污水处理工程中的数字孪生技术

工程（英文） ›› 2024, Vol. 36 ›› Issue (5) : 23 -39. DOI: 10.1016/j.eng.2024.04.012

研究论文

污水处理工程中的数字孪生技术

王爱杰 ^a^,^b ,
李贺文 ^a ,
贺哲君 ^c ,
陶彧 ^a ,
王鸿程 ^a^,^b ,
杨敏 ^a^,^d ,
Dragan Savic ^e^,^f ,
Glen T. Daigger ^g ,
任南琪 ^a^,^b

作者信息 +

Digital Twins for Wastewater Treatment: A Technical Review

Ai-Jie Wang ^a^,^b^,^* ,
Hewen Li ^a ,
Zhejun He ^c ,
Yu Tao ^a^,^* ,
Hongcheng Wang ^a^,^b ,
Min Yang ^a^,^d ,
Dragan Savic ^e^,^f ,
Glen T. Daigger ^g ,
Nanqi Ren ^a^,^b ,

Author information +

文章历史 +

Received	Accepted	Published
2023-09-30	2024-04-10
Issue Date
2024-11-11

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摘要

数字孪生技术可以通过融合实时数据及其反馈来增强建模和仿真的性能。本文阐明了数字孪生的基本要素，涵盖其概念、实体、应用领域和关键技术，提出了数字孪生在污水处理工程领域的变革潜力，重点介绍了数字孪生在污水处理厂和排水管网工程中的具体应用。这些应用包括硬件和软件两个方面。硬件包括污水处理设施、排水管网和传感器；软件包括知识和数据驱动的模型、机理模型、混合孪生、控制方法和物联网。在此基础上，以中国、意大利为例，对数字孪生技术在城市生活污水处理系统中的实际应用案例进行了分析，指出其应用的挑战和前景。本文旨在为污水处理工程领域提供一份数字化

Abstract

The digital twins concept enhances modeling and simulation through the integration of real-time data and feedback. This review elucidates the foundational elements of digital twins, covering their concept, entities, domains, and key technologies. More specifically, we investigate the transformative potential of digital twins for the wastewater treatment engineering sector. Our discussion highlights the application of digital twins to wastewater treatment plants (WWTPs) and sewage networks, hardware (i.e., facilities and pipes, sensors for water quality and activated sludge, hydrodynamics, and power consumption), and software (i.e., knowledge-based and data-driven models, mechanistic models, hybrid twins, control methods, and the Internet of Things). Furthermore, two cases are provided, followed by an assessment of current challenges in and perspectives on the application of digital twins in WWTPs. This review serves as an essential primer for wastewater engineers navigating the digital paradigm shift.

关键词

数字孪生 / 城市水系统 / 污水处理

Key words

Digital twins / Urban water systems / Wastewater treatment

引用本文

引用格式 ▾

王爱杰,李贺文,贺哲君,陶彧,王鸿程,杨敏,Dragan Savic,Glen T. Daigger,任南琪. 污水处理工程中的数字孪生技术[J]. 工程（英文）, 2024, 36(5): 23-39 DOI:10.1016/j.eng.2024.04.012

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1 引言

水是城市的基础资源，城市水系统运行状态直接影响居民生活质量，同时影响其他城市服务和管理。智慧水务是联合国可持续城市目标（建设包容、安全、有抵御灾害能力和可持续的城市和人类住区）的重要组成部分，为管控基础设施老化、气候变化、水资源短缺等加剧的城市水系统风险带来多重益处。加速水行业的数字化势在必行。数字孪生[ 1]是关键技术之一，而污水处理是城市水循环的重要部分[ 2]，因此本文将着眼于数字孪生在污水处理工程中的应用。

第四次工业革命（工业4.0）将数字技术应用到了制造业的生产过程[ 3]，对实时反馈极为依赖。在工业4.0中，数字孪生已经应用到多个领域，如制造业、医疗[ 4]、生物制造[ 5]、地球系统模拟和环境监测[ 6]、气候变化和交通[ 7]、食品加工与制造[ 8]、能源生产（尤其是厌氧共消化产甲烷）[ 9]和城市规划[ 10]等。同时，数字孪生通过集成建模、仿真和数字线程，促进了污水处理工程在规划、设计和管理方面的发展[ 11]。

数字孪生逐渐助力智慧水务管理，污水处理厂（WWTPs）借此契机不断优化其污水处理策略[ 12‒ 13]。本文介绍了数字孪生在污水处理厂和管网的研究与应用，旨在为污水处理工程领域提供一份数字化转型指南，提高污水处理效率，推动可持续发展；第二部分提出了污水处理工程中数字孪生的定义；第三部分介绍了数字孪生在污水处理厂和管网中硬件和软件方面的应用，提出了数字孪生在污水处理工程领域的变革潜力；第四部分介绍了两个数字孪生在污水处理厂中实地应用的案例，展示了数字孪生提升运营效率的能力；最后，第五部分总结了数字孪生实际应用的挑战、对未来的展望以及本文的结论。

2 数字孪生

2.1 定义

明确定义在概念新兴阶段至关重要。数字孪生和建模、仿真、网络物理系统、物联网（IoT）有一定的相似性，同时具有独特的特征和应用场景[ 14]，是污水处理厂智慧化进程的重要组成部分，此处将在第2.3节和第3节中深入探讨。

Grieves在2005年最早提出了数字孪生概念[ 1]，主要将其应用于工业和航天领域。随着时间的推移，如表1所示[ 11, 15‒ 23]，针对不同行业和相应需求，数字孪生发展出了不同的定义[ 5]，提供了多样化解释。

数字孪生现已发展为集成人员、产品、资产和流程等元素的动态技术，并在污水处理领域得到应用，有效提升了污水处理厂的运行效率和管理水平。明确的定义将持续推动该技术在此领域的应用，水务行业尚缺乏针对数字孪生的专门定义。基于污水处理工程的实际需求，我们提出以下定义：

基于污水处理设施和过程，数字孪生建立模型，分析实时数据以预测并调整污水处理工程的运行状态。随着技术的不断发展，这一系统将简化控制流程、提高数据利用效率，并积极指导环境决策制定，增强水务行业与社会环境的互动。

2.2 实体

数字孪生作为生产设备和生产网络等诸多实体的数字化表达，图1详细介绍了其中涉及的系统和过程。

在这个框架中，系统是由相互关联的实体构成的网络，通过将数字模型与实际实体整合，并配备控制和安全协议等结构化子系统，以在不同生命周期中增强决策[ 24]。系统主要分为两大类：物理实体和抽象实体。

物理实体存在于现实世界中，源自人造产品，并随着数字孪生技术的发展，其应用范围扩展到更广泛的领域，如供应链、农场和农业[ 25‒ 28]。物理实体可以进一步细分为人造实体、自然实体和社会实体。人造实体是传统的人造物理实体，它是通过转化自然实体为特定目的而制成的。社会实体则指社会群体。每种实体都具有独特的角色和起源。

相比之下，抽象实体是依据特定实体中分离出普遍规律而建立的，包括概念模型和理论模型，彼此协作[ 29]，可以实现调度和健康监控等功能。

过程是数字孪生的另一个组成部分，它指的是通过一系列相互关联的步骤或连续现象进行模拟和分析，以实现预期结果[ 17, 28]。过程分为物理过程和虚拟过程，这些过程在不同阶段模拟和实现物理和虚拟属性[ 28]，促进物理实体和虚拟实体各自以及彼此之间的交互和过渡。

2.3 系统

数字孪生框架涵盖用户域（UD）、数字孪生域（DTD）、感知与控制域（SCD）以及现实物理域（PD），其彼此关系及跨域功能实体在图2中进行了说明。

在用户域中，人机交互、界面设计、应用软件以及孪生共智等元素协同工作，促进了数字孪生的最优化利用[ 11]。数字孪生域通过模型管理、仿真服务[ 30]和孪生共智[ 31]三个关键功能，支持详细的可视化、动态仿真和资源调用，保证数据传输的安全，表现物理实体的特性。感知与控制域支持数字孪生域与物理域之间的实时通信[ 32]，包括两个主要部分：感知域和控制域。感知域负责从物理对象收集数据，通过工业物联网进行传递，控制域则负责执行数字孪生域中的策略。物理域由有形组件构成，包括人员、设备和流程等实际主体[ 33]。跨域功能确保了所有这些域之间信息的实时安全交互，促进系统的整体运行。

2.4 关键技术

随着各个领域数据激增，数字孪生利用这些数据创建物理实体的虚拟副本，并采用虚拟现实（VR）和人工智能（AI）等技术来数字化表达实体并优化决策。基于模型的系统工程（MBSE）是数字孪生的顶层框架技术，统领建模、仿真和数字线程三大核心技术；物联网作为底层伴生技术进行支持；云计算、机器学习（ML）、大数据和区块链等作为外围使能技术进一步增强了数字孪生的功能。图3展示了这些技术之间的相互关系。

建模是数字孪生的基础，通过在模型中展现系统内的因果关系或相互关联，数字化物理世界及其问题[ 34‒ 37]。建模涉及三维（3D）几何结构和形状、操作机制、接口、软件和控制算法等物理实体的详细参数[ 38]，并根据不同特性而有显著差异。目前，计算机辅助设计（CAD）和MATLAB用于基础建模，Revit用于建筑信息模型（BIM）[ 39]，CATIA则用于生命周期管理（PLM）[ 40]。

虚拟模型是数字孪生的核心，能够在多个维度和规模上高度精确地实现物理实体的数字表征[ 33]，并通过虚拟与现实的沉浸式整合增强其实用功能。虚拟现实（VR）[ 41]、增强现实（AR）[ 42‒ 43]和混合现实（MR）[ 44]等技术支持视觉与实时的结合。VR利用计算机图形对动态环境进行建模，尽可能生动逼真地描绘物理实体的各种属性、行为和规则。基于VR、AR和MR引入实时数据获取、场景捕捉和实时跟踪等功能，同步和融合虚拟模型与物理实体，有效增强检测、验证和指导功能。此外，元宇宙整合了AR、VR和MR技术及内容[ 45]，创建了一个更为扩展的虚拟环境。

在技术层面上，建模与仿真密不可分。建模用于表达我们对物理世界或特定问题的理解，而仿真则用来验证这些理解的准确性与相关性[ 36, 46‒ 47]。在工业应用中，仿真通过软件依据模型重现物理世界，这些模型在整合明确规则和复杂机制的同时，有时还包括基于知识的随机因素。如果模型精确并且输入数据齐全，仿真便能有效地映射出物理世界的特性。

传统污水处理厂在设计初期并未考虑数字孪生。为了提高这些污水处理厂的污水处理效率，将二维（2D）和三维的模型信息融合到同一个数字模型中至关重要。然而，目前很多污水处理厂仍采用二维设计，而不是使用更先进的建筑信息模型，阻碍了图形匹配和模型重用。

功能模拟接口（FMI）的引入旨在解决模拟工具碎片化、模型重用性有限和知识产权保护等问题[ 48]。FMI为模型重用提供了一个统一的接口标准，侧重于模型交换和功能与性能的协同模拟。采用FMI后，模型的整合变得更加直接和简便。使用FMI标准导出的文件，文件扩展名为“.fmu”（功能模拟单元）。

数字线程是一种具有高适应性的企业级通信框架[ 49]，是实体间的连接纽带。这种结构覆盖了整个系统生命周期的跨层级、跨规模的多视角模型。数字线程的核心功能是在系统的整个生命周期中指导活动并协助决策者。即数字线程确保系统生命周期内及时且适当地向相关利益方传递信息[ 50]。

MBSE是开发数字孪生的一种结构化方法[ 51‒ 53]。作为数字线程的基石，MBSE利用物联网数据确保仿真能够检测潜在故障，促进现有操作系统的持续改进。

物联网是数字孪生的基础[ 54]，它从互联网、传统电信及各种工具如传感器、射频识别（RFID）[ 55]、全球定位系统（GPS）[ 56]和激光扫描仪中收集信息。使得独立的物体能够链接为一个网络，实现物体与人之间的无缝交互。物联网还能智能识别和管理物品，促进数据的及时、可靠和高效传输。

只读存储器（ROM）用于存储固定的程序和数据，在非破坏性读出模式下，这些数据可以读取但不能修改。该系统结构简单，提供稳定的数据存储，确保即便在断电情况下数据也能保持不变，从而使其既可靠又便于用户使用。

数字孪生的可扩展性视需求而定。尽管单元级数字孪生可能在本地服务器上运行，系统级和更为复杂的数字孪生则需要更强大的计算及存储能力。云计算[ 57]通过提供丰富的资源和数据中心满足了这些需求，从而使数字孪生能够适应各种计算、存储和操作需求。雾计算[ 58]通过在多个分散的节点分配资源，扩展了云计算的范围，使数据处理更加靠近网络边缘。这种方式利用本地化处理来提升操作的速度和效率。此外，边缘计算直接在数据源头或其附近处理数据，进一步优化了实时数据分析，以增强边缘节点的感知、计算和控制能力[ 59]。与云计算协同工作，边缘计算将复杂的孪生数据发送到云端进行高级处理。这种云-边缘协作模式满足了多样化需求，提高了数据处理速度，减轻了云端的数据负担，并缩短了数据传输的延迟。通过这些方式，数字孪生的实时功能得到显著增强。特别是，系统级数字孪生与雾计算高度匹配，因其主要应用于具有特定地理位置的制造企业。

数据是一种动态且快速变化的资产，其处理需要采用创新技术以增强决策制定。大数据[ 60]利用数字孪生的海量数据阐释和预测现实世界的结果与过程，从而提取出宝贵的信息。作为补充，机器学习[ 61]推动了数据的自动分析。数字孪生利用机器学习通过物联网从物理域获取的数据预测未来的状态和行为。因此，大数据和机器学习紧密结合、协同工作，提供丰富的分析基础。

数字孪生也代表着数字资产，并参与到数字交易中。应用区块链[ 62]能够通过防止未经授权的修改，增强数字孪生的安全性，从而避免可能出现的错误和偏差，营造一个更安全的创新环境。此外，区块链推动的去中心化交易机制，确保了数字资产交易的安全性、分布式和实时性，为数字孪生的资产交易提供了理想的平台，增强了用户对数字孪生服务的信任。

综上所述，数字孪生的成功实施和应用依托于新兴技术的支持[ 63]。通过与这些技术的深入整合，数字孪生能够精确构建多维度、多尺度的模型，进行广泛的数据融合，提供定制化服务，并实现全面的动态实时交互。即能够真实且全面地感知物理实体。

3 污水厂和污水管网中的应用

3.1 设施

建模和仿真技术的进步推动了数字孪生广泛的实际应用。污水处理厂的运维因此获得了显著提升（详见表2 [ 64‒ 70]）。通过运用机器学习优化水泵、风机、污泥泵和搅拌机等关键设备的运行参数，有效提高了二级沉淀池、生物曝气过滤器和初级澄清器等设施的处理效率，从而增强了这些设备在实际应用中的性能。

近年来，水处理技术在各个方面都取得了显著的进展。例如，在沉降技术方面，已开发并验证了修正的Vesilind阻碍沉降函数，引入了新的指数函数来描述压缩沉降速度[ 64]。这种精进的主题延续到了对关键参数的验证和模拟上，包括水力负荷率、有机负荷率和填充材料的表面积，这些参数被用于一个包含填充床上流式厌氧污泥毯和生物曝气过滤器的项目。该项目由位于埃及开罗的国家研究中心水资源研究部门承担[ 65]。

泵技术方面的进展同样引人注目。2016年，Kim等[ 66]采用现代设计方法提高了单通道泵叶轮的流体动力性能。基于此，2020年，同一团队通过解决稳态雷诺平均纳维-斯托克斯方程[ 67]，提升了双叶片泵的水力性能和预测精度。与此同时，Lozano Avilés等[ 68]的工作利用先进的流体建模技术，针对流体分配和混合的不足进行了改进，成功将反应器所需的气流量减少了3%以上。

借助Ansys Fluent中的计算流体动力学（CFD）的进展，研究人员改进了在污泥泵中的涡轮叶轮的适用性，标志着该领域的重要进步[ 69]。叶轮的混合、推进和耐磨性能得到增强，已在中国镇江成功实施[ 70]。

3.2 管道

数字技术整合影响了地下管网的建模和管理。借助CAD、Ansys的计算流体力学X（CFX）、建筑信息模型、地理信息系统（GIS）以及集成流域建模（ICM）软件开发的二维和三维管网模型，为信息数字化设定了统一标准。该方法不仅提升了水流和水质的监测与模拟水平，还增强了污染物的预测和验证能力，简化了对这些复杂地下网络的管理（表3 [ 3, 71‒ 79]）。

中国在海南省三亚市成功推出了城市污水网络的数字管理平台。该平台在污水信息控制中心整合了数字管理、动态模拟和紧急情况网格管理，运用GIS空间管理和分析，可对排水网络的进行动态模拟，并执行细致的污水管网管理[ 71]。

广州市在东濠涌流域创建一个城市暴雨模型。该模型分析了结合污水溢流污染的截留效率，并评估了洪水缓解水平[ 72]。利用美国环保局的暴雨水管理模型（EPA-SWMM）生成的水力和水文数据集，Sun等[ 73]探讨了法国波尔多地区总悬浮固体模型的输出如何受流速、雨强和管长的影响。Fedorov等[ 74]研究了废水流和气体（空气和硫化氢）混合物的两相流动力学，确定了强烈排放的区域。此外，数据质量也是多项研究的重点。Nie的工作[ 75]强调了语义和拓扑数据的验证与精炼，确保数据损失最小化，促进各种地下建设研究人员之间的合作。综上所述，统一信息管理系统[ 76]和网络平台[ 3]标志着地下水管理向标准化数字化的重要转变。

3.3 水质传感器

为了适应日益严格的环境法规，未来的污水处理厂将需要依赖人工智能赋能的智能控制系统。智能控制的核心在于通过广泛部署水质传感器来实现数据的收集和处理[ 80]，进而建立集中化和标准化的数据库，这些数据库不仅存储持续的水质监测信息，还能与物联网软件无缝集成，形成在线管理系统，从而降低人员培训和其他相关成本[ 81]。

数据采集和控制系统（SCADA）是整合各种传感器数据的关键，它能自主优化处理参数，并监控污水处理厂的曝气系统运行（表4 [ 81‒ 89]）。利用SCADA可以简化水质监测，实现在无需采样和广泛用户培训的情况下进行实时参数测量，支持污水处理中的决策制定[ 82‒ 84]。

元数据作为数据收集和存储的关键组成部分，通过如Bluemix平台等，实现了历史与实时水数据的整合，这些数据覆盖了广泛流域的定量和定性指标，从而增强了实时水质监测[ 85]。这种包容性的数据库结构便于获取标准化、集中化的数据，全面记录与测量相关的所有重要信息[ 81]。

最近的实践表明，这些技术的应用取得了显著成果。在中国厦门，一个在线水质管理系统成功稳定了城市风景河流的水质，并利用数据分析调节了来自杏林湾的淡水供应[ 86]。同时，AquaSat也展示了其用于未来实地水质评估的丰富资源潜力[ 87]。

全球范围内，SCADA的有效性得到了验证。如在罗马尼亚，SCADA系统独立管理污水处理厂的运营，保持最佳技术参数的同时记录关键运营数据[ 84]。在韩国和中国，SCADA在监控曝气系统和提升水质方面也发挥了关键作用，证明了其是污水处理厂现代化进程中的重要角色[ 88‒ 89]。

3.4 活性污泥传感器

在污水处理厂中，传感器在识别和检测活性污泥的多种状态和性质中发挥着关键作用。如软传感器、液相色谱-串联质谱（LC-MS/MS）、低场 ¹H核磁共振以及独立成分分析等，能够对活性污泥进行一系列评估，识别污泥膨胀[ 90]、检测水相和固体污泥相中的群体感应信号物质[ 91]，以及测量污泥中的水含量和水分分布[ 92]。此外，也可通过评估相关参数，确定废水污泥中不同类型的水质[ 93]。相关进展已在波兰、中国和芬兰开展（表5 [ 90‒ 93]）。

3.5 流体动力学

污水处理厂的设计主要受到目标污染物去除率的影响，其效率在很大程度上依赖于生物反应器的流体动力学特性。计算流体动力学、隔室模型和连续槽模型（TIS）的发展使人们能够对这些反应器的流体动力学特性进行建模和预测，这对于预测相关参数值以及协助去除和降解污染物至关重要（表6 [ 94‒ 103]）。

CFD的使用促进了一系列参数的预测和确定。例如，Matko等[ 94]利用CFD改进了氧化沟和曝气器的设计，预测气液流动模式和溶解氧分布。同样，Elhalwagy等[ 95]应用CFD识别了悬浮固体（SS）与市政接触池中新消毒剂效率之间的关系。其他研究小组使用CFD优化了不同反应器设置中污染物降解的条件[ 96‒ 99]。

此外，隔室模型和TIS模型进一步提高了预测的准确性。Hormann等[ 100]改善了公共下水道系统中放射性碘移动的预测，而 Ng [101]专注于预测降水事件期间城市放射性铯的传输。此外，有研究定性分析了反应器的混合机制[ 102]，并定量确定了移动体积和非移动体积的准确值[ 103]。

3.6 节能降耗

全球人口增长、工业进步、生活方式和气候变化推动着污水处理厂设计对能源消耗问题的考量。在推动碳中和及能源限制的背景下，节能降耗需求激增为污水处理厂带来了巨大的挑战。利用数据驱动方法，结合传统时间序列和深度学习，可以为污水处理厂构建能耗预测模型，有助于在水处理的初期阶段减少能源使用（表7 [ 88‒ 89, 104‒ 111]）。

Sean等[ 88]使用电流和水质数据来预测最优的空气流量和能源消耗，为工厂运营的初始阶段提供了宝贵的参考，而Saini等[ 99]探讨了现有厌氧消化器入口平面中抽水循环的动态能耗。此外， Harrou [104]和Cheng等 [ 105]尝试使用流量、温度数据和生化需氧量预测污水处理厂的短期能源需求，促进这些工厂的数据驱动管理。De Canete等[ 106]应用机器学习确定影响进水质量的变量，如化学需氧量（COD）、总氮（TN）和总悬浮固体（TSS），从而优化能源消耗并最小化生物废水处理设施的违规情况。WEST是一个比利时的模拟平台，最初为废水处理而创建，用于动态网络建模和长期模拟开发的多功能环境[ 107]。Cechinel等[ 108]考虑了出水质量的预测，而Muoio等[ 109]确定了一个大型工业废水处理厂的最佳固体滞留时间，以尽量减少运营成本。Kovács等[ 110]对生物膜反应器进行了建模，这些反应器构成了SUMO的基础模块。Kirchem等[ 111]提出了污水处理厂的用电方案。

3.7 知识和数据驱动的模型

自引入以来，数据驱动模型在污水处理厂中主要有三个目的：故障检测、变量预测和高级控制。关于活性污泥模型（ASMs）和高级控制的更深入见解分别在第3.8节和第3.10节中介绍[ 112]。第3.7节将聚焦于基于知识和数据驱动的模型（表8 [ 113‒ 123]）。这种方法使用控制图、主成分分析、偏最小二乘法（PLS）和神经网络等工具进行故障检测，同时使用传递函数模型、多元回归和神经网络等工具进行变量预测。

故障检测对污水处理厂的平稳运行至关重要。Santos等[ 113]通过使用控制图监控膜的透性，并根据需要提出干预措施，而Trubetskaya等[ 114]则利用工业数据来确定规格限制。此外，主成分分析帮助确定了适合纸厂序批式反应器（SBR）过程的子期划分策略，并进行了污水处理厂质量参数的统计分析[ 115‒ 116]。使用主成分分析，Liu等[ 117]在具有非线性和动态特征的过程中检测了传感器故障，并提高了出水质量指标预测的性能和稳定性[ 118]。同时，神经网络帮助深入分析了伊拉克原水与处理后出水之间的复杂关系[ 119]。

在变量预测方面，多项研究尝试预测沉积池的出水浊度、不同废水处理技术的去除效率以及日常城市废水排放量，以此为污水处理厂的高效管理作出贡献[ 120‒ 122]。此外，研究人员还进行了详尽的评估，探讨了长期内水质参数的周期性波动[ 123]。

3.8 机理模型

在废水处理领域，技术进步促进了机理模型的发展，如活性污泥模型[ 124‒ 125]、厌氧消化模型（ADMs）[ 126‒ 127]和软传感器[ 128‒ 129]的进步。如表9所示[ 90, 104‒ 106, 125, 127‒ 128, 130‒ 148]，这些模型用于模拟特定处理过程，预测目标产出并协助评估整个操作流程。

ASMs和ADMs在优化和评估多个变量方面发挥了重要作用。研究人员利用这些模型改善了焦化污水处理厂的运营条件，从而降低了成本[ 130]，评价了抗生素的去除效果[ 131]，探讨了污泥垂直分层对ASM组分的空间和时间分布的影响[ 132]，并预测了厌氧反应器中不同阶段的沼气产量[ 133]。

软传感器在机理模型中占有重要地位，广泛应用于污水处理厂的多个方面。它们有助于预测复杂变量，如预测反硝化后过滤单元中硝酸盐的浓度[ 134]和模拟天气预报以控制污水处理厂[ 135]。多项研究利用软传感器设计了各种参数的实时控制策略，包括在进水低C/N比波动时调整污泥溶解液回流比[ 136]，在长期复杂废水处理过程中估算总凯氏氮（TKN）[ 137]，以及提升软传感模型在预测出水质量方面的效率和精度[ 138]。此外，软传感器还促进了难以测量但与水质相关的变量的预测[ 139]，在线监测废水处理过程中的关键变量，同时捕获非线性和非高斯数据[ 140]，并提取动态特征以预测质量变量[ 141]。

3.9 混合孪生

利用数字孪生概念，混合孪生将真实数据与数字孪生产生的数据相结合，基于物理原理并涵盖了因果关系，结合虚拟与现实，建立模拟与现实之间的反馈循环。混合孪生模型基于实际测试参数优化其模拟结果，从而降低测试成本并提高数据精度。在污水处理厂中，混合孪生在消除歧义方面发挥了核心作用[ 149]。此外，它们还助力设计针对厌氧氨氧化过程多目标控制的运行变量系统[ 150]，具体详情见表10 [ 149‒ 151]。

3.10 控制方法

污水处理厂的控制方法主要分为四个类别：线性控制、线性化控制、非线性控制和基于人工智能的控制[ 135]。每个类别涵盖了一系列策略，详见表11 [ 152‒ 165]。例如，线性控制策略应用于污水处理厂的单个参数以优化处理流程，通过有效控制活性污泥处理中的溶解氧（DO）降低曝气成本[ 152‒ 154]，在硝化过程中降低了N₂O排放[ 155]，并通过最小化出水中的COD和有机氮含量，在提高出水质量的同时降低能耗[ 156]。

线性化控制用于协调污水处理厂的多方面条件，减少进水流率和浓度的大幅波动及测量噪声和动力学的不确定性。这种控制策略不仅提高了污水处理厂的效率[ 157, 158]，还增强了厌氧-缺氧-好氧（A²/O）反应器中脱氮和除磷过程的可靠性[ 159]。此外，线性化控制还被应用于优化水资源回收设施的曝气，以满足不同的优化目标[ 160]。

非线性控制有助于预测和处理动态参数，如助力操控反渗透过程中二甲基苯酚的移除[ 161]，在不牺牲曝气效率的同时节约能源[ 162]，以及提前预测TN峰值，调整空气流量以保持出水质量[ 163]。

基于AI的控制使用映射模型进一步提升出水质量并减少工厂测量的频次[ 164]，这种策略也可以预测出水结果[ 165]。

综上所述，多样化的控制策略形成了一个全面的工具包，提升了污水处理厂运营的效率和预测能力，有效提高处理效率并节约能源。

4 案例分析

4.1 中国博爱县第二污水处理厂

中国焦作市博爱县第二污水处理厂是全球首个采用PLM技术运行的数字孪生污水处理厂[ 166]。该厂设计服务于15万居民，设计规模为60 000 t∙d ^-1，采用A²/O工艺，并辅以混凝、沉淀和过滤程序。平均进水中COD = 270 mg∙L ^-1、5天生化需氧量（BOD₅）= 140 mg∙L ^-1、SS = 200 mg∙L ^-1、NH₃-N = 35 mg∙L ^-1、TP = 4 mg∙L ^-1、TN = 50 mg∙L ^-1，出水严格遵守中国一级A类污染物排放标准[即《城镇污水处理厂污染物排放标准》（GB 18918—2002）]，处理进水中规定浓度的各种物质，以生产适合排放到自然水体的出水。

整合了设计、运营、维护和实时数据，该污水处理厂增强了以下三个核心功能：

（1）虚拟巡检：通过3D模型与实时数据协同，实时准确全面地展现数据，引入虚拟检查来完成控制和调试，简化人员培训，促进多维数据分析。

（2）数字报表：整合2D和3D模型的实时数据，降低由纸质报表导致的操作复杂问题，减少了维护工作量，并使自动控制系统能够在少人或无人监管下运行。

（3）预警分析：通过对输入/输出、设备和过程的模拟，系统能预测进水流量、溶解氧波动和出水水质，进行自我诊断，找出问题原因并预警。

这种全面的方法通过技术创新简化操作流程，确保了工厂有效运行。

4.2 意大利Nosedo污水处理厂

Nosedo污水处理厂是意大利米兰的主要市政污水处理设施，也是欧洲最大的污水处理厂，该厂设计服务于125万人[ 167]，设计规模为432 000 t∙d ^-1，干燥天气下的处理流速为5 m³∙s ^-1，雨天则可达到15 m³∙s ^-1。其中60%~70%的出水被用于农业灌溉，高效运营每年可节省约63万欧元，主要涉及三个方面：

（1）综合管控：污水处理厂实现了管网与处理过程的无缝整合，支持实时决策，精确调整生物负荷，减少人为操作，优化生化处理过程控制，工厂总能耗降低40%。

（2）运营成本降低：通过降低能源使用、化学品消耗和污泥产量，实现了显著的成本节约。具体成效包括生物处理能耗减少25%，砂粒室曝气减少9%，得益于磷沉淀过程的改进，FeCl₃使用量减少80%。此外，化学污泥产量由于沉淀减少而每年减少126 t。

（3）水力容量提升：Nosedo污水处理厂优化了其生物处理过程，在恶劣天气和暴雨期间提高了20%~30%的水力容量，提高了系统韧性和处理效率。

通过优化以上关键环节，Nosedo污水处理厂在保持处理效率的同时降低经济成本，成为可持续发展的示范性污水厂。

5 结语

数字孪生驱动包括污水处理领域在内的多个行业的改革，在探讨其应用和当今发展时，也有必要阐述现有的挑战并展望未来的前景。

数字孪生为污水处理工程带来了技术挑战。首先，实时数据与模型整合需要搭载先进控制系统以满足多变量处理的需求，同时也为工作人员增加了学习难度。其次，污水处理需要寻求处理效率和能源消耗之间的平衡，同时精准预测动态参数以确保出水达标。

数字孪生在污水处理厂中的应用标志着城市水系统管理向更智能的方向发展。博爱县第二污水处理厂和意大利Nosedo污水处理厂的预测性维护和资源优化展示了数字孪生的助力运维和决策的能力。此外，结合物联网，数字孪生将重新定义污水处理和环境管理的标准[ 89, 168‒ 169]。未来，数字孪生将融入更广泛的数字水务基础设施，开启一个高效、韧性和可持续性的数字水务新时代。

综上所述，本文概述了数字孪生在污水处理工程中的定义、实体、系统和关键技术，回顾了污水处理厂和污水管网决策的数字工具。此外，分析了数字孪生在城市污水处理厂中的实际应用案例，指出通过实时分析，数字决策工具将有望显著提升污水处理厂的效率和决策能力。建议未来工作应扩大数字整合，革新数据分析技术，并拓宽应用范围，以进一步增强数字孪生的潜力。

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