The effects of tidal cycles associated with the water level on the biocorrosion of stainless steel AISI 316L were studied. Steel coupons were exposed to different conditions of immersion in mesocosms fed by fresh seawater either continuously or in accordance with the periodicity of natural tides. After 5 and 15 weeks, all coupons were found to have undergone ennoblement associated with the formation of a biofilm. Analysis of the composition of the bacterial community using denaturing gradient gel electrophoresis (DGGE) revealed differences in the biological succession. After 15 weeks, exposure to the simulated tidal conditions resulted in biofilms with lesser bacterial richness; the corresponding rate of corrosion, as determined by weight loss, was about 40 times lower compared to the case for the continuous exposure to seawater. Phylogenetic analysis of selected DGGE bands and the inspection of biofilm morphologies revealed that the faster rate of corrosion was associated with the presence of iron-oxidizing Zetaproteobacteria and eukaryotic photosynthetic microorganisms. On the other hand, intermittent exposure to seawater resulted in the succession of microorganisms resistant to the stress associated with sudden environmental changes, which was associated with a low rate of corrosion.

The application of Artificial Neuronal Networks (ANN) offers better statistical accuracy in erosion-corrosion (E-C) predictions compared to the conventional linear regression based on Multifactorial Analysis (MFA). However, the limitations of ANN to require large training datasets and a high number of inputs pose a practical challenge in the field of E-C due to the scarcity of data. To address this challenge, a novel ANN method is proposed, structured to a small training dataset and trained with the aid of synthetic data to produce an E-C neural network (E-C NN), applied for the first time in the study of E-C wear synergy. In the process, transfer learning is applied by pre-training and fine-tuning the model. The initial dataset is created from experimental data produced in a slurry pot setup, exposing API 5L X65 steel to a turbulent copper tailing slurry. To the previously known E-C scenario for selected values of flow velocity, particle concentration, temperature, pH, and the content of the dissolved $C{u}^{2+}$, new experimental data of stand-alone erosion and stand-alone corrosion is added. The prediction of wear loss by E-C NN considers individual parameters and their interactions. The main result is that E-C ANN provides better prediction than MFA as evaluated by a mean squared error (MSE) values of 2.5 and 3.7, respectively. The results are discussed in the context of the cross-effect between the proposed prediction model and the resulting estimation of relative contribution to E-C synergy, which is better predicted by the E-C NN. The E-C NN model is concluded to be a viable alternative to MFA, delivering similar prediction with better sensitivity to E-C synergy at shorter computation times when using the same experimental dataset.