An attempt to promote intelligent petroleum processing: a look at how different programming languages works with deep learning models for gasoline quality detection

Abstract The current study explores how several programming languages affect deep learning models for gasoline quality detection, highlighting the significance of choosing the best appropriate language according to project needs. Featuring several scientific computations using an integrated development environment of the Visual Studio Code (VS Code) tool. This work has reached multiple outcomes: Python is known for its rapid development and adaptability, which are well-suited for exploring quality control techniques in petroleum products. Java is recognized for its stability and flexibility to work across many platforms, making it ideal for large-scale deployments that require dependable performance in refinery equipment. C/C++ is lauded for its speed and ability to manage hardware resources effectively, which is essential in real-time production settings, allowing for quick analysis and little delay in quality control processes. The study utilizes a deep learning architecture in Java, C, and Python, with frameworks such as Visual Studio Code to aid in model construction, with the goal of creating flexible tools for monitoring the dynamic quality of petroleum products.