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.

翻译：在能源与制造系统中，基于神经网络的优化与控制方法已逐步取代基于第一性原理的模型驱动方法，这得益于其高效、数据驱动的过程建模能力，且所需资源更少。然而，神经网络的非凸特性显著降低了优化与控制过程的速度，限制了其在实时决策中的应用。为应对这一挑战，我们提出了一种新颖的输入凸长短期记忆网络，以提升基于神经网络的优化的计算效率。通过两个采用实时神经网络优化方法对能源与化工系统进行优化的案例研究，我们证明了基于ICLSTM的优化在运行时间方面的优越性能。具体而言，在新加坡LHT控股公司一个实际太阳能光伏能源系统的实时优化问题中，基于ICLSTM的优化相比传统基于LSTM的优化实现了8倍的加速。这些结果凸显了ICLSTM网络在实际应用中显著提升基于神经网络的优化与控制效率的潜力。源代码可在https://github.com/killingbear999/ICLSTM获取。