bge job no. 2875-00 - drylet · bge job no. 2875-00 march 2017 evaluation of drylet® lift at...

BGE Job No. 2875-00 March 2017 Evaluation of DryLet® LIFT at Atascocita WWTP

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Section 1 Executive Summary ............................................................................. 1-1

Section 2 Introduction .......................................................................................... 2-1

2.1 Background ............................................................................................................. 2-1 2.2 Product Description ................................................................................................ 2-1 2.3 Testing at Atascocita Regional WWTP .................................................................. 2-2 2.4 Key Performance Indicators ................................................................................... 2-3 2.5 Stoichiometry, Mass Balances, and Yield Analysis ............................................... 2-5 2.6 Objective ................................................................................................................. 2-6

Section 3 Procedure and Analysis....................................................................... 3-1

3.1 Data Acquisition ..................................................................................................... 3-1 3.2 Raw Sludge Accounting ......................................................................................... 3-5 3.3 Biomass Yield ......................................................................................................... 3-5 3.4 Polymer Usage ........................................................................................................ 3-8 3.5 Energy Usage .......................................................................................................... 3-9 3.6 Equipment Life and Maintenance ........................................................................... 3-9

Section 4 Results ................................................................................................ 4-11

4.1 Raw Sludge Accounting ....................................................................................... 4-11 4.2 Biomass Yield ....................................................................................................... 4-14 4.3 Polymer Usage ...................................................................................................... 4-20 4.4 Energy Usage ........................................................................................................ 4-22 4.5 Equipment Life and Maintenance ......................................................................... 4-23 4.6 Other Parameters................................................................................................... 4-24

Section 5 Discussion and Observations ............................................................. 5-1

5.1 Effect on Sludge, Polymer, MLSS, BOD, SVI and SRT........................................ 5-1 5.2 Effect on Clarifier Solids Flux ................................................................................ 5-3 5.3 Continuous Improvement, Process Optimization, Increased Throughput .............. 5-5 5.4 Qualitative Benefits ................................................................................................ 5-6

Section 6 Conclusions and Next Steps ............................................................... 6-1

Section 7 Appendices ........................................................................................... 7-1

7.1 Appendix A - Calculations Spreadsheet ................................................................. 7-1 7.2 Appendix B - Financial Reports ............................................................................. 7-1 7.3 Appendix C - Operators Reports ............................................................................ 7-1 7.4 Appendix D - Test America Data ........................................................................... 7-1 7.5 Appendix E - Magna Flow Data ............................................................................. 7-1 7.6 Appendix F - Photo Journal .................................................................................... 7-1 7.7 Appendix G - Atascocita Regional Plant Schematic .............................................. 7-1


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Tables Table 1-1 - Value Proposition .................................................................................................................... 1-1 Table 2-1 – Atascocita Regional WWTP Treatment Units ......................................................................... 2-2 Table 2-2 – Key Performance Indicators ................................................................................................... 2-3 Table 3-1 –Data Sources ........................................................................................................................... 3-1 Table 3-2 – Parameters collected form Operator’s Lab Database ............................................................ 3-2 Table 3-3 – Parameters collected form Operator’s Lab Database ............................................................ 3-2 Table 3-4 – Parameters collected form Bird Nest™ .................................................................................. 3-2 Table 3-5 - Summary of Yield Analysis Methods ....................................................................................... 3-7 Table 3-6 - Digester inventory drawdown .................................................................................................. 3-7 Table 4-1 - Raw Sludge Accounting Results ........................................................................................... 4-11 Table 4-2 - Raw Sludge Accounting Results ........................................................................................... 4-12 Table 4-3 – Summary of Sludge Reduction Results ................................................................................ 4-18 Table 4-4 – Sludge Reduction Cost Savings ........................................................................................... 4-20 Table 4-5 – Historical Polymer Usage ..................................................................................................... 4-20 Table 4-6 – Study Period Polymer Usage ................................................................................................ 4-21 Table 4-7 – Polymer Usage Comparison ................................................................................................. 4-21 Table 4-8 – Polymer Usage Cost Savings ............................................................................................... 4-21 Table 4-9 – Energy Usage Comparison ................................................................................................... 4-22 Table 4-10 – Belt Press Cost Savings ..................................................................................................... 4-24 Table 4-11 –Summary of Results ............................................................................................................ 4-26 Table 4-12 - Value Proposition ................................................................................................................ 4-26 Table 5-1 – Summary of Changes before and during Study Period .......................................................... 5-2


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Figures Figure 1-1 – Effluent Quality ...................................................................................................................... 1-2 Figure 1-2 – Monthly Yield Comparison ..................................................................................................... 1-2 Figure 2-1 - Drylet Product Dispenser at AJOB ......................................................................................... 2-3 Figure 3-1 –BOD LOAD, Historical and study periods ............................................................................... 3-3 Figure 3-2 - Average Daily Flow during the historical and study period .................................................... 3-4 Figure 4-1 - Raw Sludge Accounting Dry Solids Comparison ................................................................. 4-13 Figure 4-2 - Raw Sludge Accounting Haul Comparison .......................................................................... 4-13 Figure 4-3 – Yield Results ........................................................................................................................ 4-18 Figure 4-4 - Raw Sludge Accounting Haul Comparison .......................................................................... 4-19 Figure 4-5 – Yield Results ........................................................................................................................ 4-19 Figure 4-6 – Energy Usage Comparison ................................................................................................. 4-22 Figure 4-7 – Ashbrook Belt Press Existing and Projected Depreciation .................................................. 4-23 Figure 4-8 – Andritz Belt Press Existing and Projected Depreciation ...................................................... 4-23 Figure 4-9 – TSS and VSS in the Aeration Basin during Study Period .................................................. 4-24 Figure 4-10-TSS in Aeration Basin during Steady State Period .............................................................. 4-25 Figure 4-11 – Mass Under Aeration during Study Period ........................................................................ 4-25 Figure 4-12 –Summary of KPIs over the Study Period ............................................................................ 4-27 Figure 4-13 – Effluent Quality .................................................................................................................. 4-27 Figure 5-1 - Sludge cake coming out of the press ..................................................................................... 5-1 Figure 5-2 – TSS in the Aeration Basin over the Study Period .................................................................. 5-2 Figure 5-3 – State Point Analysis ............................................................................................................... 5-3 Figure 5-4 - DO Chart During Study Period ............................................................................................... 5-4 Figure 5-5 - DO Chart after Installation of New DO Meter ......................................................................... 5-4 Figure 5-6 – Rate of Oxygen Uptake Chart ............................................................................................... 5-5


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Glossary ADF – Average daily flow rate AJOB – Atascocita joint operations board AST – Activated sludge treatment BOD – Biological oxygen demand BGE – Brown & Gay Engineers C8H12O3N2 – Chemical formula for BOD C5H7O2N – Chemical formula for biomass CBOD – Carbonaceous biological oxygen demand CIP – Capital improvement project CFM – Cubic feet per minute CFU – Colony forming units COA – Certificate of analysis COC – Chain of custody CSTR - Continuous Flow Stirred-Tank Reactor D – Dilution rate DO – Dissolved oxygen EPA – Environmental Protection Agency Fd – Biodegradable fraction HRT – Hydraulic retention time I&I – Inflow and Infiltration I/O – Input / Output KPI – Key Performance Indicator MBR – Micro bioreactor MDL – Minimum detectable level mgl – milligrams per liter MLSS – Mixed liquor suspended solids MCRT – Mean cell retention time 𝜇𝜇 - Specific growth rate (per day) MGD – Million gallons per day MT – Metric tons (2,205 lbs) MUD – Municipal utility district MW – Molecular weight NBOD – Nitrogenous biological oxygen demand NPK – Nitrogen, phosphorous, potassium OUR - Oxygen uptake rate pH – Negative log of hydrogen ion concentration

% solids – weight/volume Q – Daily flow rate (mm gallons/day) RAS – Recycled activated sludge SDEV – Standard deviation SRT – Solids retention time ST – Short tons (2,000 lbs) STS – Severn Trent Services SVI – Sludge volume index TCEQ – Texas Commission on Environmental Quality TSS – Total suspended solids TOC – Total organic carbon VSS – Volatile Suspended solids WAS – Wasted activated sludge WHO – World Health Organization WWTP – Wastewater treatment plant Y – Yield (lb sludge/lb BOD)


1-1

Section 1 Executive Summary

The AJOB engineering study set out to measure the performance and economic benefit of sludge reduction using DryLet® LIFT. DryLet® LIFT is a wastewater sludge reduction product developed and manufactured by DryLet, LLC (DryLet). DryLet is a Texas-based startup company focused on bioremediation using their proprietary platform technology. The test period began in October 2014 and concluded in April 2015. This test period spanned seven months where key performance indicators were analyzed by a third party, TestAmerica, Inc., along with the engineering analysis conducted by Brown & Gay Engineering, Inc. The operational data includes sludge hauls (as recorded by Severn Trent Services and MagnaFlow, Inc.), sludge mass produced, influent properties, and plant operational data for the aeration basins. Value Proposition The performance of the product shows an estimated 30% reduction of sludge and a 43% polymer reduction. The economic benefit (see Table 1-1) covers four key value drivers for sludge reduction including sludge disposal, polymer use, equipment life, and equipment maintenance costs. The value proposition analysis estimated the savings on an annual basis and a projected annual use of product of 2,920 pounds (8 pounds per day) of DryLet® LIFT.

Table 1-1 - Value Proposition

Value Driver Annual

Cost Pre-DryLet

Annual Cost with

DryLet

Annual Savings with

DryLet

Savings per Pound of DryLet

Sludge Disposal $146,000 $102,000 $44,000 $15 Polymer Use $35,000 $19,600 $15,400 $5 Equipment Life $52,000 $43,000 $9,000 $3 Equipment

$35,000 $23,750 $11,250 $4

TOTALS $268,000 $188,350 $79,450 $27 There are other potential value drivers that may lead to more savings per year. To better understand the potential for additional savings, further testing is required. It is believed that the potential value drivers that could have a more dramatic impact on the plant operational costs include: (i) oxygen demand and/or blower efficiency, (ii) chemical use for sanitation, and (iii) extended plant life.


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Plant Performance The product had no negative effect on the effluent quality. As shown by Figure 1-1, the BOD, TSS, and ammonia tracked well below the permitted values, and in many cases were below detection limits. Figure 1-2 shows a sustained and cumulative average reduction of 30% after the first month of applying the product. Figure 1-3 shows a sustained and cumulative average reduction of 30% after the first month of applying the product.

Figure 1-1 – Effluent Quality

Figure 1-2 – Monthly Yield Comparison

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Nov Dec Jan Feb Mar Apr May

Yiel

d (in

per

cent

)

Month

Comparison of Measured YieldsTest Period 5 Year Historical Avg.


1-3

Figure 1-3 – Sludge Haul Comparison

0

5

10

15

20

25

30

Nov Dec Jan Feb Mar Apr

Num

ber o

f Slu

dge

Haul

s pe

r Mon

th

Month

Comparison of Number of Sludge Hauls

Test Period 5 Year Historical Avg.


2-1

Section 2 Introduction

2.1 Background

Wastewater sludge consists mostly of water, typically 70-85%. So land application and other potential technologies for energy recovery require the use of fuel to transport the sludge. Sludge disposal means hauling vast quantities of water around our planet every day. The most preferred waste management practice is that the waste not be created in the first place, thus the global objective to minimize solids production. In this way, sludge reduction represents a movement towards better environmental stewardship and sustainability. In addition, sludge reduction amounts to significant water conservation, as the water content can be returned to the surface and groundwater supply rather than being evaporated thereby contributing to impending water shortages. 2.2 Product Description

DryLet® LIFT is a novel delivery platform for enhanced microbial activity in wastewater treatment plants (WWTP). Microbes do the work of stabilization of organic waste through the production of biomass sludge. The Activated Sludge (AS) treatment process relies on native microorganisms present in human flora and in storm water run-off to convert influent BOD into new biomass and old solids or dead biomass in the Return Activated Sludge (RAS) into new microorganisms. DryLet® LIFT is a dry-to-the-touch product that consists of a mixed culture of beneficial microbes immobilized on an inert stratum. The native, non-pathogenic consortium of microbial species selected is ideal for wastewater application and the stratum is a super absorbent polymer that functions as a Micro-Bioreactor All microbial species present are non-genetically modified strains. They belong to the class of Group 1 microorganisms according to the World Health Organization (WHO). The product produces blooms of bacteria when placed into an aqueous environment containing a food source like BOD and aging solids in the form of biomass or dead cells. Each DryLet® Micro-Bioreactor is highly porous and contains a huge surface area within its volume and on its surface (700,000 square feet per pound). As with any catalyst, the surface area provides a matrix upon which a reaction can be greatly accelerated. The stratum has been chosen and engineered to promote a growth quorum among the species present. The DryLet® Micro-Bioreactor stratum is also a super absorbent polymer (SAP) that is capable of drawing in organic nutrients to be used as building blocks for new bacterial cells and to sustain cellular functions. As the microorganisms bloom, they experience crowding effects within the DryLet® Micro-Bioreactor and begin to populate the surrounding medium.

For several decades, attempts have been made to reduce sludge in wastewater systems by treating with products containing enzymatic blends, liquid based microbial cultures or nutrient


2-2

based microbial cultures. These have been unsuccessful because they did not take into account that wastewater treatment is inherently a biotechnology process. DryLet® LIFT is intended to be a biotechnological means to reducing sludge. 2.3 Testing at Atascocita Regional WWTP

In early 2014, DryLet, LLC (DryLet) approached Brown & Gay Engineers, Inc., (BGE) with the intent to conduct a full-scale product trial to validate the efficacy of DryLet® LIFT for sludge reduction and measure the resulting economic benefit for plant operations. BGE identified the Harris County Municipal Utility District (MUD) #109 WWTP at Atascocita in Humble, Texas. The treatment plant has an excellent record of meeting discharge permit compliance and had undergone an extensive expansion in the mid 2000’s. The treatment plant is permitted for 9.0 million gallons per day (MGD), but receives an average daily flow rate of 4.1 million gallons. The treatment units are described in Table 2-1 and a schematic of the plant is included in Appendix G.

Table 2-1 – Atascocita Regional WWTP Treatment Units

Treatment Unit # of Units Dimensions (L x W x H) Aeration Basin 4 (Not in use) 110'x30'x15' Aeration Basin 3 110'x30'x15'

Aeration Channel 2 180'x20x15' - 270'x10'x15' Digester #1 1 (Not in use) 75'x55'x15'

Digester #1 1 90'x55'x15' Digester #2 1 (Not in use) 165'x61'x15'

Digester #3 1 (Not in use) 60'x11'x15'

Thickener 1 35'DIAx10.5'SWD

Chlorine Contact 1 142'x36'x15'

Clarifiers 2 115'DIAx10'SWD

Clarifiers 1 (Not in use) 115'DIAx10'SWD

The Atascocita Joint Operations Board (AJOB) agreed to host the product trial, as did Severn Trent Services (STS) the Operator of the WWTP. In October 2014, the test began with the introduction of DryLet® LIFT to the headworks of the plant at a rate of 8 pounds per day as seen on Figure 2-1.

TestAmerica, Inc. (TestAmerica) was contracted to collect and perform sample analytics that would provide data acquisition for the entire test period. These additional samples and analytics augmented the standard analytics performed by STS. The test period covered a total of seven (7) months, from October 2014 through April 2015. The test, its methodology, the data analysis, and the results are described herein.


2-3

Figure 2-1 - Drylet Product Dispenser at AJOB

2.4 Key Performance Indicators

Key Performance Indicators (KPIs) in wastewater treatment focus on suspended and settled solids. The most pervasive paradigm that one encounters among industry professionals is that a WWTP is a solids-handling operation.

To date, microbiology in wastewater treatment appears to be rather passive. Microbial activity occurs, but is largely ignored. There is thought given to the Solids Retention Time (SRT) because it is undesirable to waste out active bacteria. The SRT can also be too long, in which case higher order organisms become entrenched and affect the system adversely. This is the only effort made to control the nature of the microbial population. The biological process is treated as a “black box.” In plants in cold climates that are not suited for microbial activity much of the organic waste is never converted to biomass, it is just settled out in primary clarifiers.

The following KPIs are ubiquitous in the industry:

Table 2-2 – Key Performance Indicators

Process Variable Typical Range for Conventional AST TSS in basin 2,000 – 3,500 mgl DO in basin 2.0 - 3.5 mgl

Recycle Ratio (RAS/Q) 50% - 150% Blanket Height 10 – 20 % of SWD

SRT 5 – 15 days SVI 50 – 150 ml/g


2-4

Most plants are designed to have a recycle ratio between 50 and 150% of the influent flow rate. The typical range for DO is between 2 and 3.5 mgl in the aeration basin. BOD is almost never checked within the plant, just the influent determine the load coming to a plant and the effluent to confirm that permit compliance has been met at the outfall. In most cases, control is achieved by keeping a constant MLSS or a constant SRT. MLSS typically ranges between 2,500 and 3,500 mgl. SRT will usually range between 10 and 20 days.

The operator will change the waste activated sludge (WAS) rate to keep a steady-state of Mixed Liquor Suspended Solids (MLSS) in the basins. The operator will keep a constant sludge level (or blanket) in the clarifiers by changing the recycle activated sludge (RAS) ratio; raising the RAS flow rate as blanket height climbs and lowering RAS flow rate if blankets begin to fall.

Most of the VSS into the plant (80 - 90%) is organic foodstuffs like carbohydrates, lipids, and proteins. A small fraction of the VSS coming into the plant is composed of non-biodegradable VSS (nbVSS). About 10% of the TSS into the plant is composed of inorganic material like metals and silt. Neither the nbVSS nor the inert inorganics will be consumed by biological activity. These solids are not the target of AST. The non-biodegradable solids will simply pass through the plant, with the vast majority exiting in the sludge generated and a very small amount remaining suspended and exiting at the outfall per limits set by the EPA and/or TCEQ.

Biodegradable material (BOD) exits the plant as sludge, with some loss of mass in the biological process. In other words, some fraction, fd, of VSS generated in the plant remains as non-biodegradable “cell debris.” This cell debris is the major portion of the nbVSS, which along with the inert inorganics comprises sludge.

The fraction of Total Organic Carbon (TOC) in VSS that is completely biodegradable (1-fd) leaves as carbon dioxide. The process of waste stabilization involves the oxidation of organic material by bacteria with the production of carbon dioxide (CO2) and water (H2O). Thus, about 50% of the inbound BOD is converted to gas (CO2 and N2) and water. This is called “burn” or “mass to gas.”

Consequently, the biomass synthesis yield is typically less than unity.

𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑠𝑠𝑠𝑠𝑠𝑠ℎ𝑒𝑒𝑏𝑏𝑏𝑏𝑏𝑏 𝑠𝑠𝑏𝑏𝑒𝑒𝑦𝑦𝑦𝑦,𝑌𝑌 = 𝑔𝑔 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑝𝑝𝑝𝑝𝑏𝑏𝑦𝑦𝑝𝑝𝑝𝑝𝑒𝑒𝑦𝑦𝑔𝑔 𝑏𝑏𝑝𝑝𝑏𝑏𝑏𝑏𝑠𝑠𝑝𝑝𝑏𝑏𝑠𝑠𝑒𝑒 𝑝𝑝𝑏𝑏𝑠𝑠𝑏𝑏𝑝𝑝𝑏𝑏𝑒𝑒𝑦𝑦

At plant scale, on a day to day basis, the yield can be defined as:

𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑏𝑏𝑏𝑏𝑒𝑒𝑝𝑝𝑜𝑜𝑒𝑒𝑦𝑦 𝑠𝑠𝑏𝑏𝑒𝑒𝑦𝑦𝑦𝑦,𝑌𝑌 = 𝑠𝑠𝑏𝑏𝑠𝑠𝑏𝑏 𝑏𝑏𝑜𝑜 𝑦𝑦𝑝𝑝𝑠𝑠 𝑏𝑏𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏 𝑏𝑏𝑝𝑝𝑠𝑠

𝑠𝑠𝑏𝑏𝑠𝑠𝑏𝑏 𝑏𝑏𝑜𝑜 𝐵𝐵𝐵𝐵𝐵𝐵𝑏𝑏𝑠𝑠

Yield can vary greatly, but the most efficient plants seem to produce around half a ton of sludge for every ton of BOD they receive. Observed Yield can be much greater in many cases, approaching or even exceeding unity.


2-5

MW=184

MW=113

MW=113

2.5 Stoichiometry, Mass Balances, and Yield Analysis

Incoming BOD and RAS become food for the microorganisms in the plant. The bacteria either use the food for growth and reproduction or for cellular maintenance. A small population primarily in Stationary Phase will use the food to maintain cellular functions (catabolism). A large population primarily in log growth will use the food to produce more cell mass (anabolism). Given a limited food supply, a larger population will undergo more endogenous decay (predation on one another). The decay rate per unit time is increased. In this way, DryLet biotechnology drives the system towards greater endogenous decay and causes more mass to leave as gas. More microbial activity means more highly treated water. The chemical formula for BOD is C8H12O3N2. Conversion of BOD to cell biomass can be accurately represented by the following balanced chemical equation:

(1) 𝐶𝐶8𝐻𝐻12𝐵𝐵3𝑁𝑁2 + 3𝐵𝐵2𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦�⎯⎯⎯� 𝐶𝐶5𝐻𝐻7𝐵𝐵2𝑁𝑁 + 3𝐶𝐶𝐵𝐵2 + 𝐻𝐻2𝐵𝐵 + 𝑁𝑁𝐻𝐻3

This equation shows that every 184 grams of BOD treated will produce 113 grams of biomass. This exerts a stoichiometric oxygen demand, which corresponds to 3 moles of oxygen for every mole of BOD treated. This reaction produces about .61 g biomass/g BOD treated. Conversely, 1.42 g of BOD is consumed for every 1 g of biomass produced.

The complete oxidation of biomass to carbon dioxide, water, and ammonia can be accurately represented by a second balanced chemical equation:

(2) 𝐶𝐶5𝐻𝐻7𝐵𝐵2𝑁𝑁 + 5𝐵𝐵2𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦�⎯⎯⎯� 5𝐶𝐶𝐵𝐵2 + 2𝐻𝐻2𝐵𝐵 + 𝑁𝑁𝐻𝐻3

The first reaction goes essentially to completion, i.e. assume 100% of the inbound BOD is stabilized and converted to biomass during cell growth. However, the second reaction occurs to the extent that consumes the biodegradable fraction (1-fd) of VSS produced in Eqn. 1.

It is this second reaction that converts VSS “mass to gas” thereby reducing the observed yield of outbound solids further below the 60% biomass yield from Eqn. 1.

If we consider only the first reaction, assume that it goes to completion, and isolate the aeration basin, then the stoichiometry makes clear some simple overall mass balances around the aeration basins. The mass fraction of TOC in BOD is 96/184 or 52%. In other words, carbon makes up over 50% of the total BOD mass to be treated.

Similarly, there is available oxygen contained in BOD. The mass fraction of BOD that is oxygen is 48/184 or 26%.

Carbon does not accumulate, but leaves the system either as gas or sludge. The fraction of TOC in BOD that leaves as gas is 36/96 or 37.5%. The balance of the TOC in BOD that remains captured in biomass is 60/96 or 62.5%. It is this 62.5% that can be further reduced to gas in Eqn. 2.


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Of the mass of BOD into the reactor, almost 10% gets reduced to liquid water in the basins. Clearly, a significant amount of water is generated during microbial growth.

Organic nitrogen comprises 28/184 or 15% of the BOD mass load. Half of this 15% gets converted through nitrification/denitrification to N2 gas in Eqn. 1.

Therefore, adding 37.5% “mass to gas” due to carbon dioxide and 7.5% “mass to gas” due to nitrogen means that about 45% of the total inbound organic load to the plant is lost as gas as a result of the first reaction alone. A 45% reduction of BOD mass inbound indicates that the stabilization process (Eqn. 1) alone should give a biomass synthesis yield around 55% (or .55).

Any improvement in the reduction of the yield from the process occurs as the resulting biomass is then further oxidized and gasified through reaction 2. Recall that reaction 2 describes Endogenous Decay of the biomass generated within the plant. Reaction 2, if complete, would convert all the biomass generated into gas and water and half of a mole of ammonia. In this case, sludge would contain only inert inorganics and inert VSS that had entered the plant in the influent.

Note that the influent at AJOB is typical for WWTPs that receive mostly residential wastewater. Influent streams with greatly different compositional characterization, say with much higher loading concentrations of BOD and COD, and streams with much higher inert fraction in the influent could certainly show much higher biomass observed yields than the 0.5 - 0.6 range observed at AJOB before DryLet.

2.6 Objective

The primary objective of this evaluation is to study and quantify, wherever possible, the effects on the plant by the addition of DryLet® LIFT. These include:

• Change in the sludge produced and hauled (Biomass Yield) • Change in the quality of cake • Change in the energy consumption from blower demand and belt press use • Change in the consumption of polymer • Effect of these results on the expected useful life of belt presses and associated equipment • Effect of these results on the maintenance requirements for belt presses and associated

equipment • Effect of these results on the ease of operation

Ultimately, by meeting the primary objective, the operational costs savings for the plant operations can be calculated in order to determine the value proposition of the product for plant economics.


3-1

Section 3 Procedure and Analysis

3.1 Data Acquisition

Data acquisition involved both sample collection and analysis during the test period and the gathering of historical data as shown in Table 3-1. Historical data was compiled from 2009 to 2014.

Table 3-1 –Data Sources

Description Influent data Aeration basin data Effluent data Outbound solids data

Historical

Severn Trent Services

2009-2014

BOD TSS VSS DO pH

2 samples/month

Daily Flow Recorded everyday

MLSS SVI

Daily sample BOD TSS NH3 DO pH

Daily sample

Number of hauls/month

(Assume constant 14.27 wet short

tons/haul)

14.08% solids

TSS DO

BOD

2 samples/month

DO

Analyzed every 3

minutes

Test Period

TestAmerica Inc.

Oct 2014- May 2015

BOD TSS VSS DO pH

Daily sample

(M-F)

Daily Flow

+ STS data (see above)

BOD TSS VSS DO pH SVI

Daily sample (M-F)

+ STS data (see above)

BOD TSS NH3 DO pH

Daily sample

(as reported by STS)

Number of hauls/month

(Manifest data for wet short tons

produced for each haul)

14.08% solids

38 samples over 3 month period

collected by Test America

Working within these constraints, historical data was collected from three main sources: The Operator’s Lab Database, the Operator’s monthly Board Report and Bird Nest™.


3-2

The Operator’s Lab Database included the following records:

Table 3-2 – Parameters collected form Operator’s Lab Database

Parameters Collection Frequency

Influent BOD and TSS concentrations Twice per month

Effluent BOD, TSS, NH4 concentrations Every weekday

Digester Percent Solids Once per week

The Operator’s Board Report included the following records:

Table 3-3 – Parameters collected form Operator’s Lab Database

Parameters Reporting Frequency

Number of sludge hauls Monthly

Cost of sludge hauls Monthly

Number of polymer drums purchased Monthly

Cost of polymer drums purchased Monthly

Cost of belt press repair and maintenance Monthly

Bird Nest™ is an online database used by Severn Trent Services for logging routine plant information. It contains various parameters measured on a daily basis, including: flowrate, electrical consumption and blower runtimes. The meters show totalized readings, so actual values were calculated by subtracting the initial reading from the final reading during a certain time period. Flow data was collected via an ultrasonic transducer at the effluent weir. Electrical consumption data was manually logged by the operator for the two electrical meters with totalizers at the plant. The main loads for meter no. 1 are the aeration basin blowers and the main loads for meter no. 2 are the digester blowers.

Table 3-4 – Parameters collected form Bird Nest™

Parameters Reporting Frequency Flowrate Daily

Energy Usage (Meter No. 1) Daily

Energy Usage (Meter No. 2) Daily

Blower runtime (per blower) Daily


3-3

3.1.1 Cake Composition

For a 3-month period early in the test, the Sludge Cake was analyzed for moisture and % solids contents. The cake grabs were only taken on days with no precipitation so that rainfall could not affect the percent moisture.

The cake remained almost exactly 86% water content. Dry solids were reported consistently on an average basis of 140,800 ppm, or 14.08% dry solids. As a result, all net wet tonnage of sludge reported on Sludge Haul Manifests was taken to contain:

𝑊𝑊𝑒𝑒𝑠𝑠 𝑆𝑆𝑦𝑦𝑝𝑝𝑦𝑦𝑔𝑔𝑒𝑒 (𝑆𝑆𝑆𝑆) ∗ 0.1408 = 𝐵𝐵𝑝𝑝𝑠𝑠 𝑆𝑆𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏(𝑆𝑆𝑆𝑆)

3.1.2 BOD Load

During the test, the influent BOD concentration was recorded far more frequently than in the past using first a Composite Sampler and later with grab samples. The frequency of test samples for BOD in the influent occurred about 20 times per month, or 5 days a week.

Based on historical data for the plant kept in Bird Nest™, the next 2 graphs show that the ADF has slowly trended downward while the BOD concentration has slowly trended upward. This trend is most likely due to an increasing number of water-saving plumbing devices and should reflect future trends at U.S. WWTPs for the same reason.

The mass balances, however, indicate that resulting average BOD load per month has changed very little. The yield analysis takes into account any variability in loading because it calculates a “normalized” load.

Figure 3-1 –BOD LOAD, Historical and study periods


3-4

Figure 3-2 - Average Daily Flow during the historical and study period

3.1.3 Other Parameters

Dissolved oxygen (DO) in the influent grab samples varies greatly from day to day and may represent cycles of aerobic/anaerobic booms and busts in the collection system pipes. DO grab samples from the basins and the splitter box always fell within the operational set point range set by the operators.

TSS and VSS in the influent were tracked along with BOD in the influent. (TSS – VSS) gives the Inert Inorganic load to the plant. The Inorganic Suspended Solids (TSS – VSS) comprised roughly 10-15% of the solids load.

TSS and VSS were also tracked in the basins and at the splitter box. There was no significant change in the inert inorganic fraction to the plant or in its basins during the test.

Grab samples were extracted from various locations in the plant to examine the role of Suspended Solids, BOD, and DO in unit operations:

• From the Influent Rapid Mixing Channel • From the Aeration Basin • At the splitter box after the basins and before the clarifiers

The grab values reported for the splitter box were used in all basin data tabulation because this is the same location that STS has always used for their bi-monthly basin grabs for TSS and BOD.


3-5

Test America provided all sampling services, Chain of Custody, and results reporting, which are included in Appendix D

STS operators kept handwritten logbooks recording the following on-site measurements:

• Number of bins of sludge filled per day • Date for changing a polymer feed drum • “Cook-off” test used to calculate MLSS (reported as mgl) • “Set-test” used to calculate SVI (recorded as ml) • Blanket height in the 2 clarifiers • Pounds of product applied each day by STS (8 pounds per day)

On-site data logbook pages were photographed and retained in digital form as well as hard copies. (See Appendix F)

Finally, Magna Flow records provided manifests describing each 20 yd3 box that was taken for disposal at landfill. Each manifest recorded the Gross Vehicle Weight, Curb Weight, and the Freight on Board, with the waste content appearing as Short Tons. The average weight of a box of sludge was 14.35 ST/ 20 yd3 box. Records for the test period and all manifests for previous years produced this same average, and these records also are referenced in the appendix.

3.2 Raw Sludge Accounting

A simple approach was taken for raw sludge accounting that ignores the Input (BOD load) and looks only at the Output (number of hauls) produced during the test. It is broken down into two methods:

1. 5-year historical averages (2009-2014) and test period for months November through April

2. Previous Year (2013 –2014) and test period for months November through April

3.3 Biomass Yield

The objective required that a historical benchmark, or baseline, be established for biomass yield and then compare it to the yield calculated from plant test data with DryLet.

3.3.1 Method No. 1 – Limited Data for Study Period and Historical Period

This method uses the same limited data points as the historical record retention to estimate I/O response during the test. It uses an average haul weight data of 14.27.

𝑀𝑀𝑏𝑏𝑠𝑠𝑠𝑠ℎ𝑦𝑦𝑠𝑠 𝑆𝑆𝑏𝑏𝑠𝑠𝑏𝑏𝑦𝑦 𝐵𝐵𝐵𝐵𝐵𝐵 (𝑆𝑆𝑆𝑆)𝑆𝑆𝑆𝑆𝑆𝑆,𝐵𝐵𝐵𝐵𝐵𝐵 2 𝑦𝑦𝑠𝑠𝑠𝑠𝑠𝑠𝑦𝑦𝑦𝑦𝑦𝑦 = �(𝐴𝐴𝐴𝐴𝐴𝐴.𝐵𝐵𝐵𝐵𝐵𝐵2 × 𝐵𝐵𝑏𝑏𝑏𝑏𝑦𝑦𝑠𝑠 𝐹𝐹𝑦𝑦𝑏𝑏𝐹𝐹)


3-6

𝑀𝑀𝑏𝑏𝑠𝑠𝑠𝑠ℎ𝑦𝑦𝑠𝑠 𝐵𝐵𝑝𝑝𝑠𝑠 𝑆𝑆𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏 (𝑆𝑆𝑆𝑆)𝐴𝐴𝐴𝐴𝐴𝐴,𝐶𝐶𝐴𝐴𝐶𝐶𝐶𝐶= (𝑁𝑁𝑝𝑝𝑏𝑏𝑏𝑏𝑒𝑒𝑝𝑝 𝑏𝑏𝑜𝑜 𝐻𝐻𝑏𝑏𝑝𝑝𝑦𝑦𝑏𝑏 𝑒𝑒𝑏𝑏𝑝𝑝ℎ 𝑏𝑏𝑏𝑏𝑠𝑠𝑠𝑠ℎ)× 14.27 [𝐴𝐴𝑜𝑜𝑒𝑒𝑝𝑝𝑏𝑏𝑔𝑔𝑒𝑒 𝐹𝐹𝑒𝑒𝑏𝑏𝑔𝑔ℎ𝑠𝑠 𝑏𝑏𝑜𝑜 𝐹𝐹𝑒𝑒𝑠𝑠 𝑏𝑏𝑦𝑦𝑝𝑝𝑦𝑦𝑔𝑔𝑒𝑒 𝑝𝑝𝑒𝑒𝑝𝑝 ℎ𝑏𝑏𝑝𝑝𝑦𝑦 𝑏𝑏𝑠𝑠 𝑏𝑏ℎ𝑏𝑏𝑝𝑝𝑠𝑠 𝑠𝑠𝑏𝑏𝑠𝑠𝑏𝑏]× 0.1408 [𝐴𝐴𝑜𝑜𝑒𝑒𝑝𝑝𝑏𝑏𝑔𝑔𝑒𝑒 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑜𝑜𝑝𝑝𝑏𝑏𝑝𝑝𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠 𝑏𝑏𝑜𝑜 𝑏𝑏𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏]

𝑌𝑌𝑏𝑏𝑒𝑒𝑦𝑦𝑦𝑦 (𝑀𝑀𝑒𝑒𝑠𝑠ℎ𝑏𝑏𝑦𝑦 1) =𝑀𝑀𝑏𝑏𝑠𝑠𝑠𝑠ℎ𝑦𝑦𝑠𝑠 𝐵𝐵𝑝𝑝𝑠𝑠 𝑆𝑆𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏 (𝑆𝑆𝑆𝑆)𝐴𝐴𝐴𝐴𝐴𝐴,𝐶𝐶𝐴𝐴𝐶𝐶𝐶𝐶

𝑀𝑀𝑏𝑏𝑠𝑠𝑠𝑠ℎ𝑦𝑦𝑠𝑠 𝑆𝑆𝑏𝑏𝑠𝑠𝑏𝑏𝑦𝑦 𝐵𝐵𝐵𝐵𝐵𝐵 (𝑆𝑆𝑆𝑆)𝑆𝑆𝑆𝑆𝑆𝑆,𝐵𝐵𝐵𝐵𝐵𝐵 2 𝑦𝑦𝑠𝑠𝑠𝑠𝑠𝑠𝑦𝑦𝑦𝑦𝑦𝑦

3.3.2 Method No. 2 - Monthly Average for Study Period

This method uses a more accurate assessment of the load to the plant during the test by sampling BOD more frequently to improve the estimate of I/O response. This method incorporates actual haul weight data to obtain a more accurate assessment of the Output produced during the test

𝑀𝑀𝑏𝑏𝑠𝑠𝑠𝑠ℎ𝑦𝑦𝑠𝑠 𝑆𝑆𝑏𝑏𝑠𝑠𝑏𝑏𝑦𝑦 𝐵𝐵𝐵𝐵𝐵𝐵 (𝑆𝑆𝑆𝑆)𝑆𝑆𝐴𝐴,𝐵𝐵𝐵𝐵𝐵𝐵 20 𝑦𝑦𝑠𝑠𝑠𝑠𝑠𝑠𝑦𝑦𝑦𝑦𝑦𝑦 = �(𝐴𝐴𝐴𝐴𝐴𝐴.𝐵𝐵𝐵𝐵𝐵𝐵20 × 𝐵𝐵𝑏𝑏𝑏𝑏𝑦𝑦𝑠𝑠 𝐹𝐹𝑦𝑦𝑏𝑏𝐹𝐹)

𝑀𝑀𝑏𝑏𝑠𝑠𝑠𝑠ℎ𝑦𝑦𝑠𝑠 𝐵𝐵𝑝𝑝𝑠𝑠 𝑆𝑆𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏 (𝑆𝑆𝑆𝑆)𝐴𝐴𝐶𝐶𝑆𝑆𝐴𝐴𝐴𝐴𝐴𝐴,𝐶𝐶𝐴𝐴𝐶𝐶𝐶𝐶= (𝐴𝐴𝑝𝑝𝑠𝑠𝑝𝑝𝑏𝑏𝑦𝑦 𝑀𝑀𝑏𝑏𝑠𝑠𝑏𝑏𝑜𝑜𝑒𝑒𝑏𝑏𝑠𝑠 𝑊𝑊𝑒𝑒𝑠𝑠 𝑆𝑆𝑦𝑦𝑝𝑝𝑦𝑦𝑔𝑔𝑒𝑒 𝑊𝑊𝑒𝑒𝑏𝑏𝑔𝑔ℎ𝑠𝑠)× 0.1408 [𝐴𝐴𝑜𝑜𝑒𝑒𝑝𝑝𝑏𝑏𝑔𝑔𝑒𝑒 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑜𝑜𝑝𝑝𝑏𝑏𝑝𝑝𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠 𝑏𝑏𝑜𝑜 𝑏𝑏𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏]

𝑌𝑌𝑏𝑏𝑒𝑒𝑦𝑦𝑦𝑦 (𝑀𝑀𝑒𝑒𝑠𝑠ℎ𝑏𝑏𝑦𝑦 2) =𝑀𝑀𝑏𝑏𝑠𝑠𝑠𝑠ℎ𝑦𝑦𝑠𝑠 𝐵𝐵𝑝𝑝𝑠𝑠 𝑆𝑆𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏 (𝑆𝑆𝑆𝑆)𝐴𝐴𝐶𝐶𝑆𝑆𝐴𝐴𝐴𝐴𝐴𝐴,𝐶𝐶𝐴𝐴𝐶𝐶𝐶𝐶

𝑀𝑀𝑏𝑏𝑠𝑠𝑠𝑠ℎ𝑦𝑦𝑠𝑠 𝑆𝑆𝑏𝑏𝑠𝑠𝑏𝑏𝑦𝑦 𝐵𝐵𝐵𝐵𝐵𝐵 (𝑆𝑆𝑆𝑆)𝑆𝑆𝐴𝐴,𝐵𝐵𝐵𝐵𝐵𝐵 20 𝑦𝑦𝑠𝑠𝑠𝑠𝑠𝑠𝑦𝑦𝑦𝑦𝑦𝑦

3.3.3 Method No. 3 - Global Average for Study Period

This method uses all the influent BOD sample results to generate a global average of BOD concentration during the test from October to April. This concentration can then be used as the daily concentration for loading calculations throughout the test, and only the ADF would change from day to day. This method incorporates actual haul weight data to obtain a more accurate assessment of the Output produced during the test

𝑀𝑀𝑏𝑏𝑠𝑠𝑠𝑠ℎ𝑦𝑦𝑠𝑠 𝑆𝑆𝑏𝑏𝑠𝑠𝑏𝑏𝑦𝑦 𝐵𝐵𝐵𝐵𝐵𝐵 (𝑆𝑆𝑆𝑆)𝑆𝑆𝐴𝐴,𝐵𝐵𝐵𝐵𝐵𝐵 𝐴𝐴𝐴𝐴𝐴𝐴 𝑦𝑦𝑠𝑠𝑠𝑠𝑠𝑠𝑦𝑦𝑦𝑦𝑦𝑦 = �(𝐴𝐴𝐴𝐴𝐴𝐴.𝐵𝐵𝐵𝐵𝐵𝐵𝐴𝐴𝐴𝐴𝐴𝐴 × 𝐵𝐵𝑏𝑏𝑏𝑏𝑦𝑦𝑠𝑠 𝐹𝐹𝑦𝑦𝑏𝑏𝐹𝐹)

𝑀𝑀𝑏𝑏𝑠𝑠𝑠𝑠ℎ𝑦𝑦𝑠𝑠 𝐵𝐵𝑝𝑝𝑠𝑠 𝑆𝑆𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏 (𝑆𝑆𝑆𝑆)𝐴𝐴𝐶𝐶𝑆𝑆𝐴𝐴𝐴𝐴𝐴𝐴,𝐶𝐶𝐴𝐴𝐶𝐶𝐶𝐶= (𝐴𝐴𝑝𝑝𝑠𝑠𝑝𝑝𝑏𝑏𝑦𝑦 𝑀𝑀𝑏𝑏𝑠𝑠𝑏𝑏𝑜𝑜𝑒𝑒𝑏𝑏𝑠𝑠 𝑊𝑊𝑒𝑒𝑠𝑠 𝑆𝑆𝑦𝑦𝑝𝑝𝑦𝑦𝑔𝑔𝑒𝑒 𝑊𝑊𝑒𝑒𝑏𝑏𝑔𝑔ℎ𝑠𝑠)× 0.1408 [𝐴𝐴𝑜𝑜𝑒𝑒𝑝𝑝𝑏𝑏𝑔𝑔𝑒𝑒 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑜𝑜𝑝𝑝𝑏𝑏𝑝𝑝𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠 𝑏𝑏𝑜𝑜 𝑏𝑏𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏]

𝑌𝑌𝑏𝑏𝑒𝑒𝑦𝑦𝑦𝑦 (𝑀𝑀𝑒𝑒𝑠𝑠ℎ𝑏𝑏𝑦𝑦 3) =𝑀𝑀𝑏𝑏𝑠𝑠𝑠𝑠ℎ𝑦𝑦𝑠𝑠 𝐵𝐵𝑝𝑝𝑠𝑠 𝑆𝑆𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏 (𝑆𝑆𝑆𝑆)𝐴𝐴𝐶𝐶𝑆𝑆𝐴𝐴𝐴𝐴𝐴𝐴,𝐶𝐶𝐴𝐴𝐶𝐶𝐶𝐶

𝑀𝑀𝑏𝑏𝑠𝑠𝑠𝑠ℎ𝑦𝑦𝑠𝑠 𝑆𝑆𝑏𝑏𝑠𝑠𝑏𝑏𝑦𝑦 𝐵𝐵𝐵𝐵𝐵𝐵 (𝑆𝑆𝑆𝑆)𝑆𝑆𝐴𝐴,𝐵𝐵𝐵𝐵𝐵𝐵 𝐴𝐴𝐴𝐴𝐴𝐴 𝑦𝑦𝑠𝑠𝑠𝑠𝑠𝑠𝑦𝑦𝑦𝑦𝑦𝑦


3-7

The following table summarizes the differences in the analytic methods used to calculate biomass yield:

Table 3-5 - Summary of Yield Analysis Methods

Stream Method 1 Method 2 Method 3

Influent

(Mass In)

Severn Trent Services analytical: Average of 2 BOD

samples per month

Daily Flow as recorded by Severn Trent Services

Test America BOD analytics: monthly average

Daily Flow as recorded by


Test America BOD analytics: Test period (global) average

Daily Flow as recorded by


Outbound

(Mass Out)

Number of hauls

Average weight of each haul over test

(14.27 short tons per haul)

Average percent solids (14.08 percent dry solids per

haul)

Number of hauls

MagnaFlow Inc., actual manifest tonnage

Average percent solids

(14.08 percent dry solids per haul)

Number of hauls

MagnaFlow Inc., actual manifest tonnage

Average percent solids

(14.08 percent dry solids per haul)

3.3.4 Digester Drawdown

In the last half of December into January, STS operators decided to draw down their already full digester since they prefer to operate with some available freeboard capacity as a buffer. In this same period, the wasting rate was raised 40%. The increased wasting flushed through the system in January and, when combined with the digester drawdown, caused a huge spike in hauls in January. Therefore the change in sludge inventory in the digester had to be accounted for. It was confirmed with the Operator that the digester was at full capacity when the study period started and it continued to be full until January. It was then emptied throughout the month as confirmed by the sludge haul manifests. Therefore the mass of 25 ST of dry sludge that was hauled, but not produced in January, was subtracted from the dry metric tons for the month of January. The mass was estimated by calculating the volume difference between a full digester and one with 8 feet of freeboard and multiplying it by the specific weight of water, the specific gravity of sludge a 2% solids concentration and a conversion factor for metric tons. The result was then subtracted from the raw dry metric tons for January before being used to calculate a corrected yield. See below for calculations.

Table 3-6 - Digester inventory drawdown

Digester Dimensions: 90x55 ft Estimated Depth change 8 ft Specific Weight of Water: 62.4 lb/cu.ft. Specific Gravity of Sludge (Metcalf & Eddy) 1.005 Percentage Solids in Digester 2% Pound per short ton 0.0005 lb/ST


3-8

𝑀𝑀𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑜𝑜 𝑦𝑦𝑝𝑝𝑠𝑠 𝑏𝑏𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏 (𝑆𝑆𝑆𝑆) = 90𝑜𝑜𝑠𝑠 ∗ 55𝑜𝑜𝑠𝑠 ∗ 8𝑜𝑜𝑠𝑠 ∗ 62.4lb𝑜𝑜𝑠𝑠3

∗ 1.005 ∗ .02 ∗ .0005 lb𝑆𝑆𝑆𝑆

= 25 𝑆𝑆𝑆𝑆

𝑀𝑀𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑜𝑜 𝐹𝐹𝑒𝑒𝑠𝑠 𝑏𝑏𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏 ℎ𝑏𝑏𝑝𝑝𝑦𝑦𝑒𝑒𝑦𝑦 = 𝑀𝑀𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑜𝑜 𝑦𝑦𝑝𝑝𝑠𝑠 𝑏𝑏𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏

𝑃𝑃𝑒𝑒𝑝𝑝𝑝𝑝𝑒𝑒𝑠𝑠𝑠𝑠𝑏𝑏𝑔𝑔𝑒𝑒 𝑆𝑆𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏 𝑏𝑏𝑜𝑜 𝑝𝑝𝑝𝑝𝑒𝑒𝑏𝑏𝑏𝑏𝑒𝑒𝑦𝑦 𝑏𝑏𝑦𝑦𝑝𝑝𝑦𝑦𝑔𝑔𝑒𝑒

𝑁𝑁𝑝𝑝𝑏𝑏𝑏𝑏𝑒𝑒𝑝𝑝 𝑏𝑏𝑜𝑜 ℎ𝑏𝑏𝑝𝑝𝑦𝑦𝑏𝑏 =𝑀𝑀𝑏𝑏𝑏𝑏𝑏𝑏 𝑏𝑏𝑜𝑜 𝐹𝐹𝑒𝑒𝑠𝑠 𝑏𝑏𝑏𝑏𝑦𝑦𝑏𝑏𝑦𝑦𝑏𝑏 ℎ𝑏𝑏𝑝𝑝𝑦𝑦𝑒𝑒𝑦𝑦 (𝑆𝑆𝑆𝑆) 𝐴𝐴𝑜𝑜𝑒𝑒𝑝𝑝𝑏𝑏𝑔𝑔𝑒𝑒 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑝𝑝𝑒𝑒𝑝𝑝 𝐻𝐻𝑏𝑏𝑝𝑝𝑦𝑦 (𝑆𝑆𝑆𝑆)

3.3.5 Cost Analysis

The cost savings on sludge hauled were calculated based on the percentage reduction of biomass yield during the study period. The total amount spent on “Sludge Disposal” from the Bookkeeper’s report from fiscal years 2009 to 2013 was adjusted for inflation to 2015 dollars and an average yearly cost was calculated. It was then multiplied by the percent reduction of biomass yield to obtain the 2015 projected expenses. Finally, the 2015 projected savings was calculated by taking the difference of the average and the projected expenses.

3.4 Polymer Usage

Historical data for polymer usage was limited to the number of drums purchased within a year prior to the study period. The drums have a volume of 55 gallons, so an average of gallons per month was calculated.

𝐻𝐻𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑝𝑝𝑏𝑏𝑝𝑝𝑏𝑏𝑦𝑦 𝑈𝑈𝑏𝑏𝑏𝑏𝑔𝑔𝑒𝑒 �𝑔𝑔𝑏𝑏𝑦𝑦

𝑏𝑏𝑏𝑏𝑠𝑠𝑠𝑠ℎ� =

𝑁𝑁𝑝𝑝𝑏𝑏𝑏𝑏𝑒𝑒𝑝𝑝 𝑏𝑏𝑜𝑜 𝑦𝑦𝑝𝑝𝑝𝑝𝑏𝑏𝑏𝑏 𝑏𝑏𝑠𝑠𝑜𝑜𝑏𝑏𝑏𝑏𝑝𝑝𝑒𝑒𝑦𝑦𝑏𝑏𝑏𝑏𝑠𝑠𝑠𝑠ℎ

∗ 55 �𝑔𝑔𝑏𝑏𝑦𝑦𝑦𝑦𝑝𝑝𝑝𝑝𝑏𝑏

�

𝐴𝐴𝑜𝑜𝑒𝑒𝑝𝑝𝑏𝑏𝑔𝑔𝑒𝑒 𝑦𝑦𝑝𝑝𝑝𝑝𝑏𝑏 𝑦𝑦𝑏𝑏𝑜𝑜𝑒𝑒 �𝑦𝑦𝑏𝑏𝑠𝑠𝑦𝑦𝑝𝑝𝑝𝑝𝑏𝑏

� =55 � 𝑔𝑔𝑏𝑏𝑦𝑦

𝑦𝑦𝑝𝑝𝑝𝑝𝑏𝑏�

𝐻𝐻𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑝𝑝𝑏𝑏𝑝𝑝𝑏𝑏𝑦𝑦 𝑝𝑝𝑏𝑏𝑏𝑏𝑔𝑔𝑒𝑒 �𝑔𝑔𝑏𝑏𝑦𝑦𝑦𝑦𝑏𝑏𝑠𝑠�

Accurate tracking of polymer use was achieved by marking the date the polymer drum was changed. This was performed beginning February 5th and lasting until April 14th. An estimate of gallons per month was then calculated by dividing 55 gallons in a drum over the time period it lasted.

𝑆𝑆𝑠𝑠𝑝𝑝𝑦𝑦𝑠𝑠 𝑝𝑝𝑒𝑒𝑝𝑝𝑏𝑏𝑏𝑏𝑦𝑦 𝑝𝑝𝑏𝑏𝑏𝑏𝑔𝑔𝑒𝑒 �𝑔𝑔𝑏𝑏𝑦𝑦𝑦𝑦𝑏𝑏𝑠𝑠

� =55 𝑔𝑔𝑏𝑏𝑦𝑦

𝑦𝑦𝑏𝑏𝑠𝑠𝑏𝑏 𝑏𝑏𝑒𝑒𝑠𝑠𝐹𝐹𝑒𝑒𝑒𝑒𝑠𝑠 𝑦𝑦𝑝𝑝𝑝𝑝𝑏𝑏 𝑝𝑝ℎ𝑏𝑏𝑠𝑠𝑔𝑔𝑒𝑒

𝑆𝑆𝑠𝑠𝑝𝑝𝑦𝑦𝑠𝑠 𝑝𝑝𝑒𝑒𝑝𝑝𝑏𝑏𝑏𝑏𝑦𝑦 𝑝𝑝𝑏𝑏𝑏𝑏𝑔𝑔𝑒𝑒 �𝑔𝑔𝑏𝑏𝑦𝑦

𝑏𝑏𝑏𝑏𝑠𝑠𝑠𝑠ℎ� = 𝑆𝑆𝑠𝑠𝑝𝑝𝑦𝑦𝑠𝑠 𝑝𝑝𝑒𝑒𝑝𝑝𝑏𝑏𝑏𝑏𝑦𝑦 𝑝𝑝𝑏𝑏𝑏𝑏𝑔𝑔𝑒𝑒 �

𝑔𝑔𝑏𝑏𝑦𝑦𝑦𝑦𝑏𝑏𝑠𝑠

� ∗ 𝑦𝑦𝑏𝑏𝑠𝑠𝑏𝑏 𝑏𝑏𝑜𝑜 𝑠𝑠ℎ𝑒𝑒 𝑏𝑏𝑏𝑏𝑠𝑠𝑠𝑠ℎ


3-9

The study period average was then compared to the average for the previous year to obtain a percent reduction in use.

𝑃𝑃𝑒𝑒𝑝𝑝𝑝𝑝𝑒𝑒𝑠𝑠𝑠𝑠 𝑅𝑅𝑒𝑒𝑦𝑦𝑝𝑝𝑝𝑝𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠 (%) = 𝑆𝑆𝑠𝑠𝑝𝑝𝑦𝑦𝑠𝑠 𝑝𝑝𝑒𝑒𝑝𝑝𝑏𝑏𝑏𝑏𝑦𝑦 𝑝𝑝𝑏𝑏𝑏𝑏𝑔𝑔𝑒𝑒 � 𝑔𝑔𝑏𝑏𝑦𝑦

𝑏𝑏𝑏𝑏𝑠𝑠𝑠𝑠ℎ� − 𝐻𝐻𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑝𝑝𝑏𝑏𝑝𝑝𝑏𝑏𝑦𝑦 𝑝𝑝𝑏𝑏𝑏𝑏𝑔𝑔𝑒𝑒 � 𝑔𝑔𝑏𝑏𝑦𝑦𝑏𝑏𝑏𝑏𝑠𝑠𝑠𝑠ℎ�

𝐻𝐻𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑝𝑝𝑏𝑏𝑝𝑝𝑏𝑏𝑦𝑦 𝑝𝑝𝑏𝑏𝑏𝑏𝑔𝑔𝑒𝑒 � 𝑔𝑔𝑏𝑏𝑦𝑦𝑏𝑏𝑏𝑏𝑠𝑠𝑠𝑠ℎ�

The cost savings on polymer usage were calculated based on the percentage reduction of polymer use during the study period. The total amount spent on “Polymer/Sludge Treatment” from the bookkeepers report from fiscal years 2009 to 2013 was adjusted for inflation to 2015 Dollars and an average yearly cost calculated. It was then multiplied by the percent reduction of polymer use to obtain the 2015 projected expenses. Finally, the 2015 projected savings was calculated by taking the difference of the average and the projected expenses.

3.5 Energy Usage

Energy usage was obtained from the Bird Nest™ database in kWh and analyzed over the historical Period (2009-2014) and over the study period (2014-2015) on a monthly basis. The average over the historical period was calculated and compared to the actual usage over the study period.

The cost savings on energy usage were considered to be negligible since there was no appreciable decrease in energy usage. However, future plant optimization may result in more significant cost savings.

3.6 Equipment Life and Maintenance

3.6.1 Belt press Useful Life Extension

The approximate reduction in sludge (30% as documented in the biomass yield analysis) was assumed to translate to a proportional reduction in the use of the belt presses and associated equipment. Performance of proper regular maintenance was assumed as well. The straight-line depreciation method was used over the 25-year useful life of the presses with the approximate 30% reduction in use. This was done for both the smaller (Ashbrook) and larger (Andritz) presses

To perform the analysis the depreciable asset cost and the straight-line depreciation rate were calculated.

𝐵𝐵𝑒𝑒𝑝𝑝𝑝𝑝𝑒𝑒𝑝𝑝𝑏𝑏𝑏𝑏𝑏𝑏𝑦𝑦𝑒𝑒 𝐴𝐴𝑏𝑏𝑏𝑏𝑒𝑒𝑠𝑠 𝐶𝐶𝑏𝑏𝑏𝑏𝑠𝑠 = 𝐼𝐼𝑠𝑠𝑏𝑏𝑠𝑠𝑏𝑏𝑏𝑏𝑦𝑦 𝐶𝐶𝑏𝑏𝑏𝑏𝑠𝑠 − 𝑅𝑅𝑒𝑒𝑏𝑏𝑏𝑏𝑦𝑦𝑝𝑝𝑏𝑏𝑦𝑦 𝐴𝐴𝑏𝑏𝑦𝑦𝑝𝑝𝑒𝑒

𝐵𝐵𝑒𝑒𝑝𝑝𝑝𝑝𝑒𝑒𝑝𝑝𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠 𝑅𝑅𝑏𝑏𝑠𝑠𝑒𝑒 = 1

𝑈𝑈𝑏𝑏𝑒𝑒𝑜𝑜𝑝𝑝𝑦𝑦 𝑦𝑦𝑏𝑏𝑜𝑜𝑒𝑒

They were multiplied to obtain the existing annual depreciation rate.


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𝐸𝐸𝐸𝐸𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑠𝑠𝑔𝑔 𝐴𝐴𝑠𝑠𝑠𝑠𝑝𝑝𝑏𝑏𝑦𝑦 𝐵𝐵𝑒𝑒𝑝𝑝𝑝𝑝𝑒𝑒𝑝𝑝𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠 = 𝐵𝐵𝑒𝑒𝑝𝑝𝑝𝑝𝑒𝑒𝑝𝑝𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠 𝑅𝑅𝑏𝑏𝑠𝑠𝑒𝑒 ∗ 𝐵𝐵𝑒𝑒𝑝𝑝𝑝𝑝𝑒𝑒𝑝𝑝𝑏𝑏𝑏𝑏𝑏𝑏𝑦𝑦𝑒𝑒 𝐴𝐴𝑏𝑏𝑏𝑏𝑒𝑒𝑠𝑠 𝐶𝐶𝑏𝑏𝑏𝑏𝑠𝑠

It is then subtracted from the value of the press every year to project its future value.

To obtain the projected depreciation the existing depreciation was then multiplied by 70% (reflecting a 30% reduction in use).

𝑃𝑃𝑝𝑝𝑏𝑏𝑃𝑃𝑒𝑒𝑝𝑝𝑠𝑠𝑒𝑒𝑦𝑦 𝐵𝐵𝑒𝑒𝑝𝑝𝑝𝑝𝑒𝑒𝑝𝑝𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠 = 𝐸𝐸𝐸𝐸𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑠𝑠𝑔𝑔 𝐵𝐵𝑒𝑒𝑝𝑝𝑝𝑝𝑒𝑒𝑝𝑝𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠 (1 − % 𝑝𝑝𝑒𝑒𝑦𝑦𝑝𝑝𝑝𝑝𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠 𝑏𝑏𝑠𝑠 𝑝𝑝𝑏𝑏𝑒𝑒)

𝐸𝐸𝑏𝑏𝑠𝑠𝑏𝑏𝑏𝑏𝑏𝑏𝑠𝑠𝑒𝑒𝑦𝑦 𝑆𝑆𝑏𝑏𝑜𝑜𝑏𝑏𝑠𝑠𝑔𝑔𝑏𝑏 = 𝐸𝐸𝐸𝐸𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑠𝑠𝑔𝑔 𝐵𝐵𝑒𝑒𝑝𝑝𝑝𝑝𝑒𝑒𝑝𝑝𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠 − 𝑃𝑃𝑝𝑝𝑏𝑏𝑃𝑃𝑒𝑒𝑝𝑝𝑠𝑠𝑒𝑒𝑦𝑦 𝐵𝐵𝑒𝑒𝑝𝑝𝑝𝑝𝑒𝑒𝑝𝑝𝑏𝑏𝑏𝑏𝑠𝑠𝑏𝑏𝑏𝑏𝑠𝑠

The cost savings on belt press maintenance were calculated based on the percentage reduction of biomass yield during the study period. The total amount spent on maintenance and repairs on the belt pressed and associated equipment from the bookkeepers report from fiscal years 2009 to 2013 was adjusted for inflation to 2015 Dollars and an average yearly cost calculated. It was then multiplied by the percent reduction of biomass yield to obtain the 2015 projected expenses. Finally, the 2015 projected savings was calculated by taking the difference of the average and the projected expenses.

3.6.2 Belt Press Maintenance Cost Reduction

The belt press maintenance cost reduction was calculated by converting the total maintenance and repair expenses on the belt pressed and associated equipment from 2010 through 2014 to 2015 dollars and averaging over the 5-year period Assuming a 30% reduction in maintenance and repairs based on the reduction is sludge production from using the DryLet product, annual savings were calculated.


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Section 4 Results

4.1 Raw Sludge Accounting

4.1.1 5-year Historical Averages and Test Period for months November 2014 through April 2015

Table 4-1 - Raw Sludge Accounting Results

Month Historical average (5 years)

Test Period (Actual)

Test Period (January correction) Reduced hauls

November 20 15 15 5

December 26 18 18 8

January 22 33 19* 3

February 22 15 15 7

March 24 16 16 8

April 28 21 21 7 * 14 hauls subtracted in January for digester inventory drawdown, 8 to 10 feet of freeboard 2.0% solids (see Section 3 – Digester Drawdown)

From the previous table the aggregate number of hauls taken as a historical average for months November through April totaled 142. The total hauls for the same months during the test period totaled 104. The net percent reduction is 26.5% on the number of hauls over the same period from November through April. Excluding January, the net percent reduction is 28.8% over the same period.

Net Percent Reduction In Total Quantity of Hauls

26.5%

(Historical versus test period for months November through April)


28.8%

(Historical versus test period for months November through April excluding January)


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4.1.2 Previous Year (2013-2014) and Test Period for months November 2014 through April 2015

Table 4-2 - Raw Sludge Accounting Results

Month Previous year (Nov 2013 – April 2014)

Test period (January correction) Reduced hauls

November 24 15 9

December 23 18 5

January 26 19* 7

February 19 15 4

March 24 16 8

April 46 21 25

From the previous table the aggregate number of hauls taken as a historical average for months November through April totaled 162. The total hauls for the same months during the test period totaled 104. The net percent reduction is 35.8% on the number of hauls over the same period from November through April. Excluding January, the net percent reduction is 37.5% over the same period.


35.8%

(Historical versus test period for months November through April)


37.5%

(Historical versus test period for months November through April excluding January)

The results of the study show a cumulative and sustained reduction of sludge compared to historical plant operations. A variety of methods of analysis all produce the same result indicating a high degree of confidence in the analysis. The study shows that DryLet® LIFT reduced raw sludge production by 30% +/- 5 %.


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Figure 4-1 - Raw Sludge Accounting Dry Solids Comparison

Figure 4-2 - Raw Sludge Accounting Haul Comparison


4-14

4.2 Biomass Yield

4.2.1 Historical Period Yield (Method 1)

The following tabulated data was calculated using the criterion of Method 1. The percent yield was determined for each month and ranged from 35% to 47%.

The historical data presented here is used to compare the test period data and analysis utilizing Methods 1, 2, & 3 previously described. The following sections illustrate the changes in the yield during the test period using DryLet® LIFT.

4.2.2 Test Period Yield (Method 1)


The following table presents the comparison of the historical and test period yields using Method 1.

Month Historical Average Yield Method 1 Monthly Yield % Chg.

November 41% 26% 36% December 44% 29% 33% January 34% 37% -9% February 46% 27% 40% March 38% 26% 33% April 47% 36% 22%

The net percent reduction in yields using the averages for each period (historical and test period) results in a 25.8% decrease. When the yield is calculated on an overall mass balance of BOD


4-15

load and Dry Solids for the same period the reduction in the yield is 26.9%. Excluding January 2015 for both cases results in a yield reduction of 32.8% and 29.8%, respectively.

Test Period (Method 1) Net Percent Reduction in Yield

25.8% (Average of historical and test period yields for months

November through April) 32.8%

(same as above excluding January 2015)


26.9% (Comparison of historical and test period yields using total mass for each period for months November through April)

29.8% (same as above excluding January 2015)





November 41% 26% 36% December 44% 37% 15% January 34% 28% 18% February 46% 29% 37% March 38% 27% 30% April 47% 36% 22%

The net percent reduction in yields using the averages for each period (historical and test period) results in a 26.3% decrease. When the yield is calculated on an overall mass balance of BOD load and Dry Solids for the same period the reduction in the yield is 26.4%. Excluding January 2015 for both cases results in a yield reduction of 28.1% and 25.1%, respectively.









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November 41% 27% 35% December 44% 33% 24% January 34% 31% 9% February 46% 34% 26% March 38% 27% 30% April 47% 36% 23%

The net percent reduction in yields using the averages for each period (historical and test period) results in a 24.6% decrease. When the yield is calculated on an overall mass balance of BOD load and Dry Solids for the same period the reduction in the yield is 24.6%. Excluding January 2015 for both cases results in a yield reduction of 27.6% and 24.4%, respectively.









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4.2.5 Steady State Period Yield (Method 3)

The steady state period is defined by the dates of March 11, 2015 through April 30, 2015. This is the period where the change in TSS in the aeration basin was approximately 9,500 ± 1,500 milligrams per liter or a range of 8,000 to 11,000 mg/L.

The following tabulated data was calculated using the criterion of Method 3.


Month Historical Average Yield

Method 3 Monthly Yield % Chg.

Mar - Apr 43% 34% 22.3%

The net percent reduction in yields using the averages for each period (historical and test period) results in a 22.3% decrease.

Steady State (Method 3) Net Percent Reduction in Yield

22.3%

(Comparison of historical and test period yields using total mass for steady state period [Mar-Apr])

The study shows that DryLet® LIFT decreased the observed yield from the plant when outhauls are referenced to the BOD load. The data supports the hypothesis that DryLet® LIFT reduced the observed biomass Yield by 30 % +/- 5 %, from an historical average during the test months of Y= .42 - .44 to the range of .29 - .31 when including January data. It is clear that operational factors affected January results. If we exclude January as an outlier, then the reduction is further decreased, though only slightly.

Calculating the yield using the 4 months which are in agreement with very low standard deviation, a value of Y= .29 is obtained resulting in a 32 % reduction. The reduction calculated


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from the various methods gives a range of results from 24 % to 37 %, with high values corresponding to cases that exclude January, and the lowest values corresponding to the cases in which no raw data was excluded. The results then for sludge reduction can be reported with an extremely high degree of confidence as being 30 % +/- 8 %, and most likely 30 % +/- 5% with a very good degree of confidence.

Table 4-3 – Summary of Sludge Reduction Results

Method % Reduction HIGH % Reduction LOW

Sludge haul Historical 28.8 26.5 Sludge haul Prior Year 37.5 35.8

Method 1 32.8 , 29.8 25.8 , 26.9 Method 2 28.1 , 26.4 25.1 , 26.3 Method 3 27.6 , 24.6 24.4 , 24.6 Average 28.5 26.9

Figure 4-3 – Yield Results


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4.2.6 Operational Factors and Digester Drawdown

As stated in the previous section, the sludge inventory in the digester had to be accounted for in January. A similar situation occurred for much of April, because there was concern about the high solids levels. These 2 changes probably resulted in unnecessary sludge production that makes the January and April observed yields both on the high side. The amount of solids wasted is the product of the wasting rate and the wasting concentration.

Figure 4-4 - Raw Sludge Accounting Haul Comparison

Figure 4-5 – Yield Results

Increased wasting rate 40% for 3 weeks with MLSS at 4,500

Increased wasting rate 20% for 2 weeks with MLSS at 9,000


4-20

4.2.7 Cost Savings

Table 4-4 shows the cost of Sludge Disposal per fiscal year, the adjustment to 2015 dollars and the projected savings for a 22% and reduction in sludge production.

Table 4-4 – Sludge Reduction Cost Savings

Fiscal Year Sludge Disposal Inflation Adjusted to 2015

June 13 - May 14 $182,748 $181,193 June 12 - May 13 $132,903 $133,910 June 11 - May 12 $128,507 $131,377 June 10 - May 11 $121,758 $127,053 June 09 - May 10 $145,129 $156,221

Average $142,209 $145,951

% Reduction 27% 29% 2015 Projected

Expenses $106,654 $102,968 2015 Projected

Savings $39,297 $42,983

4.3 Polymer Usage

The polymer, which is used to aid in dewatering the sludge, is fed just before the presses. While this feed rate is adjustable, it is always entrained into the wasting stream to the presses. A reduction in polymer use occurs naturally from creating less pressed sludge. Table 4-5 shows the historical polymer usage

Table 4-5 – Historical Polymer Usage

Report Month Drums Gallons per Month October 2013 4 220

November 2013 4 220 December 2013 3 165 January 2014 0 0 February 2014 3 165 March 2014 0 0 April 2014 3 165 May 2014 5 275 June 2014 5 275 July 2014 4 220

August 2014 0 0 September 2014 0 0

Average 3 142


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Table 4-6 shows the study period tracking of polymer use.

Table 4-6 – Study Period Polymer Usage

Date Gallons Gallons per Day Gallons per Month

2/5/2015 N/A N/A N/A 2/24/2015 55 2.89 81 3/11/2015 55 3.67 114 4/14/2015 55 1.62 50 Average 2.73 81

Table 4-7 shows the comparison of the historical and study period polymer use. A reduction of 43% was documented during the study period along with a corresponding extension in drum life.

Table 4-7 – Polymer Usage Comparison

Polymer Use Gallons per Month Drum Life (Days) Historical 142 12 Study Period 82 20 % Reduction -43% +43%

Table 4-8 shows the cost of Polymer/Sludge Treatment per fiscal year, the adjustment to 2015 dollars and the projected savings for a 43% reduction in polymer use.

Table 4-8 – Polymer Usage Cost Savings

Fiscal year Polymer/Sludge Treatment Inflation Adjusted to 2015 June 09 - May 10 $19,425 $21,259 June 10 - May 11 $36,452 $38,672 June 11 - May 12 $15,613 $16,288 June 12 - May 13 $71,332 $73,072 June 13 - May 14 $27,670 $27,892 5-year Average $34,098 $34,843

2015 Projected Savings (43%) $15,238 2015 Projected Expenses $20,199


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4.4 Energy Usage

A shown in Table 4-9, there was minor variation on energy usage on a month to month basis and no net change in energy usage over the study period. This can also be seen on Figure 4-5.

Table 4-9 – Energy Usage Comparison

Month Historical Average (2009-2014)

Study Period (2014-2015)

% Reduction

October 738 772 5% November 719 677.9 -6% December 768 795.9 4% January 821 810.1 -1% February 713 699.8 -2% March 777 753.4 -3% April 770 760.1 -1% May 742 735.6 -1% June 715 738.3 3% July 721 712.7 -1% Average 748 746 0%

Figure 4-6 – Energy Usage Comparison

Energy usage remained constant, showing negligible 2% reduction. Increased microbial growth will increase the blower demand; but in this case, no increase in electrical consumption was detected. Whatever increase in oxygen demand that occurred was offset by operational improvement brought about by blower control tuning.

0100200300400500600700800900

Energy Usage (kWh)

Historical Average Study Period


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4.5 Equipment Life and Maintenance

The reduction of sludge produced has the ancillary effect of extending the life of sludge-handling equipment, namely the belt presses. 4.5.1 Belt Press Useful Life Extension, Associated Equipment

Replacement of the smaller (Ashbrook) belt press and polymer system is currently estimated at around $667,000 including contingency and engineering. Similarly, the purchase, installation and engineering costs for the larger (Andritz) press totaled approximately $550,000 in 2005 and $672,048 adjusted for inflation.

Using the straight-line depreciation method, the capital savings on the presses were estimated to be $8,004 a year for the Ashbrook and $8,065 for the Andritz. The projected depreciation is shown in Figures 4-6 and 4-7.

Figure 4-7 – Ashbrook Belt Press Existing and Projected Depreciation

Figure 4-8 – Andritz Belt Press Existing and Projected Depreciation

$0

$100,000

$200,000

$300,000

$400,000

$500,000

$600,000

$700,000

$800,000

0 5 10 15 20 25 30 35Year

Ashbrook Press

Existing DepreciationProjected Depreciation

$0

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$200,000

$300,000

$400,000

$500,000

$600,000

$700,000

$800,000

0 5 10 15 20 25 30 35Year

Andritz Press

Existing DepreciationProjected Depreciation


4-24

4.5.2 Belt Press Maintenance Cost Reduction

From 2010 through 2014, maintenance and repairs on the belt presses and associated equipment totaled just under $180,000. Converting that to 2015 dollars and averaging over the 5-year period, maintenance and repair costs were approximately $37,500 per year. Assuming a 30% reduction (or prolonging) in maintenance and repairs based on the reduction is sludge production from using the DryLet product, annual savings could be approximately $11,250 as seen on Table 4-10.

Table 4-10 – Belt Press Cost Savings

Fiscal year Belt Press Maintenance Inflation Adjusted to 2015 June 09 - May 10 $21,148 $23,144 June 10 - May 11 $42,175 $44,744 June 11 - May 12 $12,983 $13,494 June 12 - May 13 $66,393 $68,012 June 13 - May 14 $38,412 $38,721 5-year Average $36,222 $37,623

2015 Projected Savings (30%) $11,287 2015 Projected Expenses $26,336

4.6 Other Parameters

The following graph illustrates the dramatic change in microbial growth rates in the basins over the 8 months. The solids concentration, or carrying capacity, tripled. This graph contains the classic sigmoidal shape associated with an increasing microbial population.

Figure 4-9 – TSS and VSS in the Aeration Basin during Study Period


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Certainly, as the population ramps up during a transitional period then mass is accumulated in the system, but the last 2 months show that a new steady state has been achieved. Original MLSS readings were in the range of 2,500 – 3,500 mgl. In the new steady state system, levels are in the range of 9,000 – 11,000 for TSS.

Figure 4-10-TSS in Aeration Basin during Steady State Period

Figure 4-11 – Mass Under Aeration during Study Period


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In this new steady state, there are over 75 short tons under aeration in the plant. However, after day 160 the mass of solids under aeration is no longer accumulated in the plant. This period shows the same reduction in amount of sludge produced and shows the same reduction in the yield factor observed during the transitional phase. The plant response described here is the result of 8 pounds of DryLet® LIFT a day throughout the test period. As a new technology for wastewater, DryLet and STS cautiously allowed the solids to climb slowly during the test. It remains to be determined how quickly the carrying capacity can be raised to this or an even higher level with perhaps a higher dose of the product for transitional periods followed by a smaller maintenance dose of 8 pounds.

Regardless, the product showed the same 30% reduction after the first month of application. The following savings table is based on the performance of the product after the first month of inoculation projected out to an annual basis. The table focuses on the four main value drivers centered on sludge reduction.

Table 4-11 –Summary of Results

Operating Cost / Value Driver Result Sludge Disposal 30 % Reduction Polymer Usage 44 % Reduction

Press Equipment Life 30 % Reduction Press Maintenance 30 % Reduction

Table 4-12 - Value Proposition

Value Driver Annual Cost Pre-DryLet

Annual Cost with DryLet

Annual Savings with

DryLet

Savings per Pound of DryLet

Sludge Disposal $146,000 $102,000 $44,000 $15 Polymer Use $35,000 $19,600 $15,400 $5 Equipment Life $52,000 $43,000 $9,000 $3 Equipment

$35,000 $23,750 $11,250 $4

TOTALS $268,000 $188,350 $79,450 $27 The ancillary benefits of longer equipment life and reduced maintenance on presses and associated equipment naturally arise from simply performing the action of creating 30 % less pressed sludge. The polymer, which is used to aid in dewatering the sludge, is fed just before the presses. While this feed rate is adjustable, it is always entrained into the wasting stream to the presses. No extrapolation was required to quantify polymer reduction because operators tracked the rate of consumption. Polymer consumption was reduced 44 %.


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Additionally, all of the KPIs showed a steady, dramatic increase during the product test period.

Figure 4-12 –Summary of KPIs over the Study Period

Finally, the plant remained completely compliant throughout the 7-month test period as shown by Figure 4-13 below.

Figure 4-13 – Effluent Quality


5-1

Section 5 Discussion and Observations

5.1 Effect on Sludge, Polymer, MLSS, BOD, SVI and SRT

There were dramatic changes in several KPIs during the course of the product trial with no permit excursions and no deleterious effects. Additionally, according to STS, the plant seemed to run more smoothly with only one full time operator on-site most of the time.

Historically, the smaller Ashbrook press could not handle all the solids produced on average by itself. However, due to the significantly reduced amount of sludge generated, the Ashbrook press was used almost exclusively during 5 of the 7 months of testing. The larger Andritz press required repairs and spent most of the test period off-line. STS operators noted that this was not possible before and as a result, the urgency to repair the large press was lessened.

Polymer is delivered by an LCM pump into the wasting stream just before the presses. STS operators kept a logbook on-site to record the number of days that a 55-gallon drum of polymer would last before being emptied and replaced with a new drum. Customarily, STS operators would expect one such drum to last about 7 – 10 days. After the first month of the test, STS records show that each drum lasted longer; about 14 days on average, and some drums lasted up to 17 days.

During the test, STS operators commented repeatedly about the high quality of the sludgebeing produced. While the sludge cake remained about 85% moisture content, the operational aspects were significantly improved. The cake fell from the belt presses in large sheets of very uniform consistency as opposed to breaking off in clumps. Consequently, the operators spent less time hosing the belts down. The man hours saved could then be redirected to other operational and preventative maintenance duties.

Figure 5-1 - Sludge cake coming out of the press


5-2

The most striking measurable changes were:

• Sludge production and polymer consumption reduced 30% and 40% respectively

• Carrying capacity (MLSS) raised 3.5X

• BOD in the basins raised 8 – 10X

• Observed SRT increased 5X

Figure 5-2 – TSS in the Aeration Basin over the Study Period

The addition of the product caused the carrying capacity of the plant to increase three and a half times the normal level. This strongly suggests increased microbial activity in the plant. The increased activity naturally creates a more robust process, one that could more easily handle the BOD load to the plant and was not affected by large rain events that result in Inflow and Infiltration (I&I).

Table 5-1 – Summary of Changes before and during Study Period

Process Variable Before Study Period During Study Period TSS in basin 2,500 – 3,800 mgl 9,000 – 11,000 mgl

SRT 10 days 50 days BOD in basins 400 – 500 mgl 3,000 – 4,000 mgl DO in basin 2.0 – 3.5 mgl 2.0 - 3.5 mgl

Recycle Ratio (RAS/Q) ~ 100% ~ 120% Blanket Height 1 foot in 10 feet SWD 1 foot in 10 feet SWD

SVI ~ 100 ~ 100


5-3

BOD grab samples from the basins also increased dramatically. At the outset, BOD in the basins averaged 400 – 500. After a few months, this value increased to 3,000 or 4,000 at times. This trend may well support the notion that a much larger microbial population would release far more enzymes and VFAs into the water. These enzymes and VFAs play a significant role in the lysis of inactive biomass, which causes intracellular constituents to become solubilize. Lastly, the calculated SRT increased from 9 – 10 days to 50 days, a value that is unheard of in the industry. Again, the daily addition of blooms of young bacteria shifts the population distribution so that the actual SRT is far less than the observed SRT. This discrepancy suggests that traditional Activated Sludge Models simply do not accurately describe a population that has been changed by catalysis. The elevated percentage of young log growth bacteria appears to reduce the percentage of old or dead microorganisms because typically a long SRT will have an extremely deleterious effect on the plant.

5.2 Effect on Clarifier Solids Flux

While the aeration basins serve as chemostsat bioreactors, the secondary clarifiers function strictly for settling suspended solids. Clarifier state point analysis tells us that higher MLSS in the plant will result in a higher solids loading rate to the clarifiers. Higher mass flux will require that the operator raise the RAS rate to keep a comfortable blanket height. In this plant, 2 of 3 clarifiers are in use. Adding the third clarifier would reduce the mass flux through each clarifier 33% over the case with only 2 clarifiers. The entire test period utilized only 2 clarifiers, so it stands to reason that the settling capability of the WWTP was not a limiting factor, even though MLSS more than tripled.

Figure 5-3 – State Point Analysis


5-4

With high MLSS there is little fear of “thickening failure.” The concern as MLSS tracks up would be that without good settling characteristics, solids would spill out at the overflow (“solids washout”). The clarifiers never failed, and effluent remained in compliance throughout the test.

The 30-minute set test is employed on-site as a rough, but reliable indicator of good settling characteristics. The 30-minute set is then divided by the MLSS and multiplied by 1,000 to give the SVI as measured as the volume containing one gram of dry solids, so the solids height after 30 minutes is expected to double if MLSS doubles for the same SVI.

Blanket height also remained constant and presented no challenge to control. STS operators were easily able to adjust RAS and maintain a relatively low blanket height (1 foot) in the presence of a higher solids loading rate, indicating excellent settleability.

5.2.1 DO Control and Disinfection

The plant uses a circle chart in the control room to display DO in the basins. The chart began to show a lot of “paint brushing” or frequent on/off cycling as the MLSS tracked upward. This indicated that perhaps the Oxygen Uptake Rate (OUR) had been significantly increased as the bacterial population became more active. While increased activity does suggest a greater oxygen requirement, there is the question of efficiency of oxygen delivery coupled with biological uptake.

As can be seen in Figure 5-4, the behavior of the DO chart became obvious with use of the product. The already-in-use discrete meter, which took readings only once every 3 minutes, had kept the bower use “between the ditches” in the past. However, upon the installation of a continuous meter, the DO charts showed a marked decrease in overshoot and undershoot of the set point range. The frequent cycling stabilized as a result. The new charts with the new continuous meter are much smoother circles showing a far more energy efficient oxygen delivery, as seen in Figure 5-5.

Figure 5-4 - DO Chart During Study Period Figure 5-5 - DO Chart after Installation of New DO Meter


5-5

DryLet and STS operators were able to substantially reduce blower requirement by simply staggering set points and shifting both the upper and lower set points down half a point. The tuning capability seems to relate clearly to accelerated OUR. In fact, having “more horsepower” in a WWTP carries with it the potential to bring about continuous improvement in blower operation and increased process efficiency that would not be possible with a more sluggish microbial population.

Ammonia remained below the limit of detection throughout the entire test. BOD was also frequently below MDL. More biological activity corresponds to more highly treated water. This raises the question as to whether more microbes and more enzymes in the water might reduce Chlorine and Dechlor disinfection treatment requirements. These represented an enormous fraction (25%) of the plant operating costs at the plant in 2014. Even a 10% reduction in chlor/dechlor is very significant. These considerations fell outside the scope of this product trial, but certainly merit attention in further testing.

Figure 5-6 – Rate of Oxygen Uptake Chart

5.3 Continuous Improvement, Process Optimization, Increased Throughput

The parameters around sludge and polymer reduction present a clear savings to the WWTP. Furthermore, it is conceivable that blower tuning and chemical disinfection tuning could easily increase the value proposition.

There are 4 main areas in which the DryLet product shows the potential to provide value:

• By sludge reduction (hauls, equipment life, repairs, man hours)

• By polymer use reduction


5-6

• By potentially catalyzing process improvement (blowers, chemicals, process automation and set points, design)

• By potentially postponing Capital Improvement Projects (CIP)

AJOB is currently budgeting $667,000 for replacement of the Ashbrook belt press. Extending the life of the existing press by using it 30% less prolongs the equipment’s life cycle and delays its inevitable replacement. Increasing the effective capacity of an existing WWTP would necessarily postpone impending CIP, particularly those related to expansion and press replacement.

The following aspects should be explored further with future testing:

• Can increased microbes reduce basin volumes so plants have higher capacity rating?

• Can blowers be detuned in waning activity periods without risking a bust, or crash of the microbial activity?

• Can chemical disinfection costs be reduced?

5.4 Qualitative Benefits

The value proposition is bolstered by the water conservation aspect. As stated in the introduction, reducing biosolids makes wastewater treatment more sustainable and more environmentally responsible. Less hauling means less fuel spent to move 85% water. It means a smaller footprint for disposal. Less hauling also means fewer trucks on our roads and less stress on overall infrastructure.

The process of pressing the solids is a major function of the operator and consumes a large portion of his time. Improving operability by reducing the logistics of the outbound solids queue is a logical conclusion. In this way, the product can possibly reduce the total FTE allotted to larger facilities employing several or many workers.

A more robust microbial treatment will be better able to absorb BOD step changes and toxic loads. The dose / response curve for these events will show a faster dampening factor with increased metabolic activity. While a toxic shock load will still affect some percentage of the population, a larger population will have more viable bacteria remaining after a kill.

Finally, it is important to note that this plant operated well before DryLet. It had received a large expansion 8 years prior, at which time SCADA was implemented. Some parts of the plant were redesigned and/or repurposed as part of this plant upgrade. From the historical yield data, it appears that AJOB was already doing a great job of stabilizing and reducing the inbound BOD load. This reduction of an already low yield suggests that DryLet® LIFT technology could have an even bigger impact at other plants that might not be running as smoothly to begin with.


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Section 6 Conclusions and Next Steps

The DryLet® LIFT product displayed a significant impact on plant performance in many ways. Given the large quantity of data acquired during the test, the dramatic changes to sludge production, and several of the most important process parameters, it is very easy to attribute the beneficial sludge and polymer reduction to DryLet® LIFT.

DryLet® LIFT began to reduce biomass production after 30 days in the plant. It had a sustained 30% reduction of sludge production and a 43% reduction of polymer use for the presses. By extension from reduced sludge production, two additional savings to the plant from the product were realized: longer equipment life, and reduced equipment maintenance costs associated with the presses.

Deeper examination of bioprocess dynamics reveals DryLet® LIFT caused a 3 – 3.5 times increase in the carrying capacity of the plant in terms of the microbial population as measured by VSS, TSS, and BOD levels in the air-cut water of the basins. The product caused no increase in electrical cost overall, and caused no increase in blower run time. In fact, this study suggests that increased carrying capacity could actually result in blower savings in the future by positively effecting the OUR.

The strong positive performance of the DryLet product for sludge and polymer reduction, combined with the dramatic changes to the process, suggest that this technology could have the potential to shift the current understanding of and operation in the wastewater industry. The importance of biosolids reduction and the potential for process improvement with this technology warrant further investigation. In further study, it would be important to determine if this product can reduce the number of basins required at the plant because of increased carrying capacity.

This study supports the claims of the product for sludge and polymer reduction. Further testing is recommended to determine if the use of the product can bring about other potential benefits like savings in blower demand and chemical demand for disinfection, and to determine if increased microbial activity can actually increase a plant’s throughput rating, extending the life of existing infrastructure while postponing future CIP.


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Section 7 Appendices

7.1 Appendix A - Calculations Spreadsheet

7.2 Appendix B - Financial Reports

7.3 Appendix C - Operators Reports

7.4 Appendix D - Test America Data

7.5 Appendix E - Magna Flow Data

7.6 Appendix F - Photo Journal

7.7 Appendix G - Atascocita Regional Plant Schematic

Appendix A – Calculations Spreadsheet

Mass calculations of influent solids, wet sludge, and dry solids including haul data

avg Sludge % solids

Reference 14.27 14.1%

Month QuarterMonth/yr

(graph)Month Year Mth+Yr^2

Accum. Flow

(MGD)

ADF

(MGD)

Total

Precip. (IN)

BOD, avg

(mg/L)

Total BOD,

(St/month)

# hauls per

monthSludge (ST) Dry solids (ST)

Jan Q1 2009 Jan '09 1 2009 4036082 134 23 328 46

Feb Q1 2009 Feb '09 2 2009 4036083 121 23 328 46

Mar Q1 2009 Mar '09 3 2009 4036084 139 24 342 48

Apr Q2 2009 Apr '09 4 2009 4036085 147 32 457 64

May Q2 2009 May '09 5 2009 4036086 138 17 243 34

Jun Q2 2009 Jun '09 6 2009 4036087 128 7 100 14

Jul Q3 2009 Jul '09 7 2009 4036088 136 21 300 42

Aug Q3 2009 Aug '09 8 2009 4036089 140 25 357 50

Sep Q3 2009 Sep '09 9 2009 4036090 136 4.51 5.15 170 98 21 300 42

Oct Q4 2009 Oct '09 10 2009 4036091 153 4.94 8.65 165 108 37 528 74

Nov Q4 2009 Nov '09 11 2009 4036092 132 4.40 1.95 220 124 24 342 48

Dec Q4 2009 Dec '09 12 2009 4036093 147 4.76 6.48 200 126 24 342 48

Jan Q1 2010 Jan '10 1 2010 4040101 140 4.53 2.45 295 177 19 271 38

Feb Q1 2010 Feb '10 2 2010 4040102 130 4.64 3.87 150 83 35 499 70

Mar Q1 2010 Mar '10 3 2010 4040103 132 4.27 2.92 215 122 29 414 58

Apr Q2 2010 Apr '10 4 2010 4040104 127 4.23 1.35 160 87 28 400 56

May Q2 2010 May '10 5 2010 4040105 135 4.36 3.50 100 58 37 528 74

Jun Q2 2010 Jun '10 6 2010 4040106 129 4.18 1.95 115 64 18 257 36

Jul Q3 2010 Jul '10 7 2010 4040107 150 4.72 10.15 115 74 25 357 50

Aug Q3 2010 Aug '10 8 2010 4040108 137 4.25 0.60 130 76 28 400 56

Sep Q3 2010 Sep '10 9 2010 4040109 139 4.65 6.50 155 92 26 371 52

Oct Q4 2010 Oct '10 10 2010 4040110 129 4.22 0.00 165 91 17 243 34

Nov Q4 2010 Nov '10 11 2010 4040111 130 4.34 5.86 165 92 15 214 30

Dec Q4 2010 Dec '10 12 2010 4040112 134 4.32 3.85 195 112 24 342 48

Jan Q1 2011 Jan '11 1 2011 4044122 140 4.52 5.50 200 120 21 300 42

Feb Q1 2011 Feb '11 2 2011 4044123 122 4.36 1.05 150 78 22 314 44

Mar Q1 2011 Mar '11 3 2011 4044124 145 4.69 1.55 210 130 8 114 16

Apr Q2 2011 Apr '11 4 2011 4044125 124 4.14 0.00 190 101 24 342 48

May Q2 2011 May '11 5 2011 4044126 133 4.29 0.40 185 105 36 514 72

Jun Q2 2011 Jun '11 6 2011 4044127 127 4.24 2.30 160 87 29 414 58

Appendix B – Financial Reports

Appendix C – Operators Reports

Appendix D – Test America Data

ANALYTICAL REPORTTestAmerica Laboratories, Inc.TestAmerica Houston6310 Rothway StreetHouston, TX 77040Tel: (713)690-4444

TestAmerica Job ID: 600-117493-1Client Project/Site: Atascocita Pilot Study

For:DryLet, LLC2150 S Central Expwy, #200McKinney, Texas 75070

Attn: Scott Conley

Authorized for release by:9/8/2015 12:10:00 PM

Jodi Allen, Project Manager I(713)[email protected]

The test results in this report meet all 2003 NELAC and 2009 TNI requirements for accreditedparameters, exceptions are noted in this report. This report may not be reproduced except in full,and with written approval from the laboratory. For questions please contact the Project Managerat the e-mail address or telephone number listed on this page.

This report has been electronically signed and authorized by the signatory. Electronic signature isintended to be the legally binding equivalent of a traditionally handwritten signature.

Results relate only to the items tested and the sample(s) as received by the laboratory.

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mailto:[email protected]

Table of Contents

Client: DryLet, LLCProject/Site: Atascocita Pilot Study

TestAmerica Job ID: 600-117493-1

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Cover Page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Case Narrative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Method Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Sample Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Client Sample Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Definitions/Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

QC Sample Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

QC Association Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Lab Chronicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Certification Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Chain of Custody . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Receipt Checklists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

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Case NarrativeClient: DryLet, LLC TestAmerica Job ID: 600-117493-1Project/Site: Atascocita Pilot Study

Job ID: 600-117493-1

Laboratory: TestAmerica Houston

Narrative

Job Narrative

600-117493-1

Comments

No additional comments.

Receipt

The sample was received on 9/2/2015 12:52 PM; the sample arrived in good condition, properly preserved and, where required, on ice. The temperature of the cooler at receipt was 3.4º C.

General Chemistry

No analytical or quality issues were noted, other than those described in the Definitions/Glossary page.

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Method SummaryTestAmerica Job ID: 600-117493-1Client: DryLet, LLC

Project/Site: Atascocita Pilot Study

Method Method Description LaboratoryProtocol

EPA160.4 Total Volatile Suspended Solids TAL HOU

SMSM 5210B BOD, 5-Day TAL HOU

Protocol References:

EPA = US Environmental Protection Agency

SM = "Standard Methods For The Examination Of Water And Wastewater",

Laboratory References:

TAL HOU = TestAmerica Houston, 6310 Rothway Street, Houston, TX 77040, TEL (713)690-4444

TestAmerica Houston

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Sample SummaryTestAmerica Job ID: 600-117493-1Client: DryLet, LLC


Lab Sample ID Client Sample ID ReceivedCollectedMatrix

600-117493-1 Aeration Water 09/02/15 09:48 09/02/15 12:52

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Client Sample ResultsTestAmerica Job ID: 600-117493-1Client: DryLet, LLC


Lab Sample ID: 600-117493-1Client Sample ID: AerationMatrix: WaterDate Collected: 09/02/15 09:48

Date Received: 09/02/15 12:52

General ChemistryRL RL

TVSS 5000 330 mg/L 09/03/15 06:54 1

Analyte Dil FacAnalyzedPreparedUnit DResult Qualifier

600 mg/L 09/02/15 16:54 1Biochemical Oxygen Demand 2100

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Definitions/GlossaryTestAmerica Job ID: 600-117493-1Client: DryLet, LLC


Glossary

These commonly used abbreviations may or may not be present in this report.

¤ Listed under the "D" column to designate that the result is reported on a dry weight basis

Abbreviation

%R Percent Recovery

CFL Contains Free Liquid

CNF Contains no Free Liquid

DER Duplicate error ratio (normalized absolute difference)

Dil Fac Dilution Factor

DL, RA, RE, IN Indicates a Dilution, Re-analysis, Re-extraction, or additional Initial metals/anion analysis of the sample

DLC Decision level concentration

MDA Minimum detectable activity

EDL Estimated Detection Limit

MDC Minimum detectable concentration

MDL Method Detection Limit

ML Minimum Level (Dioxin)

NC Not Calculated

ND Not detected at the reporting limit (or MDL or EDL if shown)

PQL Practical Quantitation Limit

QC Quality Control

RER Relative error ratio

RL Reporting Limit or Requested Limit (Radiochemistry)

RPD Relative Percent Difference, a measure of the relative difference between two points

TEF Toxicity Equivalent Factor (Dioxin)

TEQ Toxicity Equivalent Quotient (Dioxin)

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QC Sample ResultsTestAmerica Job ID: 600-117493-1Client: DryLet, LLC


Method: 160.4 - Total Volatile Suspended Solids

Client Sample ID: Method BlankLab Sample ID: MB 600-171028/1Matrix: Water Prep Type: Total/NAAnalysis Batch: 171028

RL RL

TVSS ND 2.0 mg/L 09/03/15 06:54 1

MB MB

Analyte Dil FacAnalyzedPreparedDUnitResult Qualifier

Method: SM 5210B - BOD, 5-Day

Client Sample ID: Method BlankLab Sample ID: SCB 600-170951/2Matrix: Water Prep Type: Total/NAAnalysis Batch: 170951

RL RL

Biochemical Oxygen Demand ND 2.0 mg/L 09/02/15 16:54 1

SCB SCB


Client Sample ID: Method BlankLab Sample ID: USB 600-170951/15Matrix: Water Prep Type: Total/NAAnalysis Batch: 170951

RL RL

Biochemical Oxygen Demand ND 2.0 mg/L 09/02/15 16:54 1

USB USB


Client Sample ID: Lab Control SampleLab Sample ID: LCS 600-170951/16Matrix: Water Prep Type: Total/NAAnalysis Batch: 170951

Biochemical Oxygen Demand 198 201 mg/L 101 84.6 - 115.

4

Analyte

LCS LCS

DUnitResult Qualifier %Rec

Spike

Added

%Rec.

Limits

TestAmerica Houston

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QC Association SummaryTestAmerica Job ID: 600-117493-1Client: DryLet, LLC


General Chemistry

Analysis Batch: 170951

Lab Sample ID Client Sample ID Prep Type Matrix Method Prep Batch

Water SM 5210B600-117493-1 Aeration Total/NA

Water SM 5210BLCS 600-170951/16 Lab Control Sample Total/NA

Water SM 5210BSCB 600-170951/2 Method Blank Total/NA

Water SM 5210BUSB 600-170951/15 Method Blank Total/NA

Analysis Batch: 171028

Lab Sample ID Client Sample ID Prep Type Matrix Method Prep Batch

Water 160.4600-117493-1 Aeration Total/NA

Water 160.4MB 600-171028/1 Method Blank Total/NA

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Lab ChronicleClient: DryLet, LLC TestAmerica Job ID: 600-117493-1Project/Site: Atascocita Pilot Study

Client Sample ID: Aeration Lab Sample ID: 600-117493-1Matrix: WaterDate Collected: 09/02/15 09:48

Date Received: 09/02/15 12:52

Analysis 160.4 EC109/03/15 06:541 TAL HOU171028

Type

Batch

Method

Batch

Prep Type LabAnalystRun

Prepared

or Analyzed

Initial

Amount Amount

Final Batch

NumberFactor

Dil

Total/NA 3 mL 500 g

Analysis SM 5210B 1 170951 09/02/15 16:54 YHX TAL HOUTotal/NA 1 mL 300 mL

Laboratory References:

TAL HOU = TestAmerica Houston, 6310 Rothway Street, Houston, TX 77040, TEL (713)690-4444

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Certification SummaryClient: DryLet, LLC TestAmerica Job ID: 600-117493-1Project/Site: Atascocita Pilot Study

Laboratory: TestAmerica HoustonUnless otherwise noted, all analytes for this laboratory were covered under each certification below.

Authority Program EPA Region Certification ID Expiration Date

Texas T104704223-15-166NELAP 10-31-15

Analysis Method Prep Method Matrix Analyte

The following analytes are included in this report, but certification is not offered by the governing authority:

160.4 Water TVSS

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Login Sample Receipt Checklist

Client: DryLet, LLC Job Number: 600-117493-1

Login Number: 117493

Question Answer Comment

Creator: Crafton, Tommie S

List Source: TestAmerica Houston

List Number: 1

N/ARadioactivity wasn't checked or is </= background as measured by a survey meter.

Lab does not accept radioactive samples.

TrueThe cooler's custody seal, if present, is intact.

TrueSample custody seals, if present, are intact.

TrueThe cooler or samples do not appear to have been compromised or tampered with.

TrueSamples were received on ice.

TrueCooler Temperature is acceptable.

TrueCooler Temperature is recorded. 3.4

TrueCOC is present.

TrueCOC is filled out in ink and legible.

TrueCOC is filled out with all pertinent information.

TrueIs the Field Sampler's name present on COC?

TrueThere are no discrepancies between the containers received and the COC.

TrueSamples are received within Holding Time.

TrueSample containers have legible labels.

TrueContainers are not broken or leaking.

TrueSample collection date/times are provided.

TrueAppropriate sample containers are used.

TrueSample bottles are completely filled.

TrueSample Preservation Verified.

TrueThere is sufficient vol. for all requested analyses, incl. any requested MS/MSDs

TrueContainers requiring zero headspace have no headspace or bubble is <6mm (1/4").

TrueMultiphasic samples are not present.

TrueSamples do not require splitting or compositing.

N/AResidual Chlorine Checked. Check done at department level as required.

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Appendix E – Magna Flow Data

Appendix F – Photo Journal

Appendix G – Atascocita Regional Plant Schematic

JOB #: DATE: EXHIBIT:SCALE:

1194 JUNE 2012

Brown & Gay Engineers, Inc.

Co

pyrig

ht 2

00

7

TPDES PERMIT RENEWAL NO.11533-001

FLOW DIAGRAM

ATASCOCITA REGIONAL WWTP

2

HARRIS COUNTY MUD 109

bge job no. 2875-00 - drylet · bge job no. 2875-00 march 2017 evaluation of drylet® lift at...

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