The proposed project aims to develop a modular, depth-optimized macro-algae farming system to enhance growth, reduce operational costs, and support small/mid-sized aquaculture businesses

The National Oceanic and Atmospheric Administration awarded $189,024 to Ultrasea, Inc. on September 1, 2026, under the NOAA Small Business Innovation Research (SBIR) Program (CFDA 11.021) to develop a modular, depth-optimized macro-algae farming system. The recipient will design and test a depth control mechanism capable of precise vertical movement to optimize environmental conditions—light penetration, nutrient availability, and temperature gradients—that static traditional systems cannot address. Work includes numerical analysis to ensure structural stability under waves and currents, fabrication of a working prototype, and controlled-environment testing to evaluate depth control accuracy and operational efficiency. The project will also capture data to assess the feasibility of machine learning models for growth prediction and optimized harvest scheduling. By dynamically adjusting algae depth, the system aims to increase productivity while maintaining modular design for compatibility with existing infrastructure, reducing capital costs and adoption barriers for small and mid-sized aquaculture operators. The award supports NOAA's goals to promote sustainable aquaculture and enhance coastal resilience. Performance takes place in East Kingston, New Hampshire, with a completion date of February 26, 2027.

Traditional macro-algae farming systems are static, failing to adapt to dynamic environmental conditions such as light penetration, nutrient availability, and temperature gradients. This project will address these challenges by testing a depth control mechanism capable of precise vertical movement and proving the feasibility of obtaining vertical water column quality data and periodic growth measurements at set intervals. The work effort includes numerical analysis to ensure structural stability under waves and currents. A working prototype will then be fabricated and tested in a controlled environment to evaluate depth control accuracy and operational efficiency. Additionally, data will be captured to determine the feasibility of utilizing machine learning models to provide reliable growth data, allowing for optimized harvest schedules and increased annual yields. By dynamically adjusting algae depth, this system can significantly increase productivity compared to static methods. Its modular design ensures compatibility with existing infrastructure, reducing capital costs and barriers to adoption. With rising global demand for seaweed in food, BIO-stimulants, biofuels, and climate initiatives, this innovative solution aligns with noaas goals to promote sustainable aquaculture, enhance coastal resilience, and empower underserved operators with scalable, sustainable technology.