To solve the problems of the difficulty in early leakage monitoring and larger positioning error for urban hazardous chemicals pipelines, the optimized method based on the improved Inverse Transient Analysis (ITA) and Ant Lion Optimizer (ALO) was proposed. Firstly, based on the obtained experiment's results of leakage of natural gas in the non-metallic pipeline, the segment classification method was incorporated into the pressure gradient calculation. The modified method can adapt to the multi-node characteristics of urban pipe networks and help to obtain the preliminary positioning calculation results after optimization. Then the calculation results were embedded in the ITA calculation model. The input parameters of the gas pipeline such as boundary conditions, leakage rate and friction coefficient were used to establish the characteristic linear equations. Then the objective function of the least-squares criterion was defined, and the improved ITA model suitable for leakage detection of urban natural gas pipeline networks was constructed. Finally, the ALO was used to optimize the calculation process of the improved ITA model, and iteratively optimize the optimal friction coefficient and its corresponding minimum objective function (OF) value. As a result, a more precise location of the leakage source was calculated. The validation of the modified method is conducted by comparing the calculated values with the experiment's results. The results show that the method can accurately predict the location where the pipeline leakage occurs. The minimum error is 3.17%. Compared with the traditional ITA, this method not only accelerates the convergence speed of the objective function, but also improves the accuracy of location calculation.

针对城市危险化学品管道泄漏早期监测困难，定位误差较大的问题，提出了一种基于改进的逆向瞬态分析（ITA）和蚁狮优化器（ALO）的优化方法。首先，根据获得的非金属管道中天然气泄漏的实验结果，将分段分类法纳入压力梯度计算中。改进后的方法能够适应城市管网的多节点特征，优化后有助于获得初步的定位计算结果。然后将计算结果嵌入ITA计算模型中。输气管道的输入参数，例如边界条件，利用泄漏率和摩擦系数建立特征线性方程。然后定义了最小二乘准则的目标函数，并构建了适用于城市天然气管网泄漏检测的改进ITA模型。最后，将ALO用于优化改进的ITA模型的计算过程，并迭代优化最佳摩擦系数及其对应的最小目标函数（OF）值。结果，计算出泄漏源的更精确的位置。通过将计算值与实验结果进行比较，可以对修改后的方法进行验证。结果表明，该方法可以准确预测管道泄漏发生的位置。最小误差为3.17％。与传统的ITA相比，