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System to provide Intelligence to Operations, Technical Support Services, Maintenance and Asset Management and Optimize Small, Medium and Large Plants – Experiences from Development and Implementation M. Brooks1, D. Sen2, B. Angelotti1, A. Lodhi3, J. Rawlings4, L. Gold5 Contributors: S. Nguyen1, E. Schlosser1, J. Roessler1, B. Canham1 1
Upper Occoquan Service Authority, 14631 Compton Road, Centerville, VA 20121.
[email protected] Aquaregen, 1290 Bryant Avenue, Mountain View, CA 94040; MCET, La Plata, MD
[email protected] 3 Virginia Tech Research Division, 9408 Prince William Street, Manassas, VA 20110 4 City of Westminster, 56. W. Main Street, MD 21157 5 Maryland Center for Environmental Training (MCET), College of Southern Maryland, La Plata, MD 20646
2
ABSTRACT Based on needs identified by Utilities and regulators to improve efficiencies, asset utilization and reduce life cycle cost, an intelligent system that incorporates process, maintenance and asset management has been developed and implemented in phases at several BNR and ENR plants. The needs required features that extend beyond process design models, operations models and CMMS and SCADA. The system incorporates an intelligent asset hierarchy where the failure or aged condition of an individual asset such as a pump, blower or diffusers in a tank is incorporated into the daily available capacity for use with optimization; it incorporates an accurate replication of the plants pumping, piping and valving to replicate hydraulic bottlenecks that arise when some tanks or clarifiers are taken out of service; it incorporates physical, chemical and biochemical equations for the processes that run in tanks and systems. The equations for processes conform to IWA ASM2d model; the risks and asset management as specified by International Infrastructure Management (IIMM). Originally, the system was funded to optimize ENR plants, driven by a need identified by regulators to use the infrastructure assets more efficiently in winter and during wet weather. It has since been expanded such that the system assists with plant operations, provides intelligence to prioritize and improve preventive and corrective maintenance, reduces the operating and life cycle cost of assets that are part of a system by identifying optimal setpoints that can be used to revise setpoints used for SCADA, and as a planning tool to improve the efficiency and timing of capital investments. During application at a BNR plant which treats wastewater for reuse (Upper Occoquan Service Authority, operating at 140,000 m3/d), the system helped identify which basins to operate at what time of the year, how to improve maintenance of ammonium-N and nitrate-N probes by detecting when they began to lose calibration; serve as a substitute for probes for inorganic and organic nitrogen forms soluble BOD5, MLSS; and find ways to optimize the flow routing and energy use for the next day based on variable power costs. At an ENR plant (Westminster, MD, operating at 20,000 m3/d), it is being used in conjunction with data received from SCADA to provide better setpoints each day and plan the next 7 days operation, based on simulation of the increase in flow due to anticipated rainfall. At small wastewater and ENR plants, it is being used on laptops of technical assistance providers to improve onsite and remote technical assistance. INTRODUCTION In 2010, the Maryland Department of Environment (MDE) identified a need for troubleshooting and optimization systems for ENR plants. An analysis of plants with tertiary processes such as Copyright ©2014 Water Environment Federation 7033
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denitrification filters indicated that several were operating with more filters and tanks during wet weather. One possible reason was that the SCADA system logic for secondary and tertiary systems operated independently. The logic for the secondary system was geared to maximize nutrient removal in the secondary treatment system. This could generate conditions that result in poorer biofilm population in the tertiary system. There was no simulation within SCADA that considered biological reactions of biofilms in response to secondary system operations. Additionally, there were no algorithms built into secondary and tertiary systems that responded to variations in loading from solids handling. A lack of an intelligent asset data set and hierarchy to replicate how the functioning of an asset at a particular time (such as a blower or a tank) reflects on the system and its capacity, lack of replication of hydraulic network and its bottlenecks which adjusted available capacity based on which tanks, clarifiers and distribution structures are out of service, limited the accuracy of design models which generally replicated the physical, chemical and biochemical processes. This is because computations and optimization performed through the use of design models that assumed a certain asset condition, availability and hydraulic network. They were not meant to allow for daily changes that a plant sees. Optimization with design models replicated only those conditions that were assumed for design, not the time dynamic condition of equipment and tanks, and their capacities. Simultaneously, several large utilities which had assets within a plant and additional assets over a larger geography (such as reservoirs, pipelines and plants) saw the need to develop an intelligent system where the deterioration of capacity of a pumping station or pipeline or a waterway would be reflected in how the current set of assets could be operated optimally and managed and minimize the risk of failure to meet certain levels of service. By using a more intelligent system that replicated the hierarchy and variation in capacity, the day to day variation in capacity of a system could be captured to determine the level of redundancy available; it would identify ways prioritize work orders based on assets that are out of service and needed to be repaired when there was insufficient redundancy and higher risk. Also, the need to take an asset out of service would be captured in which pipes, tanks and systems would be affected when the asset was taken out of service, together with the duration that they would be out of service. These pieces of information would improve maintenance and capital investment planning. The development, testing and initial application over a three year was funded by several regulatory agencies and utilities. Today, it being applied at multiple plants, where it is configured collaboratively the plant operations and technical staff to mimic the actual configuration and overcome each plant’s daily and seasonal operational, design and management challenges. ARCHITECTURE The system developed looks at the plant at three levels – assets and their condition and capacity, hydraulic configuration and bottlenecks in each possible operating configuration, and process kinetics through models that use the framework developed by IWA ASM2d and Anaerobic Digestion, with additional equations for physico-chemical, biochemical processes and biofilms. The system looks at the current scenarios but can also run additional scenarios and do an optimization based on next day’s power costs and anticipated weather conditions and provide feedback how to operate the plant, when to refurbish and repair assets, and when and where to invest in capital projects.
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The approach to data extraction from SCADA, Lab and CMMS systems is shown in Figure 1. The analysis is performed on the IViewOps computer and the results displayed through its tables, graphs and dashboard. When used with an optimizer, the dashboard can present the results of analysis of the current setpoints and display recommended setpoints to satisfy the desired effluent and other thresholds (such as Maximum MLSS, maximum airflow, etc). Once the permit is satisfied and the other thresholds met, the optimizer looks at other scenarios to recommend the best scenario. The system is operated on desktop computers of key users. For computers on which optimization is performed, it is recommended that they run Intel i5 or i7 processors to run optimization routines. Optimization routines at UOSA and Westminster may run run 672 time steps (96 time steps of 15 minute data each day, for 7 days), and several possible flow routings over 1 to 7 days, including anticipated wet weather flows within a short time. Computational speed is important for once a day optimization to anticipated power, raw influent flow and digester centrate flow rates; it is also important for more frequent optimization (every 15 minutes to two hours) if the plant wishes to go there in the future.
Figure 1. Approach to Data Extraction from SCADA, Lab, CMMS systems and Transmit Information on Intelligent Setpoints to Operations, Maintenance and Management
TYPES OF APPLICATIONS FOR NUTRIENT REMOVAL AND REUSE Upper Occoquan Service Authority (UOSA), Centerville, VA The UOSA plant (160,000 m3/d, 42 MGD) is fairly complex with East and West trains that have been built at different times and have been upgraded with different configurations. A system as described above will have to have users in various groups (operations, technical services, asset management) within the Utility. Because of use of Distributed Control System, it will have users in
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different buildings. Different computers, such as ones performing optiomization, will need higher speed processers. The objectives of various phases of the work are described in Table 1. The system has to (a) provide intelligence to reduce meet the total nitrogen and reduce the variation between weekdays and the weekend, and reduce the variation at different times of the day, and thereby, improve the effluent total nitrogen; (b) it has to minimize power costs which vary each hour; (c) it has to find ways to time capital projects better with the changes in the economy. Between Phases I and II, it was recognized that to apply the system and accurately replicate observed conditions and determine correct solutions for optimization, there is a need to accurately simulate and replicate the asset sizes and current (aged) condition, the hydraulic network which includes flow split structures and valving and hydraulic limitations, in addition to the process kinetics, all of which can contribute to the variation. The asset layout and mode of operation at UOSA is complex. First, the plant uses an Emergency Retention Pond (ERP) on a daily basis to scalp Primary effluent load during hours with high power costs and to hold excess flows during wet weather. The ponds have some biological activity where the heterotrophs take up the ammonium-N into biomass. When water is recycled (returned) back from the ponds during the low flow hours and mixed with the rest of the plant influent, the concentration of nitrogen remaining for nitrification may be lower compared to hours when there is no recycle from the ponds. This required analysis of the whole plant with a cascading series of biological systems, where the ERP is a separate biological system upstream. Second, the plant operates anaerobic digesters and the digester effluent is dewatered only on weekdays. Third, the older plant configuration of anoxic and aerobic zones is slightly different from the newer plant. It was necessary to determine how to split the primary effluent, RAS and nitrate recycle, for which the whole plant simulations of asset, hydraulic network and process kinetics were used to determine reasons for imbalances. Table 1. Time Frame, Objectives, Analysis and Results of Different Phases Phase
Phase I
Phase II A Phase II B
Phase II C Phase II D
Phase III
Time Frame Dec 2012 to Mar 2013 Jul 2013 to Dec 2013 Oct 2013 to Nov 2013
Jan 2013 to Mar 2014 Nov 2013 to Jun 2014
July 2014 to Jun 2015
Objective
Find means to defer 75 Million Capital Upgrade of Secondary System five years at request of Board Confirm Phase I Troubleshoot and find solutions to startup and stabilize operations of East and West Trains Training Operations Improve Total Nitrogen
Improve information available to Asset Management, Operations and Maintenance
Analysis (Description of task) Steady State based on one year data from 2012; capacity analysis for pri, secondary and solids handling Dynamic Simulation Upgrade Model (Figure 3), replicate hydraulic bottlenecks Two day training On-site training program to supplement web based Determine cause of roller coaster effect on Nitrate; Identify solutions through optimizer; Assist with implementation 1. Upgraded release to include key information on assets. 2. Connect to aged capacity of each pump, blower, valve. 3. Link to key pictures and drawings of assets to understand how to troubleshoot quickly
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Status and Results Found means to defer 75 Million Capital Project five years. Confirmed. New timeline being identified Determined means to split flows and achieve consistent TN removal in East and West Scheduled Identified Causes; Identified solutions, implementing in Optimizer; Starting operations implementation Release in operation at plant has been upgraded for Asset Management. Links of Assets in IViewOps to key drawings and pictures
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Asset, Hydraulic and Process Setup Figure 2 shows the key that is used to represent various types of assets or systems in IViewOps. Figure 3 shows how IViewOps was configured in Phase I (Dec 2012). This was used to analyze timing for capital projects. The set up was simpler and geared to answer questions within one month of starting the effort. Its objective was to find ways to operate defer a 75 Million dollar capital upgrade by five years. Within each icon, there are cells in parallel tanks in trains that have the same size. For example, each bioreactor object in Figure 2 for the east side trains has three cells to represent the corresponding cells in each of three trains; the west side bioreactor objects have four cells. Additionally, a link between IViewOps and SCADA can switch cells on and off. The plant has three sets of secondary clarifiers, with the oldest four (3.65 m, 12 foot Side Water Depth, SWD) represented by one secondary clarifier object, the newer two (4 m, 13 foot SWD) by second secondary clarifier object and newest four (4.87 m, 16’ SWD) by the third. Figure 4 shows how the setup was enhanced for dynamic simulation and for starting up the upgrade. The operation of ERP basins and clarifiers were replicated more accurately. The plant uses the ERP (Emergency Retention Pond) as a flow diversion and holding basin to manage loadings on a diurnal basis. The flow distribution to the clarifiers, and primary effluent and RAS distribution to East side (older basins) and West side (newer basins) were replicated to understand imbalances in the MLSS and identify solutions. It helped simulate and plan what would happen if various valves are shut and flow is redirected, and when various tanks are taken out of service.
Key for Asset Objects
Figure 2. Key for Asset Objects used to represent tanks and systems IViewOps
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Figure 3. Initial Setup for Phase I (Steady state simulation)
Figure 4. Complex setup used to represent East and West Side Operations – Phases II and later
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Figure 5. Layout of UOSA from Google Maps
Scenario Setup for Analysis of Operations, Optimization and Planning Figure 6 shows five scenarios that are set up as standard configuration in UOSA. These include: a) Current Steady state, which displays dashboard for results operated without and with optimizer b) Current Dynamic state, which computes the 15 minute data going one day back c) Current Dynamic 7 day, which computes the 15 minute data going one week back d) Plan Dynamic Event – which allows optimization and selection of flow routing for the next 24 hours based on permit conditions and hourly power costs 1 day in advance e) Evaluate Steady State – which allows evaluation of conditions that would change or need to be changed when certain tanks are brought back in service / taken out of service Additional scenarios can be set up by the users. Each scenario can operate off the current steady state or a future condition (such as a different loading or temperature).
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Five Scenarios for UOSA
Figure 6. Five Scenarios set up as standard for UOSA
Optimizer Setup Figure 7 shows how a multi variable optimizer is set up. In this example, the operating DO in the basins (at each location within the basin) is allowed to vary in a range from 0.8 to 1.2 times current setpoint, with the analysis done in increments of 0.05. Additionally, the WAS is allowed to vary from 0.8 to 1.2 times the current wasting. The user can select more variables in the future such as nitrate recycle rate, etc. The user also sets up effluent, air and other thresholds that have to be satisfied. For example, these were run for a maximum steady state effluent ammonium-N of 0.11 mg/L and a maximum air flow of 35,700 m3/h (21,000 scfm). For a dynamic condition optimization, discussed later, the user can set up thresholds for flow weighted effluent NH4N over a 24 hour period. In this case, we used 0.15 mg/L for flow weighted effluent NH4N and 10.0 mg/L for NO3N to determine the best flow routing and minimize power costs for the next day. An example of the variation in power cost for tomorrow, as determined at 5 pm of today, is discussed below. By optimizing the flow routing, the plant can manage the energy costs better for the next day while satisfying or improving the effluent quality.
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Figure 7. set up Optimizer Pattern for DO (top) and WAS (bottom)
Westminster The four standard scenarios set up at Westminster, MD (20,000 m3/d, 5.2 MGD) are shown in Figure 8. These include: a) Current Steady state, which displays dashboard for results operated without and with optimizer b) Current Dynamic state, which computes the 15 minute data going one day back c) Forward Dynamic 7 day, which computes the 15 minute data going forward 7 days based on expected rainfall and Infiltration Inflow. This is useful for planning how to operate the plant and its septage and leachate treatment facilities during high I/I.
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d) Evaluate Steady State – which allows evaluation of conditions that would change or need to be changed when certain tanks are brought back in service / taken out of service.
Figure 8. Configuration of ENR Plant at Westminster, MD
SnowHill The Snow Hill is an example of a small ENR plant (2000 m3/d, 0.5 MGD) where the system is set up on the computers of Technical Assistance providers. The Technical Assistance providers assist with troubleshooting. They do this on-site and remotely. The system will be set up at the plant in the second half of 2014 (Figure 9).
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Figure 9. Configuration for Snow Hill WWTP, MD
RESULTS Upper Occoquan Service Authority, Centerville, VA Calibration
The model was calibrated over dry weather and winter weather and over a year (Table 2). Certain changes were made to kinetic coefficients: e.g. anoxic half sat constant for soluble biodegradable fermented and unfermented COD for OHO (ordinary heterotrophs) was increased from 60 to 100 mg/L to match effluent nitrate-N. The Arrhenius coefficient for Ammonia Oxidizers (AOB) growth rate variation with temperature was decreased from a default value of 1.09 to 1.085. Calibration is deemed good when secondary effluent concentrations are simulated accurately and simultaneously match the sludge production and air flow. Analysis of UOSA’s secondary effluent showed that there is a significant diurnal variation in effluent total nitrogen over a 24 hour period, and between weekend and weekdays. When plant was operated for denitrification, the nitrate-N levels fluctuated from 9 mg/L to 13 mg/L on weekdays, depending on the time of the day, and 7.5 to 10 mg/L on weekends (Figure 10). Stabilizing nitrogen removal is a priority because current permit requires the plant to satisfy an annual nitrogen cap of 1.3 Million pounds per year (which works out to an average of approximately 10 mg/L). However, the plant has to discharge a higher nitrate during summer months to prevent anaerobic conditions in the drinking water reservoir downstream, and discharge lower levels in winter (8.5 mg/L). Analysis for Troubleshooting Figure 11 shows the primary effluent (PE) ammonium-N profile, as measured by the probe and through simulation. The whole plant dynamic simulation evaluated how centrate recycle impacts operations on weekdays versus weekdays, and how it behaved when flow was diverted to the ERP
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basins and returned. The whole plant simulation helped explain why the primary effluent (PE) NH4N changes over a 24 h period. When flow is routed to the ERP basins which also act as bioreactors, some biomass introduced from plant recycles causes NH4N uptake into biomass, even though the TSS is low. This reduces the NH4N in the ERP return flow. During low flow hours, when the plant pumps out the ERP, the PE contains a larger mix of ERP effluent, which reduces the PE NH4N. Additionally, the centrate recycles over weekdays compared to weekends. Both model and plant data show digester NH4N of 1000 to 1350 mg/L in the digester effluent going to the centrifuges (Table 2). The analysis with the model also explained why secondary effluent nitrate-N behaves like a “roller coaster” (Figure 12). When centrate recycle and lack of dilution from ERP return during peak flow hours increases PE TKN from 35 to 45 mg/L, it increases the NH4N available for nitrification which increases secondary effluent (SE) NO3N. However, what is also observed is that nitrate-N drops each day during midnight to 8 am period when there is a higher proportion of ERP recycle added to the PE. The secondary effluent NO3N measured and simulated are shown in Figure 12. When PE NH4N decreases due to dilution by ERP flow, there is less N available for nitrification, this decreases the secondary effluent NO3N. When the Centrate recycle is turned off, it decreases the PE NH4N, which decreases the secondary effluent NO3N on weekends. The results from daily operations are shown in the Steady State and Dynamic dashboards. The steady state dashboard (Figure 13) indicates where the plant should be with respect to MLSS, effluent quality based on the operating conditions on the East and West Side. The dynamic dashboard (Figure 14) shows how various parameters trended over the last 24 hour period at various locations within the plant. This information can be used to determine if some probes have lost their calibration (such as ammonium-N and Nitrate-N probes), substitute for probes at other locations, and work in lieu of probes for parameters such as soluble BOD5, SKN, SCODbio, SCOD, MLSS, MLVSS, etc.
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Table 2. Results from Detailed Time Dynamic Calibration Dry Weather: April 5 to 12, 2012, 7 day period of 15 minute data. Swing zones operated aerobically Flow = 28.5 MGD, MLSS Temp = 18 C, WAS Rate = 260 gpm over this period. Only West Side Trains operating Winter Weather: Feb 12 to 17, 2013, 7 day period of 15 minute data. Swing zones operated anoxically. Flow = 30.7 MGD, MLSS Temp = 15.8 C, WAS Rate = 200 gpm over this period. Only West Side Trains operating MLSS MLVSS AirFlow Sec Effluent Sec Eff Dig eff Dig eff (mg/L) (mg/L) (scfm) NH4N NO3N NH4N Alkalinity (mg/L) (mg/L) (mg/L) (mg/L) Dry Weather
Plant Model Deviation
3960 3940 0.5%
3200 3150 1.5%
18470 19730 6.8%
Weighted avg
Weighted avg
0.05 0.07
15.9 15.1 5.0%
NA below 0.20 mg/L NH4N
Plant 4573 3740 19035 0.10 – 0.151 10.9 Model 4555 3713 19247 0.10 – 0.151 11.0 NA Deviation 0.5% 0.8% 1.1% 1.0% Deviation, % = [(Model – Plant) / Plant] x 100 NA = Ratios for deviation are not applicable at low NH4N levels; generally below 0.25 mg/L 1 Higher values for days with Centrate recycle Winter
1175 1210 3.0%
4350 4440 2.0%
960 980 2.0%
3960 3610 8.9%
For calibration, for the Raw Influent Concentrations measured, the simulation results must simultaneously match MLSS (verifies sludge production), Air Flow (verifies COD balance and aeration system), and Secondary Effluent quality (verifies kinetics and asset capacity) Note that additional calibration was completed to replicate results from Jan to Dec 2012, and from March and April 2014 with East and West Trains in operation
Figure 10. Daily variation in Final Effluent Nitrate-N over weekday and weekend (above), and hourly variation over a one week period (below). The increase in NO3N over the week occurs during Centrate recycle.
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Figure 11. Variation in 15 minute Primary Effluent Ammonium-N (as simulated by the model (above) and measured by the probe, in yellow, below). The Primary Effluent is affected both by Centrate recycle operations and ERP operations
Figure 12. Fifteen Minute Secondary Effluetn Nitrate-N, Actual and Simulated values for Calibration Periods
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Figure 13. Steady State Dashboard – can be customized by operator or technical staff
Figure 14. Dynamic State Dashboard – the views can be toggled from table views to graphs for last 24 hours. The flow lines (such as Raw Influent, Primary Effluent) and parameters to graph can be selected by operator.
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Optimization As stated earlier, an objective of Optimization is to figure out how to manage two objectives simultaneously – such as reduce energy costs and manage to target effluent total nitrogen. In a plant like UOSA, under certain conditions, the two objectives can be aligned and under certain other conditions the two can be in conflict with each other. UOSA is set up to have two types of optimization. The first is the steady state optimization which was introduced earlier. The steady state optimization dashboard (Figure 15) tells the operations and technical staff if a better setpoint is available and may be considered to save energy and life cycle costs while meeting the objectives for effluent quality. In this example, it asks for a lower DO and lower wasting rate and tells the staff that there can be savings of about $50 dollars ($15,000 per year) a day at 120,000 m3/d (32 MGD) of flow and an average power cost of 10 cents per KWH. The second optimization looks at the power cost for the next day. Figure 16 shows an hourly power cost for the next day UOSA receives from the PJM grid operator at 5 pm each day. This information can be used to run several flow routings for diversion at certain hours to the ERP basin, the rate of return from the ERP basin, and Centrate recycle. While this can generate an additional savings of $50 per day ($15000 per year), its principal benefit is in allowing better management of Centrate recycle in combination with ERP recycle.
Figure 15. Dashboard for Steady state optimization – User can select which flow streams and monitors to display. Each table for flow stream has two columns where the left hand side shows the current values and the right hand side shows the optimized values. Each monitor at the bottom shows two values, one for current and one for optimized state. The feedback shown is that the DO should be reduced from 2.0 mg/L to 1.6 mg/L; the Wasting should be decreased from 220 to 198 gpm, the energy use will go down from $2082 per day to $2030.
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PJM Grid Data
Figure 16. Example of Power Rates for Tomorrow obtained at 5 pm today. Power rates for the next day will vary daily. The data can be used in IViewOps to compute the optimum flow routing through the plant the next day in conjunction with nitrogen removal to save energy costs.
Figure 17. The flow routing to the ERP Basin an d return from the ERP basins can be optimized to a pattern for the next day based on the power cost to reduce the daily energy cost. In this instance, the cost decreases from 2000+ dollars a day to around 1900 dollars a day by optimizing the flow routing. While
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Westminster, Maryland Figure 18 shows an example of how the flow dynamic 7 day scenario (FD7) can be used to simulate influent flows and determine effluent quality during wet weather, based on the current set of assets and MLSS in operation. Note that the current set of assets in operation changes each day. By connecting to SCADA and lab, both the CSS and FD7 scenario reset the plant each day to the operating MLSS temperature, SVI and assets in operation.
Figure 18. Predictive Analytics at Westminster, Maryland through the scenario that goes forward 7 days starting from the current day. Plant simulates the forward flow rate based on rainfall and I/I. It simulates the effluent NH4N, OP, TN going forward 7 days based on the configuration it is running today. Using the NH4N graph, it can determine up to what flow it can continue to treat leachate and septage (Figure 8). Septage and leachate treatment generate revenues for the plant for its operation. IViewOps can be used to generate the revenues while managing the compliance risk.
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DISCUSSION Rollout There are different applications that each plant may deem important at a certain point in time. Typically, a rollout happens in phases. At UOSA, which is large and sophisticated 42 MGD plant, the system is being rolled out from Technical Services Division to Operations Division. In Phase III, the plan is to expand it further for use with Asset Management and Capital Project Planning. At Westminster, which is a midsized plant (5 MGD), in the first two years (2012 and 2013), the system was operated with data but without a link to SCADA. The system was linked to SCADA in 2014. It is being used by the Senior Operators. At Snow Hill, which is a small plant (0.5 MGD), it is being used for technical assistance. The plans are to migrate from technical assistance to in plant operation at Snow Hill. Other plants such as Kent Island, MD and Cinnamonsin, NJ are beginning the in-plant use. They would start with steady state, dynamic and wet weather simulation. Benefits While various plants have various objectives and benefits, one of the benefits that seems to apply to all plants is the ability to improve the management and reduce the maintenance cost of existing sensors, substitute for sensors at locations where a plant was not able to afford to install them, and substitute for types of sensors that may not exist or can relatively expensive (such as soluble BOD5, SKN) for some plants. Essentially, through the use of the asset based simulator, one can substitute for these. Overall, this serves as a QA/QC tool for sensors and probes that may not be giving the correct data, for lack of sensors, and serves as a QA/QC tool for setpoints specified in SCADA. For an ENR plant, the reduction in replacement and maintenance burden can be over $10,000 per year for a 10 MGD plant as the sensors age and have to be replaced and maintained through maintenance dollars. CONCLUSION 1. Plants are able to simulate the current condition and determine if better operating setpoints exist to meet their objectives for nitrogen removal and manage energy, chemical and life cycle costs. 2. Plants are able to determine how the maintenance of a certain asset or deterioration due to age will affect the overall system and optimization, and the plan for the duration a system has to be taken out of service for maintenance of each asset. 3. For plants that have significant changes in flow due to wet weather, the application can be extended forward in time to determine anticipated rainfall will affect the current operations, and how best to change the operations to manage to wet weather. 4. Plants can determine if certain probes and sensors are beginning to lose calibration by using the dynamic simulation of current condition going back 24 hours as a backup. They can use this to improve the maintenance practices. They can also use the data from the IViewOps.
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dashboard to substitute for sensors that they do not have at various locations. This helps improve the quality of setpoints and QA/QC the setpoints specified in the SCADA system. REFERENCES IIMM - International Infrastructure Management Manual (2006). Version 3.0. INGENIUM, New Zealand. IIMM - International Infrastructure Management Manual (2011). INGENIUM, New Zealand.
IWA Publishing (2002). Activated Sludge Models ASM1, ASM2, ASM2d and ASM3. M. Henze, W. Gujer, T. Mino, M van Loosdrecht. IWA Publishing (2002). Anaerobic Digestion Model 1. IWA Task Group for Mathematically Modeling of Anaerobic Digestion Processes. ACKNOWLEDGMENT The support of Mr. Neelesh Dubey and Anadi Srivastav of MPUD, India to extend the platform for planning and operations evaluation is appreciated.
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