A model-based optimization methodology was developed to systematically address all possible optimal start-up scenarios of a multistage combined cooling and antisolvent continuous MSMPR (mixed-suspension, mixed-product removal) crystallizer. The crystallization of aspirin (acetylsalicylic acid, ASA) in ethanol (solvent) and water (antisolvent) was used as a case study. A steady state optimization was firstly conducted to optimize the crystallizer design and operating conditions and set-up the reference trajectories. The discretised profiles of the jacket temperatures, antisolvent flowrates and seeding policies, which includes the seed flowrates, loading and mean size, were used as decision vectors for the dynamic optimization problem. Scenarios with single and combined decision vectors were addressed and analysed in start-up situations involving prefilled or empty vessels. The impact of simplified optimization and operating procedures or refined discretization schemes were also addressed. It was shown that several options may provide comparable performance and the start-up time can be improved by nearly 80% in the case of combined decision vectors involving all possible decision vectors.

开发了一种基于模型的优化方法，以系统地解决多级组合冷却和反溶剂连续 MSMPR（混合悬浮，混合产物去除）结晶器的所有可能的最佳启动方案。阿司匹林（乙酰水杨酸，ASA）在乙醇（溶剂）和水（反溶剂）中的结晶被用作案例研究。首先进行稳态优化以优化结晶器设计和操作条件并设置参考轨迹。夹套温度、反溶剂流量和播种策略的离散曲线，包括种子流量、负载和平均尺寸，被用作动态优化问题的决策向量。在涉及预填充或空容器的启动情况下，处理和分析了具有单一和组合决策向量的场景。还解决了简化优化和操作程序或改进离散化方案的影响。结果表明，在涉及所有可能决策向量的组合决策向量的情况下，几个选项可以提供相当的性能，并且启动时间可以提高近 80%。