Analysis of China's olefin industry using a system optimization model considering technological learning and energy consumption reduction

Xu, Z., Fang, C., & Ma, T. (2020). Analysis of China's olefin industry using a system optimization model considering technological learning and energy consumption reduction. Energy 191 e116462. 10.1016/j.energy.2019.116462.

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Abstract

Recently, China has been increasingly producing olefins from alternative resources, especially coal. Technological learning, energy consumption reduction, and environmental policies/regulations will have a great impact on the economic and environmental values of coal-to-olefin (CTO) projects. How should China configure its future olefin industry considering these factors? Little work has been performed to explore this question. This study develops a system optimization model to analyze the optimal configuration of China's olefin industry under different scenarios of technological learning, energy consumption reduction, and environmental policies/regulations. Our results show that in all scenarios, the oil-to-olefin process will remain dominant in China's olefin industry in the next two decades, and with technological learning, the CTO process is competitive in China's olefin industry, especially when CO2 emissions are not controlled. To control the CO2 emissions of China's olefin industry, our study indicates that requiring CTO implementation along with carbon capture and storage (CCS) would have both economic and environmental value compared with imposing a carbon tax (assume 20$/t CO2 from the year 2021). However, policymakers should be cautioned about the uncertainties and risks of CCS. This study also provides some insights for those who are considering investing in China's olefin industry.

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