In shale gas extraction, bottomhole liquid loading reduces gas well efficiency. Traditional time-based plunger lift methods use reservoir energy to remove liquid, but model-based optimization has since emerged. However, these methods, deployed on remote servers, lead to inefficient data transfer and high server loads. This study proposes an Adaptive Particle Swarm Optimization Model Predictive Control (APSO-MPC) for plunger lift optimization, implemented via edge computing. APSO dynamically adjusts inertia weights and learning factors, while a microprocessor-based edge architecture localizes computations at the controller, eliminating transmission delays and reducing server load. Simulations show APSO-MPC improves gas production by 18% compared to traditional methods, while edge computing increases data transmission by 24%, reduces packet loss by 83%, and lowers server memory and computational delays. [ABSTRACT FROM AUTHOR]

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