Toward intelligent and green ethylene manufacturing: an AI-based multi-objective dynamic optimization framework for the steam thermal cracking process

With growing concerns over environmental issues, ethylene manufacturing is shifting from a sole focus on economic benefits to an additional consideration of environmental impacts. The operation of the thermal cracking furnace in ethylene manufacturing determines not only the profitability of an ethylene plant but also the carbon emissions it releases. While multi-objective optimization of the thermal cracking furnace to balance profit with environmental impact is an effective solution to achieve green ethylene manufacturing, it carries a high computational demand due to the complex dynamic processes involved. In this work, artificial intelligence (AI) is applied to develop a novel hybrid model based on physically consistent machine learning (PCML). This hybrid model not only reduces the computational demand but also retains the interpretability and scalability of the model. With this hybrid model, the computational demand of the multi-objective dynamic optimization is reduced to 77 s. The optimization results show that dynamically adjusting the operating variables with coke formation can effectively improve profit and reduce CO2 emissions. In addition, the results from this study indicate that sacrificing 28.97% of the annual profit can significantly reduce the annual CO2 emissions by 42.89%. The key findings of this study highlight the great potential for green ethylene manufacturing based on AI through modeling and optimization approaches. This study will be important for industrial practitioners and policy-makers.