The feasible path optimisation algorithm is widely used for chemical process optimisation due to its effectiveness for large-sale highly nonlinear optimisation problems. The effectiveness strongly relies on the convergence of process simulation as the simulation is performed in each optimisation step. Existing feasible path optimisation algorithms often fail to converge or require expensive computational effort in process simulation in the equation-orientated environment. In this work, we propose three novel feasible path optimisation algorithms to improve both convergence and computational efficiency. The first algorithm improves the original steady-state feasible path algorithm through resetting the initial point used for process simulation when its direct precedence fails during the line search. The second algorithm is an enhancement to the time-relaxation-based optimisation algorithm through the use of the tolerances-relaxation integration method for the pseudo-transient continuation (PTC) simulation. The last algorithm is a hybrid algorithm through the effective combination of steady-state simulation and PTC simulation in the feasible path optimisation framework. The computational results demonstrate that the proposed three new variants do improve the convergence and are more robust than the existing feasible path optimisation algorithms. The first variant is more efficient than the other two and the existing PTC model-based optimisation algorithms with the same computer hardware and software.

可行路径优化算法因其对大规模销售的高度非线性优化问题的有效性而被广泛用于化学过程优化。有效性在很大程度上取决于过程仿真的收敛性，因为仿真是在每个优化步骤中执行的。现有的可行路径优化算法在面向方程的环境中进行过程仿真时通常无法收敛或需要昂贵的计算量。在这项工作中，我们提出了三种新颖的可行路径优化算法，以提高收敛性和计算效率。第一种算法通过在直线搜索过程中直接优先级失败时重置用于过程仿真的初始点，从而改进了原始的稳态可行路径算法。第二种算法是通过对伪瞬态连续（PTC）仿真使用公差-松弛积分方法，对基于时间松弛的优化算法进行了增强。最后一种算法是在可行路径优化框架中将稳态仿真与PTC仿真有效结合的混合算法。计算结果表明，与现有的可行路径优化算法相比，提出的三个新变体确实提高了收敛性，并且更健壮。第一个变量比其他两个变量和具有相同计算机硬件和软件的现有基于PTC模型的优化算法效率更高。