Leeming Ryan, Mahmud Tariq, Roberts Kevin J, George Neil, Webb Jennifer, Simone Elena, Brown Cameron J
School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, U.K.
Syngenta, Jealott's Hill, Bracknell RG42 6EY, U.K.
Ind Eng Chem Res. 2023 Jul 3;62(28):11067-11081. doi: 10.1021/acs.iecr.3c00371. eCollection 2023 Jul 19.
Fine chemicals produced via batch crystallization with properties dependent on the crystal size distribution require precise control of supersaturation, which drives the evolution of crystal size over time. Model predictive control (MPC) of supersaturation using a mechanistic model to represent the behavior of a crystallization process requires less experimental time and resources compared with fully empirical model-based control methods. Experimental characterization of the hexamine-ethanol crystallization system was performed in order to collect the parameters required to build a one-dimensional (1D) population balance model (PBM) in gPROMS FormulatedProducts software (Siemens-PSE Ltd.). Analysis of the metastable zone width (MSZW) and a series of seeded batch cooling crystallizations informed the suitable process conditions selected for supersaturation control experiments. The gPROMS model was integrated with the control software PharmaMV (Perceptive Engineering Ltd.) to create a digital twin of the crystallizer. Simulated batch crystallizations were used to train two statistical MPC blocks, allowing for in silico supersaturation control simulations to develop an effective control strategy. In the supersaturation set-point range of 0.012-0.036, the digital twin displayed excellent performance that would require minimal controller tuning to steady out any instabilities. The MPC strategy was implemented on a physical 500 mL crystallizer, with the simulated solution concentration replaced by in situ measurements from calibrated attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy. Physical supersaturation control performance was slightly more unstable than the in silico tests, which is consistent with expected disturbances to the heat transfer, which were not specifically modeled in simulations. Overall, the level of supersaturation control in a real crystallizer was found to be accurate and precise enough to consider future adaptations to the MPC strategy for more advanced control objectives, such as the crystal size.
通过间歇结晶生产的精细化学品，其性质取决于晶体尺寸分布，这需要精确控制过饱和度，而过饱和度会随着时间推动晶体尺寸的演变。与基于完全经验模型的控制方法相比，使用机理模型来表示结晶过程行为的过饱和度模型预测控制（MPC）所需的实验时间和资源更少。对六亚甲基四胺 - 乙醇结晶系统进行了实验表征，以便收集在gPROMS FormulatedProducts软件（西门子 - PSE有限公司）中构建一维（1D）种群平衡模型（PBM）所需的参数。对亚稳区宽度（MSZW）的分析以及一系列晶种间歇冷却结晶实验为过饱和度控制实验选择了合适的工艺条件。gPROMS模型与控制软件PharmaMV（Perceptive Engineering有限公司）集成，以创建结晶器的数字孪生模型。模拟间歇结晶用于训练两个统计MPC模块，从而能够进行计算机模拟过饱和度控制，以制定有效的控制策略。在0.012 - 0.036的过饱和度设定值范围内，数字孪生模型表现出优异的性能，几乎不需要控制器调整就能消除任何不稳定性。MPC策略在一个500 mL的物理结晶器上实施，模拟溶液浓度被校准衰减全反射 - 傅里叶变换红外（ATR - FTIR）光谱的原位测量值所取代。实际过饱和度控制性能比计算机模拟测试略不稳定，这与传热预期干扰一致，而传热在模拟中未进行专门建模。总体而言，发现实际结晶器中的过饱和度控制水平足够准确和精确，足以考虑未来为实现更高级控制目标（如晶体尺寸）而对MPC策略进行调整。
