Evidence map›Paper›PMID 39918294›Full record

ArticleBiotechnology and bioengineering2025

Enhancing Biomass Productivity by Forecast-Informed Pond Operations.

Hongxiang Yan, Mark S Wigmosta, Ning Sun, Song Gao, Michael H Huesemann

Abstract read
In one paragraph

Article in Biotechnology and bioengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hongxiang YanEnergy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.ORCID http://orcid.org/0000-0002-2387-403X
Mark S WigmostaEnergy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.
Ning SunEnergy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington, USA.
Song GaoMarine Sciences Laboratory, Pacific Northwest National Laboratory, Sequim, Washington, USA.ORCID http://orcid.org/0000-0002-7210-4445
Michael H HuesemannMarine Sciences Laboratory, Pacific Northwest National Laboratory, Sequim, Washington, USA.

Funding

This study was supported by the Bioenergy Technologies Office.
6 · The paper itself

Abstract

Microalgal cultivation for biofuels and proteins holds significant promise but faces challenges in achieving economically viable biomass productivity under variable environmental conditions. This study introduces a forecast-informed pond operation (FIPO) system that uses numerical weather prediction (NWP) ensemble forecasts and the biomass assessment tool (BAT) to optimize daily dilution rates for enhanced biomass production. In contrast to the current practice, where fixed dilution rates are based on operator experience, the FIPO system determines the optimal dilution rate based on future weather forecasts and biomass growth conditions. Our experiments validate the effectiveness of FIPO in both short- and long-term growth scenarios. In short-term experiments, FIPO increased biomass production by 21.3% compared to batch growth and 7.4% over fixed dilution (60% every 3 days) operations. The NWP forecast-informed operations achieved biomass production nearly identical to that using perfect weather forecasts, highlighting the accuracy of current NWP forecasts for guiding pond operations. In long-term experiments, FIPO resulted in biomass production increases of 13.3% and 17.8% compared to two fixed dilution rates (60% every 3 days and 20% daily). These findings underscore the viability of using NWP forecasts to optimize microalgal cultivation systems. By adjusting daily dilution rates in response to forecasted weather, operators can achieve higher biomass yields and mitigate risks associated with environmental variability. This study provides a foundation for future research and practical applications in commercial-scale microalgal production.

Indexed as

BiomassMicroalgaePondsBiofuelsBiofuelsbiomass assessment toolbiomass forecastingChlorella sorokinianaforecast‐informed pond operationsHuesemann algae biomass growth model

Identifiers

PMID39918294
PMCPMC11975164

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.