  Dynamic optimization of nonlinear chemical systems -- such as batch reactors
-- should be applied online, and the suitable control taken should be according
to the current state of the system rather than the current time instant. The
recent state of the art methods applies the control based on the current time
instant only. This is not suitable for most cases, as it is not robust to
possible changes in the system. This paper proposes a Deterministic Q-Learning
method to conduct robust online optimization of batch reactors. In this paper,
the Q-Learning method is applied on simple batch reactor models; and in order
to show the effectiveness of the proposed method the results are compared to
other dynamic optimization methods. The main advantage of the Q-learning method
or the proposed method is that it can accommodate unplanned changes during the
process via changing the control action; i.e. the main advantage of the
proposed method that it can overcome sudden changes during the reaction. In
general, we try to maximize the final product obtained or meet certain
specifications of the products (e.g. minimize side products).
