The optimization of process systems within the field of Chemical Engineering often confronts uncertainties that can exert significant influences on the performance and dependability of the obtained solutions. This research endeavors to investigate the application of Bayesian optimization in the realm of constrained mixed integer nonlinear problems. A comparative analysis was conducted, exploring different surrogate models, and evaluating diverse kernel functions and acquisition functions. Furthermore, a sampling strategy was devised to assess the enhancement achieved by the acquisition function. The findings of this article reveal the superior performance of sparse Gaussian processes in conjunction with computationally inexpensive acquisition functions, thereby highlighting their suitability for addressing mixed integer nonlinear programming problems characterized by noisy functions and stochastic behavior. Consequently, this article presents a computationally efficient approach to effectively tackle the challenges associated with data-driven mixed integer nonlinear programming problems within the domain of Process System Engineering.

中文翻译：

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数据驱动混合整数非线性规划问题的贝叶斯优化方法


化学工程领域内的工艺系统优化经常面临不确定性，这些不确定性可能对所获得的解决方案的性能和可靠性产生重大影响。本研究致力于研究贝叶斯优化在约束混合整数非线性问题领域的应用。进行了比较分析，探索不同的代理模型，并评估不同的核函数和获取函数。此外，还设计了采样策略来评估采集功能所实现的增强。本文的研究结果揭示了稀疏高斯过程与计算成本低廉的采集函数相结合的优越性能，从而突出了它们对于解决以噪声函数和随机行为为特征的混合整数非线性规划问题的适用性。因此，本文提出了一种计算高效的方法，可以有效解决过程系统工程领域内与数据驱动的混合整数非线性规划问题相关的挑战。