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Home  > Raw materials & technologies  > Applications  > Protective & Marine coatings  > Using artificial neural networks to evalua...

Friday, 20 September 2019
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Raw materials & technologies, Applications, Protective & Marine coatings

Using artificial neural networks to evaluate antifouling paints

Thursday, 27 March 2014

Although biofouling is a natural process, it has some disadvantages for shipping industry such as increased fuel consumption, and CO2emission.

Applying a new robust method against growth of algaes and mossels Bild: Source: Limnomar

Applying a new robust method against growth of algaes and mossels Bild: Source: Limnomar

Therefore, the ships' hull must be covered by antifouling (AF) or fouling release type coatings to overcome biofouling. In general, the so-called self-polishing AF paints contain biocides for preventing fouling organisms. Their concentrations and release rates from AF coatings are of great importance and they definitely affect both quality and cost of the coating.

Applying a new robust method

In a study, a team of Turkish researchers from Dokuz Eylül University, Izmir, aimed at applying a new robust method. They used a model biocide, i.e., "Econea”, to obtain its RP-HPLC optimization through artificial neural networks (ANN) and to see its antifouling performance. Antifouling performances were evaluated compared with the biocide free paint with a field test on the Turkish coastline. Column temperature, mobile phase ratio, flow rate, concentration and wavelength as input parameters and retention time as an output parameter were used in the ANN modeling. In conclusion, the R&D groups in AF paint industry may use RP-HPLC method supported with ANN modeling in further studies.

The study was published in: Progress in Organic Coatings, Volume 77, Issue 3, March 2014, Pages 627-635.

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