the available data which can be used for conceptual decision-making for P recovery. A model based technical and economical evaluation of struvite and brushite recovery (using BioWin™ Version 6.0) was conducted for a large Water Resource Recovery Facility (WRRF). The cost effectiveness ($/lb of Premoved) of the P recovery options was determined and sensitivity analysis was conducted to compare the options with and without the sale of the fertilizer product, for chemical cost and other key parameters. This comparative assessment focused on confirming and validating the design basis, technology providers’ estimated P recovery, and chemical needs. This paper is the first application of commercial simulator for side by side comparison of the two P recovery alternatives. Modeling results compared reasonably well with the actual WRRF data for struvite recovery. The model predicted chemical requirement for brushite recovery was similar to literature values for pilot studies. For the testing conditions modeled in this paper (20 MGD WRRF), brushite recovery appears to be slightly more cost effective in terms of $/lb Premoved. However, given the cost variations depending on the location and site specific conditions, the two alternatives appear equal in cost effectiveness.

SpeakerArabi, Sara

Presentation time

08:30:00

08:50:00

Session time

08:30:00

09:30:00

SessionSidestream Phosphorus Removal and Recovery Operations

Session number515

TopicFacility Operations and Maintenance, Municipal Wastewater Treatment Design, Nutrients, Research and Innovation

Author(s)

S. ARABIS. ARABIM. BenischC. ByeE.A. Evans

Author(s)S. ARABI1; S. ARABI1; M. Benisch2; C. Bye3; E.A. Evans4;

Author affiliation(s)HDR, IL1; HDR Engineering Inc, OR2; EnviroSim Associates Ltd., ON3; HDR, IA4

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2020

DOI10.2175/193864718825157501

Volume / Issue

Content sourceWEFTEC

Word count16

Description: Economics of Potable Reuse with Partial Reverse Osmosis Treatment Schemes for Inland...

Economics of Potable Reuse with Partial Reverse Osmosis Treatment Schemes for Inland Communities

Abstract

This paper presents a techno-economic analysis of Advanced Water Treatment (AWT) Systems for partial Reverse Osmosis (RO) treatment schemes for potable reuse applications for inland utilities. Treatment process design considerations for two scenarios of partial RO treatment schemes for potable reuse are explored and expected produced water quality was identified. Capital and operating costs for each treatment scenario are presented and compared with baseline advanced treatment alternatives using RO-based Advanced Treatment (RBAT) and Carbonbased Advanced Treatment (CBAT). This study shows a partial RO treatment train can be used cost-effectively to provide inorganics removal required for some inland communities. Treatment trains decisions can be dependent on social, environmental, technical, economic, and regulatory considerations. This study informs decision-making about potable reuse treatment trains by comparing the planning level cost estimates and technical performance of partial RO potable reuse treatment scenarios for inland communities.

This paper presents a techno-economic analysis of Advanced Water Treatment (AWT) Systems for partial Reverse Osmosis (RO) treatment schemes for potable reuse applications for inland utilities. This study shows a partial RO treatment train can be used cost-effectively to provide inorganics removal required for some inland communities. This study informs decision-making about potable reuse treatment trains by comparing the planning level cost estimates and technical performance of partial RO.

SpeakerArabi, Sara

Presentation time

08:30:00

09:00:00

Session time

08:30:00

10:00:00

SessionWater Reuse: Challenges and Opportunities

Session locationRoom S403b - Level 4

TopicPotable Reuse, Water Reuse and Reclamation

Author(s)

Arabi, Sara

Author(s)S. Arabi 1; Z. Hirani 2 ; R. McCandless 3; M. Adelman 4;

Author affiliation(s)Stantec 1; Stantec, Pasadena, CA 2 ; Stantec, Phoenix, AZ 3; Stantec, Pasadena, CA 4;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159164

Volume / Issue

Content sourceWEFTEC

Word count14

Description: Modeling Aeration and Phosphorus Removal and Lingering Precipitants in a Full-Scale...

Modeling Aeration and Phosphorus Removal and Lingering Precipitants in a Full-Scale Post-Aerobic Digester (PAD)

Abstract

Introduction: Implementation of PAD at Boulder WRRF has reduced sidestream nitrogen loading and improved overall nitrogen removal. Process modeling of PAD process is challenging given the high OUR combined with reductions in transfer efficiency, complex process control including air flow changes based on pH set point and time of the day schedule. In addition, the mechanism for nitrogen removal have not been fully understood. The aim of this modeling effort was to simulate the key process performance parameters for PAD using BioWin Controller. This study shows the importance of process control in operation and performance of PAD process as well as providing insight into key parameters and mechanism of reaction. Methodological Approach: A calibrated whole-plant model in BioWin (adopted from past work) was used for this modeling effort. Process Data from 1/1/2020 to 2/3/2020 was used, using daily data for influent, PAD and secondary digester. The average ammonia removal efficiency was 70%. Daily nitrite data showed very low concentrations of nitrite with negligible accumulation of nitrite. Average nitrate concentration was approximately 1 mg/L. For the operation of PAD, DO is not measured continuously. City staff have indicated that they were not able to detect measurable DO in PAD using handheld meters. The key operating condition for PAD is presented in Table 1. Three modeling approaches were considered in this study (1) DOSP-based model (2) Air flow-based model (3) Controller-based model. Model Setup and Calibration The model calibration focused on the performance of PAD for VSR, solids concentration, and nutrient removal. BioWin's Model Builder element was used to represent the PAD process. To mimic the increased solids destruction, aeration rate, and nutrient removal efficiency, a few modifications were required to default values including allowance for VSS components (i.e. endogenous decay products and influent undegradable particulate organics) not normally solubilized in the base model to undergo partial hydrolysis. Endogenous products decay rate increased from 0 to 0.1 d-1, temperature dependency for biomass decay and particulate substrate hydrolysis of 1.100 (from the default value of 1.029), aeration parameters adjustments: K1:2.56 and K2:0.05, Alpha factor: 0.1. The alpha factor was determined from an extensive meta-analysis of data in 10 studies for MLSS and alpha factor, as presented in Figure 1. The control logic for the BioWin Controller used a Proportional-Integral (PI) controller with aeration rate as an input and DO as the measured parameter. The DOSP and proportional gain was set at 0.03 mg/L and 100 scfm. Results and Discussion: Dynamic process modeling was also conducted using DOSP-based model and air flow-based model (using actual data). None of these strategies resulted in a nutrient profile that matched the actual data. This clearly shows the importance of simulation coupled with automation control to mimic actual data. Table 2 shows the average model predicted and measured PAD effluent parameters, using BioWin controller. Overall, the model predicted the key parameters well. The BioWin Controller air flow rate is very close to the actual value. The average soluble P predicted by the model is around 40 mg/L. No soluble P measurement was reported during the study period for PAD; however, soluble P measurements for early 2020 in centrate was between 20-50 mg/L. Ammonia removal efficiency predicted by the model is 66%, which is close to the actual value of 70%. Figure 1 shows the time variations of N species compared with actual data. Overall, the model predicts PAD ammonia, nitrate, and soluble P well. Table 3 summarizes the key model predicted rates related to nitrogen transformations by AOB, NOB, and OHOs using BioWin controller, compared with DO-based modeling. Figure 2 shows the concentration of key microorganisms in PAD based on modeling with the controller. The results of RNA analysis for three PAD samples are also presented in Figure 2. The relative distribution based on RNA analysis is consistent with BioWin predictions with the Controller, with AOBs being present in higher abundance than NOBs. This suggests NOB suppression. The rates presented in Table 3 and Figure 2 predict that AOB is the primary group responsible for ammonia removal and there is minimal NOB growth. This suggests that nitrite accumulation may happen in this process, although high nitrite measurements are not observed. This also suggests that nitritation and denitritation may be the primary route of nitrogen removal; however, the mechanism for NOB suppression is not clear and could be due to sensitivity of NOB to temperature (Lantz et al, 2021), or high FA concentration. Estimated FA concentration in this simulation is 5-13 mg/L and studies have shown NOB suppression at FA concentrations of 0.1- 5 mg/L (Liu et al., 2020). Mass Balance Tables 4 and 5 show the mass balance for N species in PAD. One route for nitrogen removal in PAD is ammonia stripping (about 4% of the influent TKN). Another route is the production of N2O. Some hydrolysis of particulate TKN is also occurring within PAD. Ammonia removal remains the main mechanism for removal of N in PAD. Nitrogen loss can also occur via assimilation which is not included in the mass balance analysis. Conclusions: This modeling effort shows the importance of control strategy coupled with adjustment of key kinetic and aeration parameters for PAD. Out of the three modeling approaches considered in this study (DOSP-based model, Air flow-based model, and Controller-based model), only the Controller-based approach was able to predict the nutrient profile and removal accurately.

This paper was presented at the WEF Residuals and Biosolids Conference, June 18-21, 2024.

SpeakerArabi, Sara

Presentation time

14:00:00

14:30:00

Session time

13:30:00

15:00:00

SessionNutrient Considerations and Digestion

Session number16

Session locationOklahoma City Convention Center, Oklahoma City, Oklahoma

TopicAerobic Digestion, Modeling

Author(s)

Arabi, Sara

Author(s)S. Arabi1, C. Sigmon2, C. Marks2, N. Brown1, S. Li1, M. Andalib1, C. Charnas1

Author affiliation(s)Stantec 1; City of Boulder 2; City of Boulder 2; Stantec 1; Stantec 1; Stantec 1; Stantec 1;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Jun 2024

DOI10.2175/193864718825159425

Volume / Issue

Content sourceResiduals and Biosolids Conference

Word count15

Description: Phosphorus Sequestration and Recovery with Brushite: Validating Chemical Equilibrium...

Phosphorus Sequestration and Recovery with Brushite: Validating Chemical Equilibrium and Process Models with Case Studies

Abstract

Abstract
The increasing prevalence of tighter effluent phosphorus limits, enhanced biological phosphorus removal, and anaerobic digestion pose a unique challenge of phosphorus release and nuisance struvite formation in solids handling processes. Phosphorus sequestration through the addition of calcium represents an alternative approach to phosphorus management in solids processes. Phosphorus equilibrium chemistry is complex, depending on the pH and the concentration of other constituents, and a variety of calcium phosphate precipitates can be formed. To provide Water Resource Recovery Facilities (WRRFs) with an improved understanding of the main calcium phosphate precipitates for recovery, this study applied a chemical equilibrium model using Visual MINTEQ to predict the main precipitates from anaerobic digester (AD) effluent. Designed systematic parameters were used as inputs into the model for existing operational conditions for three WRRFs and for three samples with different molar ratios of Ca/P. Visual MINTEQ was compared with BioWin 6.2 and with X-ray powder diffraction (XRD) analysis for phase identification and quantification. XRD analysis corresponded well with BioWin model outputs in terms of precipitates formation. Our results indicate that BioWin can be reliably used to determine the precipitates in AD samples for calcium phosphate.

Novelty and Relevance
This study applies a chemical equilibrium model for brushite precipitation in AD effluent and compares the results with BioWin and XRD analysis. Based on literature review, this is the first paper that investigates the applicability of Visual MINTEQ for calcium phosphate precipitation in wastewater and a side-by-side comparison of Visual MINTEQ with a wastewater simulator such as BioWin. To date, technology provider inputs have been used for estimating the calcium dose to sidestreams for brushite recovery. This study attempts at verifying results from BioWin and Visual MINTEQ as additional tools to estimate Ca dosing and brushite recovery using full scale data. Introduction Visual MINTEQ, a chemical equilibrium model, has been used to study struvite precipitation using bench scale testing and/or synthetic wastewater (Buchanan et al., 1994; Ali et al., 2003; Wu and Zhou, 2012; Jia et al. 2017; Celen et al. 2007; Miles and Ellis, 2001). Researchers point out that the order of precipitation is determined by kinetic factors and the importance of precipitation kinetics was stressed in many studies (Koutsoukos et al., 2001; Zawacki et al., 1989; Romero-GÃ¼iza et al. 2015; da Rocha et al., 2018). Biological wastewater simulators such as BioWin formulates precipitation processes with kinetics equations. In this study, an attempt is made to compare the predictions of BioWin and Visual MINTEQ with XRD analysis using full-scale data.

Methodology and Materials
Samples and XRD Analysis Four AD samples for City of Longmont and two Post Aerobic Digester (PAD) samples from City of Boulder WRRF and Metro Water Recovery NTP were obtained for analytical measurements and XRD analysis. The chemical characteristics of the sludge samples are shown in Table 1. Sample 2 was used for bench scale testing at Ca/P molar ratios of 1.5, 2.5, and 3.5 with addition of 20% CaCl2.

Visual MINTEQ Set up
Visual MINTEQ v3.1 (KTH Vetenskap Och Konst, Stockholm, Sweden) was used to predict the precipitates formed in samples using a constant pH approach at 35°C. Details of model set-up and revisions are described in full paper. Precipitates employed in the model include struvite, bobierrite, newberyite, monetite, brushite, monetite, MgCO3, calcite, vivianite, strengite, and ferrous sulfide.

Experimental Set up
Bench scale experiments were performed using a sample of AD (City of Longmont WRRF) at room temperature. CaCl2 was added, samples were stirred at 200r/min using a magnetic stirrer, and pH was measured after 45 minutes. Photos 1 and 2 show the samples for Conditions 2 and 5 after mixing has stopped. There was no clear visual sign for precipitants formed. Final pH for sample conditions 3, 4, and 5 were 7.2, 7.05, and 6.9, respectively. Samples were sent for XRD analysis and Ortho-P measurements.

BioWin Modeling
A whole plant BioWin model was set up in BioWin 6.2 for Condition 1 for City of Boulder WRRF (Figure 1) and Conditions 2-5 for City of Longmont WRRF (Figure 2 and 3).

Results and Discussion
Testing the Validity of Model Results In order to validate the results and confirm the presence of minerals predicted by the two models, XRD analysis was used. The quantitative crystalline phase analysis for conditions 1 to 6 is shown in Table 2. The observations made based on XRD analysis are presented in Table 3. Figure 4 shows the XRD pattern for condition 1. XRD results for conditions 2 to 5 are presented in Figures 5 to 8, respectively. Figure 9 shows the XRD pattern for condition 6.

Visual MINTEQ Results
Table 4 shows the model output for major precipitates with positive saturation index which indicates supersaturation as well as the solids in finite state (in equilibrium). The observations made based on Visual MINTEQ are presented in Table 5. The Ortho-P reduction, as predicted by Visual MINTEQ, is compared with actual data in Table 6 for Conditions 3 to 5. Visual MINTEQ underpredicted the Ortho-P removal. Visual MINTEQ estimated that the Ortho-P reduction was linked to precipitation of calcium phosphate. The % of total phosphate in dissolved form decreased from 56% for condition 2 to 36% with increasing Ca:P ratios from 1.5 to 3.5. While there are no studies available for calcium phosphate precipitation in AD, studies on brushite precipitation in CalPrexTM reactor shows high removal efficiency of 80-95% at Ca/P molar ratios of 1.2-1.4 based on pilot studies (Li et al., 2018, Cichy et al, 2019). Based on literature values, the Ortho-P reduction values predicted by Visual MINTEQ seem to be low.

BioWin Model Results
Condition 1 The BioWin model (Figure 1) was calibrated for influent fractionation, effluent quality, and solids production. A model builder unit in BioWin with modified default kinetics was used to simulate the PAD process. The model predicts ammonia, pH, and alkalinity well, overpredicts ortho-P concentration and underpredicts metals (Table 7). Ca concentration in PAD can be further adjusted by adjusting influent Ca concentration based on actual data. Figure 10 shows the precipitates and concentrations in AD and PAD. Brushite is formed in PAD, along with small concentrations of ferrous sulfide and vivianite. This is consistent with the findings of XRD analysis, indicating presence of vivianite and amorphous material which most likely includes Amorphous Calcium Phosphate. Conditions 2 to 5 Dynamic modeling for the existing conditions at City of Longmont WRRF was conducted (Figure 2). The model was calibrated for influent fractionation, effluent quality, centrate quality, digesters, and solids production. Influent Ca and Mg was measured in three samples. The model predicts digester Ortho-P, Ca, and Mg very well (Figure 11). For Conditions 3 to 5, CaCl2 was added for Ca:P molar ratios of 1.5, 2.5, and 3.5, which corresponds with Ca dosing of 180, 300, and 420 Ib/d, respectively. The results of BioWin modeling for Conditions 2 to 5 are presented in Table 8.

Comparison of Models
Comparison of two models with XRD analysis was conducted for conditions 2 to 5 (Table 9). Mineral species predicted by BioWin and Visual MINTEQ are generally in agreement. For condition 2, both models predicted both struvite and calcium phosphate which is consistent with XRD analysis. For Conditions 3 and 4, both models show increased quantities for calcium phosphate species. While BioWin predicts some small quantities of struvite formed in Conditions 3 and 4, Visual MINTEQ does not predict any struvite formation. XRD predicts some amorphous content which is likely calcium phosphate and no struvite. For Condition 5, both models predict increased quantities of calcium phosphate species with no struvite formation, which is consistent with XRD analysis.

Summary and Conclusions
The results of this study indicate that while a chemical equilibrium can be helpful in predicting the precipitation of brushite and other phosphate-bearing minerals, BioWin was able to predict precipitates formation and Ortho-P reduction reasonably well. Verification of models with XRD analysis was not possible for the PAD samples, since XRD analysis showed that the majority of the sample was in amorphous form. Verification results with XRD demonstrated that BioWin can be reliably used to determine the precipitates in AD samples for calcium phosphate. The main advantage of BioWin is considerations for kinetics for precipitation and Ca and Mg content of biomass.

This paper was presented at the WEF Residuals and Biosolids Conference, June 18-21, 2024.

SpeakerArabi, Sara

Presentation time

15:45:00

16:15:00

Session time

15:45:00

16:45:00

SessionNutrient Management Innovations

Session number20

Session locationOklahoma City Convention Center, Oklahoma City, Oklahoma

TopicResource Recovery

Author(s)

Arabi, Sara

Author(s)S. Arabi1, A. Umble1, S. Trujillo1, C. Sigmon2, C. Marks2, J. Gage3, R. Luna3, T. Worley-Morse4

Author affiliation(s)Stantec 1; Stantec 1; Stantec Inc. 1; City of Boulder 2; City of Boulder 2; City of Longmont 3; City of Longmont 3; Metro Wastewater Reclamation District 4;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Jun 2024

DOI10.2175/193864718825159426

Volume / Issue

Content sourceResiduals and Biosolids Conference

Word count16

Phosphorus Sequestration and Recovery with Calcium: Validating Chemical Equilibrium and Process Models with Case Studies

Abstract

Novelty and Relevance: This study applies a chemical equilibrium model for brushite precipitation in AD effluent and compares the results with BioWin and XRD analysis. Based on literature review, this is the first paper that investigates the applicability of Visual MINTEQ (V MINTEQ) for calcium phosphate precipitation in wastewater and a side-by-side comparison of V MINTEQ with a wastewater simulator such as BioWin. To date, technology provider inputs have been used for estimating the calcium dose to sidestreams for brushite recovery. This study attempts at verifying results from BioWin and V MINTEQ as additional tools to estimate Ca dosing and brushite recovery using full scale data. Methodology and Materials: Samples and XRD Analysis: Four AD samples for City of Longmont and two Post Aerobic Digester (PAD) samples from City of Boulder WRRF and Metro Water Recovery NTP were obtained for analytical measurements and XRD analysis. The chemical characteristics of the sludge samples are shown in Table 1. Sample 2 was used for bench scale testing at Ca/P molar ratios of 1.5, 2.5, and 3.5 with addition of 20% CaCl2. V MINTEQ Set up : V MINTEQ v3.1 (KTH Vetenskap Och Konst, Stockholm, Sweden) was used to predict the precipitates formed in samples using a constant pH approach at 35 degrees C. Details of model set-up and revisions are described in full paper. Experimental Set up: Bench scale experiments were performed using a sample of AD (City of Longmont WRRF) at room temperature. CaCl2 was added, samples were stirred at 200r/min using a magnetic stirrer, and pH was measured after 45 minutes. Photos 1 and 2 show the samples for Conditions 2 and 5 after mixing has stopped. There was no clear visual sign for precipitants formed. Final pH for sample conditions 3, 4, and 5 were 7.2, 7.05, and 6.9, respectively. Samples were sent for XRD analysis and Ortho-P measurements. BioWin Modeling : A whole plant BioWin model was set up in BioWin 6.2 for Condition 1 for City of Boulder WRRF (Figure 1) and Conditions 2-5 for City of Longmont WRRF (Figure 2 and 3). Results and Discussion: Testing the Validity of Model Results: The quantitative crystalline phase analysis for conditions 1 to 6 is shown in Table 2. The observations made based on XRD analysis are presented in Table 3. Figure 4 shows the XRD pattern for condition 1. XRD results for conditions 2 to 5 are presented in Figures 5 to 8, respectively. Figure 9 shows the XRD pattern for condition 6. V MINTEQ Results: Table 4 shows the model output for major precipitates with positive saturation index which indicates supersaturation as well as the solids in finite state (in equilibrium). The observations made based on V MINTEQ are presented in Table 5. The Ortho-P reduction, as predicted by V MINTEQ, is compared with actual data in Table 6 for Conditions 3 to 5. V MINTEQ underpredicted the Ortho-P removal. V MINTEQ estimated that the Ortho-P reduction was linked to precipitation of calcium phosphate. The % of total phosphate in dissolved form decreased from 56% for condition 2 to 36% with increasing Ca:P ratios from 1.5 to 3.5. BioWin Model Results: Condition 1 : The BioWin model (Figure 1) was calibrated for influent fractionation, effluent quality, and solids production. The model predicts ammonia, pH, and alkalinity well, overpredicts ortho-P concentration and underpredicts metals (Table 7). Figure 10 shows the precipitates and concentrations in AD and PAD. Brushite is formed in PAD, along with small concentrations of ferrous sulfide and vivianite. This is consistent with the findings of XRD analysis, indicating presence of vivianite and amorphous material which most likely includes Amorphous Calcium Phosphate. Conditions 2 to 5 : Dynamic modeling for the existing conditions at City of Longmont WRRF was conducted (Figure 2). The model predicts digester Ortho-P, Ca, and Mg very well (Figure 11). The results of BioWin modeling for Conditions 2 to 5 are presented in Table 8. For Condition 2, BioWin predicted all the key constituents very well, including Ca and Mg in AD. The Ortho-P reduction for Condition 3 at Ca/P ratio of 1.5 is about 11% higher than actual value. The Ortho-P reduction for Conditions 4 and 5 at Ca/P molar ratios of 2.5 and 3.5, respectively are overpredicted by BioWin. At such high metal/P ratios, the Ortho-P reductions predicted by BioWin do not seem unreasonable. The removal rates based on experimental data seem to be relatively low, which may be due to the operational conditions of the bench scale testing. Comparison of Models: Comparison of two models with XRD analysis was conducted for conditions 2 to 5 (Table 9). Mineral species predicted by BioWin and V MINTEQ are generally in agreement. While BioWin predicts some small quantities of struvite formed in Conditions 3 and 4, V MINTEQ does not predict any struvite formation. XRD predicts some amorphous content which is likely calcium phosphate and no struvite. For Condition 5, both models predict increased quantities of calcium phosphate species with no struvite formation, which is consistent with XRD analysis. Summary and Conclusions: The results indicate that while a chemical equilibrium can be helpful in predicting the precipitation of brushite and other phosphate-bearing minerals, BioWin was able to predict precipitates formation and Ortho-P reduction reasonably well. Verification results with XRD demonstrated that BioWin can be reliably used to determine Ca-P precipitates in AD.

This study applied a chemical equilibrium model using Visual MINTEQ to predict the main precipitates from Anaerobic Digester effluent to provide WRRFs with an improved understanding of the main calcium phosphate precipitates for recovery. The results of this project indicate that while a chemical equilibrium model can be helpful in predicting the precipitation of brushite and other phosphate-bearing minerals in AD effluent, BioWin was able to better predict OP concentration in solids streams.

SpeakerArabi, Sara

Presentation time

14:00:00

14:30:00

Session time

13:30:00

15:00:00

SessionInnovations in Phosphorus Management: From Models to Solutions

Session number603

Session locationRoom 340

TopicFacility Operations and Maintenance, Intermediate Level, Municipal Wastewater Treatment Design, Nutrients, Research and Innovation

Author(s)

Arabi, Sara, Umble, Art, Trujillo, Shelley, Marks, Christopher, Sigmon, Cole, Gage, John, Luna, Roberto, Worley-Morse, Thomas

Author(s)S. Arabi1, A.K. Umble2, S. Trujillo3, C.A. Marks4, C. Sigmon4, J.P. Gage5, R. Luna5, T. Worley-Morse6

Author affiliation(s)1Stantec, CO, 2Stantec Consulting Services, Inc., CO, 3STANTEC CONSULTING, INC., CO, 4City of Boulder, CO, 5City of Longmont, CO, 6Metro Wastewater Reclamation District, CO

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2024

DOI10.2175/193864718825159487

Volume / Issue

Content sourceWEFTEC

Word count16

Description: Process Modeling and Evaluation for Anaerobic Treatment of Vegetable Oil Refining...

Process Modeling and Evaluation for Anaerobic Treatment of Vegetable Oil Refining Wastewater with High Sulfate Concentrations

Abstract

The Vegetable Oil Refinery (VOR) wastewater has a high concentration of Chemical Oxygen Demand (COD) and contains large amounts of oil, grease, sulfates, and phosphates. VOR wastewater is usually treated by a combination of pretreatment for oil and grease, biological treatment, and advanced treatment. High sulfate concentration is a result of sulfuric acid addition during the acidulation process, which poses a challenge for treatment of this type of wastewater. A comparative modeling assessment was conducted for treatment alternatives for a VOR wastewater containing high COD and sulfate concentrations. Two treatment alternatives were evaluated: Covered Anaerobic Lagoon (CAL) followed by activated sludge and Sulfate reduction, Autotrophic Denitrification and Nitrification Integrated (SANI®) process. Biogas production, sulfate, nitrogen and COD removal, aeration and chemical demand, and other key parameters were compared. Process modeling and analysis indicated excessive H2S concentration in biogas. Dilution of acidulation wastewater, and pre-treatment for sulfate removal prior to biological system were investigated.

SpeakerArabi, Sara

Presentation time

12:30:00

13:00:00

Session time

11:30:00

12:30:00

SessionApplication of Innovative Process Technologies in the Food and Beverage Industry

Session number9C

TopicIndustrial Issues and Treatment Technologies

Author(s)

S. ARABID. DevittB. MULINIX

Author(s)S. ARABI1; D. Devitt2; B. MULINIX2;

Author affiliation(s)HDR, IL1; HDR, NE2

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2020

DOI10.2175/193864718825157371

Volume / Issue

Content sourceWEFTEC

Word count17

Significance of Process Control Strategy in Dynamic Modeling and Optimization of Full-Scale Post Aerobic Digestion (PAD)

Abstract

Introduction: Implementation of PAD at Boulder WRRF has reduced sidestream nitrogen loading and improved overall nitrogen removal. Process modeling of PAD process is challenging given the high OUR combined with reductions in transfer efficiency, complex process control including air flow changes based on pH set point and time of the day schedule. In addition, the mechanism for nitrogen removal have not been fully understood. The aim of this modeling effort was to simulate the key process performance parameters for PAD using BioWin Controller. This study shows the importance of process control in operation and performance of PAD process as well as providing insight into key parameters and mechanism of reaction. Methodological Approach: A calibrated whole-plant model in BioWin (adopted from past work) was used for this modeling effort. Process Data from 1/1/2020 to 2/3/2020 was used, using daily data for influent, PAD and secondary digester. The average ammonia removal efficiency was 70%. Daily nitrite data showed very low concentrations of nitrite with negligible accumulation of nitrite. Average nitrate concentration was approximately 1 mg/L. For the operation of PAD, DO is not measured continuously. City staff have indicated that they were not able to detect measurable DO in PAD using handheld meters. The key operating condition for PAD is presented in Table 1. Three modeling approaches were considered in this study (1) DOSP-based model (2) Air flow-based model (3) Controller-based model. Model Setup and Calibration: The model calibration focused on the performance of PAD for VSR, solids concentration, and nutrient removal. BioWin's Model Builder element was used to represent the PAD process. To mimic the increased solids destruction, aeration rate, and nutrient removal efficiency, a few modifications were required to default values including allowance for VSS components (i.e. endogenous decay products and influent undegradable particulate organics) not normally solubilized in the base model to undergo partial hydrolysis. Endogenous products decay rate increased from 0 to 0.1 d-1, temperature dependency for biomass decay and particulate substrate hydrolysis of 1.100 (from the default value of 1.029), aeration parameters adjustments: K1:2.56 and K2:0.05, Alpha factor: 0.1. The alpha factor was determined from an extensive meta-analysis of data in 10 studies for MLSS and alpha factor, as presented in Figure 1. The control logic for the BioWin Controller used a Proportional-Integral (PI) controller with aeration rate as an input and DO as the measured parameter. The DOSP and proportional gain was set at 0.03 mg/L and 100 scfm. Results and Discussion: Dynamic process modeling was also conducted using DOSP-based model and air flow-based model (using actual data). None of these strategies resulted in a nutrient profile that matched the actual data. This clearly shows the importance of simulation coupled with automation control to mimic actual data. Table 2 shows the average model predicted and measured PAD effluent parameters, using BioWin controller. Overall, the model predicted the key parameters well. The Controller air flow rate is very close to the actual value. The average soluble P predicted by the model is around 40 mg/L. No soluble P measurement was reported during the study period for PAD; however, soluble P measurements for early 2020 in centrate was between 20-50 mg/L. Ammonia removal efficiency predicted by the model is 66%, which is close to the actual value of 70%. Figure 1 shows the time variations of N species compared with actual data. Overall, the model predicts PAD ammonia, nitrate, and soluble P well. Table 3 summarizes the key model predicted rates related to nitrogen transformations by AOB, NOB, and OHOs using BioWin controller, compared with DO-based modeling. Figure 2 shows the concentration of key microorganisms in PAD based on modeling with the controller. The results of RNA analysis for three PAD samples are also presented in Figure 2. The relative distribution based on RNA analysis is consistent with BioWin predictions with the Controller(NOB suppression). The rates presented in Table 3 and Figure 2 predict that AOB is the primary group responsible for ammonia removal and there is minimal NOB growth. This suggests that nitrite accumulation may happen in this process, although high nitrite measurements are not observed. This also suggests that nitritation and denitritation may be the primary route of nitrogen removal; however, the mechanism for NOB suppression is not clear and could be due to sensitivity of NOB to temperature (Lantz et al, 2021), or high FA concentration. FA concentration in this simulation is 5-13 mg/L and studies have shown NOB suppression at FA concentrations of 0.1- 5 mg/L (Liu et al., 2020). Mass Balance: Tables 4 and 5 show the mass balance for N species in PAD. One route for nitrogen removal in PAD is ammonia stripping (4% of the influent TKN). Another route is the production of N2O. Some hydrolysis of particulate TKN is also occurring within PAD. Ammonia removal remains the main mechanism for removal of N in PAD. Conclusions: This modeling effort shows the importance of control strategy coupled with adjustment of key kinetic and aeration parameters for PAD. Out of the three modeling approaches considered in this study, the controller-based approach was able to predict the performance accurately.

Process modeling of Post Aerobic Digestion (PAD) is challenging given the high Oxygen Uptake Rate combined with reductions in transfer efficiency and complex process control. In addition, the mechanisms for nitrogen removal have not been fully understood. This modeling effort aimed to simulate PAD's key process performance parameters and provide insight into critical parameters and mechanisms of nitrogen removal.

SpeakerArabi, Sara

Presentation time

14:00:00

14:20:00

Session time

13:30:00

15:00:00

SessionMechanistic Modeling Developments for Newer Processes

Session number410

Session locationRoom 244

TopicAdvanced Level, Biosolids and Residuals, Industrial Issues and Treatment Technologies, Municipal Wastewater Treatment Design, Research and Innovation

Author(s)