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The online optimization of gasoline blending benefits refinery economies.However, the nonlinear blending mechanism, the oil property fluctuations, andthe blending model mismatch bring difficulties to the optimization. To solvethe above issues, this paper proposes a novel online optimization method basedon deep reinforcement learning algorithm (DRL). The Markov decision process(MDP) expression are given considering a practical gasoline blending system.Then, the environment simulator of gasoline blending process is establishedbased on the MDP expression and the one-year measurement data of a real-worldrefinery. The soft actor-critic (SAC) DRL algorithm is applied to improve theDRL agent policy by using the data obtained from the interaction between DRLagent and environment simulator. Compared with a traditional method, theproposed method has better economic performance. Meanwhile, it is more robustunder property fluctuations and component oil switching. Furthermore, theproposed method maintains performance by automatically adapting to systemdrift.

Reinforcement Learning Based Gasoline Blending Optimization: Achieving More Efficient Nonlinear Online Blending of Fuels