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Statistical lifetime modeling of FeNiCr alloys for high temperature corrosion in waste to energy plants and metal dusting in syngas production plants

Camperos Guevara, Sheyla Herminia. Statistical lifetime modeling of FeNiCr alloys for high temperature corrosion in waste to energy plants and metal dusting in syngas production plants. PhD, Science et Génie des Matériaux, Institut National Polytechnique de Toulouse, 2016

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Over the last decades, the corrosion control of alloys exposed to severe and complex conditions in industrial applications has been a great challenge. Currently, corrosion costs are increasing and preventive strategies have become an important industrial demand. The SCAPAC project funded by the French National Research Agency has proposed to study the corrosion for two separate processes: Steam Methane Reforming (SMR) and Waste to Energy (WtE). Although the operating conditions of both processes are different, the modeling approaches can be similar. Metallic components in the SMR process are subjected to metal dusting corrosion, which is a catastrophic form of damage that affects alloys exposed to highly carburising gases (aC>1) at high temperatures (400–800 °C).[1]. Likewise, metallic components in the Waste to Energy (WtE) process are subjected to high temperature corrosion under deposit that takes place in equipment exposed to atmospheres with high content of corrosive products of combustion. Metal dusting corrosion is considered as a critical phenomenon that has led to worldwide material loss for 50 years. A basic understanding of the degradation mechanisms is available. However, the effect of some process parameters is still not well understood in current literature and requires further study. Otherwise for high temperature corrosion, a considerable amount of literature has been published over the last few decades and the mechanisms are well documented. Also many materials and coatings have been developed. However, the material performance in different environments has not been sufficiently well understood to define suitable criteria for lifetime prediction models regarding operating conditions, due to the high complexity of the corrosion phenomena involved. Literature research in both fields revealed modeling approaches in different kinds of complex conditions and applications. Nevertheless, there are no lifetime models currently available in the open literature for commercial materials that consider a wide range of conditions and the relative weight of the variables involved in the corrosion processes. This dissertation presents a methodology to develop lifetime prediction models to evaluate materials performance under metal dusting and high-temperature corrosion conditions. Two databases were created to integrate experimental results from the SCAPAC project, as well as results from literature to enable sufficient amount of data for modeling. The databases allowed analyzing approximately 4000 corrosion rates by different statistical methods over different scenarios. The Principal Component Analysis (PCA) methodology was performed to identify the key parameters to create lifetime prediction models using Multiple Linear Regressions (MLR). For high-temperature corrosion, three models were obtained in the thermal gradient scenario for three families of alloys: low alloyed steels, Fe/Ni-based high temperature alloys and Ni-based alloys, showing agreeable results. For metal dusting corrosion, two models were obtained to explain the incubation times and the kinetic of pit depth growing, showing satisfactory results. The statistical models in both cases were compared with experimental and theoretical results showing good agreement with experimental findings, which allows performing the lifetime assessment of materials under defined conditions.

Item Type:PhD Thesis
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
Laboratory name:
Research Director:
Monceau, Daniel and Brossard, Jean-Michel
Deposited By: Thèse INPT
Deposited On:07 Jun 2016 09:24

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