The research team, led by Long Zhang of Nanjing Agricultural University together with colleagues from industry partner Three Gorges Group, worked at a 20-megawatt agrivoltaic demonstration park in Lishui District, Nanjing, in eastern China. The park, established in 2016 across roughly 47 hectares, generates about 24 million kilowatt-hours per year. Its fixed-support arrays run north-south with south-facing modules installed 2.5 meters above the ground at a 24-degree tilt, covering 53.3 percent of the soil below with panel projections. The researchers spent a full year measuring solar radiation beneath and between the panels using Onset HOBO sensors logging every ten minutes, comparing the field data against an open-field control station to define the daylighting rate, the ratio of light inside the array to light in the open.

To turn those measurements into a design tool, the team built a one-to-one three-dimensional model of the array in SketchUp and imported it into ECOTECT, an environmental simulation package capable of calculating solar radiation, daylighting and shading across complex geometries. The model simplified the panels into uniform rectangular layers of glass, silicon cells and backsheet with measured optical properties, ignored minor shading from diagonal braces, and drew its meteorological boundary conditions from historical China Meteorological Administration data for Nanjing. After a grid independence analysis settled on a 64-by-48 node resolution for the north-south vertical section, the researchers validated the simulation against twelve months of field data from July 2023 to June 2024. The agreement was striking: a coefficient of determination of 0.982, a root mean square error of 2.28, and an average relative error of just 4.9 percent, with no monthly comparison exceeding 8 percent error.

With a trustworthy light model in hand, the team defined the design problem. Four geometric parameters emerged as the truly independent variables an engineer can adjust: array span, panel tilt angle, installation height, and the transverse gap between panel rows. Azimuth was fixed by convention and site orientation, module width was locked by commercial standardization, and coverage ratio was treated as a derived quantity rather than a free variable. The chosen ranges reflected real-world engineering: spans of 8 to 12 meters to accommodate machinery, tilt angles of 18 to 36 degrees, heights of 2.5 to 4.0 meters to clear crops and equipment, and transverse gaps of 0 to 0.6 meters. Three conflicting objectives defined success: maximizing the maximum canopy daylighting rate, maximizing annual energy generation per hectare, and minimizing the coefficient of variation of daylighting, a statistical measure of how unevenly light is distributed across the crop canopy.

The first analytical pass was a single-factor sensitivity study, which revealed that no single knob moves all three objectives in the same direction. Raising the tilt angle from 18 to 36 degrees steadily improved power output and modestly improved light availability. Widening the span improved the daylighting rate substantially but eroded energy generation, because fewer panels fit per hectare. The transverse gap behaved similarly, boosting light penetration up to about 0.4 meters before power losses became steep. Installation height barely mattered for electricity but dominated light uniformity, since taller panels cast longer, softer shadows that blend more evenly across the canopy. Analysis of variance later confirmed these hierarchies: span mattered most for the daylighting rate, the transverse gap and span dominated energy generation, and installation height was the decisive factor for uniformity, with tilt acting as a moderate regulatory parameter throughout.

Because interactions between parameters proved statistically significant, the team then fitted quadratic response surface models using a Box-Behnken design of 29 simulation runs, an efficient scheme that captures linear, interaction and curvature effects while keeping every design point inside the engineering-safe range. The resulting regression models were exceptionally accurate, with coefficients of determination of 0.9978 for daylighting rate, essentially 1.0 for energy generation, and 0.9987 for the coefficient of variation, all significant at well below the 0.0001 probability level. But response surfaces alone could not guarantee a global optimum in such a nonlinear, multi-peaked landscape, so the researchers embedded their surrogate models inside a hybrid evolutionary search. The non-dominated sorting genetic algorithm II, or NSGA-II, provided the global engine, using fast non-dominated sorting, elitist preservation and crowding-distance ranking to maintain a diverse Pareto front of trade-off solutions. In each generation, the most promising layouts seeded an artificial fish swarm algorithm, whose foraging, swarming and following behaviors performed local refinement around those candidates before the improved designs were folded back into the main population.

The hybrid AFSA-NSGA-II search converged on a Pareto set of layouts balancing all three objectives. To pick a single recommended configuration without subjective judgment, the team applied the entropy weight method, which derives objective weights from the information content of the data itself, assigning weights of 0.35 to daylighting rate, 0.27 to energy generation, and 0.38 to uniformity. A TOPSIS ranking, which scores each candidate by its distance from the ideal and anti-ideal solutions, then selected the winner: a tilt angle of 30.1 degrees, a span of 9.1 meters, an installation height of 3.1 meters, and a transverse gap of 0.24 meters. Under this configuration the predicted maximum daylighting rate reached 80.1 percent with a coefficient of variation of 18.3 percent and an energy yield of 1.35 megawatt-hours per hectare. Follow-up simulation confirmed the predictions with relative errors of just 1.7 percent for light availability and 3.2 percent for uniformity, and zero error for power. Compared with the original 24-degree, 8-meter, gap-free design, the optimized layout raised light availability by 4.6 percent and cut light non-uniformity by 12.7 percent while sacrificing only 0.40 megawatt-hours per hectare of electricity.

The spatiotemporal gains carried through the growing seasons. During the overwintering crop period, the optimized array achieved a daylighting rate of 81.6 percent, up 1.9 percent from the original, while the coefficient of variation dropped from 41.9 to 25.9 percent, a 16.0 percent improvement in evenness. In the summer-sown period the daylighting rate reached 81.2 percent, up 2.2 percent, and the coefficient of variation fell 20.5 percent to 15.1 percent. To test whether these simulated benefits translate into grain and legumes, the team ran field trials with winter wheat and peanuts, the dominant rotation crops of the Yangtze River middle and lower reaches, under three panel densities: full density, high density and semi density. Light and yield tracked panel density closely. Under the semi-density layout between panels, wheat yielded 5.4 tonnes per hectare against 6.4 in the open field, a 15.6 percent loss, while the full-density between-panel treatment fell 25.0 percent to 4.8 tonnes. Peanuts showed the same gradient, dropping 20.0 percent under semi-density and 30.0 percent under full density relative to the open-field control of 4.0 tonnes per hectare.

The authors are careful about scope. Their optimum is calibrated to the subtropical monsoon climate of Nanjing and to two specific crops, and they note that shade-tolerant species such as forages, vegetables and certain high-value crops may respond differently to panel density. They also flag inter-annual climate variability and the need to couple the layout framework with crop growth models and multi-year environmental data. Still, the practical message is clear and, for a field often driven by rules of thumb, quietly radical: agrivoltaic design is a genuine multi-objective optimization problem, and treating it as one, with validated light simulation, surrogate modeling and hybrid evolutionary search, can buy meaningfully better growing conditions for a modest and quantified energy cost. As agrivoltaics scales worldwide, frameworks like this one offer engineers a numerical basis for deciding where every panel should sit.

Subject of Research: Multi-objective optimization of photovoltaic array layouts in agrivoltaic systems to balance crop light availability and solar energy generation

Article Title: Multi-objective optimization of photovoltaic array layouts on farmland via a hybrid algorithm framework for enhanced agricultural production and energy generation

Article References: Zhang, L., Geng, X., Ding, H., Cao, K., Wang, L., Chen, H., Deng, L., Wu, C., Xiao, M., & Bao, E. (2026). Multi-objective optimization of photovoltaic array layouts on farmland via a hybrid algorithm framework for enhanced agricultural production and energy generation. Artificial Intelligence in Agriculture. https://doi.org/10.1016/j.aiia.2026.09.002

Image Credits: AI Generated

DOI: 10.1016/j.aiia.2026.09.002

Keywords: agrivoltaics, photovoltaic arrays, multi-objective optimization, NSGA-II, artificial fish swarm algorithm, response surface methodology, light environment simulation, ECOTECT, crop yield, winter wheat, peanuts, solar energy

The research was published in the esteemed journal Nature Water and presents a holistic method for transforming sewage sludge, which is often difficult to process due to its complicated composition and contaminants, into economically viable and eco-friendly products. As urban populations expand, with the United Nations predicting an increase of 2.5 billion people in cities by 2050, the challenges associated with managing sewage sludge become more pressing. Traditional disposal methods, including incineration and landfilling, are deemed inefficient and harmful to the environment, thus necessitating innovative solutions.

NTU’s research team has developed a three-step solar-powered process that integrates mechanical, chemical, and biological methods to tackle these multifaceted challenges. The initial phase involves mechanically breaking down the sludge to facilitate subsequent processing. Following this, a sophisticated chemical treatment separates harmful heavy metals from the organic materials that can be repurposed for resource recovery, including proteins and carbohydrates essential for animal feed.

The third step employs a solar-powered electrochemical process, wherein specialized electrodes convert the organic materials into high-value products. This phase generates hydrogen gas, a clean energy source, along with acetic acid, which is critical in various food and pharmaceutical industries. This innovative approach not only addresses the environmental concerns linked with sewage sludge but also optimizes resource recovery and energy efficiency.

Lead researcher Associate Professor Li Hong, from NTU’s School of Mechanical and Aerospace Engineering, emphasizes that this method exemplifies the circular economy principle by transforming waste into renewable energy and sustainable food. The process promises to mitigate environmental damage while contributing significantly to resource sustainability — a crucial aim in the face of growing urban challenges.

Co-lead researcher Professor Zhou Yan from NTU’s School of Civil and Environmental Engineering further elaborates on the multi-faceted benefits of this approach. By integrating mechanical, chemical, and biological strategies, the research effectively tackles pollution while simultaneously addressing resource scarcity. This innovation is pivotal not only for wastewater management but also for global food security, showcasing how advanced research can drive meaningful change in environmental technologies.

Through laboratory tests, it has been observed that NTU’s process recovers an impressive 91.4 percent of organic carbon from sewage sludge, converting approximately 63 percent of that carbon into high-quality single-cell protein without generating detrimental by-products. In comparison, traditional methods such as anaerobic digestion typically yield only about 50 percent of the organic materials, highlighting the superior efficiency of the NTU approach.

Energy efficiency is another critical advantage of NTU’s solar-powered process, achieving a remarkable energy conversion rate of 10 percent. This translates to generating up to 13 liters of hydrogen per hour, a figure that stands about 10 percent higher than conventional hydrogen generation techniques. Such advancements underscore the potential for this method to significantly alter how we process waste and harness renewable energy.

Carbon emissions associated with traditional sludge processing methods form another area of concern; however, the NTU process reportedly reduces carbon emissions by an astounding 99.5 percent and energy use by 99.3 percent. This immense reduction is not only beneficial for the environment but also positions NTU’s method as an attractive, cost-effective alternative to existing wastewater treatment solutions, with the elimination of hazardous heavy metals further enhancing its ecological credentials.

Dr. Zhao Hu, the first author of the study, emphasizes the broader implications of this innovative method. He advocates for a shift in perspective regarding sewage sludge, encouraging stakeholders to view it not merely as waste but as a valuable resource for clean energy and sustainable food production. The transition to this mindset is critical in reshaping current waste management paradigms and fostering a more sustainable future.

Despite the promising outcomes, the researchers acknowledge the challenges that remain. Scaling up this groundbreaking process for widespread application in wastewater treatment facilities presents complex hurdles, particularly concerning the cost of utilizing electrochemical processes to comprehensively break down organic materials and extract heavy metals. Moreover, designing a robust system capable of handling the intricacies of wastewater treatment is a task that requires meticulous planning and significant investment.

NTU’s research on this solar-driven sewage sludge transformation stands as a beacon of hope amid growing environmental concerns. By addressing the dual challenges of resource scarcity and pollution, this innovative approach lays the groundwork for a new paradigm in waste management. It not only demonstrates the viability of converting waste into valuable resources but also fosters a pathway towards achieving greater sustainability in food and energy sectors, crucial for the future of our planet.

In conclusion, NTU Singapore’s research marks a significant leap towards a sustainable future, effectively turning the challenges of sewage sludge management into opportunities for innovation and growth. The development of such an integrated, eco-friendly method illustrates the power of interdisciplinary research in tackling some of humanity’s most pressing challenges, paving the way for a greener, more sustainable world.

Subject of Research: Not applicable
Article Title: Solar-driven sewage sludge electroreforming coupled with biological funnelling to cogenerate green food and hydrogen
News Publication Date: 1-Nov-2024
Web References: http://dx.doi.org/10.1038/s44221-024-00329-z
References: Not applicable
Image Credits: Credit: NTU Singapore

Keywords: Sustainable development, Industrial production, Electrode processes, Sludge, Sewage, Environmental methods, Waste conversion energy, Industrial research, Electrochemical energy, Hydrogen energy, Bacterial proteins, Environmental issues, Food resources, Heavy metals, Wastewater treatment, Pollution, Environmental sciences.