RP-HPLC optimization of econea by using artificial neural networks and its antifouling performance on the Turkish coastline

• RP-HPLC determination and optimization of econea were investigated.
• Antifouling performances of econea were evaluated compared with the biocide free paint with a field test in Turkey.
• Artificial neural network (ANN) was used for modeling of RP-HPLC data.
• ANN modeling is proposed for R&D projects related to antifouling paint development.

Coverage of artificial surfaces within seawater by fouling organisms is defined as biofouling. Although biofouling is a natural process, it has some disadvantages for shipping industry such as increased fuel consumption, and CO2 emission. 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. In the present study, we aimed at applying a new robust method. In this method, we used a model biocide, i.e., econea, to obtain its RP-HPLC optimization through artificial neural networks (ANN) and to see its antifouling performance. 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.