  Over the past three decades, numerous controllers have been developed to
regulate complex chemical processes, but they have certain limitations.
Traditional PI/PID controllers often require customized tuning for various
set-point scenarios. On the other hand, MPC frameworks involve
resource-intensive steps, and the utilization of black-box machine learning
(ML) models can lead to issues such as local minima and infeasibility. Thus,
there is a need for an alternative controller paradigm that combines the
simplicity of a PI controller with the grade-to-grade (G2G) transferability of
an MPC approach. To this end, we developed a novel LSTM controller (LSTMc) as a
model-free data-driven controller framework. The LSTMc considers an augmented
input tensor that incorporates information on state evolution and error
dynamics for the current and previous $W$ time steps, to predict the
manipulated input at the next step ($u_{t+1}$). To demonstrate LSTMc, batch
crystallization of dextrose was taken as a representative case study. The
desired output for set-point tracking was the mean crystal size ($\bar{L}$),
with the manipulated input being the jacket temperature ($T_j$). Extensive
training data, encompassing 7000+ different operating conditions, was compiled
to ensure comprehensive training of LSTMc across a wide state space region. For
comparison, we also designed a PI controller and an LSTM-MPC for different
set-point tracking cases. The results consistently showed that LSTMc achieved
the lowest set-point deviation ($<$2\%), three times lower than the MPC.
Remarkably, LSTMc maintained this superior performance across all set points,
even when sensor measurements contained noise levels of 10\% to 15\%. In
summary, by effectively leveraging process data and utilizing sequential ML
models, LSTMc offers a superior controller design approach.
