Investigation of Factors Influencing the Autoclave Tests Results of Internal Anticorrosive Polymer Coatings

Abstract

Polymer coatings are one of the most common methods for protecting metal structures from corrosion damage. For example, in the oil and gas industry, polymer coatings are used to protect the inner surfaces of oilfield pipelines. Forecasting the service life of the coating is an unsolved problem. Existing test methods allow to assess the quality of coating application and compliance with the declared properties, for example, resistance at a certain temperature, but do not allow to understand the expected service life or degradation dynamics. One solution to this problem may be the development of existing methods of autoclave testing of coatings with the addition of more criteria for assessing degradation. This paper considers the methodological features of autoclave testing with rapid pressure relief. The decompression autoclave test was considered from the point of view of the principles of its conduct and evaluation of test results. The tests were carried out in environments containing hydrogen sulfide and carbon dioxide. The main object of the tests was anticorrosive polymer powder coatings applied in industrial conditions. The work assessed the influence of the following factors on the test result: pressure relief time, test cycle, and coating quality. Attention was also paid to the evaluation methods; aside from the adhesion assessment, optical microscopy and the evaluation of the microhardness of coatings were used. As a result of the work carried out, it was shown that the pressure relief rate within 5 s affects the test results. An increase in micropores and a drop in the microhardness of coatings after cyclic autoclave tests were also shown. The method of assessing the degradation of coatings using microhardness also showed the convergence of the results with the traditional method of assessing adhesion. The results of the work can be used to modify the autoclave testing method and transition to resource forecasting.