PBS 反應器之類神經網路模擬與模型預測控制器設計

Abstract

聚丁二酸丁二醇酯 (PBS) 是一種可生物降解的塑料，以其在各種應用中的強度和多功能性而聞名。 本研究提出了一種數據驅動的方法來模擬聚合過程中半間歇反應器中的溫度控制，並將所提出方法的性能與傳統控制器（包括 PID 控制和第一原理模型 MPC 控制）進行了比較。 該研究使用 Python 和 Tensorflow 開發了基於神經網絡模型的預測控制 (NNMPC) 和基於多重神經網絡模型的預測控制 (Multi-NNMPC)。 通過使用隱藏層中具有不同數量的神經元的廣泛動態數據來訓練神經網絡模型，以研究不同模型複雜性下的過程動態。 在標稱條件下，50 神經元 NNMPC 表現出測試結構中最有效的複雜性，絕對誤差積分 (IAE) 值為 2,104.77，20 神經元多 NNMPC 的性能略有改善，IAE 降低至 2,030.52，且控制動作呈重複趨勢。 MPC 控制。 這些方法解決了 PID 控制失敗的問題，該失敗導致超調和低效的設定點跟踪。 PID控制導致聚合物超規格，分子量幾乎達到14,000，IAE值為3,271.83。 相比之下，50 神經元 NNMPC 的最佳溫度控制方法可以執行嚴格的溫度控制並產生所需的聚合物性能，顯著優於 PID 控制。 這項研究還考慮了不確定條件，包括白噪聲的干擾和模型失配，所有控制方法都成功地處理了噪聲並保持溫度等溫，50 神經元 NNMPC 表現出比 PID 控制更小的閥門運動，增強了控製作用並提高了魯棒性 並減少公用事業消耗。 當引入模型失配來表示反應器結垢時，總傳熱係數降低了 30%，與其他控制器相比，50 個神經元 NNMPC 實現了控制變量更快地收斂到設定值。 它產生的 IAE 為 2,892.41，而 MPC 顯示的 IAE 為 3,009.59。 此外，神經網絡模型展示了高效學習高度非線性動力學的能力，與使用順序最小二乘規劃 (SLSQP) 方法的數學模型相比，預測最佳操縱變量的速度最高可達 5 至 20 倍。

Polybutylene succinate (PBS) is a biodegradable plastic known for its strength and versatility in various applications. This research presents a data-driven approach to simulate temperature control in a semi-batch reactor during polymerization, the performance of the proposed approaches was compared against conventional controllers, including PID control and first-principles model MPC control. The study developed neural network model-based predictive control (NNMPC) and multiple neural network model-based predictive control (Multi-NNMPC), using Python and Tensorflow. Neural network models were trained by using a wide range of dynamic data with varying numbers of neurons in hidden layers to investigate the process dynamics under different model complexities. Under nominal conditions, 50 neuron NNMPC demonstrated the most efficient complexity among the tested structures, exhibiting an Integral of Absolute Error (IAE) value of 2,104.77, 20 neuron Multi-NNMPC provided slightly improved performance as IAE reduced to 2,030.52 and the control action trended duplicating MPC control. These approaches addressed the failure of PID control, which caused overshoot and inefficient setpoint tracking. The PID control resulted in polymer over-specification, with the molecular weight reaching almost 14,000 and an IAE value of 3,271.83. In contrast, the optimal temperature control approach of the 50 neuron NNMPC could perform tight temperature control and yield the desired properties of the polymer, significantly outperforming PID control. This research also considers uncertain conditions, including the interference of white noise and model mismatch, all control approaches successfully handled the noise and maintained temperature isothermally, the 50 neuron NNMPC exhibited less aggressive valve movement than PID control, enhancing control action and leading to increased robustness and reduced utility consumption. When model mismatch was introduced to represent reactor fouling, reducing the overall heat transfer coefficient by 30%, the 50 neuron NNMPC achieved faster convergence of control variable to setpoints compared to other controllers. It yielded an IAE of 2,892.41, while MPC showed an IAE of 3,009.59. Moreover, the neural network model demonstrated the ability to learn highly nonlinear dynamics efficiently, enabling the prediction of optimal manipulated variables up to 5 to 20 times faster than a mathematical model using the Sequential Least Squares Programming (SLSQP) method.