Naphtha Cracking Center Scheduling Optimization using Multi-Agent Reinforcement Learning

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.

Authors

• Sunghoon Hong
• Deunsol Yoon LG AI Research
• Whiyoung Jung LG AI Research
• Jinsang Lee LG AI Research
• Hyundam Yoo LG AI Research
• Jiwon Ham LG AI Research
• Suhyun Jung LG AI Research
• Chanwoo Moon LG AI Research
• Yeontae Jung LG AI Research
• Kanghoon Lee Korea Advanced Institute of Science and Technology
• Woohyung Lim LG AI Research
• Somin Jeon LG Chem
• Myounggu Lee LG Chem
• Sohui Hong LG Chem
• Jaesang Lee LG Chem
• Hangyoul Jang LG Chem
• Changhyun Kwak LG Chem
• Jeonghyeon Park LG Chem
• Changhoon Kang LG Chem
• Jungki Kim LG Chem

Keywords

• Multi-Agent Reinforcement Learning
• MacDec-POMDP
• Petrochemical Feedstock Production
• Agent-based Modeling and Simulation