This document summarizes research investigating the use of effluents from anaerobic digestion (ADE) and polyhydroxyalkanoate production (PHAE) processes to cultivate algae. The objectives were to determine the effects of light attenuation, nitrogen concentration, and nitrogen species on algal growth rate and yield. Experiments tested combinations of absorbance and nitrogen levels in ADE, PHAE, and mixtures. Principal components analysis showed absorbance and ammonium influenced one component, while nitrate and phosphate influenced another. Regression models predicted growth rate decreased with the first principal component, while biomass increased with both components. Supplementing with PHAE eliminated freshwater needs and doubled annual biomass productivity compared to ADE

1. Table 4. Predictions based on 10,000,000 L
system, operating semi-continuously, using
optimal growth rate for each effluent to
calculate hydraulic retention time and
productivity.
Objective 1: Determine effect of light
attenuation on growth kinetics (μ) and
yield.
• Previous data showed light
attenuation from effluents effected
growth rate (Passero 2015).
• Examined a range of absorbance
levels that would be used in
operation.
Objective 2: Determine effect of [N] on
growth kinetics (μ) and yield.
• Each absorbance level was amended
with additional nitrogen.
• Added N at concentrations to be
equivalent to next treatment level.
(Fig. 3).
This allowed transmittance to remain
the same while [N] was manipulated.
Objective 3: Create effluent mixture
• Supplement process water using
PHAE
Treatments were designed based on
absorbance, nitrogen species, & nitrogen
concentration of the ADE and PHAE.
To investigate the influence of effluent
characteristics we tested combinations of
absorbance and nitrogen concentrations in a
sequential factorial design (n=4).
1Boise State University, USA, 2University of Idaho, USA, 3Idaho National Laboratory, USA
Characteristics of effluent used for algal cultivation
Project Overview Research Goals
Experimental Outcomes
Industry Relevance
Predictions
Acknowledgements
 Integrate algal biomass production operation into
manure treatment process
 Improve large scale algal operational costs
• Supply nutrients
• Supplement process water
 Maximize operation
• Increase algal biomass productivity
• Increase nutrient remediation
 Develop predictive model to forecast operation
 Provide decreased nutritional costs
• Fertilizer requirements account for 61.1% of algal
operational cost (Liu 2009)
 Lower process water usage
• Supplementing process with wastewater can reduce
water usage by as much as 90% (Yang 2011)
 Predict annual yield and operation time using model
• Based on effluent optical and nitrogen properties
Figure 1. Integrated process schematic for manure management,
nutrient remediation and alternative product production (i.e. Bio-
plastics, Bio-Power, Algae Biomass).
Anaerobic Digestor effluent
(ADE)
Polyhydroxyalkanoate
effluent (PHAE)
Optical Characteristics
Absorbance (680 nm) 0.334 ± 0.014 0.135 ± 0.025
Nutrient Characteristics (mg/L)
Total Nitrogen (Ntot) 1164.23 ± 45.07 36.25 ± 3.13
Ammonium (NH4-N) 1146.03 ± 42.61 19.74 ± 0.81
Nitrate (NO3-N) 18.20 ± 2.46 36.32 ± 2.32
Orthophosphate (PO4-P) 5.36 ± 1.18 5.99 ± 1.10
N:P 217 : 1 6 : 1
NH4
+
:NO3
-
63 : 1 0.5 : 1
Table 1. Initial nutrient species and concentrations (± standard deviation) for AD and PHA
effluents (n=3).
Figure 4. Each absorbance level (circle) had
nitrogen added (dashed bars) to match the
nitrogen concentration of the next highest
absorbance level (solid bars). ADE/PHAE
mixtures (green bars) were created based on
μ and yield performance.
Figure 3. Chlorella vulgaris (UTEX# 2714)
was grown at 60 μmolm-2s-1 on a 18:6 light
dark cycle, constantly aerated at 0.3 vvm
with air containing 2.5% CO2 at 25ᵒC.
Principal Components Analysis
Figure 5. Initially growth rate increased,
likely due to the increased availability of a
limiting nutrient such as nitrate, until
reaching a threshold Abs680 value of 0.07.
After which there was a distinct inverse
relationship between absorbance and
growth rate. Abs680 values greater than
0.07, resulted in decreased growth rates as
the light attenuation increased.
Figure 6. There was a positive relationship
with nitrogen and ash free dry weight.
Nitrogen addition led to an increase in
biomass accumulation. The effect was
greatest in PHAE (blue) where most of the
N was in the form of nitrate. ADE biomass
production (red) on the other hand was
less responsive to nitrogen addition as
only a small fraction of the N was nitrate.
Our objectives were to determine the influence
of effluent characteristics, (i.e. Light Attenuation,
Nitrogen Concentration and Nitrogen Species) on
growth rate and biomass productivity.
Both effluents were analyzed for:
• Optical properties
─ Absorbance (PAR λ) with focus on 440
& 650 nm
• Nutrient species & concentrations
─ NH4
+, NO3
-, PO4
3-
Effects of N and optical property treatments on growth rate and yield
GR R2 = 0.46 AFDW R2 = 0.73
Estimate ± SE t P Estimate ± SE t P
Intercept 0.162 ± 0.004 38.23 <0.0001 Intercept 1.919 ± 0.065 29.59 <0.0001
PC1 -0.021 ± 0.003 -6.88 <0.0001 PC1 0.361 ± 0.047 7.61 <0.0001
PC2 0.007 ± 0.003 2.07 0.0430 PC2 0.508 ± 0.049 10.36 <0.0001
GR, growth rate; AFDW, ash free dry weight
Figure 8. A Principal Components Analysis for the 4
predictor variables. The arrows show correlation
between values along each principal component axis. For
example, Effluent abs & NH4-N both contribute to PC1,
while NO3-N & PO4-P contribute to PC2. Each color dot
represents an effluent type (red = ADE, blue = PHAE,
green = Mix) with lighter colors being nitrogen addition
treatments. PC1 and PC2 explain 47.5% and 44.5% of
the variation, respectively.
Table 2. The loading matrix for the influence (positive or
negative) of the 4 predictor variables on each of the
principal components (Figure 8). Bolded numbers
represent significant loading. NH4-N and Effluent
absorbance influence PC1. NO3-N and PO4-P both
influence PC2.
Figure 7. Correlations and distributions among the 4
predictor variables. Each color dot represents an
effluent type (red = ADE, blue = PHAE, green = Mix) with
lighter colors being nitrogen addition treatments. NH4-N
and Effluent absorbance had a high positive correlation
(0.8524). NO3-N and PO4-P also were highly
correlated (0.7668) The amount of correlation
between variables made traditional analysis
problematic so a Principal Components Analysis (PCA)
was employed.
Table 3. Regression model ability to predict growth rate and ash free dry weight.
Figure 2. Undiluted ADE (left) and
PHAE (right).
 Work supported through the INL Laboratory Directed Research & Development (LDRD)
Program under DOE Idaho Operations Office Contract DE-AC07-05ID14517.
• Liu, J., & Ma, X. (2009). The analysis on energy and environmental impacts of microalgae-based fuel methanol in
China. Energy Policy, 37(4), 1479-1488.
• Yang, J., Xu, M., Zhang, X., Hu, Q., Sommerfeld, M., & Chen, Y. (2011). Life-cycle analysis on biodiesel production from
microalgae: water footprint and nutrients balance. Bioresource technology, 102(1), 159-165.
• Passero, M., Cragin, B., Coats, E. R., McDonald, A. G., & Feris, K. (2015). Dairy Wastewaters for Algae Cultivation,
Polyhydroxyalkanote Reactor Effluent Versus Anaerobic Digester Effluent. BioEnergy Research, 1-14.
0.00
1.00
2.00
3.00
4.00
0 50 100 150 200 250 300
AFDW(g/L)
N total (mg/L)
AD
AD +N
PHA
PHA +N
Mix
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.00 0.05 0.10 0.15 0.20 0.25
GrowthRate(u)
Absorbance (680 nm)
AD
AD +N
PHA
PHA +N
Mix
Experimental design
Experimental Approach
0.00
0.05
0.10
0.15
0.20
0.00
50.00
100.00
150.00
200.00
250.00
300.00
Absorbance(680nm)
Ntot(mg/L)
10% 50% 100% 5% 10% 15% o:100 5:95 12:88 20:80
PHA AD Mix
Cont
+ N
Absorbance
10% 50% 100% 5% 10% 15%
PHA AD Mix
(AD:PHA)
Figure 9. Regression plots for the model effect on growth rate (A) and AFDW (B).
Increases in PC1 values (i.e. Total N and NH4-N) result in decreased growth rate.
Increases in PC1 and PC2 values (i.e. effluent absorbance, Total N, NH4-N & NO3-
N, PO4-P, respectively) result in increased biomass production.
Effect on water consumption
Effluent
Biomass
Productivity
(kg/yr)
Fresh Water
Consumption
(gal/yr)
5% ADE 1.44E+05 4.35E+07
50% PHAE 3.19E+05 2.29E+07
5:95
ADE:PHAE
3.04E+05 0.00E+00
• Productivity
• Using PHAE to
supplement NO3
- results
in 2x increase in annual
yield.
• Water consumption
• Supplementing process
with PHAE eliminates
fresh water
requirement.
A B
Forecast using Principal Components model
Fertilizers and water usage are recognized as two of the greatest hurdles in large-scale algal production.
Research into methods to supplement nutrients and limit water usage is paramount to the successful
implementation of large scale algal operations. In the United States alone, nearly 227 million metric tons of
manure was produced by the 9.1 million dairy cows on farms across America in 2010. A biological treatment
platform being developed by a collaborative effort incorporates Anaerobic Digestion (AD) and
Polyhydroxyalkanoate production (PHA) to treat large volumes of manure. Both options produce valuable
co-product streams while not diminishing the Nitrogen and Phosphorous levels in the resulting effluents,
which still pose a problem for conventional disposal. Our research focuses on integrating an algal growth
platform into the manure treatment process in an attempt to lower operational costs by utilizing these
waste streams to supplement nutrients & process water, and remediate N and P from the effluents.
GrowthRate
AshFreeDryWeight
PC1 PC2 PC1 PC2