IRORERE ANDREW OSAYANDE

Corrosive damage remains a critical issue across various industries, especially in remote oil and gas pipeline infrastructures.This study presents the design and implementation of anIoT-based wireless sensor network (WSN) integrated with machine learning Model (SVM) for corrosion monitoring and prediction. The system architecture involved deploying sensor nodes utilizing electromagnetic techniques for real-time corrosion data acquisition. These nodes communicated with an ESP32 microcontroller equipped with wireless transmission capabilities to relay data to the Thing Speak cloud platform for storage and visualization. Subsequently, MATLAB was used to preprocess the acquired data, enabling the training and validation of a supervised machine learning model for corrosion classification and prediction. With the help of the SVM model, corroded pipeline samples could be easily dif erentiated from a corrosion-free pipeline. 80% of the recorded data was used to train the algorithm, and the rest 20% was kept for testing the data without corrosion. The first graph displayed by the model shows that the resistance values from the corroded sample fluctuate only slightly over time Additionally, the chlorine level ranged between (1000–1500)ppm, showing emission of chlorine gas from the sample. There was a significant drop in resistance in the corrosion- free sample for the second graph, with values falling below 1000ohms and No chlorine data was indicated When the model was tested and validated, the model correctly classified 59 out of 60 test samples whileone incorrectly indicating an accuracy of 98.33%.. When unseen samples were used, the model was still able to predict the presence of corrosion with almost the same amount of precision and gave results showing the state of the pipelines with a 50% chance of them being either corroded or not from a 40 sample prediction.. The results obtained af irm the ef ectiveness of both processes for corrosion monitoringinremote pipeline networks. The system’s autonomous operation, real-time data handling, and intelligent decision-making capabilities highlight its potential as a cost-ef ective and ef icient
alternative to traditional, labor-intensive methods. Moreover, its predictive capabilities enable proactive maintenance scheduling and safer operational planning, significantly reducing the risk of pipeline failure. This research thus lays a strong foundation for scalable, field-deployable corrosion monitoring systems leveraging modern IoT and AI tools

Corrosive damage remains a critical issue across various industries, especially in remote oil and gas pipeline infrastructures.This study presents the design and implementation of anIoT-based wireless sensor network (WSN) integrated with machine learning Model (SVM) for corrosion monitoring and prediction. The system architecture involved deploying sensor nodes utilizing electromagnetic techniques for real-time corrosion data acquisition. These nodes communicated with an ESP32 microcontroller equipped with wireless transmission capabilities to relay data to the Thing Speak cloud platform for storage and visualization. Subsequently, MATLAB was used to preprocess the acquired data, enabling the training and validation of a supervised machine learning model for corrosion classification and prediction. With the help of the SVM model, corroded pipeline samples could be easily dif erentiated from a corrosion-free pipeline. 80% of the recorded data was used to train the algorithm, and the rest 20% was kept for testing the data without corrosion. The first graph displayed by the model shows that the resistance values from the corroded sample fluctuate only slightly over time Additionally, the chlorine level ranged between (1000–1500)ppm, showing emission of chlorine gas from the sample. There was a significant drop in resistance in the corrosion- free sample for the second graph, with values falling below 1000ohms and No chlorine data was indicated When the model was tested and validated, the model correctly classified 59 out of 60 test samples whileone incorrectly indicating an accuracy of 98.33%.. When unseen samples were used, the model was still able to predict the presence of corrosion with almost the same amount of precision and gave results showing the state of the pipelines with a 50% chance of them being either corroded or not from a 40 sample prediction.. The results obtained af irm the ef ectiveness of both processes for corrosion monitoringinremote pipeline networks. The system’s autonomous operation, real-time data handling, and intelligent decision-making capabilities highlight its potential as a cost-ef ective and ef icient
alternative to traditional, labor-intensive methods. Moreover, its predictive capabilities enable proactive maintenance scheduling and safer operational planning, significantly reducing the risk of pipeline failure. This research thus lays a strong foundation for scalable, field-deployable corrosion monitoring systems leveraging modern IoT and AI tools

Industrialization is the backbone of any meaningful global development while material selection forms the basis or bedrock for industrial development and improvements (Ashby & Johnson, 2013). In the industries, there are key aspect of industrial processes among which is the transportation of materials (Roemer, 1979). Getting the right selection of materials for transporting a specific and the right kind of materials is one of the challenges that has bedeviled the industrial sector, which the engineers have been looking for ways to proffer solutions to this humongous challenge for several decades (Constable & Somerville, 2003). A lot of research has been done in getting materials that are corrosion resistant that is, having high resistance to
chemical attacks (Mévrel, 1989). Nonetheless, this has not been eradicated as we still have lots of materials that are still highly corrosive. In the oil and gas sector for example, both the crude materials and refined products are mostly transported through pipes over long distance (Koch, 2017).