  Neural network-based optimization and control have gradually supplanted
first-principles model-based approaches in energy and manufacturing systems due
to their efficient, data-driven process modeling that requires fewer resources.
However, their non-convex nature significantly slows down the optimization and
control processes, limiting their application in real-time decision-making
processes. To address this challenge, we propose a novel Input Convex Long
Short-Term Memory (ICLSTM) network to enhance the computational efficiency of
neural network-based optimization. Through two case studies employing real-time
neural network-based optimization for optimizing energy and chemical systems,
we demonstrate the superior performance of ICLSTM-based optimization in terms
of runtime. Specifically, in a real-time optimization problem of a real-world
solar photovoltaic (PV) energy system at LHT Holdings in Singapore,
ICLSTM-based optimization achieved an 8-fold speedup compared to conventional
LSTM-based optimization. These results highlight the potential of ICLSTM
networks to significantly enhance the efficiency of neural network-based
optimization and control in practical applications. Source code is available at
https://github.com/killingbear999/ICLSTM.
