  The persistent depletion of fossil fuels has encouraged mankind to look for
alternatives fuels that are renewable and environment-friendly. One of the
promising and renewable alternatives to fossil fuels is bio-diesel produced by
means of the batch transesterification process. Control of the batch
transesterification process is difficult due to its complex and non-linear
dynamics. It is expected that some of these challenges can be addressed by
developing control strategies that directly interact with the process and
learning from the experiences. To achieve the same, this study explores the
feasibility of reinforcement learning (RL) based control of the batch
transesterification process. In particular, the present study exploits the
application of twin delayed deep deterministic policy gradient (TD3) based RL
for the continuous control of the batch transesterification process. These
results showcase that TD3 based controller is able to control batch
transesterification process and can be a promising direction towards the goal
of artificial intelligence-based control in process industries.
