Biological Wastewater Treatment for Food Processing · PDF fileEnergy requirement to treat 1000 gallons of water at CSUF dairy wastewater treatment ... dairy wastewater treatment plant

PG&E’s Emerging Technologies Program ET 14PGE1511

Biological Wastewater Treatment for Food

Processing Industry

ET Project Number: ET 14PGE1511

Project Manager: Sam Newman Pacific Gas and Electric Company Prepared By: Dr. Fayzul Pasha, P.E. Department of Civil and Geomatics Engineering California State University, Fresno 2320 E. San Ramon Ave, MS/EE94 Fresno, CA 93740

Dr. Dilruba Yeasmin Center for Irrigation Technology at Fresno State 5370 North Chestnut Avenue - M/S OF 18 Fresno, CA 93740

Dr. David Zoldoske Center for Irrigation Technology at Fresno State 5370 North Chestnut Avenue - M/S OF 18 Fresno, CA 93740

Issued: December 30, 2014

Copyright, 2014, Pacific Gas and Electric Company. All rights reserved.

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ACKNOWLEDGEMENTS

Pacific Gas and Electric Company’s Emerging Technologies Program is responsible for this project. It was developed as part of Pacific Gas and Electric Company’s Emerging Technology – Technology Evaluation program under internal project number ET14PGE1511. The Center for Irrigation Technology on the campus of California State University, Fresno conducted this technology evaluation for Pacific Gas and Electric Company with overall guidance and management from Sam Newman. For more information on this project, contact [email protected].

LEGAL NOTICE

This report was prepared for Pacific Gas and Electric Company for use by its employees and agents. Neither Pacific Gas and Electric Company nor any of its employees and agents:

(1) makes any written or oral warranty, expressed or implied, including, but not limited to those concerning merchantability or fitness for a particular purpose;

(2) assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, process, method, or policy contained herein; or

(3) represents that its use would not infringe any privately owned rights, including, but not limited to, patents, trademarks, or copyrights.

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ABBREVIATIONS AND ACRONYMS

B Boron

BIDA®

Dynamic Aerobic Biofilter

BOD Biochemical Oxygen Demand

Ca Calcium

CIT Center for Irrigation Technology at California State University, Fresno

COD Chemical Oxygen Demand

CSUF California State University, Fresno

CV Coefficient of Variation

EC Electrical Conductivity

GHG Green House Gas

gph Gallons per hour

gpd Gallons per day

K Potassium

kWh Kilowatt-hour

Mg Magnesium

MGD Million gallons per day

Na Sodium

NH4 Ammonium Nitrogen

NO3-N Nitrate Nitrogen

P Phosphorus

PG&E Pacific Gas and Electric Company

SALT-SOL Soluble Salts

TDS Total Dissolved Solids

TKN Total Kjeldahl Nitrogen

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TSS Total Suspended Solids

UAL University Agricultural Lab

U.S. DOE United States Department of Energy

U.S. EPA United States Environmental Protection Agency

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FIGURES

Figure 1. Schematic of BIDA system (Source: BioFiltro 2014) ........... 2

Figure 2. Non-Exceedance probability curve for energy

requirement for the treatment of wastewater (by

Pump2) at CSUF dairy wastewater treatment plant ........... 5

Figure 3. CSUF wastewater treatment plant site ............................ 9

Figure 4. Process diagram of CSUF wastewater treatment plant ....... 9

Figure 5. System under construction: CSUF wastewater

treatment plant .......................................................... 10

Figure 6. Wastewater source: CSUF wastewater treatment plant ... 10

Figure 7. Top layer of the treatment plant: CSUF wastewater

treatment plant .......................................................... 11

Figure 8. Sprinker at the treatment plant: CSUF wastewater

treatment plant .......................................................... 11

Figure 9. A large tomato processor wastewater treatment plant

site ........................................................................... 12

Figure 10. Wastewater Treatment Plant: A large tomato processor

site ........................................................................... 12

Figure 11. Wastewater source: A large tomato processor site ......... 13

Figure 12. Top layer of the sastewater plant: A large tomato

processor site ............................................................ 13

Figure 13. Concentration with confidence limits before and after

the treatment at CSUF: Boron (B) ............................... 26


the treatment at CSUF: BOD5 ..................................... 26


the treatment at CSUF: Calcium (Ca) ........................... 27


the treatment at CSUF: COD ...................................... 27


the treatment at CSUF: EC ......................................... 28


the treatment at CSUF: Potassium (K) ......................... 28


the treatment at CSUF: Magnesium (Mg) ..................... 29


the treatment at CSUF: Sodium (Na) ........................... 29

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Figure 21. Concentration with confidence limits before and after the

treatment at CSUF: Ammonium Nitrogen (NH4) ............ 30


the treatment at CSUF: Nitrate Nitrogen (NO3-N) ......... 30


treatment at CSUF: Phosphorus (P) ............................. 31


treatment at CSUF: pH .............................................. 31


the treatment at CSUF: Soluble Salts (Salt-Sol) ............ 32


the treatment at CSUF: Total Dissolved Solids (TDS) ..... 32


the treatment at CSUF: Total Kjeldahl Nitrogen (TKN)... 33

Figure 28. Removal efficiency at CSUF: Boron (B) ........................ 35

Figure 29. Removal efficiency at CSUF: BOD5 .............................. 35

Figure 30. Removal efficiency at CSUF: Calcium (Ca) .................... 36

Figure 31. Removal efficiency at CSUF: COD ................................ 36

Figure 32. Removal efficiency at CSUF: EC .................................. 37

Figure 33. Removal efficiency at CSUF: Potassium (K) .................. 37

Figure 34. Removal efficiency at CSUF: Magnesium (Mg) ............... 38

Figure 35. Removal efficiency at CSUF: Sodium (Na) .................... 38

Figure 36. Removal efficiency at CSUF: Ammonium Nitrogen

(NH4) ....................................................................... 39

Figure 37. Removal efficiency at CSUF: Nitrate Nitrogen (NO3-N) ... 39

Figure 38. Removal efficiency at CSUF: Phosphorus (P) ................. 40

Figure 39. Removal efficiency at CSUF: pH .................................. 40

Figure 40. Removal efficiency at CSUF: Soluble Salts (Salt-Sol) ..... 41

Figure 41. Removal efficiency at CSUF: Total Dissolved Solids

(TDS) ....................................................................... 41

Figure 42. Removal efficiency at CSUF: Total Kjeldahl Nitrogen

(TKN) ....................................................................... 42

Figure 43. Energy requirement by Pump1 to pre-treat wastewater

at CSUF .................................................................... 43

Figure 44. Energy requirement by Pump2 to treat wastewater at

CSUF ........................................................................ 44

Figure 45. Energy requirement by Pump3 to distribute treated

water at CSUF ........................................................... 44

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Figure 46. Time series plot of energy requirement by each pump

and flow at CSUF dairy wastewater treatment plant

after discarding some data due to mechanical failure ...... 46

Figure 47. Sorted flow and energy at CSUF dairy wastewater

treatment plant after discarding some data due to

mechanical failure ...................................................... 47


requirement for the pre-treatment of wastewater (by

Pump1) at CSUF dairy wastewater treatment plant ......... 49


requirement for the treatment of wastewater (by



requirement for the distribution of treated water (by



the treatment at A large tomato processor: BOD5 ......... 52


the treatment at A large tomato processor: TSS ........... 52

Figure 53. Removal efficiency at A large tomato processor: BOD5 .. 53

Figure 54. Removal efficiency at A large tomato processor: TSS ..... 54

Figure 55. Energy requirement by Pump2 to treat wastewater at A

large tomato processor ............................................... 55

Figure 56. Energy requirement by Pump3 to distribute treated

water at A large tomato processor ................................ 55

Figure 57. Time series plot of energy requirement by each pump

and flow at A large tomato processor wastewater



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TABLES

Table 1. Removal efficiency of water quality parameters at CSUF

dairy wastewater treatment plant ................................... 3

Table 2. Energy requirement to treat 1000 gallons of water at

CSUF dairy wastewater treatment plant .......................... 4

Table 3. Removal efficiency of water quality parameters at a

large tomato processor wastewater treatment plant ......... 5

Table 4. Energy requirement to treat 1000 gallons of water at a

large tomato processor wastewater treatment plant ......... 5

Table 5. Basic statistical parameters in CSUF water quality data

analysis .................................................................... 24

Table 6. Removal efficiency of water quality parameters at CSUF

dairy wastewater treatment plant ................................. 34

Table 7. Energy supply statistics for CSUF dairy wastewater

treatment plant .......................................................... 43

Table 8. Energy–water statistics at CSUF dairy wastewater



Table 9. Energy requirement to treat 1000 gallons of water at

CSUF dairy wastewater treatment plant ........................ 48

Table 10. Basic statistical parameters in A large tomato processor

water quality data analysis .......................................... 51

Table 11. Removal efficiency of water quality parameters at A

large tomato processor wastewater treatment plant ....... 53

Table 12. Energy supply statistics for A large tomato processor

wastewater treatment plant ......................................... 54

Table 13. Energy–water statistics at A large tomato processor

wastewater treatment plant after discarding some data

due to mechanical failure ............................................ 56

Table 14. Energy requirement to treat 1000 gallons of water at A

large tomato processor wastewater treatment plant ....... 56

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EQUATIONS

Equation 1. Mean of data ............................................................. 21

Equation 2. Standard deviation of data .......................................... 21

Equation 3. Coefficient of variation (CV) of data .............................. 22

Equation 4. Confidence interval of data .......................................... 22

Equation 5. Removal efficiency ..................................................... 23

Equation 6. Energy requirement for 1000 gallons of water ................ 23

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CONTENTS

ABBREVIATIONS AND ACRONYMS __________________________________________________________ II

FIGURES _____________________________________________________________________________ IV

TABLES _______________________________________________________________________________ I

EQUATIONS ___________________________________________________________________________ I

CONTENTS ___________________________________________________________________________ 1

EXECUTIVE SUMMARY _____________________________________________________ 2

INTRODUCTION __________________________________________________________ 7

BACKGROUND __________________________________________________________ 8

Biofiltro .................................................................................. 8

Description of Sites ................................................................. 8

EMERGING TECHNOLOGY/PRODUCT ________________________________________ 14

Description of the Product ...................................................... 14

Advantages .......................................................................... 15

Risk and Chanlleges .............................................................. 15

ASSESSMENT OBJECTIVES _________________________________________________ 16

Objectives ............................................................................ 16

Technology Assessment ......................................................... 17

TECHNOLOGY EVALUATION _______________________________________________ 19

TECHNICAL APPROACH/TEST METHODOLOGY _________________________________ 20

Instrumentation Plan ............................................................. 20

DATA ANALYSIS - METHODS .................................................. 21

Water Quality Data Analysis Method ................................... 21 Energy Data Analysis Method ............................................. 23

RESULTS_______________________________________________________________ 24

DATA ANALYSIS - Results ...................................................... 24

Data Analysis – Result (CSUF Dairy) ................................... 24 Data Analysis – Result (A large tomato processor)................ 51

EVALUATIONS __________________________________________________________ 57

RECOMMENDATIONS ____________________________________________________ 58

APPENDICES ___________________________________________________________ 58

REFERENCES ___________________________________________________________ 59

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EXECUTIVE SUMMARY

PROJECT GOAL

The goal of this project is to test the performance of an earthworm-based wastewater

treatment system known as Dynamic Aerobic Biofilter (BIDA®) or BioFiltro for its ability to

reduce organic contaminants in agricultural waste and to measure the energy use to

achieve these reductions.

PROJECT DESCRIPTION

The BIDA® system consists of a few layers (listed from bottom to top) including an air

pocket, rocks/gravel, wood shavings/sawdust, with worms/castings within the upper layer of

the wood shavings (Figure 1). Sprinklers, which use most of the energy required in the

treatment process, are used to apply wastewater to the surface of the treatment system.

Once the wastewater is sprinkled on the surface of the system, wastewater infiltrates to the

bottom layer by gravity. During the infiltration process, the wastewater is filtered through

different layers as the earthworm-microbe interaction takes place. The entire process takes

around four hours.

FIGURE 1. SCHEMATIC OF BIDA SYSTEM (SOURCE: BIOFILTRO 2014)

This study evaluated the application of the BIDA® system to organic effluents from the

Fresno State dairy farm (Fresno, California) and a large tomato processor (Firebaugh,

California).

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PROJECT FINDINGS/RESULTS

Table 1 and Table 3 summarize the water quality analysis results for CSUF and a large tomato processor. Specifically, the tables present the removal efficiency in percent. The performance (in terms of

removal efficiency) of BioFiltro (or BIDA® technology) for treating wastewater at the CSUF dairy farm is found to be consistent and high (removal efficiency is above 90%) for the nitrogen-based water quality parameters such as NH4, NO3-N, and TKN. Removal efficiencies for other important water quality indicators such as BOD5 and COD are promising. The performance (in terms of removal efficiency) of BioFiltro at a large tomato processor site was found consistently high as well. Two water quality parameters, BOD5 and TSS were considered for this site (tomato processor) and the removal efficiency for each these parameters is 90% or above.

The energy requirements to treat a unit amount of water (kWh/1000 gallons) are summarized in Table 2, Table 4, and Figure 2. The calculated results have been compared to the baseline study (PG&E 2006) information. For the CSUF site, about 25 percent of the data (i.e., 25 percent of the total days) shows extraordinary performance compared to the baseline study (PG&E 2006) and the 50 percent energy requirement data are lower than the baseline requirement (2.5 kWh/1000 gallons for overall treatment) showing better performance than the baseline information. However, about 50 percent of the data did not perform as the plant was designed. Refer to Table 5 and Figure 37 in the Results section for detailed analysis. Mechanical failure is suspected to be one of the main reasons why about 50 percent of the data at both sites (CSUF and the tomato processor) did not outperform the baseline study. Mechanical failure included issues such as pipe and system leakage, plugged filters and pipe and pump clogging.

TABLE 1. REMOVAL EFFICIENCY OF WATER QUALITY PARAMETERS AT CSUF DAIRY WASTEWATER TREATMENT PLANT

Water Quality Parameter

Removal Efficiency

Mean Std. Dev CV

B (ppm) 7.4% 3.8% 0.52

BOD5 (mg/L) 23.5% 14.8% 0.63

Ca (ppm) 16.8% 9.5% 0.56

COD (mg/L) 46.9% 15.2% 0.32

EC (mmhos/cm) 12.1% 11.2% 0.92

K (ppm) -2.6% 4.4% -1.69

Mg (ppm) 2.7% 7.6% 2.84

Na (ppm) -2.9% 4.4% -1.52

NH4 (mg/L) 98.4% 3.8% 0.04

NO3-N 96.2% 4.3% 0.05

P (ppm) 8.9% 23.9% 2.69

pH 2.9% 1.9% 0.67

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Removal Efficiency

Mean Std. Dev CV

Salt-Sol (ppm) 12.1% 11.2% 0.92

TDS (mg/L) 10.1% 11.6% 1.15

TKN (ppm) 90.6% 18.0% 0.20

TABLE 2. ENERGY REQUIREMENT TO TREAT 1000 GALLONS OF WATER AT CSUF DAIRY WASTEWATER TREATMENT PLANT

Statistics Energy requirement (kWh) to treat 1000 gallons of waste water

Pre-treatment (Pump1)

Treatment (Pump2)

Distribution (Pump3)

Days in operation 104 104 104

Average 3.7 3.2 1.7

Max 12.2 14.4 8.4

Standard deviation 2.5 2.9 2.0

25 percentile 2.0 1.0 0.3

50 percentile (median) 3.1 1.8 0.4



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FIGURE 2. NON-EXCEEDANCE PROBABILITY CURVE FOR ENERGY REQUIREMENT FOR THE TREATMENT OF WASTEWATER (BY

PUMP2) AT CSUF DAIRY WASTEWATER TREATMENT PLANT

TABLE 3. REMOVAL EFFICIENCY OF WATER QUALITY PARAMETERS AT A LARGE TOMATO PROCESSOR WASTEWATER

TREATMENT PLANT


Removal Efficiency Confidence Interval

Mean Std. Dev CV 99% LB 99% UB

BOD5 (mg/L) 96.4% 6.9% 0.07 83.9% 100.0%

TSS (mg/L) 89.7% 9.5% 0.11 69.3% 100.0%

TABLE 4. ENERGY REQUIREMENT TO TREAT 1000 GALLONS OF WATER AT A LARGE TOMATO PROCESSOR WASTEWATER

TREATMENT PLANT

Statistics

Energy requirement (kWh) to treat per 1000 gallons


Treatment (Pump2)


Days in operation Not used

21 21

Average 6.7 8.0

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0

No

n-E

xce

ed

an

ce

Pro

bab

ilit

y (

%)

Energy Requirement (kWh/1000 gallons of wastewater)

Bas

elin

e Se

con

dar

y

Bas

elin

e O

vera

ll

Bas

elin

e Te

rtia

ry

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Statistics



Treatment (Pump2)


Max 11.3 10.3

Min 1.4 0.2

Standard deviation 2.6 2.4

25 percentile 5.6 7.7

50 percentile (median) 7.0 8.8



PROJECT RECOMMENDATIONS

Although the performance of the BioFiltro was found extraordinary for some water quality parameters (such as nitrogen-based parameters at the CSUF site and all the parameters at the large tomato processor site), the performance for some parameters was not found to be high or satisfactory. Please refer to the Analysis section under Results for details. Measurement errors and probable contaminations are suspected to be the reasons for this poor performance. Therefore, extra caution should be taken in future deployments, and the testing protocol for sampling, lab analysis, and data entry must be carefully maintained throughout the process.

In terms of energy requirement, while about 50% of the data (i.e., days) shows better performance indicating energy efficiency savings, the other 50% shows poor performance. Both the CSUF and large tomato processor sites are pilot studies. As such, some system parameters, such as the system’s required head and design flow need to be fine-tuned before starting the test. Lack of a fine-tuned system caused significant energy waste. Calibrating the system after remedying observed mechanical failures (such as pipe and system leakage, pipe and pump clogging, plugged filter etc.) was a challenge, particularly re-calibrating the system after replacing a broken pump.

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INTRODUCTION Like other industrial processes, energy consumption is not the main focus in wastewater

treatment processes, which must meet rigorous effectiveness and reliability requirements.

The treatment process itself - the use of chemicals in the process and the byproducts

resulting from the treatment process - are also of interest in the research community. The

discussion is centered on how to reduce the energy consumption in the wastewater

treatment process and how the treatment should be done.

As much as 10 percent of the total local government’s annual budget can be used for

energy use of which a significant amount is used for treating wastewater (U.S. DOE 2005).

This involves energy consumption for wastewater treatment and transport and energy

consumption for equipment and plant operation (U.S. EPA 2013). This energy consumption

makes wastewater treatment among the largest contributors to the community’s total

greenhouse gas (GHG) emissions (U.S. EPA 2013). One study reported that about 35

percent of the U.S. municipal energy budget is used for water and wastewater utilities

(NYSERDA, 2008). PG&E (Pacific Gas and Electric Company) and other utility companies are

exploring ways to reduce the total energy consumption in wastewater treatment process by

making the plant more efficient (PG&E 2009).

Reducing energy consumption in the wastewater treatment process, minimizing the adverse

effects of chemicals and byproducts on the natural ecology, increasing the treatment

efficiency, and identifying how the treatment should be conducted are the main research

topics in wastewater treatment technology. Researchers are not only considering ways to

increase the efficiency of the conventional wastewater treatment process but they are also

considering other types of wastewater treatment processes in which natural processes such

as earthworms can be used. Earthworms, which were called the friends of farmers by Sir

Charles Darwin, can tolerate toxicity and can routinely devour the microorganisms which

help the wastewater to be cleaned (Sinha et al 2010). Researchers are now testing the

potential use of earthworms for treating urban and agricultural wastewaters (Wang et al

2011, Tomar and Suthar 2011, Sinha et al 2007). They are conducting experiments to

understand the mechanisms of microorganism-earthworm interaction in depth (Liu et al

2012, Oh et al 2010, Zhao et al 2010). The performance and efficiency of earthworm-based

wastewater treatment is still under investigation (Xing et al 2011, Xing et al 2010, Yang et

al 2009, Sinha et al 2007). This research study can be added to this list.

The purpose of this study is to test the performance of an earthworm-based wastewater

treatment system known as Dynamic Aerobic Biofilter (BIDA®) or BioFiltro in reducing

organic contaminants in agricultural waste and to measure the amount of energy used to

achieve these reductions. The BIDA system utilizes red worms and microbes within a

biological filter to scrub wastewater organic contaminants.

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BACKGROUND BioFiltro is a Clean Tech Company based in Chile and the United States that provides a

solution for treating wastewater from communities and from the food processing and animal

husbandry industries (BioFiltro 2014). BioFiltro USA was launched by its mother company

BioFiltro Chile in Fresno, California in August of 2013, establishing its headquarters at the

Water, Energy and Technology (WET) Center located at California State University, Fresno

(CSUF). BioFiltro is a wastewater treatment company that builds, maintains and operates a

biological treatment technology known as the BIDA® System. This patented technology has

been used in five countries and 100 facilities since its inception in Santiago Chile in 1994.

The following section describes the process used in the BioFiltro.

BIOFILTRO The layers of the BIDA system consists of (listed from bottom to top) an air pocket,

rocks/gravel, wood shavings/sawdust, with worms/castings within the upper layer of

the wood shavings (see Figure 1) Specialized sprinklers are used to apply

wastewater to the surface of the treatment system. From the applied wastewater

larger solids/contaminants are trapped within the wood shavings where it is then

becomes accessible to the earthworms. The earthworms eat this material and

convert it to their manure/humus/castings which are teaming with billions of

microbes that eat away at the dissolved organic contaminants. The rest of the layer

is inhabited by microbial flora which assists in the treatment process before outflow.

The entire process from wastewater to treated water takes four hours, as gravity

pulls the wastewater through the different filter layers. Aeration and aerobic

conditions are maintained by perforated pipes throughout the filter media, and by

the burrowing of the earthworms in the upper layer (BioFiltro 2014).

DESCRIPTION OF SITES The California State University, Fresno wastewater treatment plant is located on the

1,000-acre University Agricultural Laboratory (UAL). Specifically the plant is near the

northwest corner of the intersection of Barstow and Chestnut Avenue. This plant was

aimed to treat the effluent from UAL dairy farm. This dairy farm was established in

1954. It maintains two different breeds of dairy cattle, Holstein and Jersey. There

are approximately 275 to 300 cows. About 150 of these cows are used in the milk

string, and each cow in the milk string is milked twice a day. The following figure

(Figure 3) shows the location and plant’s components. Figure 4 shows the process

diagram with the location of data loggers. Briefly the process is as follows.

The water is taken from the secondary accumulation lagoon situated in the northeast

at the dairy farm. The water from this lagoon is pumped to the accumulation tank

passing through the Parabolic Screen. Then the water is pumped to the pumping

tank passing through another final screen. The wastewater from the pumping tank

is moved through sprinklers and spread evenly across the sawdust/gravel bed. The

water goes into the sawdust/gravel bed and gravity takes four hours for final

treatment. The clean (treated water) is then returned to the lagoon.

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The second site is a large tomato processor located in Firebaugh, California that

produces diced tomatoes, whole peeled tomatoes, catsup, sauces, puree and paste.

The tomato processor is considering BioFiltro as an alternative wastewater treatment

to reduce the capital costs. Figure 9 shows the overall process diagram and the site

of the plan. A few pictures of the test set-up and equipment schematics are included

from Figure 3 to Figure 12.

FIGURE 3. CSUF WASTEWATER TREATMENT PLANT SITE

FIGURE 4. PROCESS DIAGRAM OF CSUF WASTEWATER TREATMENT PLANT

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FIGURE 5. SYSTEM UNDER CONSTRUCTION: CSUF WASTEWATER TREATMENT PLANT

FIGURE 6. WASTEWATER SOURCE: CSUF WASTEWATER TREATMENT PLANT

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FIGURE 7. TOP LAYER OF THE TREATMENT PLANT: CSUF WASTEWATER TREATMENT PLANT

FIGURE 8. SPRINKLER AT THE TREATMENT PLANT: CSUF WASTEWATER TREATMENT PLANT

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FIGURE 9. A LARGE TOMATO PROCESSOR WASTEWATER TREATMENT PLANT SITE

FIGURE 10. WASTEWATER TREATMENT PLANT: A LARGE TOMATO PROCESSOR SITE

13


FIGURE 11. WASTEWATER SOURCE: A LARGE TOMATO PROCESSOR SITE

FIGURE 12. TOP LAYER OF THE WASTEWATER PLANT: A LARGE TOMATO PROCESSOR SITE

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EMERGING TECHNOLOGY/PRODUCT

DESCRIPTION OF THE PRODUCT The BIDA® System comes from biotechnology, which is technology based on biology.

It uses different natural, microbiological layers which are designed according to the

characteristics of the wastewater to be treated. Every source of wastewater has its

own specialized microbiological flora. This model filters the domestic and industrial

wastewater in order to return it to the environment as clean water where it can be

reused for irrigation, which is where 70 percent of the world's water resources are

consumed (Clarke Prize 2003, Crites and Tchobanoglous 1998, BioFiltro 2014).

This technology does not use chemicals and thus is expected to save energy when

compared to the conventional systems. It can be applied in an efficient and

sustainable manner in small and large volumes. This can make BioFiltro an

alternative for industry as well as rural and isolated communities. The BIDA® System

generates two byproducts which can be used in the agricultural and feeding

industries. Firstly an organic fertilizer and secondly worm protein which is a source of

nutrients with high protein and amino acid content (Clarke Prize 2003, Crites and

Tchobanoglous 1998, BioFiltro 2014).

The layers of the patented BIDA® system listed from bottom to top consist of an air

pocket, rocks/gravel, wood shavings/sawdust, and worms/castings within the upper

layer of the wood shavings. Specialized sprinklers are used to apply wastewater to

the surface of the treatment system. When the wastewater is applied, larger

solids/contaminants are trapped within the wood shavings where it is then accessible

to the earthworms. The earthworms then eat this material and convert it to their

manure/humus/castings which are teaming with billions of microbes which eat away

at the dissolved organic contaminants. The rest of the layer is inhabited by microbial

flora which assists in the treatment process before outflow. The entire process from

wastewater to treated water takes around four hours, as gravity pulls the

wastewater through the different filter layers. Aeration and aerobic conditions are

maintained by perforated pipes throughout the filter media, and by the burrowing of

the earthworms in the upper layer (Clarke Prize 2003, Crites and Tchobanoglous

1998, BioFiltro 2014).

It is expected that this product can be an alternative to the old technologies based

on mechanical aeration (activated sludge, aeration ponds, etc.)

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ADVANTAGES All the following advantages offer environmental benefits from biological

wastewater treatment approaches.

- Affordable CAPEX (capital expenditure) and OPEX (operational

expenditure) (today a lot dairies, food processing companies and

small/medium towns cannot install conventional technologies because it is

not viable from an economic perspective).

- Less energy consumption and thus improved sustainability.

- No sludge generation (traditional systems generate sludge which is a very

contaminated product that needs to be shipped and taken to special

landfills to dispose of).

- No chemical products are required in the process in comparison with

conventional technologies.

- The byproducts are organic fertilizer and worm protein, so the system can

generate valuable byproducts that have a market value and not pollutants

as traditional systems.

- Simple to operate compared to conventional system. Conventional

technologies are complicated and require qualified people to operate that

in rural areas especially may be difficult to find. On the other hand, the

BIDA® system does not required qualified people to operate.

RISK AND CHALLENGES There are some technologies for decentralized wastewater management such

as activated sludge, anaerobic digesters and aeration ponds. But the main

problems of those are:

- High CAPEX (capital expenditure) and OPEX (operational expenditure)

requirements.

- High energy consumption.

- Sludge generation (environmental pollutant).

- Chemical inputs

In general, all of these technologies do not provide sustainable solutions.

The BIDA® system is not a fuel switching technology. It is a technology that is

expected to consume about 85 percent less energy than conventional

technology to get the same quality effluent.

Upon successful completion of the pilot and full-scale tests, the system is

expected to have wider adoption with no risk and challenges.

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ASSESSMENT OBJECTIVES

OBJECTIVES There were four (4) major objectives identified in the Scope of Work for the project:

1. Installation of project monitoring equipment:

o Install the water- and energy-monitoring equipment at Fresno State dairy

by May 1st, 2014.

o Install the water- and energy-monitoring equipment at a large tomato

processor plant by June 1st, 2014.

2. Monitoring and data collection:

Collect data from project sites via installed power and water meters, as well

as submeters to be installed where appropriate. Conduct sampling on a

weekly basis (total of four events) with both the source water and treated

water being collected during each visit.

Collect grab samples of source and treated water weekly during the

evaluation period and analyze for key constituents listed below. The project

includes establishing an approved QC/QA procedure to assure samples are

collected, transported and analyzed appropriately.

o BOD (bio-chemical oxygen demand)

o COD (chemical oxygen demand)

o TSS (total suspended solids)

o TDS (total dissolved solids)

o Nitrogen/nitrates

o Salts/salinity

o Phosphorous

Energy and water use data will be collected using data monitors and water

samples will be collected weekly and analyzed by commercial laboratory.

3. Monitoring and data collection:

Analyze data weekly to determine data quality. The water quality, water flow,

and energy usage data will be used to support the following analysis

objectives:

(1) Energy analysis

o Energy usage for BioFiltro process

o Energy usage per gallon of water treated for BioFiltro process

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o Energy savings compared to conventional wastewater treatment

process (for both Fresno State dairy case and a large tomato processor

case). Baseline equipment standards and energy usage expectations

shall be identified in collaboration with PG&E’s wastewater treatment

baseline document and input from PG&E engineering team.

(2) Water quality verification

o Effectiveness of BioFiltro process at meeting water quality standards

o Volume of water recovered

(3) Business case assessment

o Land usage requirements for dairy and a large tomato processor

installation

o Clean water production per unit area

o System cost (up front)

o System cost (ongoing)

o System revenue streams

o Risk assessment: As bio-systems can be prone to fail when operating

conditions change (e.g., pH, temperature, organic levels), any

variations in water quality should be identified and ongoing

maintenance costs should be documented.

o Scalability assessment: To the extent possible, Fresno State will

provide recommendations for target customer types (industries,

locations, regulatory areas) and utility program needs.

4. Reporting:

o Interim Reports. CIT will provide the following interim reports to the

project team: Report of Site Selection and Operation Success/failure.

o Interim Report: Summarize project data to-date and analysis of

wastewater treatment effectiveness, energy and water usage comparison

to baseline treatment system, and business case evaluation. Report will

also discuss recommendations for future utility energy efficiency program

incorporation.

o Final Report. Summarize key findings and data using template provided by

PG&E Emerging Technologies Program, including the scope described

above.

TECHNOLOGY ASSESSMENT This would be a technology assessment as it is investigating the performance of the

new wastewater treatment technology that will provide opportunities to PG&E

customers for energy use improvement.

18


Data is analyzed to determine the energy requirement to treat per 1000 gallons of

wastewater. It provides a basis to compare with the energy requirement found in the

wastewater treatment baseline document.

19


TECHNOLOGY EVALUATION Technology will be implemented on the customer’s site, where the wastewater

is generated. Wastewater will be treated on-site and reused for irrigation or

other proposes in the same area. Otherwise, all the wastewater from a facility

needed to be tracked down and taken to a centralized wastewater treatment

plant which is very expensive primarily due to the trucking cost. The process

takes time and can generate additional pollutants and does not promote

reusing the water on-site what is very important to achieve sustainability in

the future.

Decentralized wastewater treatment management is the only way to achieve

sustainability in the future. Decentralized wastewater treatment i) allows the

reuse of wastewater that can help alleviate water shortages due to drought,

ii) reduces CAPEX intensive model like the centralized wastewater treatment

model where one needs to invest huge amounts of money to install sewage

lines, iii) reduces a significant amount of energy in comparison with the

centralized wastewater model where wastewater must be pumped from miles

away until it arrives at the centralized wastewater treatment plant, iv) there

is a big reduction in the carbon footprint emission of the decentralized

wastewater management model in comparison to the centralized wastewater

treatment model or with the trucking model. There may not be any risk to the

customers. Many industries today have implemented decentralized on-site

wastewater treatment (Clarke Prize 2003, Crites and Tchobanoglous 1998).

For each case, BioFiltro needs to know i) surface area available to install a

treatment plant, ii) volume of wastewater that will be treated, iii)

concentration of the contamination parameters of the wastewater that will be

treated, and iv) quality of the treated water that needs to be achieved.

The installation of the system is simple. BioFiltro needs contractors with

minimal experience in concrete, electrical, and piping work. BioFiltro does not

need PG&E to do this work. BioFiltro always uses small and local construction

companies to do the main job related to civil works. The media filters of

BIDA® system are implemented on-site through BioFiltro technical staff.

BioFiltro also does the start-up of the system and the O&M of the system.

20


TECHNICAL APPROACH/TEST METHODOLOGY

INSTRUMENTATION PLAN The following instrumentations have been used in the data collection.

Variables that were used for measurement include:

- BattV for Battery Voltage in Volts

- Pulse for flow of water in gallons

- Pulse_T for total flow in gallons

- DiffVolt(3) for three current transformers

Sensors and devices used for measurement:

- CR1000 Datalogger for BattV and Pulse_T

- Seametrics M120 flow meter for Pulse

- YHDC-SCT-013 Current transformer for DiffVolt(3)

Location of each device and sensors are as follows:

- CR1000 Datalogger -> On an antenna pole next to Tank2 (refer to the

Results section for layout of the system)

- Seametrics M120 flow meter -> On pipe out from Pump3 (refer to

Results section for layout of the system)

- YHDC-SCT-013 Current transformer -> On power supply line of

Pump1, Pump2 and Pump3 (refer to Results section for layout of the

system)

Expected range of measurement for variables includes:

- BattV = 0 ~ 7,999 V

- Pulse = 0 ~ 7,999 Gallons

- Pulse_T = 0 ~7,999 Gallons

- DiffVolt(3) = -non-zero ~0~ non-zero/Integers

Data was scanned every second and data was stored every 30 seconds for

DiffVolt(3) and BattV and every minute for Pulse and Pulse_T

21


DATA ANALYSIS - METHODS The following statistical methods have been used to analyze the water quality

and energy data. The methods are described below.

WATER QUALITY DATA ANALYSIS METHOD

The trend in the data has been observed by time series plot. This plot

provides an estimate of the quality of the data.

Basic statistical parameters such as mean, standard deviation, and

coefficient of variation have been calculated to quantify the uncertainty

associated with the data. Mean has been calculated by the following

equation:

EQUATION 1. MEAN OF DATA

�̅� = ∑ 𝑥𝑖

𝑛𝑖=1

𝑛

Where,

�̅� = mean

xi = any data i from a sample of n data

n = sample size

The standard deviation for sample has been calculated by the following

equation:

EQUATION 2. STANDARD DEVIATION OF DATA

𝜎 = ∑(𝑥𝑖 − �̅� )2

(𝑛−1)

Where,

𝜎 = standard deviation

�̅� = mean

xi = any data i from a sample of n data

n = sample size

The coefficient of variation (CV) which determines the extent of

variability is calculated by the ratio of standard deviation to the mean

as shown in the following equation:

22


EQUATION 3. COEFFICIENT OF VARIATION (CV) OF DATA

𝐶𝑉 = 𝜎

�̅�

Where,

CV = coefficient of variation

𝜎 = standard deviation

�̅� = mean

Standard deviation and coefficient of variation provide estimates of

uncertainties associated with the data. However, to infer the

population parameter, confidence interval analysis can be made on the

sample data. Confidence interval provides a limit or a range in which

the population mean can be bounded for a level of confidence. The

following equation has been used to calculate the confidence interval

following the normal distribution:

EQUATION 4. CONFIDENCE INTERVAL OF DATA

𝐵𝐿 = �̅� − 𝑧 ∗𝜎

√𝑛

𝐵𝑈 = �̅� + 𝑧 ∗𝜎

√𝑛

Where,

BL, BU = lower and upper bounds of the confidence

interval respectively

𝜎= standard deviation

�̅� = mean

z = critical value of standard normal cumulative

distribution function for a confidence level such as for

confidence level 99%.

The efficiency of the wastewater treatment plant has been observed by

calculating the removal efficiency of each water quality parameter

separately. The following equation has been used to calculate the

removal efficiency:

23


EQUATION 5. REMOVAL EFFICIENCY

Efficiency (%) =(𝐶𝑏𝑒𝑓𝑜𝑟𝑒− 𝐶𝑎𝑓𝑡𝑒𝑟)

𝐶𝑏𝑒𝑓𝑜𝑟𝑒∗ 100%

Where,

Cbefore, Cafter = parameter’s concentration before and

after the treatment.

Please note that three sets of efficiencies can be calculated for each

week for a particular parameter; (1) using the data directly collected

or observed, (2) using the upper bound of the confidence interval, and

(3) using the lower bound of the confidence interval. These three

efficiencies for a particular week have been calculated to observe the

range of the efficiency for that particular week. A common

denominator i.e., Cbefore in this case the observed value has been used

for all these three efficiency calculations.

Overall mean efficiency and standard deviation of the efficiency for a

particular parameter have also been calculated considering all the

efficiencies calculated for each week separately. This calculation

provides the general removal efficiency of that parameter and thus

helps to identify the robustness of the plant.

ENERGY DATA ANALYSIS METHOD

The trends in the energy consumption and water pumped have been

observed by time series plots. The mean of daily energy consumption

and water pumped have been calculated by Equation 1 and plotted in

the same time series plots to show the variabilities observed in these

two data sets.

Daily energy requirements to pretreat (mechanical separation), treat

(biological treatment), and to distribute (after treatment) unit amount

of water (in this case 1000 gallons of water) have been calculated by

the following equation:

EQUATION 6. ENERGY REQUIREMENT FOR 1000 GALLONS OF WATER

𝐸𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑑 =𝑇𝑜𝑡𝑎𝑙 𝑑𝑎𝑖𝑙𝑦 𝑒𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛

𝑇𝑜𝑡𝑎𝑙 𝑑𝑎𝑖𝑙𝑦 𝑤𝑎𝑡𝑒𝑟 𝑝𝑢𝑚𝑝𝑒𝑑∗ 1000

Where,

Erequired = Daily energy consumption in kWh/1000 gallons

of water

The basic statistical parameters such as mean, median, and some

percentile values of the unit energy requirements have been

calculated. These parameters would provide an estimate of the sample

data that is within the baseline energy requirement. In this regard, the

exceedance probability analysis on unit energy consumption has also

been completed to quantify the probability exceedance of a certain

unit energy requirement.

24


RESULTS

DATA ANALYSIS - RESULTS The following sections analyze and present water quality and energy

requirement results.

DATA ANALYSIS – RESULT (CSUF DAIRY)

Sixteen water quality parameters have been considered for the CSUF dairy

site. These parameters include; B, BOD5, Ca, COD, EC, K, Mg, Na, NH4, NO3-

N, P, pH, Salt-Sol, TDS, TKN, and TSS. Water samples from the CSUF dairy

site have been collected from May 9th to October 15. Samples were collected

on a weekly basis i.e., once in a week. Due to mechanical failure, some

weeks’ samples were discarded, resulting in a maximum number of useful

data points seventeen (17) for some parameters. For some of the

parameters, the number of useful data points may be lower than 17. The

water samples were collected before and after the treatment hence the term

“Before” and “After” in the following sections of the report.

Table 5 shows the basic statistical parameters of the water quality data used

in this study. Although the standard procedure and protocols have been

followed during sample collection, transportation, and laboratory analysis,

some data still shows outliers. The outliers have been discarded during the

basic statistical parameters calculation. As seen from Table 5 the variability in

the data ranges from CV of 0.02 to CV of 0.42. The lowest variability is

observed for pH and the highest variability is observed for BOD5.

TABLE 5. BASIC STATISTICAL PARAMETERS IN CSUF WATER QUALITY DATA ANALYSIS


Before After

Mean Std. Dev CV Mean Std. Dev CV

B (ppm) 0.71 0.11 0.16 0.66 0.11 0.17

BOD5 (mg/L) 888.64 374.42 0.42 775.73 309.01 0.40

Ca (ppm) 91.62 10.19 0.13 75.94 9.83 0.13

COD (mg/L) 1478.57 86.11 0.06 775.73 309.01 0.40

EC (mmhos/cm) 4.49 0.74 0.16 3.92 0.63 0.16

K (ppm) 527.06 85.00 0.16 539.65 81.73 0.15

Mg (ppm) 83.59 11.25 0.13 81.38 13.00 0.16

25



Before After


Na (ppm) 208.59 28.07 0.13 214.18 26.89 0.13

NH4 (mg/L) 124.39 22.88 0.18 0.50 0.00 0.00

NO3-N 55.18 15.04 0.27 0.82 0.46 0.57

P (ppm) 43.21 6.26 0.14 36.95 4.12 0.11

pH 8.11 0.20 0.02 7.88 0.23 0.03

Salt-Sol (ppm) 2870.59 471.20 0.16 2507.29 403.12 0.16

TDS (mg/L) 3036.53 530.14 0.17 2714.41 494.51 0.18

TKN (ppm) 182.27 25.64 0.14 0.50 0.00 0.00

The trend in an entire data set for each of the parameters can be observed in

figures from Figure 13 to Figure 27. It is expected that the data would have

some measurement errors during the sampling and laboratory testing

processes. To account for this error, confidence limits have been calculated

considering the weekly recorded value as the mean. The confidence limits

would provide a range in which the true or population mean would fall. The

upper and lower limits of the confidence intervals for each week of the data

have been calculated for the 99% confidence level following a normal

distribution. The standard error of the sample (SES) has been calculated

using the statistical parameters listed in Table 5. As a result the time series

plots from Figure 13 to Figure 27 show three curves for each “Before” and

“After” water quality data. One curve is to show the mean (i.e., the recorded

data); one is to show the upper bound of 99% confidence limit; and the other

is to show the lower bound of 99% confidence limit.

Some figures show consistent trends in the data sets. These data sets either

have no spike or trough at all or have a single or minimum outliers. These

data sets include; B, BOD5, Ca, COD, NH4, NO3-N. Some data sets show an

upward trend as the weeks increase. The other data sets may have more than

one spike, trough or inconsistency in the trend. The local conditions such as

the weather pattern and the farming processes can contribute to the

inconsistencies in the trends.

26


FIGURE 13. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT CSUF: BORON (B)

FIGURE 14. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT CSUF: BOD5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Co

ncen

trati

on

(p

pm

)

Week

After - Mean Before - Mean

Before - LB 99% Before - UB 99%

After - LB 99% After - UB 99%

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

Co

ncen

trati

on

(m

g/L

)

Week


After - LB99% After - UB99%

Before - LB99% Before - UB99%

27


FIGURE 15. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT CSUF: CALCIUM (CA)

FIGURE 16. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT CSUF: COD

0

20

40

60

80

100

120

140

Co

ncen

trati

on

(p

pm

)

Week

Before - Mean After - Mean



0

1000

2000

3000

4000

5000

6000

7000

8000

9000

Week1 Week2 Week3 Week4 Week5 Week6 Week7 Week8

Co

ncen

trati

on

(mg

/L)

Week




28


FIGURE 17. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT CSUF: EC

FIGURE 18. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT CSUF: POTASSIUM (K)

0

1

2

3

4

5

6

7

8

Co

ncen

trati

on

(mm

ho

s/c

m)

Week




0

100

200

300

400

500

600

700

800

Co

ncen

trati

on

(pp

m)

Week

Concentration Before and After Treatment: K




29


FIGURE 19. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT CSUF: MAGNESIUM (MG)

FIGURE 20. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT CSUF: SODIUM (NA)

0

20

40

60

80

100

120

Co

ncen

trati

on

(pp

m)

Week




0

50

100

150

200

250

300

Co

ncen

trati

on

(pp

m)

Week




30


FIGURE 21. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT CSUF: AMMONIUM

NITROGEN (NH4)

FIGURE 22. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT CSUF: NITRATE

NITROGEN (NO3-N)

0

20

40

60

80

100

120

140

160

180

200

Co

ncen

trati

on

(mg

/L)

Week

After

Before

0

100

200

300

400

500

600

700

800

900

1000

Co

ncen

trati

on

(mg

/L)

Week




31


FIGURE 23. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT CSUF: PHOSPHORUS (P)

FIGURE 24. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT CSUF: PH

0

10

20

30

40

50

60

70

80

Co

ncen

trati

on

(pp

m)

Week




6.6

6.8

7.0

7.2

7.4

7.6

7.8

8.0

8.2

8.4

8.6

8.8

pH

Week




32


FIGURE 25. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT CSUF: SOLUBLE SALTS

(SALT-SOL)

FIGURE 26. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT CSUF: TOTAL DISSOLVED

SOLIDS (TDS)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Co

ncen

trati

on

(p

pm

)

Week




0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Co

ncen

trati

on

(m

g/L

)

Week




33


FIGURE 27. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT CSUF: TOTAL KJELDAHL

NITROGEN (TKN)

The removal efficiency has been calculated separately for each of the water

quality parameters. For each water quality parameter weekly efficiency has

been calculated first and then the overall mean and standard deviation in the

removal efficiency was also calculated. The upper and lower bounds of the

removal efficiencies were also calculated. While Table 6 shows the overall

mean and standard deviation of the removal efficiencies, the figures from

Figure 28 to Figure 42 show the upper and lower limits of the removal

efficiency for each week. Any negative (i.e., concentration was higher in

“After” than “Before”) and outlier efficiencies have been discarded in the

overall efficiency calculation and also discarded in the plot.

The overall mean efficiency for all the water quality parameters ranges from

2.7% to 98.4%. While NH4 has the highest overall efficiency, Mg has the

lowest overall efficiency. The standard deviation, which represents the

uncertainty, is found lowest in pH. However, since the mean is already lower

for pH, this does not reflect the variation. Therefore a normalized parameter

such as CV has been used to compare the variability between the parameters.

CV is found lowest for NH4 removal efficiency. The highest CV of the removal

efficiency is observed for P showing a very high uncertainty in the P removal

process. Some of the parameters that are to be found with higher removal

efficiency include NH4 (98.4%), NO3-N (96.2%), TKN (90.6%), and COD

(46.9%). The uncertainty, in terms of CV, is found 0.04, 0.05, 0.2, and 0.32

respectively for NH4, NO3-N, TKN, and COD. A few parameters such as K and

Na are found with overall negative removal efficiency. Either the sample was

contaminated specifically for these parameters or somehow these parameters

were generated in the removal efficiency. Another important water quality

parameter BOD5 is found to have an overall efficiency of 23.5% with 0.63 of

0

50

100

150

200

250

300

350

400

450

Co

ncen

trati

on

(p

pm

)

Week




34


CV. The 99% upper and lower limits provide a range in which the population

mean of the removal efficiency can fall. Figure 28 to Figure 42 show the

weekly mean removal efficiency with upper and lower limits.

TABLE 6. REMOVAL EFFICIENCY OF WATER QUALITY PARAMETERS AT CSUF DAIRY WASTEWATER TREATMENT PLANT


Removal Efficiency

Mean Std. Dev CV

B (ppm) 7.4% 3.8% 0.52

BOD5 (mg/L) 23.5% 14.8% 0.63

Ca (ppm) 16.8% 9.5% 0.56

COD (mg/L) 46.9% 15.2% 0.32

EC (mmhos/cm) 12.1% 11.2% 0.92

K (ppm) -2.6% 4.4% -1.69

Mg (ppm) 2.7% 7.6% 2.84

Na (ppm) -2.9% 4.4% -1.52

NH4 (mg/L) 98.4% 3.8% 0.04

NO3-N 96.2% 4.3% 0.05

P (ppm) 8.9% 23.9% 2.69

pH 2.9% 1.9% 0.67

Salt-Sol (ppm) 12.1% 11.2% 0.92

TDS (mg/L) 10.1% 11.6% 1.15

TKN (ppm) 90.6% 18.0% 0.20

35


FIGURE 28. REMOVAL EFFICIENCY AT CSUF: BORON (B)

FIGURE 29. REMOVAL EFFICIENCY AT CSUF: BOD5

0%

10%

20%

30%

40%

50%

60%

Eff

icie

ncy (

%)

Week

Mean LB 99% UB 99%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Eff

icie

ncy (

%)

Week

Mean

LB 99%

UB 99%

36


FIGURE 30. REMOVAL EFFICIENCY AT CSUF: CALCIUM (CA)

FIGURE 31. REMOVAL EFFICIENCY AT CSUF: COD

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Eff

icie

ncy (

%)

Week

Mean

LB 99%

UB 99%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Week1 Week2 Week3 Week4 Week5 Week6 Week7 Week8

Eff

icie

ncy (

%)

Week

Mean LB 99% UB 99%

37


FIGURE 32. REMOVAL EFFICIENCY AT CSUF: EC

FIGURE 33. REMOVAL EFFICIENCY AT CSUF: POTASSIUM (K)

0%

5%

10%

15%

20%

25%

Eff

icie

ncy (

%)

Week

Mean LB 99%UB 99%

0%

1%

2%

3%

4%

5%

6%

7%

Eff

icie

ncy (

%)

Week

Mean LB 99% UB 99%

38


FIGURE 34. REMOVAL EFFICIENCY AT CSUF: MAGNESIUM (MG)

FIGURE 35. REMOVAL EFFICIENCY AT CSUF: SODIUM (NA)

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

Eff

icie

ncy (

%)

Week

Mean LB 99% UB 99%

0%

1%

1%

2%

2%

3%

3%

4%

4%

5%

Eff

icie

ncy (

%)

Week

Mean LB 99% UB 99%

39


FIGURE 36. REMOVAL EFFICIENCY AT CSUF: AMMONIUM NITROGEN (NH4)

FIGURE 37. REMOVAL EFFICIENCY AT CSUF: NITRATE NITROGEN (NO3-N)

0%

20%

40%

60%

80%

100%

120%

Eff

icie

ncy (

%)

Week

Mean LB 99% UB 99%

0%

20%

40%

60%

80%

100%

120%

Eff

icie

ncy (

%)

Week

Mean LB 99% UB 99%

40


FIGURE 38. REMOVAL EFFICIENCY AT CSUF: PHOSPHORUS (P)

FIGURE 39. REMOVAL EFFICIENCY AT CSUF: PH

0%

5%

10%

15%

20%

25%

30%

35%

Eff

icie

ncy (

%)

Week

Mean LB 99% UB 99%

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

Eff

icie

ncy (

%)

Week

Mean LB 99% UB 99%

41


FIGURE 40. REMOVAL EFFICIENCY AT CSUF: SOLUBLE SALTS (SALT-SOL)

FIGURE 41. REMOVAL EFFICIENCY AT CSUF: TOTAL DISSOLVED SOLIDS (TDS)

0%

5%

10%

15%

20%

25%

Eff

icie

ncy (

%)

Week

Mean LB 99% UB 99%

0%

5%

10%

15%

20%

25%

30%

Eff

icie

ncy (

%)

Week

Mean LB 99% UB 99%

42


FIGURE 42. REMOVAL EFFICIENCY AT CSUF: TOTAL KJELDAHL NITROGEN (TKN)

Three pumps have been used to supply energy to the overall wastewater

treatment process. Specifically, Pump 1 provides energy for pre-treatment

(mechanical separation); Pump 2 provides energy for the main treatment

process using the BIDA® system, and Pump3 supplies energy to distribute

treated water. Pump2 is the main source of energy for the wastewater

treatment process using BIDA® system. The following table (Table 7) shows

the energy supply statistics by the three pumps. Pump 2 which is the main

source of energy to treat wastewater supplied the highest amount of energy.

Pump 3 that supplies energy to distribute the treated water supplied the

lowest amount of energy. The days in operation for all three pumps are not

same which means some pumps might not be in operation on some days.

There were various reasons why all the pumps were not in operation on same

days. Mechanical failure caused pumps to be non-operable while the other

means were used to supply wastewater and treated water.

The daily average energy requirements by three pumps are also listed in

Table 7. While Pump 3 requires the least daily average, Pump 2 requires the

highest daily average. Please note that pumps were running intermittently on

any given day meaning that not all three pumps were following the same

schedule with the same operating time in a given day. Pump 1 was used to

pump water for mechanical separation (pre-treat) and Pump 2 was used to

pump water to the treatment system. Since there was four hours of time

requirement to flow the water through the treatment system, the Pump 1,

Pump 2, and Pump 3 (for distributing treated water) were not likely to follow

the same operational schedule. As a result, the pump operation hours and

energy requirements vary. Also due to mechanical failure such as leaks,

plugged filters, and clogging some pumps needed to be turned off. During the

test, Pump 2 needed to be replaced as a result of mechanical failure. The

newer pump was not fine-tuned with the system requirement and as a result,

0%

20%

40%

60%

80%

100%

120%

Eff

icie

ncy (

%)

Week

Mean LB 99% UB 99%

43


it supplied more energy than the older pump which is reflected in Figure 44

showing higher energy requirements towards the end of the test.

TABLE 7. ENERGY SUPPLY STATISTICS FOR CSUF DAIRY WASTEWATER TREATMENT PLANT

Pump Pump1 Pump2 Pump3


Average daily hours in operation (hr) 14.1 10.4 10.4

Total consumption (kWh) 1040.7 1060.15 623.54

Daily average consumption (kWh) 7.8 7.3 4.2

Standard deviation (kWh) 3.8 6.7 4.1

FIGURE 43. ENERGY REQUIREMENT BY PUMP1 TO PRE-TREAT WASTEWATER AT CSUF

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

En

erg

y (

kw

h)

Date

Energy consumed (kWh) Daily average (kwh)

44


FIGURE 44. ENERGY REQUIREMENT BY PUMP2 TO TREAT WASTEWATER AT CSUF

FIGURE 45. ENERGY REQUIREMENT BY PUMP3 TO DISTRIBUTE TREATED WATER AT CSUF

0.0

5.0

10.0

15.0

20.0

25.0

En

erg

y (

kw

h)

Date


0.0

2.0

4.0

6.0

8.0

10.0

12.0

En

erg

y (

kw

h)

Date


45


Pump 3 has been used to collect the flow data. Since there is no loss of water,

except minimum evaporation and consumption by the earthworms, it is

assumed that a steady state flow was maintained in the system. As a result,

the same amount of flow was flowing through Pump 1, Pump 2, and Pump 3

(ignoring the minimum evaporation and the minimum water consumption by

the earthworms). Please note that due to mechanical failure on some days,

the flow data is not consistent with the pump data i.e., for any given day

while Pump 3 was running, the flow data shows no value. On an average,

Pump 3 was running about 10.4 hours but for some days the recorded flow

amount was very minimal. It is reported that this discrepancy also resulted

due to mechanical failure of the pump and the system. Mechanical failure

included system leakage, pipe clogging, plugged filters etc. A test was

conducted later to record the Pump 3 pressure. It was found that Pump3 was

running at approximately 3.25 psi (7.5 ft of head). Considering this operating

pressure (7.5 ft of head), maximum design head of the pump (20 ft), and the

maximum discharge capacity of the pump (1670 gph), the average daily

discharge was calculated following a linear pump curve. The calculated

average daily discharge is 1044 gpd. It was assumed that any discharge

lower than the 1044 gpd would be due to the mechanical failure at the pump

or in the system. As the main objective is to find out the energy requirement

to treat a unit amount of water (i.e., 1000 gallons) this assumption still can

maintain the correlations between the amount of water treated and the

energy requirement since the entire data for the days with lower flow would

be discarded. As a result, a total 104 days of data was considered in the

energy requirement analysis.

Table 8 shows the statistics of the amount of energy required by each pump

(after discarding some data due to mechanical failure). Figure 46 shows the

time series plot of these data. Generally in the beginning of the experiment,

energy requirements by each pump were lower than the energy requirement

towards the end of the experiment. Mechanical failure caused the old broken

pump to be replaced towards the end of test. The newer pump was not fine-

tuned with the system causing a higher energy requirement. Figure 47 shows

the sorted energy and flow data to illustrate any correlation between pump

energy usage and water flow. The energy requirement is high with the

increase of flow. This relationship is almost linear for Pump 1 but not for

Pump 2 and Pump 3 showing some mechanical issues with these two pumps

(Pump 2 and Pump 3). This is due to the mechanical failures that were

described earlier.

TABLE 8. ENERGY–WATER STATISTICS AT CSUF DAIRY WASTEWATER TREATMENT PLANT AFTER DISCARDING SOME DATA

DUE TO MECHANICAL FAILURE

Statistics Energy data (kWh) Flow

(gpd) Pump1 Pump2 Pump3

Days in operation 104 104 104 104

Daily average 7.9 7.8 4.1 2481.0

46


Daily maximum 13.2 19.6 9.7 4027.0

Daily minimum 0.0 0.1 0.0 1044.5

Standard deviation 3.8 6.8 4.1 848.9

Total 825 813 427 258,028

FIGURE 46. TIME SERIES PLOT OF ENERGY REQUIREMENT BY EACH PUMP AND FLOW AT CSUF DAIRY WASTEWATER

TREATMENT PLANT AFTER DISCARDING SOME DATA DUE TO MECHANICAL FAILURE

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0

5

10

15

20

25

1 11 21 31 41 51 61 71 81 91 101

Flo

w (

gp

d)

En

erg

y (

kw

h)

Days

Pump1 Pump2

Pump3 Flow

47


FIGURE 47. SORTED FLOW AND ENERGY AT CSUF DAIRY WASTEWATER TREATMENT PLANT AFTER DISCARDING SOME DATA

DUE TO MECHANICAL FAILURE

The energy required to treat 1000 gallons of wastewater has been calculated

for; pre-treatment (by Pump 1), main treatment (by Pump 2), and distribution

of treated water (by Pump 3). Table 9 shows the summary of the energy

requirements for these three conditions. On an average, while distribution of

treated water requires the least amount of energy (1.7 kWh/1000 gallons) the

pre-treatment requires the highest (3.7 kWh/1000 gallons). The energy

supplied by Pump 2 was used to treat the wastewater. The average energy

requirement (kWh/1000 gallons) to treat the wastewater (by Pump 2) is 3.2

kWh/1000 gallons. On a percentage basis, about 25% of the energy

requirement data is lower than the 1.0 kWh/1000 gallons and 50% of the

energy requirement data is lower than the 1.8 kWh/1000 gallons.

In the baseline study it is reported that the annual energy requirements for

secondary, tertiary, and overall treatments are about 771,357 kWh/MGD,

1,144,277 kWh/MGD, and 907,836 kWh/MGD (PG&E 2006). Converting these

numbers to the energy requirement per 1000 gallons of water per day gives us

2.1, 3.1, 2.5 kWh/1000 gallons. These numbers can be directly compared with

the energy requirement by Pump 2 in this study. About 55% of the data energy

requirement data are found lower than the baseline study. Figure 48 to Figure

50 show the non-exceedance probability plot of these energy requirements to

treat 1000 gallons of wastewater. As seen from the figures, about 50% of the

energy requirement data are found lower than the baseline information. It is

important to note that this is a pilot test and the plant was not running at a full

scale. Therefore, it is possible that some energy might have been wasted due

to lack of fine tuning (calibrating) the system parameters. Mechanical failure

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0

5

10

15

20

25

1 11 21 31 41 51 61 71 81 91 101

Flo

w (

gp

d)

En

erg

y (

kw

h)

Rank

Pump1 Pump2

Pump3 Flow

48


might be another reason for seeing some energy requirement data higher than

the baseline study.

TABLE 9. ENERGY REQUIREMENT TO TREAT 1000 GALLONS OF WATER AT CSUF DAIRY WASTEWATER TREATMENT PLANT

Statistics Energy requirement (kWh) to treat 1000 gallons of waste water


Treatment (Pump2)



Average 3.7 3.2 1.7

Max 12.2 14.4 8.4



50 percentile (median) 3.1 1.8 0.4



49


FIGURE 48. NON-EXCEEDANCE PROBABILITY CURVE FOR ENERGY REQUIREMENT FOR THE PRE-TREATMENT OF

WASTEWATER (BY PUMP1) AT CSUF DAIRY WASTEWATER TREATMENT PLANT

FIGURE 49. NON-EXCEEDANCE PROBABILITY CURVE FOR ENERGY REQUIREMENT FOR THE TREATMENT OF WASTEWATER

(BY PUMP2) AT CSUF DAIRY WASTEWATER TREATMENT PLANT

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

No

n-E

xce

ed

an

ce

Pro

bab

ilit

y (

%)


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0

No

n-E

xce

ed

an

ce

Pro

bab

ilit

y (

%)


Bas

elin

e Se

con

dar

y

Bas

elin

e O

vera

ll

Bas

elin

e Te

rtia

ry

50


FIGURE 50. NON-EXCEEDANCE PROBABILITY CURVE FOR ENERGY REQUIREMENT FOR THE DISTRIBUTION OF TREATED

WATER (BY PUMP3) AT CSUF DAIRY WASTEWATER TREATMENT PLANT

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0

No

n-E

xce

ed

an

ce

Pro

bab

ilit

y (

%)


51


DATA ANALYSIS – RESULT (TOMATO PROCESSOR)

Weekly samples were collected from August 6 to October 16. Only two water

quality parameters, including BOD5 and TSS, were considered for this

wastewater treatment plant. The same method that was applied to analyze the

CSUF wastewater water quality data has been applied to this site. Table 10 lists

the basic statistical parameters and Figure 51 and Figure 52 show the time

series plot of the water quality data with its upper and lower 99% confidence

bounds. Except for a few spikes, both parameters show consistency throughout

the sample collection period. The statistics of removal efficiency were tabulated

in Table 11 and the weekly removal efficiencies are plotted in Figure 53 and

Figure 54. Unlike the CSUF wastewater treatment plant, the removal efficiency

of BOD5 and TSS for this plant is significantly higher as seen from the table and

figures. The overall mean removal efficiency for BOD5 is 96.4% and for the TSS

is 89.7% with lower CVs. The system design and the biological process of the

treatment plant are assumed to be the main reasons for the higher removal

rate.

Like the CSUF wastewater treatment plant, the same methodology was applied

to analyze energy data for the large tomato processor wastewater treatment

plant. Table 12 lists the energy requirement statistics and the energy

requirement time series (daily data) plots are shown in Figure 55 and Figure

56. Please note that only two pumps were used for this site. No pump was

assigned for the pre-treatment. For the consistency, these two pumps were

labeled Pump 2 (for treatment) and Pump 3 (for the distribution of treated

water). Similar to the CSUF wastewater treatment, some data was discarded

due to mechanical failure. Table 13 lists the statistics of the energy requirement

and water pumped by the pumps. Figure 57 shows the entire data set (both

energy and water) after some data has been discarded due to the mechanical

failure. As seen from the table and figure, the daily average energy

requirement for Pump 2 is lower than Pump 3. The energy requirement to treat

1000 gallons of wastewater has been tabulated in Table 14. Generally this

wastewater site requires more energy than the CSUF site.

TABLE 10. BASIC STATISTICAL PARAMETERS IN A LARGE TOMATO PROCESSOR WATER QUALITY DATA ANALYSIS


Before After


BOD5 (mg/L) 1928.6 170.4 0.09 23.0 4.7 0.21

TSS (mg/L) 260.0 67.0 0.26 20.9 7.4 0.35

52


FIGURE 51. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT A LARGE TOMATO

PROCESSOR: BOD5

FIGURE 52. CONCENTRATION WITH CONFIDENCE LIMITS BEFORE AND AFTER THE TREATMENT AT A LARGE TOMATO

PROCESSOR: TSS

0

500

1,000

1,500

2,000

2,500

3,000

Co

nc

en

tra

tio

n (

mg

/L)

Week

Before - Mean Before - LB 99%

Before - UB 99% After - Mean


0

100

200

300

400

500

600

700

800

900

1,000

Co

nc

en

tra

tio

n (

mg

/L)

Week




53


TABLE 11. REMOVAL EFFICIENCY OF WATER QUALITY PARAMETERS AT A LARGE TOMATO PROCESSOR WASTEWATER

TREATMENT PLANT


Removal Efficiency Confidence Interval

Mean Std. Dev CV 99% LB 99% UB

BOD5 (mg/L) 96.4% 6.9% 0.07 83.9% 100.0%

TSS (mg/L) 89.7% 9.5% 0.11 69.3% 100.0%

FIGURE 53. REMOVAL EFFICIENCY AT A LARGE TOMATO PROCESSOR: BOD5

0%

20%

40%

60%

80%

100%

120%

Eff

icie

nc

y (

%)

Week

Mean LB 99% UB 99%

54


FIGURE 54. REMOVAL EFFICIENCY AT A LARGE TOMATO PROCESSOR: TSS

TABLE 12. ENERGY SUPPLY STATISTICS FOR A LARGE TOMATO PROCESSOR WASTEWATER TREATMENT PLANT

Pump Pump1 Pump2 Pump3

Days in operation

Not in

used

87 86

Average daily hours in operation (hr) 9.7 10.4

Total consumption (kWh) 418.87 352.55

Daily average consumption (kWh) 4.8 4.1

Standard deviation (kWh) 2.0 2.3

0%

20%

40%

60%

80%

100%

120%

Eff

icie

nc

y (

%)

Week

Mean LB 99% UB 99%

55


FIGURE 55. ENERGY REQUIREMENT BY PUMP2 TO TREAT WASTEWATER AT A LARGE TOMATO PROCESSOR

FIGURE 56. ENERGY REQUIREMENT BY PUMP3 TO DISTRIBUTE TREATED WATER AT A LARGE TOMATO PROCESSOR

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

8/18/2014 9/18/2014 10/18/2014

En

erg

y (

kw

h)

Date


0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

8/18/2014 9/18/2014 10/18/2014

En

erg

y (

kw

h)

Date


56


TABLE 13. ENERGY–WATER STATISTICS AT A LARGE TOMATO PROCESSOR WASTEWATER TREATMENT PLANT AFTER

DISCARDING SOME DATA DUE TO MECHANICAL FAILURE

Statistics

Energy data (kwh) Flow (gpd)

Pump1 Pump2 Pump3

Days in operation

Not in

used

21 21 24

Average 4.6 5.4 717.4

Max 7.0 6.2 1471.0

Min 0.9 0.2 557.0


FIGURE 57. TIME SERIES PLOT OF ENERGY REQUIREMENT BY EACH PUMP AND FLOW AT A LARGE TOMATO PROCESSOR

WASTEWATER TREATMENT PLANT AFTER DISCARDING SOME DATA DUE TO MECHANICAL FAILURE

TABLE 14. ENERGY REQUIREMENT TO TREAT 1000 GALLONS OF WATER AT A LARGE TOMATO PROCESSOR WASTEWATER

TREATMENT PLANT

Statistics



Treatment (Pump2)


Days in operation

Not used

24 24

Average 5.9 7.1

Max 11.3 10.3

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

0

2

4

6

8

10

12

1 11 21

Flo

w (

gp

d)

En

erg

y (

kw

h)

Days

Pump2 Pump3 Flow

57


Statistics



Treatment (Pump2)


Min 0.1 0.2

Standard deviation 3.2 3.4


50 percentile (median) 6.5 8.7



EVALUATIONS

The performance of the BioFiltro or BIDA® wastewater treatment technology

was observed for its removal efficiency in terms of water quality parameters.

The removal efficiencies of the nitrogen-based water quality parameters such

as NH4, NO3-N, TKN are significantly high (above 90%). Other water quality

indicators such as BOD5 and COD show promising results. However, the

plant’s performance on other parameters was poor. Negative removal

efficiency was found for a few parameters showing the probable

contamination or generation of these parameters during the treatment

process. Since the CSUF site is a dairy farm, the interest perhaps is on the

nitrogen-based parameters. The BIDA® performance on these nitrogen-based

water quality measures is significantly high. The performance of BioFiltro

(BIDA® technology) in terms of water quality was very high at a large tomato

processor site. Two water quality parameters, BOD5 and TSS were considered

for this site and the removal efficiencies for both the parameters are found

90% or above.

The energy requirements to treat 1000 gallons of wastewater were calculated

for both CSUF and the large tomato processor site. Results were compared to

the baseline study information. For the CSUF site, about 25% of the data

shows better or extraordinary performance than the baseline study as the

energy requirement to treat 1000 gallons of wastewater was found to be

much lower. Refer to Table 9 and Figure 49 in the Results section for a

detailed analysis. About 50% of the data at both CSUF and the large tomato

processor site did not outperform the baseline study. Mechanical failure is

suspected to be one of the main reasons.

58


RECOMMENDATIONS Although the performance of the BioFiltro was found extraordinary for some

water quality parameters (such as nitrogen-based parameters at the CSUF

site and all the parameters at the large tomato processor site), the

performance for some parameters were not found high or satisfactory. Refer

to the Analysis section under Results for details. Measurement errors and

probable contaminations are suspected to be the reasons for this poor

performance. Therefore, extra caution and attention to testing protocols for

sampling, lab analysis, and data entry need to be maintained throughout the

process.

Both CSUF and the large tomato processor site are pilot studies. As such,

some system parameters were not fine-tuned and might have been the cause

of wasting some energy. Mechanical failure was also observed during the

testing process. It is important to understand the system parameters in depth

which will provide an insight to fine tune or calibrate the parameters properly.

Extra caution also needs to be maintained to avoid any mechanical failure.

Next steps: A full scale system is being built at a dairy near Hilmar, CA. It is

recommended that additional data be collected on a system of full,

commercial size to verify scalability of system and needed improvements in

operational efficiencies. The Hilmar system is planned to be fully operational

in January of 2015. This provides a perfect opportunity to collect data and

determine feasibility widespread adoption.

APPENDICES

All the data including raw and processed are attached in spreadsheet format.

The Appendices also includes all the statistical analysis and figures that were

made for this study.

59


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