A simple pseudo-dynamic surrogate model is developed in the framework of the state space model with the feed-forward neural network to replace the complex free radical pyrolysis model. The surrogate model is then applied to investigate the multi-objective optimization of two key performance objectives with distinct contradiction: the mean yields of key products and the day mean profits. The epsilon-constraint method is employed to solve the multi-objective optimization problem, which provides a broad range of operation conditions depicting tradeoffs of both key objectives. The Pareto-optimal frontier is successfully obtained and five selected cases on the frontier are discussed, suggesting that flexible operations can be performed based on industrial demands.