Naphtha Cracking Center Scheduling Optimization using Multi-Agent Reinforcement Learning

AAMAS Conference (2024)

Sunghoon Hong, Deunsol Yoon, Whiyoung Jung, Jinsang Lee, Hyundam Yoo, Jiwon Ham, Suhyun Jung, Chanwoo Moon, Yeontae Jung, Kanghoon Lee, Woohyung Lim, Somin Jeon (LG Chem), Myounggu Lee (LG Chem), Sohui Hong (LG Chem), Jaesang Lee (LG Chem)

Abstract

The Naphtha Cracking Center (NCC) is central to petrochemical feedstock production through the intricate process. It consists of receipt stage for unloading naphtha, blending stage for mixing  naphtha, and furnace stage for producing marketable products. It is crucial to make an optimal schedule for NCC for profitability and efficiency. Traditionally managed by human experts, challenges arise in predicting complex chemical reactions and navigating real-world complexities. To address these issues, this paper aims to develop autonomous NCC operation using multi-agent reinforcement learning, where each agent is responsible for each stage and collaborates to achieve common objectives, while adhering to real-world constraints. We developed an online web service to allow the staff in LG Chem Daesan NCC facility to obtain an NCC schedule in real-time, and the staff are now operating the facility based on the schedules generated by the online web service.