Size and Cost Optimization of AutoCAD Oil and Gas Control Flow Designs Using Constraint Satisfaction Problem and Machine Learning - Kore, HA; Mane, SB; Madkaikar, A (International Journal of Computer Sciences and Engineering, 2025)
The aim of automating the task of generating flow control designs for oil and gas flow comes with the different dimension constraints and cost factors. The resulted designs should satisfy given dimension boundaries and pre- specified conditions. To make the module more efficient and automate we combine the machine learning and constraint satisfaction module which resulted in reduction of time complexity and how the accuracy gets maintained. The result shows how the separate module of machine learning and optimization module work and how the results get vary when we combine both modules. The constraint we want to optimize are size and cost of the design. The main factors we considered for measuring performance are time complexity and accuracy.