3 minute read
Corrosion prediction models
Soil corrosion brings new challenges to corrosion research.
Franck Nozahic
Corrosion prediction models
The excellent corrosion properties of the Magnelis® coating in harsh environments resulted in an increased demand for Magnelis® to be used in large ground-mounted and floating solar photovoltaic parks. Depending on the soil and water corrosivity, differences in the Magnelis® coating’s lifetime can be expected. There is a clear need from customers for guidelines on possible expected lifetimes of Magnelis® coated structures and the degradation rate of the Magnelis® coating in these different soil and water environments. To meet the customer needs, the development of corrosion prediction models for soil and water have been initiated.
EXCAVATE KNOWLEDGE
Soils are complex multi-phase environments that depend on location and the governing climate, and so it is extremely difficult to evaluate soil corrosivity due to the large diversity of variables. Supervised machine learning methods are being applied to generate a soil corrosion prediction model, which is based on a soil corrosion database that draws on available historic data from lab soil corrosion tests, field exposures and results from external sources. Based on the Magnelis® soil corrosion database, a regression model to predict the corrosion rate is obtained. Strengthening of the first model’s robustness is ongoing for different sources, due to insufficient long-term data and little variability in soils. A long-term workplan has been launched to triple the number of datapoints in 5 years by performing additional field and lab soil corrosion exposures. Two large experimental matrices have also been launched to generate data for the soil corrosion model. First, a long-term field soil corrosion exposure in 3 soils at the OCAS Zelzate site has been started for a large variety of materials and duration up to 15 years of exposure. The long-term vision allows us to benchmark Magnelis® to other materials, which will aid future metallic coating development. Secondly, a large lab design of experiments is ongoing to evaluate the effect of multiple soil corrosion variables in a larger variety of soils. Additionally, continuous monitoring of the environmental soil parameters will be combined with the Magnelis® corrosion data. Over the next few years, the generated data will be fed into the soil corrosion database and prediction model. In addition to enriching the model with new data, further model improvements are in the pipeline based on the modern machine learning developments. The developed model helps support the expansion of Magnelis® structures in the fast-growing market of renewable energy.
Floating solar structures offer power generation companies a flexible solution that can be deployed quickly and in areas where there is high demand on land use. The structures shield the water from the sun, limiting evaporation. At the same time, the water keeps the PV modules cool, thanks to the system’s design, which helps maximise the efficiency of the solar panels. Magnelis® is currently being used in the support structures for floating solar PV panels and the walkways used by maintenance personnel. The use of Magnelis® for this application is motivated by its excellent environmental and corrosion resistance properties. Magnelis® parts can be exposed to aggressive environments depending on the water’s composition and other exposure conditions such as temperature, rain and wind-caused wave splash. In the near future, floating solar PV will be installed in more demanding environments. As a result, there is a need to strengthen our understanding of the corrosion behaviour of Magnelis® in floating solar environments. In addition, a tool allowing us to estimate the lifetime of Magnelis® in these conditions will be valuable. The development of a lake water database with the chemical composition of the waters of >6000 lakes in Europe and accelerated tests in the laboratory will be a significant step forward. The results of these studies were used to design a large experimental matrix that will generate enough corrosion data to build a corrosion lifetime prediction model. In parallel, long-term field exposures – at OCAS as well as at customers’ sites – will be launched in 2022.
Our soil corrosion research underpins the global transition to renewable energy to combat climate change.
Ansbert De Cleene
