Primm, Christian

Christian Primm is the Group Product Manager for Mechanical Biosolids Treatment at HUBER Technology, Inc. where he focused on solids screening, thickening, and dewatering equipment. Christian is a member of the Water Environment Federation, and participates on the Young Professionals and Solids Separation Subcommittees.

Titles from this speaker

Description: Increasing the Accuracy of Project Planning Through Meta-Analysis of Dewatering...

Increasing the Accuracy of Project Planning Through Meta-Analysis of Dewatering System Performance

Abstract

KEYWORDS Sludge dewatering, data collection, screw press, meta-analysis BACKGROUND Performance estimation for Biosolids dewatering is of great importance for project planning and dewatering system design. Given the cost implications associated with dewatered solids hauling, polymer consumption, and capture rate efficiency, identifying accurate ranges for predicted equipment performance is advantageous to facilities that produce and process biosolids. Equipment manufacturers are able to collect data from existing installations and pilot scale testing for utilization in large scale meta-analysis. This analysis increases predictive capability as it yields insight into how upstream processes and sludge composition influence dewatering performance. Bench scale testing can also be utilized, however should be viewed with more scrutiny, as it cannot exactly replicate onsite performance. METHODS Bench, Pilot, and Full Scale installation testing data was collected from hundreds of facilities across the United States, during a period of time between 2014 and 2022. A Screw Press dewatering device was used on all tests. Figure 1 below shows the number of data points for each type of testing: Bench Scale Testing Pilot Scale Testing Full Scale Installation Testing ~360 ~200 ~100 Table 1: Number of data points for each type of testing conducted in this analysis Figure 1. Dewatered Cake Solids from Screw Press Equipment To assist in the statistical analysis of performance data, multiple descriptive 'tags' were created and assigned to various parameters of the sludge being tested. These tags allowed for the storage, categorization, and comparison of performance data between similar sludges. Each parameter used in the meta-analysis was obtained from a questionnaire filled out by the facility contact, and is one of the 23 following: Sludge Parameter Tags 1. Cake Solids (%) 2. Polymer Dose (lb active/DT) 3. Capture Rate (%) 4. Feed Solids Concentration (%) 5. Throughput (gpm) 6. Solids loading (lb/hr) 7. Machine Size 8. Facility Flow (MGD) 9. Waste Sludge Flow (GPD) 10. Sludge Age (days) 11. Sludge Type (WAS, Primary, Blend) 12. Volatile Solids Ratio (%) 13. Digestion Type (Aerobic, Anaerobic) 14. Total Dissolved Solids (mg/L) 15. Sludge pH 16. Chloride Value (mg/L) 17. Phosphate Value (mg/L) 18. Nitrate/Nitrite (mg/L) 19. Sludge Temp (F) 20. Bench Cake/Bench Polymer Dose (For comparison to field values) 21. Biological Process Type 22. Type of sludge storage (Day tank, Holding tank, Lagoon) 23. Digestion temperature range (Mesophilic, Thermophilic, TPAD, None) Table 2: Sludge Parameter Tags Recorded parameters were tabulated and analyzed using Microsoft Excel, as this provided the easiest means for exporting data collected from online forms. The programming language Visual Basic was utilized as a tool to filter and plot all of the data, allowing for comparison and determination of trends between two different sludge parameters. RESULTS The operator of this analysis tool is able to define X-axis and Y-axis parameters for comparison. Once selected, data points will be plotted along with a trend line to describe the behavior of the data set. Any additional filters applied to the data will further narrow the data set, allowing the operator to view only those data points which fall within a desired range of sludge characteristics. Figure 2. Data Analysis Main Page Further analysis employing the use of standard deviations for each data set allowed for the creation of statistically derived ranges that aid in estimating performance to a more accurate degree than sludge type alone. One such example is shown below in Figure 3, where Cake Solids (%) is compared with Volatile Solids Ratio (%). This search is further refined by only looking at Waste Activated Sludges (no primary component) with Aerobic Digestion. This returns a truncated list that provides a clear relation between Cake Solids and VSS for the sludge type specified. Figure 3. Cake Solids vs. VSS Ratio for Aerobically Digested WAS Sludges Another example of this can be found in Figure 4, which shows a trend of polymer dose compared to feed solids. This is further filtered by looking only at anaerobically digested sludges which fall within the mesophilic temperature range. Figure 4. Polymer Dose vs. Feed Solids for Anaerobically Digested Sludges in the Mesophilic Temperature Range DISCUSSION The tool shown above is useful in making performance determinations and estimations for new and existing projects by leveraging past data from multiple sources. The accuracy of this tool will continue to increase as the data set becomes more robust, meaning that the usefulness of this meta-analysis for project planning will continue to increase. Cooperation between manufacturers, consultants, municipalities, and academia in combination with tools similar to the one described herein has the potential to bridge the gap between dewatering system expectation and actual performance, as each party is able to bring unique contributions to the discussion. The intent of this project was to continue the conversation on ways to create more accurate projections for municipal sludge dewatering performance. Further, this project aimed to explore and qualify the intricacies that are inherent in the municipal wastewater treatment process, and provide a way to identify the factors that can positively or negatively affect dewatering system performance. It should be noted that while this project aimed to be as thorough as possible in terms of data collection and analysis, the study of sludge Dewaterability is ongoing, and as such some factors which influence dewaterability were not included at the time of writing. SIGNIFICANCE/IMPLICATIONS Manufacturers typically have the greatest access to large quantities of data, due to the many projects that they entertain. Through coordinated efforts between all involved parties, a qualitative approach to data analysis can allow for a better understanding of how to estimate performance, especially at new facilities where sludge is not available for testing. Statistical meta-analysis can provide a higher degree of certainty regarding performance expectations as compared to traditional estimations based on sludge type alone.

This paper was presented at the WEF/IWA Residuals and Biosolids Conference, May 16-19, 2023.

SpeakerPrimm, Christian

Presentation time

15:45:00

16:15:00

Session time

13:30:00

16:45:00

SessionSession 08: Dewatering and Polymer Optimization

Session number08

Session locationCharlotte Convention Center, Charlotte, North Carolina, USA

TopicThickening & Dewatering