Journal of Process Control最新文献

Observer switching strategy for enhanced state estimation in CSTR networks CSTR网络中增强状态估计的观测器交换策略

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2026-01-30 DOI: 10.1016/j.jprocont.2026.103640

Lisbel Bárzaga-Martell , Francisco Ibáñez , Angel L. Cedeño , Maria Coronel , Francisco Concha , Norelys Aguila-Camacho , José Ricardo Pérez-Correa

Accurate state estimation in nonlinear chemical reactors is essential for advanced monitoring and control, yet sensor limitations and model uncertainties pose significant challenges. This paper presents a novel multi-observer switching framework that operates four state estimators in parallel—Extended Luenberger Observer, Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter—and dynamically selects the most reliable estimate at each sampling instant. The switching mechanism employs a composite cost function combining

L_{2}

and

L_{\infty}

norms of the output estimation error: the

L_{2}

component captures sustained deviations while the

L_{\infty}

component enables rapid response to transient peaks, together providing robust adaptation to changing operating conditions. The framework is validated on continuous stirred-tank reactor networks with up to three reactors in series, under partial global observability where only downstream concentrations and temperatures are measured. Monte Carlo simulations demonstrate that the switching observer achieves superior estimation accuracy compared to individual estimators while maintaining computational efficiency suitable for real-time implementation. Parametric robustness analyses confirm reliable performance under kinetic and thermal uncertainties. The proposed approach offers a scalable solution for state estimation in complex chemical processes, with potential applications in fault detection and model predictive control.

在非线性化学反应器中，精确的状态估计对于先进的监测和控制至关重要，但传感器的局限性和模型的不确定性带来了重大挑战。本文提出了一种新的多观测器切换框架，该框架并行运行四个状态估计器（扩展Luenberger观测器、扩展卡尔曼滤波器、Unscented卡尔曼滤波器和粒子滤波器），并在每个采样时刻动态选择最可靠的估计。切换机制采用结合输出估计误差的L2和L∞范数的复合代价函数：L2分量捕获持续偏差，而L∞分量能够快速响应瞬态峰值，同时提供对不断变化的操作条件的鲁棒适应。该框架在连续搅拌槽反应器网络上进行了验证，该网络有多达三个串联反应器，在局部全局可观测性下，仅测量下游浓度和温度。蒙特卡罗仿真表明，与单个估计器相比，切换观测器在保持适合实时实现的计算效率的同时，获得了更高的估计精度。参数鲁棒性分析证实了在动力学和热不确定性下的可靠性能。该方法为复杂化工过程的状态估计提供了一种可扩展的解决方案，在故障检测和模型预测控制方面具有潜在的应用前景。

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引用次数: 0

Sparse optimization assisted adaptive and smart hybrid data-driven modeling for process systems 稀疏优化辅助过程系统的自适应和智能混合数据驱动建模

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2026-01-30 DOI: 10.1016/j.jprocont.2026.103642

Shubhasmita Behera, Santhosh Kumar Varanasi

Integration of data-driven and physics-based modeling approaches has become essential for achieving intelligent monitoring and control in modern process industries. This paper presents an adaptive hybrid data-driven identification framework for process systems that operate under varying conditions. The proposed method uses B-spline representations along with model-based regularization to ensure consistency. A sparsity constraint on model parameters improves interpretability and simplicity. To handle process variations, we developed an error-triggered adaptive mechanism that automatically updates the model structure and parameters when significant deviations occur. The resulting framework effectively captures dynamic behavior across multiple operating regimes. Validation on a quadruple-tank system and a non-isothermal continuous stirred-tank reactor shows improved prediction accuracy and greater robustness compared to standard methods. These results highlight the potential of the proposed framework as a tool for adaptive process modeling and predictive control in the context of Industry 4.0.

在现代过程工业中，数据驱动和基于物理的建模方法的集成对于实现智能监测和控制至关重要。本文提出了一种自适应混合数据驱动的识别框架，用于在不同条件下运行的过程系统。该方法使用b样条表示和基于模型的正则化来确保一致性。模型参数的稀疏性约束提高了可解释性和简单性。为了处理过程变化，我们开发了一种错误触发的自适应机制，它在发生重大偏差时自动更新模型结构和参数。生成的框架有效地捕获跨多个操作机制的动态行为。在四槽系统和非等温连续搅拌槽反应器上的验证表明，与标准方法相比，预测精度更高，鲁棒性更强。这些结果突出了所提出的框架作为工业4.0背景下自适应过程建模和预测控制工具的潜力。

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引用次数: 0

Mathematical model for optimal operation of an ex-situ hydrogenotrophic methanation bio-trickling filter reactor 非原位氢化甲烷化生物滴滤反应器优化运行的数学模型

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2026-01-29 DOI: 10.1016/j.jprocont.2026.103631

Fernando Aarón Ortiz-Ricárdez , Karla María Muñoz-Páez , Alejandro Vargas

To upgrade biogas to biomethane, a mathematical model approach for a hydrogenotrophic methanation process is formulated in a biotrickling filter (BTF) reactor. The model partitions the trickling bed (TB) space in arbitrary levels of possibly different volumes and uses the first Fickian diffusion law along the vertical axis and through the biofilm layer attached to the inert bed material. To calculate gas flows among TB levels, the model is subject to the ideal gas law and to the partial pressures Dalton’s law, assuming a fixed amount of gaseous moles in each TB level. In addition to the available control variables, such as the liquid recirculation rate and the gaseous inflow rate, the gaseous effluent recirculation rate is tested as a new control variable for the model. The simulations performed of the model accurately describe experimental results of an ex-situ hydrogenotrophic methanation in a BTF. Finally, an optimal steady-state operation study for BTFs with certain physicochemical parameters is provided for any TB reactor size, and other operational improvements for the model, such as additional gaseous influent injections along the TB, are outlined.

为了将沼气转化为生物甲烷，在生物滴滤反应器中建立了氢营养化甲烷化过程的数学模型。该模型将滴床（TB）空间划分为可能不同体积的任意水平，并使用沿垂直轴和通过附着在惰性床材料上的生物膜层的第一菲克扩散定律。为了计算TB水平之间的气体流动，该模型遵循理想气体定律和分压道尔顿定律，假设每个TB水平中有固定数量的气体摩尔。除了现有的控制变量，如液体再循环速率和气体流入速率外，还测试了气体流出再循环速率作为模型的新控制变量。该模型的模拟结果准确地描述了BTF中非原位氢营养化甲烷化的实验结果。最后，对具有特定物理化学参数的btf进行了最佳稳态运行研究，并对该模型的其他操作改进进行了概述，例如沿TB注入额外的气体。

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引用次数: 0

Efficient learning-based predictive control for acid gas abatement in waste to energy processes 废化能过程中酸性气体减排的基于学习的高效预测控制

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2026-01-27 DOI: 10.1016/j.jprocont.2026.103638

Andrea Wu , Andres Cordoba-Pacheco , Senem Ozgen , Fredy Ruiz

Waste-to-energy plants have become a strategic resource to reduce the volume of non-recyclable solid waste in municipalities. Flue gas treatment is a key component in making these plants clean and sustainable. In particular, acid gas abatement is a fundamental process for complying with emission standards. However, developing models of the abatement process is challenging due to the complexity of the phenomena and reactions occurring inside the pollutant abatement system. In this work, a predictive control strategy is proposed to regulate the concentration of hydrogen chloride in the flue gas of a waste-to-energy plant by manipulating the reactant flow rate. Black-box models for simulation and prediction tasks are derived from experimental data from a real WtE plant in Italy. A learning strategy is proposed to update an autoregressive model of the process in real-time using Set Membership identification techniques, and a Model Predictive Controller is formulated to optimally manipulate the reactant feed rate, guaranteeing that emissions comply with regulatory constraints while minimizing the reactant dosage. The performance of the resulting control strategy is compared with a standard PI plus FeedForward controller, currently employed in this kind of process. The results show that the adaptive MPC improves the tracking performance, reducing the Mean Integrated Absolute Error by up to 57.1% and reactant consumption by 3%, while ensuring better compliance with emission regulations.

废物发电工厂已成为减少城市不可回收固体废物数量的战略资源。烟气处理是使这些工厂清洁和可持续发展的关键组成部分。特别是，酸性气体减排是符合排放标准的基本过程。然而，由于污染物减排系统内部发生的现象和反应的复杂性，开发减排过程模型具有挑战性。在这项工作中，提出了一种预测控制策略，通过控制反应物流量来调节垃圾焚烧发电厂烟气中氯化氢的浓度。用于模拟和预测任务的黑箱模型来源于意大利一个实际污水处理厂的实验数据。提出了一种学习策略，利用集合隶属度识别技术实时更新过程的自回归模型，并制定了模型预测控制器，以优化控制反应物进给量，保证排放符合监管约束，同时使反应物用量最小化。将所得到的控制策略的性能与目前在这类过程中使用的标准PI +前馈控制器进行了比较。结果表明，自适应MPC系统提高了车辆的跟踪性能，使平均综合绝对误差降低了57.1%，减少了3%的反应物消耗，同时更好地符合排放法规。

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引用次数: 0

Offset-free Model Predictive Control with parametric models: Augmented disturbance estimates with tunable dynamics and impact on noise sensitivity 参数模型的无偏移模型预测控制：具有可调动态和对噪声灵敏度影响的增强干扰估计

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2026-01-27 DOI: 10.1016/j.jprocont.2026.103637

Piotr Tatjewski

Design of offset-free model predictive control (MPC) with parametric process models is concerned in the paper, in the presence of deterministic constant or asymptotically constant external and internal (modeling errors) disturbances. For linear state-space models, two established methods assuring offset-free control and relations between them are briefly recalled, including very recent results. It yields also a new result concerning disturbance estimation when full-state is measured. This is needed for presentation of the main result of the paper, but jointly, yields also a unified approach to the mentioned design problem, for linear parametric models (state-space models, difference equations models). The main new result of the paper is formulation of the augmented formula for unmeasured disturbance estimate assuring offset-free control in the GPC (generalized predictive control) algorithm. The new formula is parametrized, which gives possibility to tune dynamics of the estimate and influence substantively sensitivity to noise of the control system. It is essential, as the GPC algorithm in existing formulation is known to be sensitive to noise. Theoretical foundation of the proposed algorithm is given. Theoretical results are validated and illustrated by simulation results of a MIMO control system. In particular, it is shown that tuning the augmented disturbance estimate reduces sensitivity to noise of the GPC algorithm. Finally, formulae for new, augmented and parametrized unmeasured disturbance estimates in the MPC algorithms with nonlinear parametric models are proposed.

本文研究了在存在确定性常数或渐近常数外部和内部（建模误差）扰动的情况下，具有参数过程模型的无偏移模型预测控制（MPC）的设计。对于线性状态空间模型，简要回顾了两种已建立的确保无偏移控制的方法以及它们之间的关系，包括最近的结果。给出了一个关于全状态下扰动估计的新结果。这是展示本文主要结果所必需的，但联合起来，也为线性参数模型（状态空间模型、差分方程模型）提供了解决上述设计问题的统一方法。本文的主要新成果是建立了广义预测控制算法中保证无偏置控制的不可测扰动估计增广公式。新公式是参数化的，这使得估计的动态调整成为可能，并对控制系统的噪声灵敏度产生实质性的影响。这是必要的，因为已知现有公式中的GPC算法对噪声敏感。给出了该算法的理论基础。理论结果得到了验证，并通过MIMO控制系统的仿真结果进行了验证。特别地，研究表明，调整增广干扰估计可以降低GPC算法对噪声的敏感性。最后，给出了具有非线性参数模型的MPC算法中新的、增广的和参数化的未测扰动估计公式。

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引用次数: 0

Observer design for lactic-acid bacteria population balances with non-uniformly delayed measurements 具有非均匀延迟测量的乳酸菌种群平衡的观察者设计

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2026-01-27 DOI: 10.1016/j.jprocont.2026.103639

Arthur Lepsien , Lucas Holtorf , Alexander Schaum

The paper addresses the problem estimating the cell-mass distribution density, glucose and lactate concentration, as well as of the total biomass concentration in lactic-acid fermentation. The estimate is based on the combination of a cell population balance model with the available measurements. The model shows a cascade structure of a nonlinear finite-dimensional subsystem and a linear infinite-dimensional subsystem. The measurements are available on different time scales. On a quasi-continuous time scale optical density and conductivity are measured. The cell-size distribution is measured with a considerably lower frequency and is furthermore subject to non-uniform delays. The proposed estimation strategy exploits the cascade structure and consists of two cascaded discrete-time extended Kalman filters (EKFs). The performance of the proposed approach is demonstrated using experimental data from batch experiments with Streptococcus thermophilus. The estimation strategy improves the mean normalized root mean squared error of the distribution by approximately 41.6 % compared to a pure simulation.

本文研究了乳酸发酵过程中细胞质量分布密度、葡萄糖和乳酸浓度以及总生物量的估算问题。该估计是基于细胞种群平衡模型与可用测量值的结合。该模型为非线性有限维子系统和线性无限维子系统的级联结构。这些测量结果适用于不同的时间尺度。在准连续时间尺度上测量了光密度和电导率。单元大小分布以相当低的频率测量，并且进一步受到非均匀延迟的影响。所提出的估计策略利用级联结构，由两个级联的离散扩展卡尔曼滤波器（ekf）组成。利用嗜热链球菌的批量实验数据证明了该方法的性能。与纯模拟相比，该估计策略将分布的平均归一化均方根误差提高了约41.6%。

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引用次数: 0

Enabling robust mixed-integer nonlinear model predictive control via self-supervised learning and combinatorial integral approximation 利用自监督学习和组合积分逼近实现鲁棒混合整数非线性模型预测控制

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2026-01-27 DOI: 10.1016/j.jprocont.2026.103636

Joshua Adamek, Lukas Lüken, Sergio Lucia

We present a novel approach that enables the solution of nonlinear model predictive control with integer decisions in real time even when when the model is subject to many uncertainties. Our approach tightly integrates three different ideas.

First, we use combinatorial integral approximation as a powerful heuristic to approximate the mixed-integer nonlinear problems with two nonlinear problems. Next, we formulate a scenario tree formulation to deal with uncertain parameters. To tackle the large number of uncertainties, we propose a scenario decomposition method to solve each scenario problem in parallel. We integrate the combinatorial approximation within this scenario decomposition method to provide a method for uncertain parameters within mixed-integer model predictive control. This method leads to many smaller optimization problems that can be solved in parallel. As the third idea, we propose the use of learned iterative solvers, as opposed to traditional numerical solvers, to solve each subproblem. This methodology can be massively parallelized by evaluating neural networks on powerful GPUs. As a result, the proposed approach leads to an order of magnitude faster solutions when compared to a solution of the entire robust problem with a traditional numerical solver, as well as to improved accuracy in comparison to a supervised learning approach. This is illustrated in the simulation example of an uncertain nonlinear reactor with mixed-integer decisions.

本文提出了一种新颖的方法，使具有整数决策的非线性模型预测控制即使在模型具有许多不确定性的情况下也能实时求解。我们的方法紧密结合了三种不同的想法。首先，我们将组合积分近似作为一种强大的启发式方法，用两个非线性问题近似混合整数非线性问题。接下来，我们制定了一个场景树公式来处理不确定参数。为了解决大量的不确定性，我们提出了一种场景分解方法来并行解决每个场景问题。在此场景分解方法中引入组合逼近，为混合整数模型预测控制中的不确定参数提供了一种方法。这种方法导致许多较小的优化问题可以并行解决。作为第三个想法，我们建议使用学习迭代求解器来解决每个子问题，而不是传统的数值求解器。这种方法可以通过在强大的gpu上评估神经网络来大规模并行化。因此，与传统的数值求解器解决整个鲁棒问题相比，该方法的求解速度要快一个数量级，而且与监督学习方法相比，该方法的精度也有所提高。通过一个不确定非线性混合整数决策电抗器的仿真实例说明了这一点。

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引用次数: 0

Fuzzy advanced control: Boosting efficiency and economic benefits in offshore gas compression systems 模糊先进控制：提高海上天然气压缩系统的效率和经济效益

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2026-01-19 DOI: 10.1016/j.jprocont.2026.103635

Thamires A.L. Guedes , Sergio A.C. Giraldo , Marcelo L. de Lima , Mario C.M.M. Campos , Daniel M. Lima , Leonardo D. Ribeiro , Argimiro R. Secchi

In this work, an advanced control system based on fuzzy logic is analyzed and implemented for a gas compression system on an offshore platform. This solution was applied after identifying an operational problem causing substantial economic losses due to unscheduled stops from high-temperature events at the compressor discharge. A detailed analysis of process variables that could be employed within the controller to mitigate this phenomenon was conducted, identifying compressor discharge temperatures as controlled variables and the machine’s discharge pressure setpoint as the primary manipulated variable. Through dynamic simulations and using a digital twin, it was possible to validate the behavior and effect of the variables, along with implementing the control system. Operational tests on the platform were conducted to verify the proposal and confirm the simulation results. The open-loop implementation of the control algorithm, i.e. the computed control action was not sent to the process, allowed the tracking and observation of the control system’s reaction to critical incidents, which validated its expected behavior. The activation of the closed-loop control successfully prevented machine stops, avoiding economic losses in production. This preventive approach avoided operational stops and highlighted the potential of advanced control systems to significantly improve safety, efficiency, and reliability in complex industrial environments.

本文分析并实现了一种基于模糊逻辑的海洋平台气体压缩系统的先进控制系统。该解决方案是在确定了一个运行问题后应用的，该问题由于压缩机排气高温事件导致的计划外停机而造成了巨大的经济损失。为了缓解这一现象，研究人员对控制器中的过程变量进行了详细分析，将压缩机排放温度确定为受控变量，将机器的排放压力设定点确定为主要操纵变量。通过动态模拟和使用数字孪生，可以验证变量的行为和效果，以及实现控制系统。在平台上进行了运行测试，验证了该方案并验证了仿真结果。控制算法的开环实现，即计算控制动作不发送到过程中，允许跟踪和观察控制系统对关键事件的反应，从而验证其预期行为。闭环控制的启动成功地防止了机器停机，避免了生产中的经济损失。这种预防性方法避免了作业停止，并突出了先进控制系统在复杂工业环境中显著提高安全性、效率和可靠性的潜力。

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引用次数: 0

A predictive modular approach to constraint satisfaction under uncertainty — with application to glycosylation in continuous monoclonal antibody biosimilar production 不确定条件下约束满足的预测模块化方法-应用于连续单克隆抗体生物仿制药生产中的糖基化

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2026-01-18 DOI: 10.1016/j.jprocont.2026.103632

Yu Wang , Xiao Chen , Hubert Schwarz , Véronique Chotteau , Elling W. Jacobsen

The paper proposes a modular-based approach to constraint handling in process optimization and control. This is partly motivated by the recent interest in learning-based methods, e.g., within bioproduction, for which constraint handling under uncertainty is a challenge. The proposed constraint handler, called predictive filter, is combined with an adaptive constraint margin and a constraint violation cost monitor to minimize the cost of violating soft constraints due to model uncertainty and disturbances. The module can be combined with any controller and is based on minimally modifying the controller output, in a least squares sense, such that constraints are satisfied within the considered horizon. The proposed method is computationally efficient and suitable for real-time applications. The effectiveness of the method is illustrated through a realistic case study of glycosylation constraint satisfaction in continuous monoclonal antibody biosimilar production using Chinese hamster ovary cells, employing a metabolic network model consisting of 23 extracellular metabolites and 126 reactions. In the case study, the average constraint-violation cost is reduced by more than 60% compared to the case without the proposed constraint-handling method.

提出了一种基于模块化的过程优化控制约束处理方法。这部分是由于最近对基于学习的方法的兴趣，例如，在生物生产中，在不确定性下的约束处理是一个挑战。所提出的约束处理程序称为预测滤波器，它与自适应约束裕度和约束违反代价监视器相结合，以最小化由于模型不确定性和干扰而违反软约束的代价。该模块可以与任何控制器组合，并且基于最小二乘意义上的最小修改控制器输出，从而在考虑的范围内满足约束。该方法计算效率高，适合实时应用。采用23种细胞外代谢物和126种反应组成的代谢网络模型，通过对中国仓鼠卵巢细胞连续生产单克隆抗体生物仿制药中糖基化约束满足的实际案例研究，证明了该方法的有效性。在案例研究中，与没有提出约束处理方法的情况相比，平均违反约束成本降低了60%以上。

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引用次数: 0

An explicit model predictive control framework based on physics-informed neural networks 基于物理信息神经网络的显式模型预测控制框架

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2026-01-17 DOI: 10.1016/j.jprocont.2026.103634

Argyri Kardamaki , Teo Protoulis , Alex Alexandridis , Haralambos Sarimveis

This paper presents a novel control framework that integrates Physics-Informed Neural Networks (PINNs) with Model Predictive Control (MPC) for nonlinear dynamical systems. Unlike traditional MPC, which requires solving optimization problems in real time, the proposed method trains a single feedforward neural network to serve as an explicit controller that directly maps the current state, set-point, and disturbance signals to optimal control actions. The network is trained using a composite loss function that enforces the governing differential equations while incorporating control-oriented objectives such as set-point tracking, control smoothness, and soft constraints on states, inputs, and outputs. The proposed controller is validated on both single-input single-output (SISO) and multi-input multi-output (MIMO) water-tank benchmark systems, demonstrating accurate set-point tracking, effective measured disturbance rejection, and strong generalization across thousands of randomized test scenarios. A runtime comparison with a nonlinear MPC performing online optimization confirms that the explicit PINN-MPC approach achieves comparable control performance while requiring several orders of magnitude less computation time. These results highlight the scalability and computational efficiency of the proposed framework, positioning it as a novel paradigm for real-time control of nonlinear systems.

本文提出了一种将物理信息神经网络（PINNs）与模型预测控制（MPC）相结合的非线性动态系统控制框架。与需要实时解决优化问题的传统MPC不同，该方法训练单个前馈神经网络作为显式控制器，直接将当前状态、设定点和干扰信号映射到最优控制动作。该网络使用复合损失函数进行训练，该函数强化了控制微分方程，同时结合了面向控制的目标，如设定点跟踪、控制平滑性以及对状态、输入和输出的软约束。该控制器在单输入单输出（SISO）和多输入多输出（MIMO）水箱基准系统上进行了验证，证明了精确的设定点跟踪，有效的测量干扰抑制，以及在数千个随机测试场景中的强泛化。通过与在线优化的非线性MPC的运行时比较，证实了显式PINN-MPC方法在减少几个数量级的计算时间的同时获得了相当的控制性能。这些结果突出了所提出框架的可扩展性和计算效率，将其定位为非线性系统实时控制的新范例。

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引用次数: 0