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Maximizing productivity and reducing environmental impacts of full-scale algal production through optimization of open pond depth and hydraulic retention time Quentin Béchet, Andy Shilton, and Benoit Guieysse Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b05412 • Publication Date (Web): 29 Feb 2016 Downloaded from http://pubs.acs.org on March 4, 2016

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Environmental Science & Technology

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Maximizing productivity and reducing environmental

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impacts of full-scale algal production through optimization

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of open pond depth and hydraulic retention time

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Quentin Bécheta,b,*, Andy Shiltona, Benoit Guieyssea

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a

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Palmerston North 4442, New Zealand

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b

INRIA BIOCORE, BP 93 06902 Sophia Antipolis Cedex, France

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*

Corresponding author

School of Engineering and Advanced Technology, Massey University, Private Bag 11 222,

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Telephone: +33 4 92 38 71 74

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Fax: + 33 4 92 38 78 58

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Email: [email protected]

ACS Paragon Plus Environment

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Abstract

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The ability to dynamically control algal raceway ponds to maximize biomass productivity and

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reduce environmental impacts (e.g. land and water use) with consideration of local constraints

16

(e.g., water availability, climatic conditions) is an important consideration in algae

17

biotechnology. This paper presents a novel optimization strategy that seeks to maximize

18

growth (i.e. optimize land use), minimize respiration losses, and minimize water demand

19

through regular adjustment of pond depth and hydraulic retention time (HRT) in response to

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seasonal changes. To evaluate the efficiency of this strategy, algae productivity and water

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demand were simulated at 5 different climatic regions. In comparison to the standard

22

approach (constant and location-independent depth and HRT), dynamic control of depth and

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HRT was shown to increase productivity by 0.6 - 9.9% while decreasing water demand by 10

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- 61% depending upon the location considered (corresponding to a decrease of the water

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footprint by 19 - 62%). Interestingly, when adding as a constraint that the water demand was

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limited to twice the local annual rainfall, higher net productivities were predicted under

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temperate and tropical climates (15.7 and 16.7 g-m-2-d-1, respectively) than from

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Mediterranean and subtropical climates (13.0 and 9.7 g-m-2-d-1, respectively); while algal

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cultivation was not economically feasible in arid climates. Using dynamic control for a full-

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scale operation by adjusting for local climatic conditions and water constraints can notably

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affect algal productivity. It is clear that future assessments of algae cultivation feasibility

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should implement locally optimized dynamic process control.


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Introduction

34 35

In the wake of massive investments to increase algae biofuel production, numerous studies

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assessed the economic feasibility and environmental impacts of microalgae cultivation in

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raceway ponds (Table 1). In contrast to this study, the majority of these assessments (Table 1)

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set a constant pond depth (typically 0.2 - 0.3 m) and did not consider the hydraulic retention

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time (HRT) despite the well-acknowledged impact of these parameters on algal

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productivity1,2. Likewise, geographic locations have been compared on the basis of how

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climatic conditions impact productivity using identical process parameters regardless of their

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practical suitability for a particular climate3,4,5,6. It follows that biomass productivities in these

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assessments may have underestimated the maximum productivity practicably achievable.

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More importantly, underestimating productivity may negatively impact the conclusions of

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economic and impact assessments. For example, the water footprint and land use efficiency of

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full-scale algal cultivation may have been overestimated by previous studies. Process

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optimization is therefore critical to any algal biotechnology applications from high-value

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metabolite production to algae biofuel and algal wastewater treatment7.

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Different optimization strategies have been investigated to maximize system productivity by

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ensuring that growth conditions are near optimal at all times during cultivation. While

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controlling pH and nutrient concentrations may be feasible at full-scale7, temperature control

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is economically prohibitive for biomass production (e.g., biofuel feedstock). As a result,

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temperature fluctuations should be considered in any attempt to optimize process operation8.

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A second strategy is to optimize the geometry of the cultivation system by for example

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adjusting the position of photobioreactors according to latitude and time of the year to

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minimize mutual shading between reactors9. Ritchie and Larkum10 adjusted pond depth to


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optimize water temperatures. While useful, these optimization strategies were based on

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annually averaged meteorological data. Further optimization can be achieved by dynamically

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adjusting pond depth and HRT over time as environmental conditions change. A third strategy

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is to adjust algal concentrations to optimize light availability while minimizing biomass

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respiration losses11. In practice, this requires constant adjustment of the harvest rate as

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proposed by Muñoz-Tamayo et al.12 because algal growth and respiration vary with

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meteorological conditions. Altogether, these past studies demonstrate that process

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optimization yields quantitatively significant productivity gains but also requires models that

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accurately predict productivity as a function of design, operational, and environmental

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parameters. Because these previous optimization studies did not account for the impact of

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temperature variation during full-scale cultivation and/or were not validated against full-scale

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data, additional development is required. Furthermore, it is also critical to consider

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environmental impacts during optimization because full-scale feasibility can be severely

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limited by freshwater availability and other environmental concerns13.

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In view on the above-mentioned uncertainties, this paper presents a novel optimization

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strategy based on the regular adjustment of pond operation in response to seasonal

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meteorological fluctuations to maximize algal productivity and minimize water demand. This

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study focuses in particular on the dynamic control of pond depth and HRT that are easy to

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regulate in practice. We examine how dynamic control yields materially different predictions

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of productivity and water demand. For this purpose, the algal productivity and process-water

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demand were simulated under various control strategies and 5 different climates using a

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predictive model of algal productivity accounting for temperature and validated against full-

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scale data14,8.


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1

Materials and methods

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1.1

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This study focuses on algal cultivation in open raceway ponds mixed with paddle wheels15 as

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these systems are the most economical for large-scale, bulk biomass cultivation16,17. Chlorella

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vulgaris was used as a commercially relevant species18,19 given the availability of a predictive

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productivity model validated at full-scale for this species (in particular, this model can be

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adapted to any geometry and requires the fewest biological inputs, see S1 for further

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discussion). The ponds were operated in a semi-continuous regime from the first day of

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operation by withdrawing a volume of culture at 7 pm every day (to minimize respiration

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losses at nighttime) and immediately replacing this volume with fresh medium. Fresh water

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was added before harvesting to compensate for daily evaporation losses. The hydraulic

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retention time (HRTi, in days) is:

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HRTi =

95

where Vi, S, and di are the culture volume (m3), surface area (m2), and depth (m), respectively,

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and Vs,i is the volume of culture extracted daily (m3-d-1); the subscript i indicates the day.

Cultivation system

Vi S ⋅ di (1) = Vs,i Vs,i

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1.2

Water demand and water footprint

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The annual water demand (WD, m3-yr-1) was calculated as:

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WD = Qev + 0.25⋅ Qout (2)

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where Qev (m3-yr-1) represents the rate of water evaporation and Qout (m3-yr-1) the rate of

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culture withdrawal from the pond. As a portion of the liquid medium withdrawn is recycled

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back into the pond following cell harvesting, and in the absence of consensus on the medium

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recycling ratio that can be achieved during full-scale cultivation, we assumed 75% recycling

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based on the literature13. The rate of water lost by leaks should be relatively minor for a pond


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with a well-constructed liner and was therefore neglected to focus on the impact of climate

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and process operation on water demand. The rate of evaporation Qev was calculated using the

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model of Béchet et al.14. By assuming that open ponds are operated 365 days of the year, Qout

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can be calculated as the sum of the volumes Vs,i (m3-d-1) extracted from the pond each day

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(see Equation 1). The impact of precipitation was accounted for by assuming that the

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freshwater inflow to the pond would be reduced in proportion to the rain landing on the pond.

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The HRT was thus maintained and no freshwater resource was unnecessarily wasted. When

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precipitations exceeded freshwater needs, the algal broth was slightly diluted and this had no

114

impact on predictions.

115 116

The water footprint (in m3-kg-1) was calculated as the ratio of the annual water demand over

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the annual biomass productivity (section 1.3). This corresponds to the 'blue-green' water

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footprint as defined by Gerbens-Leenes et al.20 ('grey' water footprint was excluded as it

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requires locally dependent data).

120 121

1.3

Biomass productivity

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Biomass productivities were predicted based on the model of Béchet et al.21, which essentially

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computes net productivity as the difference between "photosynthetic output" (i.e., the biomass

124

produced through photosynthesis) and "respiration loss" (i.e., the biomass consumed by the

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algae for non-growth related processes; see S1 for details). In this paper, the negative impact

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of respiration losses on productivity is expressed as the percentage of respiration loss divided

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by the photosynthetic output over a given period of time.

128 129

Yearly net productivities were computed at different locations by predicting (see S1 for

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details): (1) hourly temperatures using the model developed by Béchet et al.14 and validated


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against one year of data collected in an open pond in New Zealand; (2) hourly light intensities

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within the culture using a model based on the Beer-Lambert law21–23; and (3) hourly

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productivities using the model of Béchet et al.21 using the temperature and light distribution

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profiles determined in steps 1 and 2. The pond surface area was set to 100 m2 because the

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temperature model for ponds was validated on a system of similar size14. The estimated areal

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productivities (in g-m-2-d-1) are considered representative of full-scale ponds. The initial algal

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concentration was set to 0.3 g-L-1 in all simulations to ensure optimal light utilization (this

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initial value had a negligible impact on overall productivity predictions, S2).

139 140

The productivity model of Béchet et al.21 was validated in indoor photobioreactors (accuracy

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of +/- 15% over 163 days8) and in outdoor photobioreactors (an accuracy of +/- 8.4% over

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148 days of cultivation21). These results indicate that the model can predict productivity for

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different geometries, under variable weather conditions, and over the full ranges of light and

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temperature conditions experienced in open ponds. For these reasons, the model should

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theoretically predict productivity in open ponds with an accuracy on the order of +/- 10%.

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Measured productivity of Chlorella sp. in an outdoor thin-layer photobioreactor located in

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Czech Republic in summer time24,25,26 varied between 11.1-32.2 g m-2 d-1, which is in the

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range of our predictions between 15.4 and 25.4 g m-2 d-1 in an open pond (assuming a depth of

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0.1m and a HRT of 7d). Similarly, Moody et al.27 predicted productivities of 11.7 and 12.8

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g.m-2-d-1 in open ponds in Arizona and Hawaii, respectively, against 12.5 and 11.2 g.m-2-d-1

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with our model. Even if this comparison is based on systems with different geometries,

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operational regimes, locations and most likely algal strains, the fact that very similar

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productivities were predicted contributes to indirectly validate the productivity model in open

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ponds (see S1 for further details).


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The model predicts that photosynthesis of C. vulgaris over a short period of time (~1 hour) is

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theoretically maximal at 38oC with a maximal temperature around 42oC. Experimental

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evidence however shows temperatures over 35oC negatively affect productivity only when

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algae are exposed to these temperatures over 1-2 days. While our biological model cannot

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predict the impact of these ‘long duration high temperature’ events, we accounted for this

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potential stability issues by introducing an "overheating criterion." This criterion was defined

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as the duration of daytime exposure to temperatures higher than 35oC was therefore computed

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along with the predicted productivities at the five climatic locations.

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Simulations were performed at five locations representative of five climates: Yuma, Arizona,

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USA (arid climate); Redding, California, USA (Mediterranean); Hilo, Hawaii, USA

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(tropical); Sebring, Florida, USA (subtropical); and Hamilton, New Zealand (temperate; see

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Béchet28 for more details on the climatic characteristics of each location). Hourly weather

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data used in the productivity model were obtained from the US National Climate Data Centre

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(NCDC) and from the New Zealand National Institute for Water and Atmospheric Research

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(NIWA). For the US locations, the hourly relative humidity data were not available and

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monthly averages were used instead.

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1.4

Impact of depth and HRT on productivity and water demand

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The examples shown in Boxes 1 and 2 illustrate how pond (or water-column) depth and pond

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HRT impact the productivity and the water demand in raceway ponds. The concepts

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introduced through these examples have already been presented in previous studies but their

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comprehension is necessary to fully understand the novel optimization strategy presented in

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this study.

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Box 1: Impact of pond depth on productivity and water demand

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For a given cultivation surface area, the thermal inertia is directly correlated to the mass of

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water in the pond and, therefore, the water-column depth. Hence, the shallower the pond, the

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greater the diurnal temperature fluctuations experienced by the algae. For a given HRT,

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decreasing depth slightly improves overall productivity by enabling daytime temperatures to

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climb close to the optimal temperature for photosynthesis and inducing a faster decrease of

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temperature at nighttime, thereby reducing nighttime respiration. Productivity can therefore

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be increased by 8% in a Mediterranean climate when the pond depth decreases from 0.5 m to

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0.1 m (Figure 1). Decreasing pond depth, however, also increases overheating risks as shown

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in Figure 1. The frequent occurrence of high temperatures may lead to culture collapse, which

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would affect yearly productivity and inoculation costs. The reduction in water demand

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evidenced in Figure 1 is largely driven by a reduction in the rate of water withdrawal from the

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pond (see Equation 1) despite an increase in daytime evaporation losses as temperature

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increases.

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Box 2: Impact of HRT on productivity and water demand

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Increasing HRT by reducing the rate of culture withdrawal from the pond enables algae cell

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concentration to increase. In practice, the cell concentration at which all the available light is

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captured is relatively low (e.g. 0.1 kg m-3) so relatively high HRT values negatively affect net

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productivity by increasing respiration losses as algae stay in the pond longer. For example, the

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optimal HRT was predicted to approximately 4 days in a Mediterranean climate (Figure 2), in

201

agreement with empirical observations reported in the literature11,29.

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Because a higher HRT entails a lower withdrawal rate from the pond, water demand

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decreases when HRT increases as seen in Figure 2 (HRT has a negligible impact on the rate

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of evaporation from the pond surface).

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1.5

Process optimization

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Simulations were conducted for each location to (1) quantify the impact of varying depth and

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HRT on productivity and water demand and to (2) compare "locally-optimized" operation

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against "standard conditions" defined as a pond depth of 0.25 m and a HRT of 10 d based on

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literature values (Table 1). For locally-optimized operation, pond depth and HRT were altered

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on a monthly basis with the objectives of minimizing the water demand and maximizing

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productivity by reducing overheating risks and respiration losses (the time step of one month

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is arbitrary and may be further refined in future studies). This optimization was undertaken by

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first optimizing depth and HRT independently as described in Table 2. The co-optimization of

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depth and HRT was then performed by determining the optimal depth monthly profile before

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optimizing the HRT monthly profile. A final case study investigated the co-optimization of

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depth and HRT under the constraint of a limit on water demand (see section 2.5 for details).

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In practice, the optimal HRT and depth monthly profiles were determined by using iterative

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dichotomic searches.

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2

Results and Discussion

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2.1

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The primary objective of this paper is to demonstrate a new operation optimization strategy

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for full-scale algal cultivation in raceway ponds. Process optimization involves maximizing

Objective and criterion for optimization


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productivity (and thus revenues) while minimizing resource use (and thus costs and

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environmental impacts). Productivity can be severely impacted if the pond overheats or if

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high-HRT operation entails excessive biomass respiration losses (Box 2). The following

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simulations were therefore conducted under the criteria that "daytime pond temperature must

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not exceed 35oC more than 20 hours per month for any month of the year" to minimize

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overheating risks (criterion 1) and "respiration must consume less than 50% of the

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photosynthetic output over the year" to maximize productivity (criterion 2). With regard to

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resource use, water demand is particularly site-specific as it depends on availability and cost

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of freshwater supply, the capacity of the receiving environment to re-assimilate process

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wastewater, and the degree of associated local authority regulations. While these constrains

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are variable (Guieysse et al. 2013), the impact of water use should, to a large degree, be

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linked to the scarcity of local freshwater resources. Thus for the simulations presented in this

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paper, we have adopted a third criterion that "yearly water demand must not exceed two times

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the yearly rainfall at the location considered" (criterion 3).

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While the first criterion is based on experimental observations21, acceptable levels of

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respiration losses and water demand are subjective with other alternatives opened to being

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assessed. The modeling platform used in this paper however enables examining the

245

implication of varying these criteria and this is discussed in section 2.5.

246 247

2.2

Productivity under constant depth and HRT operation

248

Productivity and water demand were simulated at five different climatic locations under

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constant depth and HRT operation. Depending on the location, algal productivity under

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standard conditions varied between 10.7 and 12.5 g-m-2-d-1. Interestingly, these productivities

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are relatively low compared to values used in prior assessments found in the literature (Table


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1). These low values are unlikely due to predictions inaccuracy as the model accuracy should

253

be around +/- 10% (see section 1.3). As discussed by Béchet et al.8, the overestimation of

254

productivity by prior studies is most likely due to the common assumption that temperature

255

has a negligible impact on productivity. Water demand was between 1.4 and 4.4 m3-m-2-yr-1

256

(Figure 3), which is within the range of values reported in the literature (Table 1).

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Significantly lower values reported in previous studies are mostly explained by the fact that

258

water demand was computed as water evaporation and process water was not included, as

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detailed by Guieysse et al.13.

260 261

For each depth and HRT combination, Figure 4 shows compliance against the three criteria

262

defined in section 2.1 (central part of each sub-figure). No combination of constant depth-

263

HRT operation were predicted to satisfy all three criteria for arid, Mediterranean, and

264

subtropical climates while several combinations ensuring low biomass losses, low

265

overheating risk, and low water demand were predicted for temperate and tropical climates

266

(Figure 4). These two climates therefore have potential for algal cultivation if pond depth and

267

HRT are maintained constant over the year.

268 269

2.3

Dynamic depth control

270

As explained in Box 1, decreasing pond depth efficiently minimizes water demand (related to

271

criterion 3), generates modest productivity gains (related to criterion 2), but also increases

272

overheating risks (related to criterion 1). In this simulation, pond depth was therefore

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dynamically adjusted on a monthly basis to the lowest possible value that still ensured pond

274

temperature did not exceed 35oC more than 20 hours during the month considered (criteria 1).

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As seen in Figure 4 (right column of each sub-figure), dynamic depth control was predicted to

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remove overheating risks while still enabling operation at relatively small depths (< 0.25 m,

278

Figure 5) at all locations considered. Compared to standard operation (constant depth = 0.25

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m, constant HRT = 10 d), dynamic depth control at a constant HRT of 10 d was predicted to

280

slightly increase productivity (4-11%) and significantly reduce water demand (15-53%) as

281

shown in Figure 3. Similarly, water footprint decreased from 19-58% depending on the

282

location. As explained in Box 1, the smaller thermal mass associated with a shallower pond

283

increased daytime temperatures (0.5-1.0oC on average over the year) and decreases nighttime

284

temperatures (1.4-2.1oC on average over the year). These changes in temperature profile are

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predicted to cause an increase of daytime photosynthetic output and a decrease of nighttime

286

respiration loss, which explains the gain in net productivity evidenced in Figure 3. The

287

reduction in water demand was achieved by decreasing the culture volume and thus the

288

culture withdrawal rate required to maintain the HRT at 10-d.

289 290

As shown in Figure 4, dynamic depth control operation was simulated under a broad range of

291

constant HRTs. As can be seen from the "Depth control" column on the right hand side on

292

each sub-figure, dynamic depth control at fixed HRT was predicted to significantly improve

293

the feasibility of algal cultivation in hot climates (e.g., all criteria were satisfied using

294

dynamic depth control at 10-d HRT in the Mediterranean climate, Figure 4). Dynamic depth

295

control was also forecasted to expand the range of acceptable HRT values under tropical and

296

temperate climates (Figure 4).

297 298

2.4

Dynamic HRT control

299

As explained in Box 2, decreasing the HRT minimized respiration losses (criterion 2), but

300

also increased water demand (criterion 3). The optimization strategy investigated in this


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section therefore involved dynamically adjusting the HRT on a monthly basis to the highest

302

possible value while still ensuring that respiration losses were lower than 50% of the monthly

303

photosynthetic output at the standard depth of 0.25 m (criterion 2).

304 305

At locations where the average HRT was higher than 10 d (Mediterranean and temperate

306

climates, see S5), dynamic HRT control was predicted to reduce the water demand by 6-13%

307

but slightly decreased productivity in comparison to standard 10 d HRT operation.

308

Conversely, productivity gains were obtained at the cost of a higher water demand at the other

309

climatic locations considered. Similarly, water footprint variation was either positive or

310

negative (-4.7-8.8%) depending on the average value of the HRT. These differences between

311

locations illustrate how the criterion for acceptable respiration losses (here 50%) impacts

312

water demand and productivity. For example, increasing this arbitrary threshold would lead to

313

lower productivity but also to reduce water demand. This threshold must therefore be adjusted

314

by considering the availability of freshwater resources as further discussed in section 2.5.

315 316

Dynamic HRT control was subsequently applied at different pond depths and the resulting

317

water demands and productivities were assessed. As illustrated by the upper "HRT control"

318

row of each sub-figure disclosed on Figure 4, dynamic HRT control was predicted to

319

efficiently reduce respiration losses, but the overheating risks and water demand criteria could

320

still not be met in the arid, Mediterranean, or subtropical climates.

321 322

2.5

Dynamic depth and HRT co-control

323

This section considers the possibility of dynamically adjusting both depth and HRT on a

324

regular basis. This co-control was forecasted to reduce water demand by 10-61% compared to

325

standard operation under the five climates considered (Figure 3). Productivity was also


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increased by 0.6-9.9% depending on the location considered, which is mostly explained by

327

the monthly adjustment of the depth as described in section 2.3. The water footprint was in all

328

cases reduced by 19-62%.

329 330

Of particular interest, dynamic co-control of depth and HRT allowed low water demand under

331

Mediterranean, tropical, and temperate climates. As a result, respiration losses could be

332

further minimized at these locations while still respecting the rainfall criterion. In contrast, a

333

respiration loss higher than 50% must be accepted under arid and subtropical climates for the

334

rainfall criterion to be respected (top right corner of each subfigure of Figure 4).

335 336

In the final case study, depth and HRT co-control was used to maximize productivity while

337

ensuring that water demand met the rainfall criterion. In these simulations, the maximum

338

amount of water that can be used was fixed by the amount of available freshwater resources

339

(defined as twice the amount of annual rainfall) instead of the maximum respiration losses

340

(previously 50%). Under this constraint, the fraction of biomass lost by respiration was, as

341

expected, lower than 50% under Mediterranean, tropical, and temperate climates (Table 3). In

342

arid climates, no operation could meet the rainfall criterion because the rate of evaporation

343

alone exceeded twice the annual rainfall. Surprisingly, productivities predicted at temperate

344

and tropical locations exceed the productivities predicted at Mediterranean and subtropical

345

locations (Figure 3). This is explained by the fact that high rainfalls and low evaporation rates

346

at the tropical and temperate locations allow for low-HRT operation, which reduces

347

respiration and therefore boosts productivity. The potential impact of freshwater availability

348

was confirmed when the amount of available freshwater resources was set to 1-3 times the

349

amount of annual rainfall (see S6 for details). For example, these simulations showed that

350

allowing for a lower water use (by reducing the rainfall criterion from 2 to 1) would make


15


351

impossible algal production in arid, Mediterranean, and subtropical climates as evaporation

352

would be higher than rainfall.

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353 354 355

2.6

Does optimization impact assessment of feasibility, environmental impacts, and economics?

356

Figure 4 illustrates how locally targeted process optimization using dynamic depth and HRT

357

co-control has the potential to reduce overheating risks and water demand. For instance, while

358

algal cultivation under standard conditions was shown to be unviable in a Mediterranean

359

climate (section 2.2), optimization through depth and HRT co-control is predicted to allow all

360

criteria to be satisfied (Figure 4) with increased productivity and reduced water demand

361

(Figure 3). In addition, accounting for environmental constraints such as water availability

362

can significantly change productivity estimates as detailed in section 2.5. While the

363

simulations presented here are dependent upon the criteria set, the results indicate that

364

attempting to assess feasibility without considering process optimization or local constraints

365

may lead to materially different outcomes. This raises the risk that prior assessments may

366

have inaccurately quantified full-scale practically achievable field outcomes.

367 368

List of supporting information

369

S1: Description of the productivity model; S2: Impact of initial concentration on productivity

370

predictions; S3: Impact of pond depth on productivity, water demand, and overheating risks at

371

five climatic locations; S4: Impact of pond HRT on productivity and water demand at five

372

climatic locations; S5: Optimal HRT profiles at constant depth at five climatic locations; S6:

373

Impact of the rainfall criterion on yearly productivity when depth and HRT are co-controlled

374

under a constraint on the water demand

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Acknowledgements

377

Q.B. was supported by a Massey University Doctoral Scholarship. The world map used in the

378

Abstract and graphical (TOC) originated from the website openclipart.org. We also thank the

379

Reviewers for their contribution.


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500 501


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502

Table 1: Pond depths and HRTs in recent assessments of algal growth for energy production

503

(NS: not specified, but accounted for in calculations; NC: not considered in the study) End product

Location/

HRT

Depth

Productivity

Water demandb

considered

climate

(d)

(m)

(g m-2 d-1)

(m3-m-2-yr-1)

30

Biofuel

NS

NC

0.3

24

NS

31

Biofuel

Australia

NC

0.3

30

7.7

16

Biogas

Europe

NC

0.3

25

28

32

Biofuel

US

NC

0.2

25

2.5

33

Biofuel

US

NC

NC

27

NS

34

Algae

NS

10

0.35

11

11.5

35

Biofuel

Europea

NC

0.3

20-30

NC

36

Biodiesel

NS

NC

NC

3-25

NS

37

Biofuel

NS

NC

NC

11.4-33.6

NC

38

Biofuel

US

NC

0.2

25

NS

39

Biofuel

US

10

0.3

15

0.13-1.1

40

Biofuel

NS

NC

NC

10-20

3-3.6

41

Biofuel

UK

10-13

0.28

36.5

0.015

42

Biofuel

NS

NS

0.2

20

NS

17

Biodiesel

Europe

NC

0.2

30

NS

43

Biofuel

Japan

NS

NS

38.9-43.9

NC

5

Biofuel

US

NC

0.3

3.8-14

0.0055-2

6

Biofuel

US

NC

NC

18

0.99

44

Biogas

NS

2

0.2

25.5-40

NS

Study

504

a

Mediterranean climate

505

b

Water demand was often not directly available from publications and was re-calculated from

506

available data provided by studies.


23


Page 24 of 32

507

Table 2: Characteristics of the scenarios (fmax: maximum fraction of biomass lost through

508

respiration during the HRT optimization; NA: Not applicable)

509 HRT (d)

Pond depth (m)

fmax (%)

Standard conditions

10

0.25

NA

Depth control

10

Optimized

NA

HRT control

Optimized

0.25

50

Depth and HRT

Optimized

Optimized

50

Optimized

Optimized

Variablea

Scenario

control Depth and HRT control under constrained water demand 510

a

See Table 3 for details


24

Page 25 of 32


511

Table 3: Yearly productivities and fractions of the photosynthetic output lost through

512

respiration f and fmax and in 'Standard conditions' and under water-demand control,

513

respectively. Standard conditions Climate

f (%)

Productivity

Water-demand control fmax (%)

(g-m-2-d-1)

Productivity (g-m-2-d-1)

Arid

55

12.5

NA

NA

Mediterranean

49

11.7

46

13.0

Subtropical

55

11.6

63

9.7

Tropical

51

11.2

20

16.4

Temperate

46

10.7

23

15.7

514 515


25


Page 26 of 32

516

Figure 1: Top: Impact of pond depth on net biomass productivity; Middle: Impact of pond

517

depth on water demand; Bottom: Impact of pond depth on the number of hours when pond

518

temperature exceeds 35oC. Pond depth and HRT are held constant over the year (HRT = 10 d)

519

- Case of a Mediterranean climate (other climates are shown in S3).

520

Figure 2: Impact of HRT on net biomass productivity and water demand (WD) during algal

521

cultivation in open ponds when pond depth and HRT are held constant over the year (pond

522

depth = 0.25 m; White bar: net biomass productivity; Grey bar: respiration loss; Blue dashed-

523

line: water demand) - Case of a Mediterranean climate (other climates are shown in S4).

524

Figure 3: Net productivity and water demand of algal cultivation in open ponds operated

525

under standard conditions or different optimization strategies (Standard: HRT = 10 d; pond

526

depth = 0.25m; Depth control at 10 d HRT; HRT control at a depth of 0.25m; Depth and HRT

527

control; and depth and HRT control with constrained water demand).

528

Figure 4: Impact of pond depth and HRT on overheating risks, water demand, and respiration

529

loss during algal cultivation in open ponds (see section 2.1 for details).

530

Figure 5: Minimum pond depths ensuring pond temperature never exceeds 35oC for > 20

531

hours each month during depth control (HRT = 10 d).

532 533 534 535 536 537 538 539 540


26

Page 27 of 32

541


Figure 1

542


27


543 544

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Figure 2

545


28

Page 29 of 32

546


Figure 3

547 548


29


549

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Figure 4

550


30

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551


Figure 5

552


31



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