## 2 Materials and Methods  
### Historical Field Data Input  
The historical field data was initially collected from three gas processing plants (GPP) with a total of 11,000 m of pipe length on the east coast of the Malaysia Peninsula (5\({}^{\circ}\)50\({}^{\circ}\)30\({}^{\circ}\) N, 104\({}^{\circ}\)07\({}^{\circ}\)3, 7\({}^{\circ}\)30\({}^{\circ}\) E) near the South China Sea which experiences equatorial climate. Both visual inspection data and wall thickness data types were involved in the historical field data collection after the removal of insulation on the piping surface. In visual inspection data, CUI data were treated as binary data. When CUI was found, data was denoted as 1, and 0 when CUI was not found. An example of visual inspection data collected from the field where CUI was heavily detected is shown in Figure 1.  
Figure 1: Corroded pipes found after the removal of insulation at some of the field inspection locations. For wall thickness data, the detailed value for nominal thickness, \(Nt\) in millimeters, and actual thickness (average wall thickness), \(At\) in millimeters, and year in service denotes by \(Y_{service}\) were recorded to calculate the field CUI rate, \(CR_{f}\) in mm/year as given in Equation (1). \[CR_{f}=\frac{Nt-At}{Y_{service}}. \tag{1}\]  
Other than that, the field data were configured and classified for each parameter which involves the six groups of operating temperature (less than \(-12\)\({}^{\circ}\)C, \(-12\)\({}^{\circ}\)C to \(16\)\({}^{\circ}\)C, \(17\)\({}^{\circ}\)C to \(48\)\({}^{\circ}\)C, \(49\)\({}^{\circ}\)C to \(93\)\({}^{\circ}\)C, \(94\)\({}^{\circ}\)C to \(121\)\({}^{\circ}\)C, and above than \(121\)\({}^{\circ}\)C) as prescribed in API 581 practices, age of the pipe (15 to 20 years of pipe age), type of insulation (cellular glass, calcium silicate, perlite and rockwool), design of pipe (with elbow or no elbow in pipe design), pipe material (carbon steel or stainless steel), and small (less than 2 inches pipe diameter) or big bore pipe (more than 2 inches pipe diameter). For field data analysis and simulation, the CUI data recorded were then compared with the plant design and operating data to ensure the CUI collected data were satisfactory for further analysis. Data were tabulated and analyzed using pivot table functions in Microsoft Excel. As the input data from the field were still limited in generating an effective final CUI prediction model, results obtained were simulated through the prediction profiler simulator function in Statistical Discovery JMP 10.0 2012 SAS Integration 9.1.3 Server software package. The steps are simplified as:  
1. Determination of artificial neural network (ANN) parameters: Available parameters were operating temperature (\({}^{\circ}\)C), insulation type, pipe design, piping thickness (mm), type of pipe material, type of environment (weather condition either rainy or sunny), elapsed time (years), and CUI rate (mm/year) data. This surface profiler is suitable when the data contain more than one continuous factor. In this study, the operating temperature and corrosion rate were set as a continuous factor. 2. Definition of ANN architecture: The basic structure of the ANN was identified and determined. The layer of hidden nodes, the input parameter, and the output parameter were clearly prescribed. For this study, one hidden layer was used, and a hyperbolic tangent function (TanH) with a sigmoid type was used as an activation function for the ANN test. 3. Data normalization, filtration, and initialization: Field data collected from the actual plant in Kerteh, Malaysia, were simulated. Before that, the incomplete, missing information from the database or outlier values were removed to ensure only significant data were included. Usually, the field data are collected without the thickness measurement. Thus, data were simulated to generate sufficient data to continue with normalization and filtration.