论文标题
气体网络的排放感知优化：输入 - 传输神经网络方法
Emission-Aware Optimization of Gas Networks: Input-Convex Neural Network Approach
论文作者
论文摘要
在排放限制下优化的气体网络规划优化优先考虑CO $ _2 $强度的气体供应。由于此问题包括复杂的气流物理定律，因此标准优化求解器无法保证与可行解决方案的收敛。为了解决此问题，我们开发了一个输入 - 传感器神经网络（ICNN）辅助优化例程，该例程结合了一组训练有素的ICNN，以高精度近似于气流方程。比利时气体网络上的数值测试表明，ICNN ADED优化主导了非凸和基于松弛的求解器，其最佳增长较大，与更严格的发射目标有关。此外，每当非凸线求解器失败时，ICNN ADED优化为网络计划提供了可行的解决方案。
Gas network planning optimization under emission constraints prioritizes gas supply with the least CO$_2$ intensity. As this problem includes complex physical laws of gas flow, standard optimization solvers cannot guarantee convergence to a feasible solution. To address this issue, we develop an input-convex neural network (ICNN) aided optimization routine which incorporates a set of trained ICNNs approximating the gas flow equations with high precision. Numerical tests on the Belgium gas network demonstrate that the ICNN-aided optimization dominates non-convex and relaxation-based solvers, with larger optimality gains pertaining to stricter emission targets. Moreover, whenever the non-convex solver fails, the ICNN-aided optimization provides a feasible solution to network planning.
