ABSTRACT
Myriads of forms of corrosion have always been a deterrent to the functionality and longevity of structures. Among them, by far, biocorrosion has been designated as a lethal one. The progressive deterioration of a substrate after being subjected to redox reaction, facilitated by biofilms formed by the action of colonizing bacteria, is known as microbial induced corrosion (MIC), alternatively known as biocorrosion. The conventional electrochemical corrosion involves the release of metal cations resultant of electron transfer from a zero-valent metal to an external electron acceptor. Whereas biocorrosion is synergistic in nature, involving the interactions between the metal substrate, abiotic corrosion products, microbial cells, and the extracellular polymeric substance (EPS) secreted by them. EPS aids the adhesion of the microbial cells to the substrate. The process of biocorrosion starts with the formation of biofilm (EPS) by the bacteria, followed by the release of various metabolites including enzymes, exopolymers, organic, and inorganic acids, as well as volatile compounds like ammonia and hydrogen sulfide. This accelerates the otherwise slow electrochemical reactions taking place at the metal–biofilm interface; resulting in the corrosion of the metal surface ensuing in considerable strength reduction of the structure. The microbial species like sulfate reducing bacteria (SRB) and acid producing bacteria (APB) are mainly responsible for MIC in anoxic conditions. Across all industrial sectors, 20%–30% of corrosion in metal structures could be attributed to MIC, amounting to 2%–3% of the gross domestic productivity (GDP) of developing nations. Its effect is more prominent in the energy industry, as the corrosion in the oil and gas pipelines cause a loss in the range of hundreds of billion dollars. To curb these losses mathematical models have been developed to predict the behavior and kinetics of the MIC process. For brevity, some of the methods used in the modeling process could be enumerated as Adomian decomposition of nonlinear differential equations such as the Michaelis–Menten kinetics model for the substrate, derivation of corrosion rate models from Plank–Nerst equation, by separation of variables, followed by Laplace transformation, corrosion rate model using Butler–Volmer equation for charge and mass transfer resistance etc. This is applied to raw datasets by manifesting them in some programming language or software, with the favorite seed being MATLAB. In this chapter, the feasibility and efficiency of these models are studied. A critical comparison of various mathematical models is used to analyze the parameters and unravel their mechanisms. This chapter aims at presenting an optimal combination of models to efficiently summarize and predict the MIC process in a nutshell.
