How does industrial engineering optimize microalgae production in oyster hatcheries?

Oyster aquaculture critically depends on the constant availability of high-quality seed from hatcheries. However, many hatcheries, particularly in the U.S. Gulf region, face significant challenges related to access and availability of this seed.

A fundamental bottleneck in these systems is the efficient production of microalgae, the essential food for oyster larvae and broodstock, which can for 30% to 60% of the total oyster seed production costs in a hatchery. In this regard, a recent study published in the Journal of Applied Phycology by scientists from Louisiana State University and the Louisiana Sea Grant Oyster Research Lab explores how the application of industrial engineering methodologies can make a substantial difference in optimizing this vital process.

The research focused on the Michael C. Voisin Oyster Hatchery (MCVOH) in Louisiana, USA, with the aim of increasing the hatchery’s production and efficiency by evaluating its microalgae cultivation processes. The findings, although specific to this hatchery, offer a valuable roap for other aquaculture facilities looking to improve their performance.

Microalgae production in a hatchery

Oyster hatcheries are not only responsible for the multiple developmental stages of juvenile oysters but must also ensure a sufficient and constant supply of high-quality microalgae. Efforts to optimize microalgae cultivation for oyster seed production are not new.

At MCVOH, five microalgae species are cultivated: Chaetoceros muelleri, Chaetoceros calcitrans, Pavlova lutheri, and two strains of Tisochrysis lutea. This production, although essential, competes for limited resources such as time, personnel, equipment, and space, which can hinder efficient operations.

Efficiency, from an industrial engineering perspective, is achieved by increasing production capacity with the same working hours or maintaining capacity with fewer hours. Therefore, evaluating the system to identify bottlenecks and resource competition is crucial.

Applying industrial engineering to microalgae cultivation

The study adopted a methodical approach based on industrial engineering tools:

• Process mapping: Researchers identified and delineated the main microalgae production processes at MCVOH. These include daily Start of Day (SOD) and End of Day (EOD) tasks – such as microalgae maintenance, water quality management, and broodstock care – and three weekly microalgae transfer processes: Erlenmeyer Transfers, Fernbach Transfers, and Bag Transfers. Process flow diagrams were created to visualize each step.
• Time studies: The time required to complete each identified step in the cultivation processes was recorded by observing multiple operators with similar experience.
• Discrete-event simulation modeling: Using Simio software, simulation models (Models A-D) were built, representing four types of typical workdays (10 hours), each with different combinations of microalgae processes to be completed. These models analyzed time, personnel, and equipment requirements to assess resource efficiency.
• Identification of constraints and “Waste”: The models were analyzed to identify bottlenecks (steps with the longest processing times) and “waste” (delays, inefficiencies, or congestion) according to industrial engineering principles, such as waiting time or unnecessary movement.
• Evaluation of modifications: Based on the findings, three main modifications were evaluated to improve hatchery operations:
  • Balancing workloads by pairing operators.
  • Analyzing historical algae production data to inform cultivation processes.
  • Implementing an alternative microalgae cultivation system (bioreactors).

Identifying opportunities for improvement

The simulation analysis shed light on several critical areas and potential solutions:

• Constraints in daily tasks:
  • Manual counting of algal cell density (with a hemocytometer and microscope) and manual water quality measurement (with test strips and portable meters) were identified as significant bottlenecks in SOD tasks. These methods are not only time-consuming but can also introduce variability and operator bias.
  • Cleaning “Bubba” filters in EOD tasks also represented a constraint due to frequent breakages and the consequent machine downtime and higher operating costs.
  • Suggested improvement: Incorporating automated equipment, such as spectrophotometers for algae counting or multiparameter water quality monitors, could standardize data collection and reduce times, although initial cost and training must be considered. Replacing the problematic filtration system would eliminate waiting waste and reduce costs.
• Movement waste in transfers:
  • Erlenmeyer and Bag transfers showed a high percentage of “movement waste,” with travel time constituting a considerable part of the total process time (over 35% of the total time to complete both transfer types). This was due to operators repeatedly going back and forth between workstations and storage shelves or carts.
  • Suggested improvement: Reorganizing workstations to minimize movement, using mobile workstations, or even tool belts for small supplies could significantly reduce this waste.
• Impact of adding an operator (Paired work):
  • Modifying models to include a second operator working concurrently (Model A.1 for SOD/EOD tasks and Model D.1 for bag transfers) demonstrated a significant reduction in the total time to complete tasks. For example, Model A.1 was 30% faster than Model A (single operator), and Model D.1 was 41% faster than Model D. This not only speeds up microalgae processes but also allows operators to start other hatchery activities earlier.
  • Suggested improvement: Instead of necessarily hiring more staff (which is costly), programming could be optimized for existing operators to work in pairs on specific tasks, thus creating level workloads (“level loading”).
• Analysis of historical microalgae production data (2020-2023):
  • Scientists observed an inverse trend between average cell densities and variation in culture “bag lifespan.” System cell densities were lower in 2022-2023 compared to 2020-2021. Coincidentally, the variation in bag lifespan was much greater in 2022.
  • This was due to a change in standard operating procedures: before 2022, culture bags were removed after 3-4 months, a standard practice to prevent algae growth on container walls from blocking light. Starting in 2022, bags were kept for as long as possible, which may have resulted in lower cell densities.
  • Suggested Improvement: Reinstating previous procedures (removing bags after 3-4 months) could increase cell density and return efficiency to previous levels, underscoring the importance of maintaining consistency in protocols.
• Comparison with an alternative system: Bioreactors
  • Scientists conducted an efficiency study comparing MCVOH’s bag culture system with a bioreactor system (six 1250 L bioreactors each).
  • The bioreactor system showed a “Weekly Output” (microalgae cells harvested per hour of labor) 27 times higher than the MCVOH system. This was due to an average cell density 5.4 times higher and weekly labor requirements 50% lower in the bioreactor system.
  • Considerations: Although promising, investing in bioreactors is costly. A careful analysis of monetary and opportunity costs is needed, including equipment downtime, training, and the impact of a potential system failure (if one bioreactor fails, 20% of production capacity is lost, compared to 1.3% if one bag fails in the current system). Previous studies suggest that tubular bioreactors can reduce costs by 40-50% compared to bubble-column systems, with labor being the largest cost component.

Conclusions and implications for aquaculture

This study convincingly demonstrates the value of applying industrial engineering methodologies to identify bottlenecks and improve operational efficiency in oyster hatcheries, specifically in the crucial and costly process of microalgae cultivation. Recommendations such as selective automation, workflow optimization to reduce unnecessary movement, scheduled teamwork, and procedure standardization can lead to significant productivity improvements.

The implementation of bioreactors emerges as an option with high potential to drastically increase microalgae production capacity, although it requires a thorough economic and risk assessment before adoption.

Beyond microalgae, the authors suggest that evaluating all major hatchery activities (spawning, larval rearing, etc.) using simulation modeling is essential to understand system-wide time, personnel, and equipment requirements. This would allow for the creation of level and balanced work schedules, even considering the seasonal nature of hatchery operations (more activity in spring/summer for spawning, and time for maintenance in fall/winter).

Ultimately, greater efficiency in hatcheries not only reduces the risk of oyster seed shortages but also s the sustainability and growth of the oyster aquaculture industry. The knowledge derived from such studies can also guide the design and development of new commercial hatcheries, filling gaps in existing manuals that often do not cover aspects such as personnel training, hatchery scheduling, or detailed economic considerations.

Elizabeth M. Robinson
Louisiana Sea Grant College Program, Louisiana State University
Baton Rouge, LA, 70803, USA

Louisiana Sea Grant Oyster Research Lab,
135 LSU Drive, Grand Isle, LA, 70358, USA
Email: [email protected]

Reference (open access)
Bodenstein, S., Waguespack, S. & Robinson, E.M. Simulating microalgae production to evaluate oyster hatchery efficiency. J Appl Phycol (2025). https://doi.org/10.1007/s10811-025-03527-8