FAU’s College of Engineering and Computer Science has received an $800,475 grant from the Gulf of America Division of the U.S. Environmental Protection Agency for a three-year project titled “AI-Guided Use of 3D-Printed Adsorbents for Phosphate Removal.” The research began July 1 and will focus on creating reusable structures capable of extracting phosphorus from freshwater systems before nutrient pollution triggers explosive algal growth.

The project is led by Masoud Jahandar Lashaki, an associate professor in FAU’s Department of Civil, Environmental and Geomatics Engineering. Yalan Liu and Mohammed Abdellatef, also assistant professors in the department, are serving as co-principal investigators. Huichun “Judy” Zhang of Case Western Reserve University will participate as the project’s sub-awardee.

Phosphorus reaches waterways through agricultural runoff, wastewater discharges, failing septic systems and urban stormwater. Once concentrated in a lake or river, it can stimulate rapid growth of algae and cyanobacteria. These harmful algal blooms may reduce water clarity, consume dissolved oxygen as they decay and produce toxins capable of endangering people, pets, fish and other wildlife. The resulting damage can extend beyond the water itself, affecting recreation, tourism, commercial fishing and municipal water treatment.

Lake Okeechobee will serve as the project’s principal field-testing site. The large Florida lake has experienced recurring harmful algal blooms associated with elevated nutrient levels. Because it lies at the headwaters of the Gulf of America watershed, phosphorus leaving the lake can move into downstream rivers, estuaries and coastal ecosystems. Reducing the nutrient load in Lake Okeechobee could therefore improve water quality across a much larger region.

The FAU team plans to manufacture durable, three-dimensional structures from sargassum, a naturally abundant seaweed. The material will be modified with lanthanum, an element that binds strongly with phosphate. In technical terms, the structures will function as adsorbents: phosphorus-containing compounds will attach to their surfaces rather than remaining dissolved in the water. Their three-dimensional architecture is intended to increase contact with contaminated water while allowing the units to be recovered after use.

That retrievability is central to the proposed technology. Lanthanum-based materials can remove phosphorus efficiently, but they are commonly deployed as fine powders that settle into sediments. Over time, those powders may release lanthanum back into the environment and are difficult to collect. The FAU researchers aim to avoid this problem by producing larger, reusable structures that can be placed in the water column or near lake sediments, removed, regenerated and redeployed, reducing waste and limiting uncontrolled dispersal.

Artificial intelligence and machine learning will guide nearly every stage of the work. Algorithms will analyze experimental results to identify the most effective combinations of sargassum, lanthanum and other material properties. The system will also evaluate where phosphorus-removal structures should be placed and predict which locations are most likely to produce meaningful reductions in bloom risk. Researchers plan to combine water-quality measurements with data on land use, weather, fertilizer application, livestock operations and septic systems to map nutrient movement more precisely.

The technology will be tested under real-world conditions in Lake Okeechobee. Researchers will track phosphorus concentrations, water clarity and indicators of harmful algal blooms through field sampling, publicly available environmental data and observations collected by unmanned aerial vehicles. The resulting information will be shared through the EPA’s Water Quality Exchange, creating a dataset that may help other communities evaluate similar interventions. If the system performs as expected, it could offer a scalable approach for lakes, reservoirs and watersheds worldwide where nutrient pollution is turning clean water into a recurring public-health and environmental crisis.

Subject of Research: Artificial intelligence-guided phosphorus removal from freshwater systems using reusable, 3D-printed sargassum-based adsorbents modified with lanthanum.

Article Title: AI-Guided 3D-Printed Structures Aim to Stop Harmful Algal Blooms at Their Source

Web References: Florida Atlantic University; FAU College of Engineering and Computer Science; Image and media resource: EurekAlert multimedia

References: U.S. Environmental Protection Agency Gulf of America Division grant; Florida Atlantic University project information.

Image Credits: Brian Lapointe, Ph.D.

Keywords

Harmful algal blooms, phosphorus pollution, Lake Okeechobee, artificial intelligence, machine learning, water quality, phosphate removal, sargassum, lanthanum, 3D-printed adsorbents, freshwater ecosystems, environmental engineering, watershed pollution, Florida Atlantic University

Traditional woodchip bioreactors have long been employed to mitigate nitrogen pollution by fostering denitrifying bacteria that transform nitrate—a prevalent and damaging nitrogen species in runoff—into benign nitrogen gas. However, these systems fall short when it comes to phosphorus removal, a critical gap given phosphorus’s role in exacerbating water quality degradation. Moreover, initial deployment of woodchip bioreactors can unintentionally elevate phosphorus levels, likely due to leaching from the wood itself, raising concerns for environmental managers seeking holistic nutrient pollution solutions.

Leading the charge, Wei Zheng and postdoctoral researcher Hongxu Zhou from the Illinois Sustainable Technology Center developed a “designer biochar” module to complement the woodchip bioreactor’s capabilities. This two-tiered approach involves routing drainage water through the bioreactor as usual, reducing nitrogen load through microbial processes, before passing it through a biochar-sorption channel engineered specifically to trap dissolved phosphorus. The chemical basis of phosphorus capture involves reactions that convert soluble phosphate into stable mineral compounds such as magnesium phosphate and calcium phosphate, which offer the added benefit of potential reuse as sustainable fertilizer inputs.

The biochar itself, crafted through a meticulous process of thermally treating a mixture of lime sludge—an industrial byproduct from water treatment—and fine sawdust under oxygen-limited conditions, is designed to maximize phosphorus affinity. After pyrolysis, the resulting biochar powder is pelletized, a critical design feature ensuring the material remains intact within the sorption channels, resisting washout and maintaining consistent nutrient capture capabilities. This innovation not only boosts treatment efficacy but also enhances operational resilience across variable flow regimes.

During a one-year, hectare-scale field trial, this hybrid bioreactor-biochar system demonstrated remarkable reductions in nutrient runoff. Nitrate-nitrogen levels dropped by 58%, while ammonium-nitrogen—a related, often overlooked pollutant—was reduced by 72%. Phosphorus removal was even more strikingly variable yet potent, with dissolved phosphorus decreasing between 3% and 92%, and total phosphorus loads curtailed by 20% to 92%, depending heavily on seasonal flow conditions and phosphorus concentrations. These results mark a major leap forward in multifunctional treatment technology critical for comprehensive nutrient management on agricultural lands.

Economic feasibility, a major consideration for farm-scale adoption, was rigorously assessed through techno-economic modeling. The unit cost of nitrate-nitrogen removal stood at an estimated $90.30 per kilogram per year, with dissolved reactive phosphorus removal costing $63.90 per kilogram annually. Importantly, projections suggest that scaling the system from one hectare to larger operational scales—such as 10 hectares—would drastically reduce these unit costs, making widespread implementation more financially viable, especially when coupled with the agronomic benefits of biochar reuse.

The potential for circular nutrient management does not end at water treatment. The biochar that captures phosphorus can be reapplied directly to farm soils, effectively turning a waste product into a valuable fertilizer resource. This nutrient recycling not only offsets the environmental footprint but can also enhance soil health, improve crop yields, and promote carbon sequestration. Farmers could additionally benefit from carbon credit markets, providing further incentives for adoption of this technology.

Scientists caution that while this combined system offers promising performance, larger-scale commercial testing is underway to validate effectiveness across diverse farming operations and hydrological conditions. Such investigations will help refine operational parameters, explore long-term stability, and evaluate maintenance requirements. The involvement and endorsement of regulatory bodies like the U.S. Environmental Protection Agency underscore the system’s potential environmental significance and policy relevance.

The ingenuity of this bioreactor-biochar system exemplifies how sustainable engineering coupled with novel material science can address complex environmental challenges. By tackling nitrogen and phosphorus pollution simultaneously through biologically and chemically synergistic pathways, this approach could reset standards in agricultural runoff management, protecting aquatic ecosystems from nutrient over-enrichment and the cascade of ecological disruptions that follow.

As farmers, policymakers, and environmental managers grapple with the escalating consequences of nutrient pollution worldwide, innovations such as this offer hope for scalable, economically viable solutions. Beyond mere mitigation, the technology envisions a regenerative future where wastes become resources and agriculture harmonizes more closely with natural ecosystem processes.

In conclusion, the Illinois team’s extensive experimental study and techno-economic analysis not only validate the functional and financial promise of this combined bioreactor-biochar approach but also highlight the indispensable role of interdisciplinary research in fostering next-generation environmental technologies. The full implications of this system’s deployment could extend beyond water quality, shaping broader strategies for sustainable nutrient management and climate-smart agriculture in the decades ahead.

Subject of Research: Not applicable

Article Title: Performance, stability, and cost-effectiveness of a bioreactor-biochar (B2) system for nutrient removal from agricultural drainage

News Publication Date: 4-Dec-2025

Web References:

• Journal of Water Process Engineering Article
• DOI link

Image Credits: Photo by Michelle Hassel

Keywords: Agricultural runoff, nutrient removal, bioreactor, biochar, phosphorus removal, nitrogen removal, water treatment, biochar pelletization, lime sludge, techno-economic analysis, sustainable agriculture, algal blooms, environmental chemistry

Phosphorus, a key nutrient, triggers ecosystem disruptions at extremely low concentrations, often measured in parts per billion. Hence, achieving near-complete removal of phosphate from impacted lakes and reservoirs remains one of the most formidable challenges in contemporary water treatment. Modified biochar has emerged as a highly promising adsorbent due to its porous structure and surface functionalities, but cost barriers associated with the use of rare earth elements like lanthanum have limited its practical application. The necessity for economically viable, scalable solutions motivates the integration of data-driven design principles to identify optimal material formulations in unprecedented timeframes.

The research team embarked on an extensive data mining campaign, aggregating datasets from various published studies that examine the synthesis conditions, metal loadings, and phosphate uptake efficiencies of lanthanum-modified biochars. By training an ensemble of eight machine learning models—including decision trees, random forests, gradient boosting machines, and other tree-based algorithms—the team achieved highly accurate predictions of adsorption performance based on experimental parameters. These models illuminated intricate, non-linear relationships between synthesis variables and removal efficacy that traditional experimentation alone would scarcely reveal.

Tree-based ensemble methods, in particular, excelled in predictive accuracy, providing reliable guidance to optimize metal composition and processing variables. The capacity to rapidly simulate thousands of hypothetical material variants enabled the researchers to pinpoint composite biochars combining lanthanum with calcium and iron that deliver superior phosphate adsorption efficiency while markedly lowering production costs. This computational approach circumvents the tedious trial-and-error cycles characteristic of conventional materials development, dramatically accelerating the innovation timeline.

Experimental validation of the machine learning–guided designs confirmed that the lanthanum-calcium and lanthanum-iron composite biochars can effectively reduce phosphate concentrations to environmentally safe levels. Notably, the adsorption capacities closely corresponded to model forecasts, underscoring the robustness of the data-driven approach. Beyond efficacy, the synthesis of composite biochars achieved cost reductions exceeding 50% relative to traditional lanthanum-modified biochars, demonstrating the economic viability of this optimization paradigm.

The study further explored the performance of these materials within simulated natural water bodies exhibiting diverse phosphorus loads and chemical compositions. Results indicate that targeted selection of composite biochars tailored to specific regional water chemistry can maximize remediation effectiveness while minimizing overall expenditure. This bespoke, site-specific approach equips water resource managers with a potent toolkit to balance ecological restoration goals and fiscal constraints across varied environmental contexts, from heavily eutrophicated lakes to waters with modest nutrient influx.

In addition to practical advancements, the research exemplifies how AI-driven materials science can elucidate fundamental mechanistic insights. Machine learning analyses identified key factors influencing phosphate adsorption kinetics, including solution pH, competing ion concentrations, and total metal loading percentages. These findings demystify the complex interplay of variables often inaccessible through standard empirical methods, charting new pathways for rational biochar design.

The deployment of such engineered biochars must also consider environmental safety parameters to mitigate potential negative impacts. Continuous monitoring of metal leaching, especially of lanthanum and iron, will be critical to ensure that adsorbent use does not introduce secondary contaminants. The study advocates for integrated life cycle management strategies, including the recovery and recycling of phosphorus-laden biochars as nutrient-rich soil amendments or fertilizers, thereby closing the phosphorus cycle and fostering circular economy principles.

This pioneering convergence of environmental engineering, materials science, and artificial intelligence not only enables the rapid discovery of cost-effective adsorbents but also exemplifies a transformative model for sustainable water treatment innovation. By integrating predictive modeling and experimental validation, this framework accelerates development cycles and propels technology readiness toward real-world application.

Ultimately, this work signals a profound shift in addressing nutrient pollution in freshwater systems—machine learning–guided material design promises to dismantle previous economic barriers, facilitating widespread deployment of advanced biochar adsorbents. This approach can catalyze the restoration of nutrient-impaired lakes globally, securing vital freshwater resources in the face of mounting environmental pressures.

Looking forward, the continued refinement and adaptation of AI methodologies in environmental remediation hold immense potential. As datasets grow richer and models become increasingly sophisticated, the precision tuning of adsorbent materials will deepen. Coupled with robust monitoring and sustainable operational frameworks, such innovations could dramatically mitigate eutrophication challenges while promoting ecological resilience and public health.

In conclusion, the integration of machine learning in the design of lanthanum-based composite biochar represents a seminal advance in phosphorus removal technologies. By optimizing performance and cost simultaneously, the research offers a scalable and economically sound solution for water quality restoration. This convergence of disciplines underscores the importance of embracing data-driven strategies to address complex environmental issues efficiently, paving the way toward healthier aquatic ecosystems and sustainable water management worldwide.

Subject of Research: Not applicable

Article Title: Machine learning–aided design of La-based composite modified biochar: Efficient materials and cost optimization for low-phosphorus water treatment

News Publication Date: 29-Jan-2026

Web References: http://dx.doi.org/10.1007/s42773-025-00534-3

References: Fu, W., Yao, X., Zhang, X. et al. Machine learning–aided design of La-based composite modified biochar: Efficient materials and cost optimization for low-phosphorus water treatment. Biochar 8, 19 (2026).

Image Credits: Credit: Weilin Fu, Xia Yao, Xueyan Zhang, Shiyu Lv, Tian Yuan, Yi An & Feng Wang

Keywords

Bioremediation, Chemical engineering, Environmental remediation, Waste management, Water treatment, Machine learning