We provide insights for the development of a fast, dynamic, and adaptive way of modeling and optimizing chemical processes. We investigate two data-driven methodologies to optimize the energy cost of an extractive distillation process using an Aspen simulator. The first method uses surrogate-based optimization to explore two model-building techniques: Automated Learning of Algebraic Models’s (ALAMO) generalized linear models and neuron network models with rectifier (ReLU) activation function. We compare the accuracy and performance of these models when embedded into mathematical programming models. The second approach uses black-box optimization (BBO) to optimize problems directly using simulation results. We compare four BBO solvers and three different penalty functions to address constraints. We find that ALAMO performs well for a less complex system, whereas the ReLU network performs better for a more complex one. BBO with a smooth penalty function for constraints is the more effective approach for problems considered in this study.

我们为开发一种快速、动态和自适应的方式来建模和优化化学过程提供见解。我们研究了两种数据驱动的方法，以使用 Aspen 模拟器优化萃取蒸馏过程的能源成本。第一种方法使用基于代理的优化来探索两种模型构建技术：代数模型的自动学习 (ALAMO) 广义线性模型和具有整流器 (ReLU) 激活函数的神经元网络模型。我们比较了这些模型嵌入数学规划模型时的准确性和性能。第二种方法使用黑盒优化 (BBO) 直接使用模拟结果来优化问题。我们比较了四个 BBO 求解器和三个不同的惩罚函数来解决约束。我们发现 ALAMO 对于不太复杂的系统表现良好，而 ReLU 网络对于更复杂的系统表现更好。对约束具有平滑惩罚函数的 BBO 是解决本研究中考虑的问题的更有效方法。