To address freezing failures in decentralized rural wastewater treatment systems in severe cold regions and reduce the computational burden of design optimization, this study proposes a surrogate-assisted multi-objective optimization framework driven by high-fidelity multiphysics simulations. A three-dimensional transient phase-change heat transfer model of the coupled foundation-soil-equipment system is established to generate training data under extreme winter conditions. Using 2500 Latin Hypercube Sampling points, a Bayesian Optimization-Random Forest (BO-RF) surrogate model is developed and coupled with NSGA-II to optimize life-cycle cost, thermal risk, treatment efficiency, user experience, carbon emissions, and land use. The MARCOS method is applied to rank 200 Pareto-optimal solutions, and a robust shallow-burial design with passive insulation is identified. A case study in Harbin, China, shows that the optimized scheme attenuates more than 85% of external temperature fluctuation. The BO-RF model achieves high accuracy (R^2=0. 961–0. 985). Compared with conventional heuristic strategies, the proposed framework reduces life-cycle cost by 38%, improves user experience by 34%, and shifts the thermal risk indicator from 1. 85 to a safe margin of -5. 5, with only marginal increases in carbon emissions and land use. The results offer practical quantitative support for resilient and cost-effective infrastructure deployment in cold regions.
Su et al. (Sun,) studied this question.