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<p>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 (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(R^{2}=0.961\)</EquationSource> </InlineEquation>–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 <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(-5.5\)</EquationSource> </InlineEquation>, 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.</p>

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Surrogate assisted optimization of decentralized wastewater facilities in severe cold regions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月19日

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  • Yikun Su,
  • Xinhao Gao,
  • Zhizhe Zheng

摘要

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.