## 1 Introduction  
Carbon steel pipelines are the main facilities for submarine oil and gas transportation. The economic losses and environmental pollution caused by the failure of oil and gas transport pipelines are severe, and corrosion caused by the transportation medium is the main cause of pipeline failure, as shown in Figure 1. Therefore, studying the corrosion failure prediction model and method for submarine oil and gas pipelines is important to the safety of submarine oil and gas transportation. Figure 1: Corrosion of submarine oil and gas transport pipeline. Until recently, research on the corrosion failure prediction of carbon steel rigid oil and gas pipelines at home and abroad has undergone four stages [1]: an initial empirical model, a semi-empirical model, a mechanism model, and a corrosion failure prediction model established using machine learning algorithms. The initial empirical model was proposed based on experiments and field data [2]. Many improvements have been made since its establishment, such as parameter optimization and making prediction software easier to apply [3; 4; 5]. However, this type of model cannot be widely used in practice because it does not consider electrochemical reaction kinetics, which increases prediction error. Based on the empirical model, fluid dynamics and other factors must be considered to study the transfer law of corrosive mediums inflow [6]; this is the semi-empirical model. Continuous improvement involves considering the pH value of the medium, corrosion product film, and specific gravity of crude oil for prediction accuracy [7; 8]. With the development of computer technology, experts have combined computational methods with experimental and theoretical analysis to reveal the mechanism of corrosion and the established mechanism model. We considered the influence of factors such as the pH value, medium flow rate, and temperature on the reduction reaction of hydrogen ions, water, and oxygen at the cathode and the dissolution process of iron at the anode in the mechanism model [9; 10; 11; 12]. In addition, we propose dimensionless film-forming tendency factors based on the circulation experiment to establish a kinetic model of deposition growth for corrosion product films and better understand the corrosion process [13]. Since data processing has become easier and more efficient, researchers have used machine learning algorithms to analyze experimental or statistical data, building a variety of corrosion prediction models. These algorithms are widely used in numerical modeling and predicting interest fields [14; 15; 16]. [PERSON] et al. [17] established a prediction model based on a regression analysis and neural networks [18], where they analyzed the sensitivity of each influencing factor to pipeline corrosion failure. [PERSON] et al. [19] established a Markov model with non-uniform linear growth in continuous time to predict pit depth distribution after pipeline corrosion. [PERSON] et al. [20] proposed a new algorithm by determining which model of the pipeline's corrosion area could be established directly. A mechanistic model and an electrochemical model for the carbon dioxide corrosion of mild steel are proposed in [21; 22], respectively. [PERSON] et al. [23] conducted failure pressure prediction for pipelines containing corrosion defects based on a DE-BPNN model. However, a predictive model based on machine learning algorithms cannot obtain an explicit reaction formula; it has poor adaptability and requires repeated training. Although the empirical model and semi-empirical model have relational expressions between factors influencing corrosion and corrosion rate, these factors are not fully considered. Uniform corrosion is the main corrosion form of carbon steel oil and gas pipelines in an acidic solution. We discuss the influence of various factors on uniform corrosion rate in this paper based on carbon steel corrosion simulations and reactor experiments.