AI 中文总结
针对过程优化模型计算需求大及GPU求解器应用受限问题，利用现有GPU优化软件栈构建工作流程，在二氧化碳吸收器设计问题中验证，最大情况下GPU工作流程比单线程CPU基线加速约21倍。
AI 中文摘要
本文提出了一种面向方程的过程优化的概念验证工作流程，该流程完全在GPU上运行。过程优化模型通常包含复杂的相互关联的单元操作、动力学和不确定性，导致大型非线性程序，传统基于CPU的求解器计算需求大。新兴的基于GPU的求解器虽有计算优势，但因缺乏GPU兼容的过程建模工具，其在过程优化中的应用受限。我们通过使用现有的具有GPU能力的优化软件栈（包括ExaModels、MadNLP和cuDSS）对GPU兼容的过程优化模型进行原型设计来解决这一差距。在进料不确定的二氧化碳吸收器设计问题上进行了演示，对于最大的情况，GPU工作流程比使用JuMP、Ipopt和MA57的单线程CPU基线实现了约21倍的加速。
英文摘要
This paper presents a proof-of-concept workflow for equation-oriented process optimization that runs entirely on a GPU. Process optimization models often incorporate complex interconnected unit operations, dynamics, and uncertainties, resulting in large nonlinear programs that can be computationally demanding for conventional CPU-based solvers. Although emerging GPU-based solvers offer substantial computational benefits, their application to process optimization has been limited by the lack of GPU-compatible process modeling tools. We address this gap by prototyping the GPU-compatible process optimization models using an existing GPU-capable optimization software stack, including ExaModels (algebraic modeling system), MadNLP (optimization solver), and cuDSS (linear solver). ExaModels formulates the process optimization problem in a GPU-compatible way by exposing its repeated algebraic structure, while MadNLP and cuDSS solve the resulting nonlinear program on the GPU. This workflow is demonstrated on a CO2 absorber design problem under feed uncertainty, in which a shared column diameter is minimized subject to equilibrium and hydraulic constraints in all scenarios. For the largest case with 5,000 scenarios and 1.5 million variables, the GPU workflow achieves a speedup of approximately 21\times over a single-threaded CPU baseline using JuMP, Ipopt, and MA57.
CommentsSubmitted to FOCAPO-CPC 2027
