In the past three decades, proportional‐integral/PI‐differential (PI/PID) controllers and model predictive controller (MPCs) have predominantly governed complex chemical process control. Despite their advancements, these approaches have limitations, with PI/PID controllers requiring scenario‐specific tuning and MPC being computationally demanding. To tackle these issues, we introduce the long‐short‐term‐memory (LSTM)‐controller (LSTMc), a model‐free, data‐driven framework leveraging LSTM networks' robust time‐series prediction capabilities. The LSTMc predicts subsequent manipulated inputs by evaluating state evolution and error dynamics from both the current and previous time‐steps, which proved effective in our dextrose batch crystallization case study. Remarkably, the achieves less than 2% set‐point deviation, three times better than MPCs, and retains robustness even with 10%–15% sensor noise. With these results, LSTMc emerges as a promising alternative for process control, adeptly adjusting to changing process conditions and set‐points, providing efficient computation for an optimal input profile, and effectively filtering out common industrial process noise.

中文翻译：

---

机器学习与过程控制的结合：揭示 LSTMc 的潜力

在过去三十年中，比例积分/PI 微分 (PI/PID) 控制器和模型预测控制器 (MPC) 主要控制复杂的化学过程控制。尽管取得了进步，但这些方法仍然存在局限性，PI/PID 控制器需要针对特定​​场景进行调整，而 MPC 的计算要求很高。为了解决这些问题，我们引入了长短期记忆 (LSTM) 控制器 (LSTMc)，这是一种无模型、数据驱动的框架，利用 LSTM 网络强大的时间序列预测功能。 LSTMc 通过评估当前和之前时间步的状态演化和误差动态来预测后续的操纵输入，这在我们的葡萄糖批量结晶案例研究中被证明是有效的。值得注意的是，它的设定点偏差小于 2%，比 MPC 好三倍，并且即使在 10%–15% 的传感器噪声下也能保持鲁棒性。凭借这些结果，LSTMc 成为过程控制的一种有前途的替代方案，能够熟练地适应不断变化的过程条件和设定点，为最佳输入曲线提供有效的计算，并有效地滤除常见的工业过程噪声。