Biofilms and beyond: a comprehensive review of the impact of Sulphate Reducing Bacteria on steel corrosion

Abstract

Sulphate-reducing bacteria (SRB) are known to cause severe corrosion of steel structures in various industries, resulting in significant economic and environmental consequences. This review paper critically examines the impact of SRB-induced corrosion on steel, including the formation of SRB biofilms, the effect on different types of steel, and the various models developed to investigate this phenomenon. The role of environmental factors in SRB-induced corrosion, molecular techniques for studying SRBs, and strategies for mitigating corrosion are discussed. Additionally, the sustainability implications of SRB-induced corrosion and the potential use of alternative materials were explored. By examining the current state of knowledge on this topic, this review aims to provide a comprehensive understanding of the impact of SRB-induced corrosion on steel and identify opportunities for further research and development.

Keywords:

• Sulphate reducing bacteria
• corrosion
• steel
• biofilm formation

Introduction

The term microbiologically influenced corrosion (MIC) refers to a type of corrosion that occurs when microorganisms interact with metals and alloys (Little and Lee 2015). Discovered over 100 years ago (Gaines 1910), it is a widespread phenomenon that affects a variety of industries, including oil and gas, marine, aviation, and water treatment. MIC is believed to account for around 10% to 20% of corrosion (Javaherdashti 2011), and a study conducted by NACE International estimated it to account for US$2.5 trillion, which is equivalent to 3.4% of the global GDP in 2013 (Bowman et al. 2016). Despite its economic impact is significant, estimates of its cost reach billions of dollars annually.

In addition to the direct costs associated with pipeline failures, equipment damage, and safety hazards, MIC can also have indirect economic impacts. The costs of inspection and monitoring programs to detect and prevent MIC can be substantial. Furthermore, the economic impact of MIC can extend beyond individual companies and industries. The environmental consequences of MIC, such as the release of toxic metals and chemicals, can have a significant impact on ecosystems and human health, leading to additional costs related to cleanup and remediation efforts. Despite the economic impact of MIC, there are still limitations in assessing it, and obtaining globalized data in the literature is next to impossible, and most data remains central to industries based in the west, ignoring the global South (Thompson et al. 2022).

A bibliometric analysis of MIC in oil and gas engineering systems by Hashemi et al (2018) revealed that the United States and China are the top two countries with MIC research, followed by Canada, France, and the United Kingdom (Hashemi et al. 2018). Traditional corrosion testing methods may not accurately detect or quantify the contribution of microbes to the corrosion process (Vinagre et al. 2022). Additionally, identifying the specific microbes responsible for MIC can be challenging due to the complexity and diversity of microbial communities in affected environments, and the interdisciplinary nature of the corrosion process (Núñez et al. 2023). Addressing these knowledge gaps is critical for effective corrosion control and infrastructure protection, emphasizing the importance of further research in this area. This includes identifying the microorganisms involved in MIC, understanding the environmental factors that influence corrosion rates, and developing methods for detecting and monitoring MIC. Additionally, there is a need for new materials and coatings that are resistant to MIC, as well as improved maintenance and inspection practices to reduce the risk of corrosion-related failures. Sulphate-reducing bacteria (SRB) are one of the important types of microorganisms responsible for corrosion. These are diverse groups of microorganisms with playing key roles in biogeochemical cycles. They significantly influence both environmental and human health. Desulfomonas pigra, recently known as Desulfovibrio piger, was first isolated in 1976 from the intestinal microbiome. Particularly, the sulfide resulting from these bacteria causes impairment of oral mucosa and intestinal cells (Kushkevych et al. 2021). Sulphate-reducing bacteria can be grouped into seven phylogenetic lineages, five within the bacteria and two within the Archaea. Most of the sulfate reducers belong to the twenty-three genera within the Deltaproteobacteria, followed by the Gram-positive SRB within the Clostridia (Desulfotomaculum, Desulfosporosinus, and Desulfosporomusa genera). Three lineages, Nitrospirae (Thermodesulfovibrio genus), Thermodesulfobacteria (Thermodesulfobacterium genus) and Thermodesulfobiaceae (Thermodesulfobium genus), only contain thermophilic sulphate reducers. Within the Archaea, SRB belongs to the genus Archaeoglobus in the Euryarchaeota, and to the genera Thermocladium and Caldirvirga in the Crenarchaeota (Muyzer and Stams 2008). One of the most notable effects of SRBs is their ability to induce corrosion in metallic structures, including steel pipelines, oil rigs, and other infrastructure. MIC caused by SRB is the most intensively investigated topic in MIC research due to its practical importance and complexity (Anandkumar et al. 2016) (Enning and Garrelfs 2014). New genus of SRBs were successively isolated and named, with the improvement of biotechnology. The Bergey’s systematic bacteriology manual, published in 1984, updated SRB to 8 genera: Desulfovibrio, Desulfomonas, Desulfobulbus, Desulfotomaculum, Desulfococcus, Desulfobacter, Desulfosarcina and Desulfonema. In 2000, with the rapid development of molecular biology, few researchers suggested that SRB could be divided into six clusters: Desulfotomaculum, Desulfobubus, Desulfobacterium, Desulfobacter, Desulfococcus-Desulfonema-Desulfosarcina, and Desulfovibrio-Desulfomicrobium. Furthermore, based on 16S rRNA gene sequence, Sulphate-reducing bacteria could be divided into four groups: Gram- negative mesophilic SRB; Gram-positive spore-forming SRB; thermophilic bacterial SRB; and thermophilic archaeal SRB (Diao et al. 2023).

Sulfate-reducing bacteria (SRB) reduce sulfate to sulfide in sewers, under anaerobic environments which is present in the forms of H₂S, HS, and S₂ in sewage. In sewers, Sulfide could accumulate to high levels, especially in networks with a long transport time. When transferred to sewer air, hydrogen sulfide causes sewer corrosion via oxidation to sulfuric acid at the inner surface of concrete pipes, odour nuisance and health hazards when emitted to the surrounding environment (Zhang et al. 2023). SRB is known to shorten the service life of metal. SRB can accelerate corrosion in steel rebar by producing reduced sulfur that enhances pitting (Etim et al. 2021). In oil and gas industries, most SRB comes from auto-tone microbiota via subsurface fluid movement in reservoirs or are introduced by drilling operations and by secondary oil recovery methods. Properties of sulfides affect the oil production, pipelines, and storage tanks, and quality of the gas and oil produced are all affected by the corrosive (Almeida et al. 2006). Despite this importance, the mechanisms underlying SRB-induced corrosion are not yet fully understood, and there is a need for continued research to better understand the biological, chemical, and physical factors that contribute to this phenomenon. In recent years, significant progress has been made in developing different scientific approaches (Table 1), and mathematical and mechanistic models to predict and quantify the effects of SRBs on metallic surfaces. However, many questions remain, and there is a need for continued interdisciplinary collaborations between biologists, chemists, engineers, and other experts to address the challenges posed by SRB-induced corrosion. Yet, there do not exist a lot of standards for these tests, and a wide range of test conditions are used in existing literature, making it difficult to compare results. This may unintentionally cause the selection of those specific test conditions to directly affect the outcome and interpretation of the test (Wade et al. 2017) instead of the actual focus of the work.

Table 1. Various measurement techniques applied to study MIC due to Sulphate reducing bacteria.

Sl. No	Topic	Method of corrosion analysis	Reference
1	Anaerobic phases of marine immersion corrosion	Corrosion rate measurement, Pit depth	(Melchers and Wells 2006)
2	Mitigation of SRB induced corrosion	Electrochemical measurements	(Yang et al. 2023)
3	Effect of SRB on galvanic corrosion behavior on steel	Electrochemical measurements	(Wan et al. 2023)
4	Corrosion of aluminum anode alloys in SRB containing sea mud	Rate of corrosion, Pit depth	(Liu et al. 2014)
5	Comparison of flow regimes on biocorrosion of steel pipe weldments	Most probable number (MPN) technique, Statistical analysis, and pitting investigation according to the American Society for Testing and Materials Standard G-46/94	(Liduino et al. 2019)
6	Study of microbiologically induced corrosion of 5052 aluminum alloy	Open Circuit Potential (OCP). SEM, polarization, and EIS	(de Andrade et al. 2020)
7	Carbon steel biocorrosion at 80 °C by a thermophilic sulfate-reducing archaeon biofilm	Weight loss measurements, electrochemical analysis- Linear polarization resistance	(Jia, Yang, et al. 2018)
8	Studies on the soils biocorrosion activity	Biometric analysis, Soils microbiological testing	(Chesnokova et al. 2015)
9	Analysis of corrosion defects on oil pipeline surface	Scanning electron microscopy, raster electron microscopy, Biometric analysis toolkits -STATISTICA-6 and BIOSTATISTICS	(Chesnokova et al. 2016)
10	Effects of biogenic H2S on the microbiologically influenced corrosion of C1018 carbon steel	Scanning electron microscope (SEM) with energy-dispersive X-ray spectroscopy (EDS), most probable number (MPN) method	(Jia, Tan, et al. 2018a)
11	Microbiologically Induced Corrosion of X80 Pipeline Steel in a Near-Neutral pH Soil Solution	Electrochemical techniques and microbiological tests	(Wu et al. 2015)

In this review, the impact of SRB-induced corrosion on steel has been examined. This is begun by discussing the biology of SRBs and their role in corrosion, followed by examining the different types of steel that are susceptible to SRB-induced corrosion and the mechanisms that underlie this process. The various mathematical and mechanistic models that have been developed to study SRB-induced corrosion along with their strengths and limitations have been discussed. Based on this, the implications of SRB-induced corrosion for sustainability have been considered. By synthesizing the existing literature and identifying gaps in our knowledge, this review aims to provide a comprehensive resource for researchers and practitioners working in the field of SRB-induced corrosion. Previous reports and reviews on SRB have focused on the mechanism of corrosion on metals. Nevertheless, the metabolic pathways and genes involved in metal degradation is not discussed in detail. Therefore, this review attempts to illuminate the metabolic mechanism of SRB, explore molecular techniques used to study SRB. The systematic review gives the important information about the computational modelling of SRB biofilms.

Microbial communities and biofilm formation

A key factor in the corrosion process is the formation of biofilms. Studies on metallic surfaces such as galvanized steel, stainless steel, and copper have confirmed that over time, SRB biofilm formation increases (Ilhan Sungur et al. 2010). Biofilms are complex communities of microorganisms that adhere to surfaces and secrete extracellular polymeric substances (EPS), which provide a protective matrix and enable the exchange of nutrients and signals between cells. SRBs are known to form biofilms on steel surfaces, which can lead to localized corrosion and pitting.

Corrosion of iron metals are characteristically covered with biofilms of diverse structures and functions that primarily promote degradation of the metal by oxidizing Fe⁰ to ferrous ion (Fe²⁺): (1) $${\text{Fe}}^0\to {\text{Fe}}^{2+}+{\text{2e}}^{-}$$(1)

For this reaction to proceed, the electrons (e^–) released from Fe⁰ must be moved to potential electron-accepting molecules (Xu et al. 2023)

Biofilm formation is generally established through several steps. The growth model used to be considered (Figure 1) that proposed a cyclic process occurring in a five-stage-specific and progressive manner (Aiyer et al. 2018). In the initial stage, planktonic cells adhere to the surface through a process called surface attachment. Following this, biofilms are constructed with the aid of EPS during the maturation phase, resulting in a well-defined architecture. In the dispersal phase, the cell exits the biofilm.

Figure 1. The original five-step model of biofilm development (Aiyer et al. 2018).

While the schematic conceptual biofilm developmental model is straightforward and has been widely used to explain biofilms, it may not fully capture the intricacies of biofilms in practical scenarios such as industrial, natural, and clinical settings.

The accumulation of biofilm is dynamic, and corrosion is initiated with changes in interface chemistry between the metal and the biofilm. Sauer et al. (2022) proposed an expanded version of the model, to develop an understanding of biofilms and anti-biofilm strategies that can be tailored to the microenvironment under investigation. Once the biofilm is formed, corrosion is affected in several ways: (1) enzymes that can promote cathode reaction are produced due to electrochemical; (2) compounds that can promote (or inhibit) corrosion are produced to microbial metabolic processes; and (3) the biofilm formed on the metal surface changes its thermodynamic state (C. Wu et al. 2021). Thus, the dynamic flux of the interaction between various compartments results in MIC (Pal and Lavanya 2022). Surface properties of the metal (chemical composition, hydrophobicity, and roughness) and environmental conditions (static magnetic field and shear stress, pH, ionic strength) also play an important role (Lv et al. 2017).

Several studies have investigated the composition and structure of SRB biofilms and have identified a range of factors that can influence their formation and stability. These include environmental factors such as temperature, pH, and nutrient availability, as well as the presence of other microorganisms that can interact with SRBs and modify the biofilm structure. In addition, recent research has highlighted the role of quorum sensing in SRB biofilm formation. Quorum sensing is a process by which bacteria communicate with one another and coordinate their behavior through the production and detection of signaling molecules (Dobretsov et al. 2009). Studies have shown that SRBs use quorum sensing to regulate the formation and dispersal of biofilms and that this process can be disrupted using targeted inhibitors.

Overall, the formation and stability of SRB biofilms is a complex and dynamic process that is influenced by a range of environmental and biological factors. A better understanding of these processes is critical for developing effective strategies to prevent and mitigate SRB-induced corrosion.

The presence of SRB in biofilm may trigger or accelerate the corrosion process by creating conditions suitable for the same (Rubio et al. 2006). SRB on the metal surface affects the dissolution of metal through the SRB metabolites by modifying the local chemistry beneath the biofilm. This leads to intrinsic heterogeneity and causes critical localised corrosion on the metal (Dong, Shi, et al. 2011). SRB contribute to corrosion by using released molecular hydrogen as a substrate and inducing further hydrogen dissociation from the metal. According to Hamilton, this depolarises the cathodic region of steel, giving rise to biomineralized iron sulphides as corrosion products (Hamilton 2003). Consequently, SRB begin forming colonies while also attracting other molecules to bind to the metal (Dordević et al. 2021), inducing more corrosion.

Recent studies done on MIC induced due to SRB do not account a lot for the effects due to SRB biofilm formation. Chiefly, laboratory tests conducted attempt to simulate corrosion by SRB simulate the effects of H₂S as the corrosion product and ignore the presence of biofilm and other products. Certainly, H₂S plays an important role in producing corrosion products, but there are other organic sources that can enable corrosion (Zhang et al. 2015) through biofilm. While literature has many studies with iron as an electron donor, the use of other donors (that is more applicable to natural conditions) is not well investigated (Liu, Gu, et al. 2018). Eduok et al (2019) made use of a general organic source instead of the conventional iron source as electron donor to understand MIC when SRB are present in limited carbon sources, as seen in oil fields. Overall, it was observed that 100% carbon source resulted in weaker biofilms, reducing corrosion (Eduok et al. 2019). SRB biofilms may also result in MIC pitting under it, on the material surface (Xu and Gu 2014). A lot of corrosion studies done on biofilms also ignore the presence of multiple species mixed in the biofilm. While studies are conducted on the mono-species and even individual strains of the bacteria, interspecies interactions are ignored (Lv et al. 2022). Madirisha et al. (2022) thus attempted to study SRB-induced corrosion by simulating a biofilm made with materials mimicking the SRB environment (Madirisha et al. 2022). However, their results contradicted the role of a biofilm and attributed to the use of simulated materials. Despite of this, the corrosion rates obtained by these authors matched that of experiments that study SRB-induced MIC (Madirisha et al. 2022). This has resulted in difficulties elucidating mechanisms for SRB, leading to multiple mechanisms being proposed.

Metabolic pathways and SRB genes involved in corrosion

SRBs are capable of utilizing a variety of organic compounds as electron donors for energy production, including hydrocarbons, fatty acids, and alcohols (Almeida et al. 2006). In the absence of oxygen, SRBs utilize sulfate as a terminal electron acceptor, which results in the production of sulfide and protons. The accumulation of sulfide can lead to the formation of corrosive species such as hydrogen sulfide and iron sulfide. Figure 2 illustrates dissimilatory sulfate reduction in SRB. Several key genes and enzymes are involved in the metabolic pathways of SRBs, including the dissimilatory sulfate reduction pathway and the sulfide oxidation pathway (Tran et al. 2021). The dissimilatory sulfate reduction pathway involves the reduction of sulfate to sulfide, which is catalyzed by the enzyme adenosine-5′-phosphosulfate reductase (AprA). The sulfide oxidation pathway involves the oxidation of sulfide to sulfate, which is catalyzed by the enzyme sulfide-quinone oxidoreductase (Sqr). In addition to these core metabolic pathways, SRBs possess a range of other genes and enzymes that are involved in stress responses, biofilm formation, and other cellular processes that can impact corrosion (Tran et al. 2021). For example, several studies have identified genes involved in the production of extracellular polymeric substances (EPS) that can help to protect biofilms from external stresses.

Figure 2. Schematic representation of dissimilatory sulfate reduction in SRB (Tran et al. 2021).

Overall, the metabolic pathways and genes involved in SRB-induced corrosion are complex and highly interconnected and are influenced by a range of environmental and biological factors. A better understanding of these processes is critical for developing effective strategies to prevent and mitigate corrosion in industrial settings.

Molecular techniques used to study SRB

Molecular techniques have played a critical role in advancing our understanding of SRBs and their role in corrosion. These techniques allow researchers to study the microbial community composition, diversity, and activity in situ and provide insight into the molecular mechanisms that underpin SRB-induced corrosion (Dobretsov and Rittschof 2023). One of the most commonly used molecular techniques is polymerase chain reaction (PCR), which allows researchers to amplify specific DNA fragments of interest from environmental samples. PCR can be used to detect and quantify the abundance of SRBs in a sample and to track changes in their abundance over time. Another powerful molecular tool is high-throughput sequencing, which enables the sequencing of millions of DNA fragments in a single run. This technique has revolutionized our ability to study complex microbial communities and has been used to identify and characterize the diversity of SRBs in various environments, such as marine sediments, oil reservoirs, and industrial pipelines. Other molecular techniques that have been used to study SRBs include fluorescence in situ hybridization (FISH), which allows researchers to visualize the spatial distribution of specific microorganisms in a sample, and metagenomics, which involves the sequencing of DNA extracted directly from environmental samples without prior culturing (Rao and Feser 2023). Overall, molecular techniques have provided valuable insights into the diversity and activity of SRBs in various environments and have helped to unravel the molecular mechanisms that underlie SRB-induced corrosion. These techniques are likely to continue to play a critical role in advancing our understanding of these microorganisms and their impact on industrial infrastructure.

Modelling and mechanisms

Traditional MIC models are based on extending many of the assumptions and principles developed for abiotic corrosion to MIC, failing to predict either the initiation or the propagation of MIC under field conditions, in all but a few cases (Aiyer et al. 2018). Over time, various mechanisms have been proposed to understand MIC due to SRB. These include cathodic depolarization theory (CDT), corrosive metabolites theory (CMT), biocatalytic cathodic sulfate reduction (BCSR), and direct electron transfer theory (DETT) (Chen et al. 2021). MIC models continue to be a field actively researched. The widely accepted MIC mechanisms of Sulphate Reducing Bacteria (SRB) are the cathodic depolarization theory (CDT) and the biocatalytic cathodic sulfate reduction (BCSR) theory (Gu et al. 2019).

Environmental and laboratory conditions used to investigate SRB MIC mechanisms

SRBs are one of the earliest anaerobic bacteria that evolved on the planet. As such, most of them prefer low-grade carbon sources such as volatile fatty acids. A minority of these microorganisms have undergone evolutionary changes that enable them to utilize hexoses such as glucose as both a carbon source and a source of energy. Additionally, certain species have evolved to use O₂ as an electron acceptor to oxidize organic carbon (Gu et al. 2019). However, no growth is observed as this form of respiration is used to only maintain energy. SRB was found to grow in an open-to-air system due to the presence of aerobes and facultative microbes providing a locally oxygen-free shelter, implying a mixed-culture system (Thauer et al. 2007). To encourage the growth of SRBs and prevent microbial contamination, it is suggested that an environment free of oxygen be maintained. Experts in the industrial sector have emphasized the importance of keeping the dissolved O₂ level below 40 ppb in the context of steel corrosion caused by CO₂, as this ensures that the contribution of O₂ to the corrosion process is insignificant (Thauer et al. 2007). This standard is generally accepted as a criterion for establishing strictly oxygen-free conditions in studies investigating abiotic CO₂ corrosion (Jia et al. 2017). Over the years this has been proven a good practice for oxygen-free MIC investigations as well.

Mechanisms of SRB-induced corrosion

SRBs are known to induce corrosion through a complex interplay of biological, chemical, and physical processes. The exact mechanisms by which SRBs cause corrosion are still not fully understood, but several hypotheses have been proposed. One of the most widely accepted mechanisms is the production of hydrogen sulfide (H₂S) by SRBs (Tran et al. 2021). H₂S is a toxic gas that can react with iron to form iron sulfides, which are less stable and more prone to corrosion than the original metal. Additionally, SRBs produce organic acids and other metabolites that can contribute to the corrosion of metal surfaces (Zhang et al. 2023). Another proposed mechanism involves the formation of biofilms on metal surfaces. SRBs are known to form thick, complex biofilms that can shield the bacteria from oxygen and other antimicrobial agents, creating a microenvironment that is favorable for corrosion. The biofilm matrix also provides a surface for the accumulation of minerals and other corrosive agents. Other potential mechanisms include the production of extracellular polymeric substances (EPS), which can bind to metal surfaces and facilitate the attachment and growth of SRBs, and the formation of localized electrochemical cells that can accelerate corrosion (Beech et al. 2006). It is important to note that the mechanisms of SRB-induced corrosion are likely to be highly dependent on the specific environment and the type of metal involved. Therefore, further research is needed to fully understand the complex interactions between SRBs and metal surfaces and to develop effective strategies for mitigating corrosion in industrial settings.

Mathematical modelling of SRB-induced corrosion

Mathematical modelling is a powerful tool for understanding the complex processes involved in SRB-induced corrosion and for predicting the rate and extent of corrosion under different conditions (Aiyer et al. 2018). Several mathematical models have been developed to simulate the corrosion behaviour of metals in the presence of SRBs. One common approach is to model the corrosion process as a combination of electrochemical and biological reactions. These models typically include equations that describe the mass transport of reactants and products, the kinetics of the chemical and biological reactions, and the electrochemical behaviour of the metal surface. The parameters in these models can be adjusted to match experimental data and to predict the effect of different variables such as temperature, pH, and oxygen concentration on corrosion rates.Another approach is to use computational fluid dynamics (CFD) to simulate the fluid flow and mass transport in biofilms and to predict the distribution of corrosive agents on metal surfaces. These models can provide valuable insights into the microscale processes that govern the corrosion behaviour of SRBs and can be used to optimize the design of corrosion-resistant materials and coatings.

More recently, machine learning techniques have been applied to predict the corrosion behaviour of metals in the presence of SRBs. These models use large datasets of experimental data to train algorithms to identify patterns and relationships between different variables and to make predictions about corrosion rates and mechanisms (Anguita et al. 2022). Overall, mathematical modelling provides a powerful tool for understanding and predicting the complex processes involved in SRB-induced corrosion. By combining experimental data with computational models, researchers can gain a deeper understanding of the mechanisms of corrosion and develop more effective strategies for preventing and mitigating the effects of SRBs in industrial settings. Anguita et al. (2022) conducted a study and developed a mechanistic mathematical model of microbial corrosion induced by SRB through EET. The research reported the effect of EET on MIC using a hybrid differential-discrete model. The model represents a SRB biofilm’s morphology and the variation in the surface topography due to MIC. The results also illustrated that the maintenance coefficient of SRB is a valuable parameter to quantify the electrons gained from iron oxidation (Anguita et al. 2022).

Computational modelling of SRB biofilms

Biofilms are complex, heterogeneous communities of microorganisms that play a critical role in SRB-induced corrosion (Vinagre et al. 2022). Computational modelling provides a powerful tool for simulating the behavior of these biofilms and predicting their impact on metal corrosion.

Early efforts on the mathematical modelling of biofilms can be traced back to the1980s. Biofilm formation is overseen by various chemical, physical principles and biological protocols and is a complex dynamical process (Zhang et al. 2023). Biofilm development mostly depends on the environmental conditions and properties of different types of bacteria inside the biofilm. Bacteria inside the biofilms are very resistant to antimicrobial agents leading to the limited penetration of antimicrobial agents. Mathematical modelling of biofilms is crucial in attaining a broader and deeper understanding of this complicated microorganism. The mathematical models can not only be used to corroborate experimental findings but also to make qualitative and quantitative predictions that might well serve as guidelines for experimental design (Wang and Zhang 2010).

One common approach to modelling biofilms is to use agent-based models (ABMs), which simulate the behavior of individual bacteria within a biofilm and the interactions between them. ABMs can provide insights into the dynamics of biofilm formation, the distribution of bacteria and corrosive agents within the biofilm, and the effect of different environmental conditions on biofilm growth and activity (Wang and Zhang 2010). Another approach is to use lattice-based models (LBMs), which represent the biofilm as a grid of discrete cells and simulate the transport of nutrients and metabolites within the biofilm (Aiyer et al. 2018). LBMs can be used to investigate the effect of different parameters such as nutrient availability and oxygen concentration on biofilm growth and activity and to predict the distribution of corrosive agents within the biofilm. More recently, multi-scale models have been developed that combine both ABMs and LBMs to simulate the behavior of biofilms at different spatial and temporal scales. These models can provide a more detailed and comprehensive understanding of the complex processes involved in biofilm formation and activity and can be used to optimize the design of materials and coatings to prevent or mitigate SRB-induced corrosion. Overall, computational modelling provides a powerful tool for understanding and predicting the behavior of SRB biofilms and their impact on metal corrosion (Wang and Zhang 2010). By combining computational models with experimental data, researchers can gain a deeper understanding of the mechanisms of corrosion and develop more effective strategies for preventing and mitigating the effects of SRBs in industrial settings.

Molecular mechanisms of SRB-induced corrosion

Sulphate-reducing bacteria (SRB) are known to cause significant damage to metallic structures through the process of microbial-induced corrosion (Thauer et al. 2007). The molecular mechanisms underlying this process involve a complex interplay of factors, including the production of corrosive metabolites, the formation of biofilms, and the expression of specific genes and proteins. One key factor in SRB-induced corrosion is the production of hydrogen sulphide (H₂S) and other sulphur-containing metabolites. H₂S is a highly corrosive compound that can react with metal surfaces to form sulphide compounds (Zhang et al. 2023), leading to corrosion and degradation of the material. SRBs are also known to produce organic acids, which can further exacerbate the corrosion process by lowering the pH of the surrounding environment. Another important factor is the formation of biofilms, which provide a protective environment for SRBs and promote the growth and activity of these bacteria. Biofilms can also act as a barrier to corrosion inhibitors and other treatment strategies, making it difficult to prevent or mitigate corrosion in industrial settings.

At the molecular level, several genes and proteins have been identified that play a role in SRB-induced corrosion. These include genes involved in sulfur metabolism, such as the dissimilatory sulphite reductase (dsr) gene, as well as genes involved in biofilm formation and quorum sensing, such as the luxI/luxR system (Dobretsov et al. 2009). Overall, the molecular mechanisms underlying SRB-induced corrosion are complex and multifaceted. Understanding these mechanisms is essential for developing effective strategies to prevent and mitigate the effects of SRBs in industrial settings. Recent advances in molecular techniques, such as metagenomics and proteomics, have provided new insights into the genetic and protein-based factors involved in SRB-induced corrosion, opening up new avenues for research and treatment. Predicting MIC is a challenging task because the activities of microorganisms are influenced by various biotic and abiotic factors, leading to changes in their interaction and either an increase or decrease in MIC activity over time. A predictive model that can account for the wide range of independent factors affecting the predicted corrosion rate would be more practical.

Cathodic depolarization theory (CDT)

CDT was first initiated by von Wolzogen Kühr and van der Vlugt and was further adopted by several studies on MIC (Xu and Gu 2014). The mechanism of cathodic depolarization is depicted in Figure 3. In 1934, von Wolzogen Kühr and van der Vlugt proposed CDT after observing high corrosion rates due to SRB in the natural environment. They saw that anaerobic corrosion was due to SRB activity and proposed that SRB catalysed a depolarization reaction where atomic hydrogen from the cathode surface reacted with sulfates in the electrolyte to form sulphides per Equation 5(5) $${\text{SO}}_4{}^{2-}+\text{8H}\to {\text{S}}^{2-}+{\text{4H}}_2\mathrm{O}$$(5) below, resulting in the rate-limiting reaction of corrosion (Costello 1974).

Figure 3. Illustration of the cathodic depolarization mechanism of SRB activity (Lv & Du 2018).

This theory contemplates hydrogen in either atomic or molecular form produced by spontaneous iron dissolution or by water dissociation reactions (reactions 2–4) is consumed by SRB for dissimilatory sulfate reduction pathway (Victoria et al. 2021). (2) $${\text{8H}}_2{\text{O →\hspace{0.5em}8OH}}^{-}+{\text{8H}}^{+}$$(2) (3) $${\text{4Fe →\hspace{0.5em}4 Fe}}^{++}+{\text{8e}}^{-}\left(\text{Anodic reaction}\right)$$(3) (4) $${\text{8e}}_{-}+{\text{8H}}^{+}\text{\hspace{0.5em}→\hspace{0.5em}8H}\left(\text{Cathodic reaction}\right)$$(4)

The hydrogen that is generated from reaction (4) is utilized by the SRB as shown by reaction 5. (5) $${\text{SO}}_4{}^{2-}+\text{8H}\to {\text{S}}^{2-}+{\text{4H}}_2\mathrm{O}$$(5)

In steel, SRB are recognized as promoters of the cathodic depolarization process. This first includes the consumption of the hydrogen formed by the electrons reducing the protons on the metallic surface. Further reduction of protons is then reduced due to the hydrogen film, causing electrostatic isolation (passivation) that can prevent further corrosion. But SRB breaks this balance by the consumption of hydrogen. In recent years, the original theory and interpretation have been highly debated (Little et al. 2020), and instead, it seems that SRB plays a more indirect role in the mechanism of steel corrosion. This is due to the following factors (Kakooei et al. 2012) not being considered in the classic theory: (a) the effect of the corrosion products on the anodic reaction; (b) the effect of elemental sulfur produced on the further corrosion product; (c) changes in the environment between anaerobic and aerobic conditions; (d) the production of other corrosion products by other elements.

The theory also reports that corrosion rates for hydrogenase⁻ and hydrogenase⁺ SRB are identical, which is not the case as evidenced by several studies. Further, it does not agree with the known electrochemical mechanism of hydrogen evolution on steel substrates.

EET-MIC

Extracellular Electron Transfer Microbiologically Influenced Corrosion (EET-MIC) has two important types of electron transfer: Direct electron transfer (DET) and Mediated electron transfer (MET) as depicted in Figure 4 (Gu et al. 2021).

Figure 4. Schematic representation of DET and MET in EET [Gu et al. 2021].

DET relies on either outer membrane-bound redox-active protein such as c-cytochromes in direct contract with a conductive surface, or conductive pili (conductive nanowires). When sessile cells attach to the iron directly, the c-cytochrome is applied to transfer electrons and when sessile cells are very close to the iron surface, conductive nanowires (pili) will be secreted to link cells to the iron surface only when the SRB culture medium is deficient of organic carbon. MET utilizes soluble electron shuttles (electron mediators or electron carriers) such as H+/H₂, ribofavin and favin adenine dinucleotide (FAD), which are redox-active chemicals. EET is a bottleneck for electron transfer in MIC on the macrolevel (Li and Ning 2019).

The Extracellular Electron Transfer Microbiologically Influenced Corrosion (EET-MIC) model is a recent approach that has been proposed to explain the underlying mechanisms of MIC. According to this model, MIC occurs when microorganisms use metal as an electron acceptor in their respiratory chain. This process involves extracellular electron transfer (EET) from the metal surface to the microorganisms, which are typically members of biofilms. The EET-MIC model proposes that the electrons are transferred through conductive nanowires or pili, which are specialized appendages that allow the microorganisms to connect with the metal surface (Gu et al. 2019). One key aspect of the EET-MIC model is the role of biofilms. The EET-MIC model suggests that biofilms are essential for MIC to occur, as they provide a platform for electron transfer between the metal surface and the microorganisms. The model proposes that the microorganisms in biofilms use EET as a mechanism for energy generation, in addition to traditional metabolic pathways. Another important feature of the EET-MIC model is the role of microbial diversity. The model suggests that MIC is a complex process that involves multiple microorganisms, each with its specific metabolic capabilities. The model proposes that the diversity of microorganisms in biofilms is crucial for MIC to occur, as it allows for a range of metabolic pathways that can contribute to the corrosion process (Gu et al. 2019). Additionally, the model suggests that the diversity of microorganisms can influence the formation and composition of biofilms, which can, in turn, impact the corrosion process.

The EET-MIC model provides a new framework for understanding the mechanisms of MIC and has important implications for corrosion control strategies. The model suggests that targeting specific microorganisms or metabolic pathways may be a promising approach for mitigating MIC. Additionally, the model emphasizes the importance of understanding the role of biofilms in MIC and the need to develop methods for controlling biofilm formation and composition. Despite the potential of the EET-MIC model, further research is needed to fully understand the underlying mechanisms of MIC and the factors that influence microbial diversity and biofilm formation. In the last few years, there has been significant progress in comprehending the mechanisms of SRB MIC due to well-planned and executed mechanistic investigations. A recent study on the modification of a gene encoding an electron mediator has provided clarity on EET in aerobic P. aeruginosa MIC against stainless steel (Jia et al. 2017). Therefore, it is anticipated that in the future, convincing genetic-level proof will emerge for EET-MIC caused by SRB. However, manipulating the genetics of SRBs is a more challenging task.

Extracellular polymer substance (EPS)

Beech and colleagues suggested that EPS containing iron has a positive effect on promoting the corrosion process (Beech et al. 2006). Since SRBs are anaerobes, they require an anaerobic environment to grow. In an open-air system, SRBs can grow beneath an aerobic biofilm that provides a locally anaerobic environment.

Sulphate Reducing Bacteria biofilm consists of proteins with minimal EPSs. Few research works have been conducted on genes and proteins on a molecular level in SRB other than Desulfovibrio vulgaris Hildenborough (DVH) and Desulfovibrio alaskensis G20 (DA-G20) (Tripathi et al. 2021)

Dong and colleagues (Dong, Liu, et al. 2011) found that low concentrations of EPS secreted by SRB could inhibit, while the high concentration of EPS enhanced the corrosion of carbon steel. The effect of EPS produced by various bacteria and archaea on metal corrosion varies considerably. Some studies have shown that certain functional groups in EPS can complex metal ions like iron and copper, thereby accelerating the anode’s dissolution and corrosion (Jin and Guan 2014). Conversely, other studies have found that EPS attachment has an inhibitory effect on metal corrosion. This is because EPS adsorbs onto the material’s surface and forms a protective film. The negatively charged groups in EPS chelate metal cations like Ca²⁺ and Mg²⁺ in the solution, create a dense protective film on the material surface that reduces cathodic polarization and, thus, inhibits corrosion (Stadler et al. 2008).

Biocatalytic cathodic Sulphate reduction (BCSR)

Traditionally, MIC studies search for a physical anode and a cathode (2009). In opposite, Gu et al. (2009) proposed the BCSR theory, which suggested that the utilization of electrons from iron oxidation by sulfate reduction in SRB cells is the cause for MIC due to SRB. The mechanism is diagrammatically shown in Figure 5.

Figure 5. Schematic illustration of BCSR microbially influenced corrosion mechanism (Gu et al. 2019).

Reactions (2) and (3) make use of sulfate as the terminal electron acceptor to explain the energy production of MIC due to SRB

Anodic: Fe → Fe²⁺ + 2e^- (3)

Cathodic: SO₄^2- + 9H⁺ + 8e- → HS^- + 4H₂O (4)

The half-reaction in Reaction (3) shows the consumption of protons. However, there is no net consumption or generation of protons in the overall sulfate reduction pathway with organic carbon donating the electron in D. vulgaris.

It was found that upon being starved of an organic carbon source, SRB biofilms increased corrosion rates despite the starvation leading to reduced sessile cell densities on carbon steel coupons. This was due to the biofilms using to elemental iron as a substitute for organic carbon as an electron donor.

Effect of SRB on various types of steel

Sulphate-reducing bacteria (SRB) are known to cause corrosion and degradation of a wide range of metallic structures, including various types of steel (Wang et al. 2014). The specific effects of SRBs on different types of steel can vary depending on factors such as the chemical composition and surface properties of the metal, as well as the growth conditions and metabolic activity of the bacteria. One of the most studied types of steel for SRB-induced corrosion is carbon steel (Zhang et al. 2023). Carbon steel is widely used in industrial applications and is particularly susceptible to corrosion by SRBs due to its high iron content and relatively low resistance to environmental factors such as pH and temperature. In addition to carbon steel, other types of steel that are vulnerable to SRB-induced corrosion include stainless steel, low-alloy steel, and galvanized steel. The specific mechanisms underlying the effects of SRBs on different types of steel can vary depending on the type of steel and the conditions of exposure. In general, however, SRBs can cause damage to steel through a combination of direct and indirect mechanisms. Direct mechanisms may include the production of corrosive metabolites such as H₂S (Zhang et al. 2023), while indirect mechanisms may involve the formation of biofilms and the inhibition of protective coatings and inhibitors. The effects of SRB-induced corrosion on different types of steel can have significant economic and safety implications in industrial settings. In the oil and gas industry, for example, corrosion of pipelines and other equipment due to SRBs can lead to costly repairs and shutdowns, as well as safety hazards such as leaks and explosions. Understanding the specific effects of SRBs on different types of steel is therefore critical for developing effective strategies to prevent and mitigate corrosion in these settings. MIC is a challenge across various materials and metal grades, as seen in example studies conducted on 1010 carbon steel (Wang et al. 2014), aluminum alloys (Rosales and Iannuzzi 2008), copper and copper alloys (Liu, Xu, et al. 2018), API 5LX70 carbon steel (Liu and Cheng 2017), 1018 carbon steel (Jia, Tan, et al. 2018), etc. In the upcoming sections, the ways MIC caused due to SRB acts on various types and grades of steel were reviewed.

Carbon steel/X70/X80

Carbon steel is one of the most widely used materials in the construction of pipelines and other industrial structures. However, its susceptibility to corrosion by SRBs has led to significant challenges in maintaining the integrity and safety of these structures (Islam and Rashed 2019). In recent years, researchers have focused on developing new materials with better resistance to SRB-induced corrosion. One such material is X70 steel, which has better resistance to corrosion than carbon steel. X80 steel, another high-strength steel, has even better resistance to corrosion than X70 steel. Several studies have investigated the effect of SRBs on carbon steel, X70 steel, and X80 steel. These studies have used different techniques, such as electrochemical measurements, weight loss analysis, and surface characterization, to evaluate the extent of corrosion induced by SRBs. The results of these studies have shown that the corrosion rate of carbon steel is higher than that of X70 and X80 steel. Furthermore, X80 steel is the most resistant to SRB-induced corrosion among the three types of steel. In addition to investigating the effect of SRBs on these types of steel, researchers have also developed mathematical and computational models to predict the corrosion rate of these materials under different environmental conditions (Torres and de França 2002). These models have been used to optimize the design of pipelines and other industrial structures, taking into account the potential for SRB-induced corrosion. Overall, the studies on carbon steel, X70 steel, and X80 steel have highlighted the importance of developing materials with better resistance to SRB-induced corrosion. The use of X70 and X80 steel has shown promise in mitigating the effects of SRB-induced corrosion in industrial applications. However, further research is needed to fully understand the mechanisms of corrosion and to develop more effective materials and mitigation strategies. Table 2 presents SRB corrosion in various media and the findings.

Table 2. SRB corrosion in various materials and medium and their findings.

Medium	Material	Comments	Ref
Simulated sewage	concrete	The biological sulfuric acid produced by microbial metabolism corrodes concrete to produce gypsum and other corrosion products.	(Wang et al., 2022)
Artificial seawater	Ti-6Al-4V	The evolution of corrosion pits and the maximum pit depth enhanced with the increase of electron shuttle concentration	(Xu et al., 2023)
Culture medium	Stainless steel	Exogenous flavins promote iron-to-microbe electron transfer, accelerating microbiological corrosion	(Jin et al., 2024)
Seawater	09CrCuSb low alloy steel (LAS) and Q235 carbon steel (CS)	Fewer sessile cells on the steel surface led to a lower extracellular electron transfer (EET) rate so less corrosion occurred. The combined effect of alloying elements on grain refinement and passive film formation also improved the anti-corrosion property of the steel.	(Lu et al., 2023)
Simulated shale gas production environment	X65 pipeline steel	The synergistic effect of sand deposit and sulfate-reducing bacteria was investigated. the formation of biofilm altered the protectiveness of the surface mixed layer	(Liao et al., 2023)
Seawater	Concrete	Abundant DO (∼4 mg/L) would enhance the compressive strength of bacterial corroded concrete in the early stage by generating products of gypsum and ettringite. While the excessive products destruct the inner structure of concrete and restrain the compressive strength.	(Sun et al., 2023)
Soil solution	X80 steel	Uniform corrosion dominated the corrosion process of the control coupon, while the local corrosion sensibility of the corrosion products (CP) and microbial film (MF) coupons increased in the same environment.	(Zhou et al., 2021)
Artificial Marine Environments	Ni-P-Ag Coatings	Ni-P-Ag could not inhibit the growth of SRB, which were still able to proliferate within a short period	(Yang et al., 2020)
Groundwater	AISI304 and 316	Higher corrosion rates of steel coupons were detected in the higher alloyed stainless-steel grade 316 compared to the lower alloyed grade 304. Pitting was the main corrosion form.	(Rajala et al., 2022)

Carbon steel is an important material used in several industries, such as the marine industry and the petroleum industry. This is primarily due to its cost-effectiveness, toughness, strength, and efficiency (Islam and Rashed 2019). Despite this, carbon steel is relatively limited in its resistance to MIC, which has led to several failures and economic losses (Rios et al. 2015). This is also concerning the environment that carbon steel is usually used in, which is usually facilitator of EPS. Carbon steel is also relatively less resistant to MIC and is susceptible to accelerated corrosion after the initial corrosion products have formed (Javaherdashti 2009). Torres and De Franca observed that the concentration of oxygen directly influences the number of SRB formed in biofilms when carbon steel was exposed to seawater (Torres and de França 2002). In a different study by Liduino and colleagues, a similar result was observed, where the highest corrosion rates due to SRB were noted in conditions of water with high dissolved oxygen content (Liduino et al. 2021). Thus, the presence of SRB biofilms enables different corrosion products and altered the kinetic behavior of the system, while acting as a biocatalyst to increase the corrosion rate significantly (AlAbbas et al. 2013). Two popular kinds of carbon steel used are of the X70 and X80 kind. They both undergo different levels of corrosion, and studies have shown that X70 undergoes a higher rate of corrosion as compared to X80 in similar conditions. For example, Cai and colleagues conducted a comparative analysis of the effect of Desulfovibrio desulfuricans-induced corrosion behaviors in pipeline steels and found that the relative pitting severity (RPS) value of X70 was 32.52, a value much larger than the 11.64 RPS value of X80 (Cai et al. 2022). Through microstructure analysis, they proposed this to be due to the less uniform structure of X70 when compared to other types of steel. As carbon steel is frequently used in the marine industry, the limitation of organic carbon sources also results in the acceleration of corrosion due to SRB, confirmed by other researchers (Xu and Gu 2014; Dou et al. 2019). Xu and colleagues conducted a study on MIC of carbon steel by bacterium Desulfotomaculum nigrificans under carbon starvation conditions and found that the corrosion rate due to SRB varied between 6 to 7.7 times as that of the control (Liu et al. 2019). In such situations of carbon limitation, SRB may directly use the electrons of the metal for corrosion, causing a synergistic effect of carbon starvation and redox mediation on the corrosion. Guan and colleagues studied the corrosion due to Desulfovibrio singaporenus on X70 pipeline steel and found a corrosion rate increase of up to 1.87 times in case of 100% carbon starvation (Cai et al. 2022). Aside from the above, mild steel typically shows pitting corrosion with rates varying from 0.13 mm/year to 0.45 mm/year based on on-site studies (Jiang et al. 2020), with under-deposit corrosion being the main cause for further corroding of the metal.

Stainless steel (SS)

SRB has been widely identified as a major bacterial group causing corrosion in SS. Colonization of SRB on SS has been found to cause micro-pitting corrosion, by depleting iron and enriching chromium (Williams et al. 2010). The adhesion of SRB onto the SS surface seems to be influenced by the surface free energy of the metal, as observed by Liang et al., where SS showed more affinity towards SRB interaction as compared to hydrophobic interaction. This further leads to the use of inhibitors that decrease the hydrophobic interaction while increasing the surface free energy, to prevent SRB adhesion (Liang et al. 2018). Possible forms of corrosion seen in SS may be etching of the metal, pitting, and crevice attack, as reported by Antony and others on investigating the corrosion of 2205 duplex SS in a chloride medium containing SRB (Antony et al. 2007). This may also be influenced by pH, with increasing pH slowing down the process of corrosion of metals, as seen in a study on the corrosion behavior of 2205 duplex SS in artificial acidic seawater (Tran et al. 2021). Aside from the 2005 duplex SS, other grades of SS such as 316 L (Xu et al. 2007, 2008; C. Wu et al. 2021), 304 SS (Ismail et al. 1999), and SS AISI 316 (Sheng et al. 2007) have been studied, although to a lesser degree. Table 3 presents the various types of bacteria affecting the different types of steel and the type of corrosion caused.

Table 3. Different types of corrosion caused by various bacteria on steel.

Metal	Bacteria/medium	Type of Corrosion	Reference
X52 carbon steel and 2205 stainless steel	Sulfate-reducing bacteria	Galvanic corrosion	(Wan et al. 2023)
X80 steel	Sulfate-reducing bacteria in seawater	MIC	(Liu et al. 2022)
X70 pipeline steel	Sulfate-reducing bacteria	Stress corrosion	(Sun et al. 2023)
X70 steel	Sulfate-reducing bacteria/marine mud environment	Stress corrosion	(Xie et al. 2023)
X80 steel	Sulfate-reducing bacteria/injected water simulation solution	Corrosion	(Zhao et al. 2022)
Carbon steel	Sulfate reducing bacteria/Test medium	Under-deposit corrosion (UDC)	(Yang et al. 2022)
X80 pipeline steel	Sulfate reducing bacteria	Corrosion	(Wei et al. 2021)
Q235 carbon steel	Sulfate reducing bacteria/	Corrosion	(Li et al. 2022)
X80 steel	Sulfate-reducing bacteria/coastal saline soil	Corrosion	(Qin et al. 2021)
X80 pipeline steel	Sulfate-reducing bacteria	Stress corrosion cracking	(Xie et al. 2021)
X80 carbon steel	sulfate reducing bacteria/ simulated seawater	Crevice corrosion	(Zhang et al. 2021)
980 high-strength steel	Sulfate-reducing bacteria/seawater	Stress corrosion cracking	(Lv et al. 2021)
2205 duplex stainless steel	Sulfate-reducing bacteria	Stress-assisted microbiologically influenced corrosion	(Yang et al. 2020)
X100 steel	Sulfate reducing bacteria/sea mud environment	Stress-assisted corrosion behaviour	(M. Wu et al. 2021)

Crevice corrosion and SRB

Crevice corrosion is one of the most common types of corrosion seen in metals, especially in steel. Crevices have environments that are oxygen-free and of low pH environment, making it vulnerable to corrosion. The pH value, the oxygen content of the environment, temperature (Jakobsen and Maahn 2001) and geometric size of the crevice has an impact on the rate of crevice corrosion (Chang et al. 2000). Crevice width is also an important factor since the crevice environment changes with differences in crevice width, influencing the activity and reproductive behaviour of SRB (C. Wu et al. 2021). Yet, there are only a few studies conducted on the effect of SRB on SS corrosion in crevice conditions.

Mechanistic models developed

Mechanistic models are often a preferred method of predicting MIC rate because they provide straightforward predictions. However, this makes mechanistic models more rigid as they require many assumptions to reduce their complexity. Over the years, the development of mechanistic models to investigate the progression of microbiological corrosion rate has been preferred owing to their straightforward predictions (Dawuda et al. 2021). The modelling of corrosion loss and maximum pit depth over the short term and long term has a number of applications, including (i) enabling interpretation of on-site corrosion rates to understand the significance, (ii) estimation and prediction of corrosion rates in the future, and (iii) assessment of acceptable corrosion rates (Melchers and Jeffrey 2008). The development of these models would help in mitigating MIC by managing corrosion risk. A numerical model explaining SRB-induced MIC in anaerobic environments of water bodies including marine and freshwater (Al-Darbi et al. 2008), with CDT being the model basis was developed by Al-Darbi et al. On a similar note, Xu and others adopted the BCSR theory to develop a mechanistic model of biocorrosion due to biofilms, using charge transfer and mass transfer concepts (Xu et al. 2016). These models enable long-term planning and worst-case scenarios after making use of short-term pit depth data caused due to SRB, enabling the simulation of various parameters, and enabling mathematical modelling (Anguita et al. 2022).

In most research investigations, the prediction modes for are divided into empirical models, semi-empirical models, and mechanism models based on the type of mechanism involved and the degree of the mechanism involved (Wang et al. 2023). Further, with the development of information technology, data-driven models based on Machine Learning (ML) have progressively succeeded. The development history of the models can be explained by Figure 6. High-throughput computing combined with ML also shows immense potential (Wang et al. 2023)

Figure 6. Evolution of various model types (Wang et al. 2023).

Molecular modelling techniques were also used to capture the presence and activities of microorganisms. Molecular Microbiological Methods (MMM) monitor microorganisms’ distribution and help analyze MIC risk factors and pitting corrosion rates. These calculations estimate the number of MIC microorganisms with reaction stoichiometric and electron flow (Bernt et al. 2012). A CDT mathematical model represents the role of SRB in pit development (Wang et al. 2000). This model included bacterial consumption of sulfate as a boundary condition and described pit geometry in cylindrical coordinates with rotational symmetry about the longitudinal axis (Wang et al. 2000). In the past decade, MIC has been increasingly recognized as a significant form of corrosion. However, detecting and predicting MIC can be challenging due to the complex behaviour of microorganisms involved. Current MIC risk assessment models consider the interdependencies of various parameters and their synergistic interactions. A data-driven approach is needed to utilize available operational and microbiological data and continuously learn as the data changes. In this study, a model is proposed to strengthen the correlation of variables and their features to assess the likelihood of MIC. The model can integrate field and laboratory data into a Learning-based Bayesian network (LBN) model (Kamil et al. 2021).

Conclusion

In conclusion, this review has provided a critical analysis of the impact of sulfate-reducing bacteria (SRB) induced corrosion on steel. The study revealed that SRB-induced corrosion has significant consequences for the environment and infrastructure and that the mechanisms underlying this phenomenon are complex and multifaceted. Through an examination of the available literature, it can be found that biofilm formation plays a crucial role in the corrosion process and different types of steel exhibit varying degrees of susceptibility to SRB-induced corrosion. Furthermore, this review has presented several different mathematical and mechanistic models used to study SRB-induced corrosion and discussed their strengths and limitations. Although there is still much to learn about the mechanisms of SRB-induced corrosion, these models have proven to be valuable tools for understanding and predicting the behavior of SRBs in different environments.

Looking ahead, further research is needed to refine our understanding of the biological, chemical, and physical factors that contribute to SRB-induced corrosion, and to develop new methods for detecting and preventing this phenomenon. These efforts will require interdisciplinary collaborations between biologists, chemists, engineers, and other experts, and will be crucial for ensuring the long-term sustainability of our infrastructure and the environment. In summary, this review highlights the importance of continued research into SRB-induced corrosion and provides a valuable resource for researchers and practitioners working in this area. By deepening the understanding of this phenomenon and its underlying mechanisms, the challenges posed by SRB-induced corrosion can be addressed and effective strategies for mitigating its impact can be developed.

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