Journal of Process Control最新文献

Understanding the role of multi-agent technology on quality of manufacturing organizations: A hybrid MCDM analysis 理解多智能体技术对制造组织质量的作用：一个混合MCDM分析

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

Journal of Process Control

Pub Date : 2026-01-16 DOI: 10.1016/j.jprocont.2026.103628

Vikram Singh, Somesh Kumar Sharma

Maintaining quality in the manufacturing system has become a critical challenge in today’s rapidly evolving technological landscape. To overcome this, current research examines the role of Multi-Agent Technology (MAT) in improving the quality of manufacturing processes. For this, a conceptual framework consisting of eight factors and thirty-seven variables of MAT, identified from the literature, was analyzed using the Analytical Hierarchical Process (AHP), Sensitivity Analysis, Decision-Making Trial and Evaluation Laboratory (DEMATEL), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). AHP findings revealed ‘Production Planning’ as the highest-priority factor, followed by ‘Process Monitoring, Control, and Data Acquisition.’ DEMATEL established the interrelationships among variables, ensuring a collaborative approach to maintaining quality. Sensitivity analysis and TOPSIS validated the AHP results for consistency and robustness. The findings also indicated that Virtual Manufacturing, Distributed Digital Manufacturing, and Adaptive Agent-Based Architecture were the globally top ranked variables in the framework that help to ensure the quality of manufacturing processes. These findings contribute to developing autonomous, high-precision manufacturing systems for long-term competitiveness and quality assurance. This study provides valuable insights for researchers and managers, demonstrating that MAT and its parameters can be customized to optimize manufacturing quality.

在当今快速发展的技术环境中，保持制造系统的质量已成为一个关键的挑战。为了克服这一点，目前的研究考察了多智能体技术（MAT）在提高制造过程质量方面的作用。为此，利用层次分析法（AHP）、敏感性分析法、决策试验与评价实验室法（DEMATEL）和理想解相似性排序偏好法（TOPSIS）对从文献中确定的由8个因素和37个变量组成的MAT概念框架进行了分析。AHP调查结果显示，“生产计划”是最优先考虑的因素，其次是“过程监控、控制和数据采集”。DEMATEL建立了变量之间的相互关系，确保了保持质量的协作方法。敏感性分析和TOPSIS验证了AHP结果的一致性和稳健性。研究结果还表明，虚拟制造、分布式数字制造和基于自适应代理的体系结构是框架中全球排名最高的变量，有助于确保制造过程的质量。这些发现有助于开发自主的高精度制造系统，以实现长期竞争力和质量保证。本研究为研究人员和管理人员提供了有价值的见解，表明MAT及其参数可以定制以优化制造质量。

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引用次数: 0

A tutorial overview of model predictive control for continuous crystallization: Current possibilities and future perspectives 连续结晶模型预测控制的教程概述：当前的可能性和未来的观点

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

Journal of Process Control

Pub Date : 2026-01-15 DOI: 10.1016/j.jprocont.2026.103630

Collin R. Johnson , Kerstin Wohlgemuth , Sergio Lucia

Continuous crystallization processes require advanced control strategies to ensure consistent product quality, yet deploying optimization-based controllers such as model predictive control remains challenging. Combining spatially distributed crystallizer models with detailed particle size distributions leads to computationally demanding problems that are difficult to solve in real-time. This tutorial provides a comprehensive overview of how to address this challenge. Topics include numerical methods for solving population balance equations, modeling of crystallizers, and data-driven surrogate modeling. We show how these elements combine within a model predictive control framework to enable real-time control of particle size distributions. Two case studies illustrate the complete workflow: a well-mixed crystallizer that allows comparison with established methods, and a spatially distributed plug-flow crystallizer that demonstrates application to more complex systems. Readers gain a practical roadmap for implementing model predictive control in continuous crystallization, supported by open-source code and interactive examples. The tutorial concludes by outlining open challenges and emerging opportunities in the field.

连续结晶过程需要先进的控制策略来确保一致的产品质量，但部署基于优化的控制器（如模型预测控制）仍然具有挑战性。将空间分布结晶器模型与详细的粒度分布相结合，导致难以实时解决的计算要求很高的问题。本教程提供了如何应对这一挑战的全面概述。主题包括解决人口平衡方程的数值方法，结晶器的建模和数据驱动的代理建模。我们展示了这些元素如何在模型预测控制框架内组合，以实现粒度分布的实时控制。两个案例研究说明了完整的工作流程：一个充分混合的结晶器，允许与已建立的方法进行比较，以及一个空间分布的塞流结晶器，演示了在更复杂系统中的应用。读者获得了在连续结晶中实现模型预测控制的实用路线图，由开源代码和交互式示例支持。本教程最后概述了该领域的公开挑战和新出现的机会。

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引用次数: 0

A microbial fuel cell with an optimal controller based on improved reptile search algorithm 一种基于改进爬行动物搜索算法的最优控制器微生物燃料电池

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

Journal of Process Control

Pub Date : 2026-01-14 DOI: 10.1016/j.jprocont.2026.103629

Chenlong Wang, Fengying Ma

Microbial fuel cells (MFCs) are novel energy technologies that convert the chemical energy of organic matter in wastewater into electrical energy. However, MFC systems generally require external control to achieve stable voltage output. In this paper, an optimal controller for MFC systems is designed. By adopting the

θ

–

D

technique, the intractable Hamilton–Jacobi–Bellman (HJB) equation is transformed into a set of algebraic equations, which enables the solution of the optimal control problem with large initial states. To address parameter uncertainty in the optimal controller, an optimization algorithm is employed to tune its parameters. Furthermore, to overcome the limitations of existing optimization algorithms, including slow convergence speed, low solution accuracy, and premature convergence, an improved reptile search algorithm is proposed by integrating chaotic mechanisms, an elite-guided differential perturbation strategy, and an adaptive crossover probability control mechanism. Simulation results demonstrate that the improved algorithm achieves faster convergence and higher accuracy. Moreover, the designed optimal controller exhibits smaller overshoot and steady-state error in the MFC.

微生物燃料电池（mfc）是一种将废水中有机物的化学能转化为电能的新型能源技术。然而，MFC系统通常需要外部控制来实现稳定的电压输出。本文设计了MFC系统的最优控制器。采用θ-D技术，将棘手的Hamilton-Jacobi-Bellman （HJB）方程转化为一组代数方程，使具有大初始状态的最优控制问题得以求解。为了解决最优控制器中参数的不确定性，采用了一种优化算法对其参数进行整定。此外，针对现有优化算法收敛速度慢、求解精度低、过早收敛等局限性，提出了一种综合混沌机制、精英引导微分摄动策略和自适应交叉概率控制机制的改进爬行动物搜索算法。仿真结果表明，改进后的算法具有更快的收敛速度和更高的精度。此外，所设计的最优控制器在MFC中具有较小的超调量和稳态误差。

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引用次数: 0

An inter-domain feature discrepancy method for multi-source partial domain fault diagnosis 多源局部域故障诊断的域间特征差异方法

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

Journal of Process Control

Pub Date : 2026-01-13 DOI: 10.1016/j.jprocont.2026.103627

Shuai Tan , Shuxuan Zeng , Jijie Han , Qingchao Jiang , Weimin Zhong , Jiayi Wang

Multi-source domain adaptation poses more complex challenges compared to traditional single source domain adaptation. While constrained target domain labeling and limited information from a single source can be mitigated, the inherent discrepancies among multiple domains exacerbate the difficulty of fault diagnosis under varying operating conditions, particularly in real industrial systems with diverse and intricate environments. To tackle these issues, a novel Multi-source Inter-domain Feature Discrepancy (MIFD) model is proposed in this paper, which differs from existing multi-source adaptation methods by explicitly modeling inter-domain feature discrepancies instead of solely enforcing a unified shared feature space through global or marginal distribution alignment. In the proposed framework, a three-scale alignment mechanism is introduced to jointly align feature representations, class semantics, and domain distributions, thereby constraining domain shifts at multiple semantic levels while preserving domain-pair-specific characteristics. A discrepancy-aware feature matching module is developed to enable the extraction of reliable and transferable features tailored to specific source–target domain pairs. Furthermore, a class-center and domain alignment strategy is designed to constrain conditional distributions and alleviate pseudo-label bias. In addition, a dual-level weighting scheme is proposed, by which domain contributions are adaptively quantified and irrelevant classes are automatically filtered. Experimental results on two benchmark fault diagnosis scenarios under partial label space settings demonstrate that the proposed MIFD model outperforms state-of-the-art multi-source domain adaptation methods by up to 5.13% on the CWRU dataset and achieves an improvement of 2.16% on the TEP dataset, effectively reducing negative transfer and domain conflicts while enhancing diagnostic robustness under label space inconsistency.

与传统的单源域自适应相比，多源域自适应面临更复杂的挑战。虽然可以减轻目标域标记的约束和单一来源的有限信息，但多域之间固有的差异加剧了在不同运行条件下的故障诊断困难，特别是在具有多样化和复杂环境的实际工业系统中。为了解决这些问题，本文提出了一种新的多源域间特征差异（MIFD）模型，该模型不同于现有的多源自适应方法，它明确地建模域间特征差异，而不是仅仅通过全局或边缘分布对齐来强制实现统一的共享特征空间。在该框架中，引入了一种三尺度对齐机制来联合对齐特征表示、类语义和领域分布，从而在保留领域对特定特征的同时约束多个语义级别的领域移动。开发了一个差异感知特征匹配模块，以实现针对特定源-目标域对提取可靠且可转移的特征。此外，还设计了类中心和领域对齐策略来约束条件分布和减轻伪标签偏差。此外，提出了一种自适应量化领域贡献和自动过滤不相关类的双级加权方案。在两个部分标签空间设置的基准故障诊断场景下的实验结果表明，本文提出的MIFD模型在CWRU数据集上的性能比目前最先进的多源域自适应方法提高了5.13%，在TEP数据集上的性能提高了2.16%，有效地减少了负迁移和域冲突，同时增强了标签空间不一致下的诊断鲁棒性。

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引用次数: 0

On data-driven robust optimization with multiple uncertainty subsets: Unified uncertainty set representation and mitigating conservatism 多不确定性子集数据驱动鲁棒优化：统一不确定性集表示和缓和保守性

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

Journal of Process Control

Pub Date : 2026-01-06 DOI: 10.1016/j.jprocont.2025.103611

Yun Li , Neil Yorke-Smith , Tamas Keviczky

Constructing uncertainty sets as unions of multiple subsets has emerged as an effective approach for creating compact and flexible uncertainty representations in data-driven robust optimization (RO). This paper focuses on two separate research questions. The first concerns the computational challenge in applying these uncertainty sets in RO-based predictive control. To address this, a monolithic mixed-integer representation of the uncertainty set is proposed to uniformly describe the union of multiple subsets, enabling the computation of the worst-case uncertainty scenario across all subsets within a single mixed-integer linear programming (MILP) problem. The second research question focuses on mitigating the conservatism of conventional RO formulations by leveraging the structure of the uncertainty set. To achieve this, a novel objective function is proposed to exploit the uncertainty set structure and integrate the existing RO and distributionally robust optimization (DRO) formulations, yielding less conservative solutions than conventional RO formulations, while avoiding the high-dimensional continuous uncertainty distributions and the high computational burden typically associated with existing DRO formulations. Given the proposed formulations, numerically efficient computation methods based on column-and-constraint generation (CCG) are also developed. Extensive simulations across three case studies are performed to demonstrate the effectiveness of the proposed schemes.

在数据驱动鲁棒优化（RO）中，将不确定性集构造为多个子集的并集已成为创建紧凑和灵活的不确定性表示的有效方法。本文主要关注两个独立的研究问题。第一个问题是在基于ro的预测控制中应用这些不确定性集的计算挑战。为了解决这个问题，提出了不确定性集的整体混合整数表示，以统一描述多个子集的并集，从而能够在单个混合整数线性规划（MILP）问题中计算所有子集的最坏情况不确定性情景。第二个研究问题侧重于利用不确定性集的结构来减轻传统RO公式的保守性。为了实现这一目标，提出了一种新的目标函数，利用不确定性集结构，将现有的RO和分布鲁棒优化（DRO）公式集成在一起，产生比传统RO公式更少的保守解，同时避免了高维连续不确定性分布和现有DRO公式通常相关的高计算负担。在此基础上，提出了基于列约束生成（CCG）的高效数值计算方法。在三个案例研究中进行了广泛的模拟，以证明所提出方案的有效性。

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引用次数: 0

Gain-scheduled tube-based MPC for quasi-LPV systems using vertex models 使用顶点模型的准lpv系统的增益调度管MPC

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

Journal of Process Control

Pub Date : 2026-01-05 DOI: 10.1016/j.jprocont.2025.103617

Rangoli Singh , Sandip Ghosh , Devender Singh , Pawel Dworak

This work develops a tube-based model predictive control (MPC) scheme for quasi–linear parameter-varying (quasi-LPV) systems affected by bounded disturbances and time-varying but measurable scheduling parameters. The controller uses a polytopic model together with a gain-scheduled feedback law to maintain robustness against parameter variations and external disturbances. To describe the terminal region more flexibly, a parameter-dependent terminal cost is introduced. In addition, an auxiliary cost function, evaluated only at the vertices of the polytope, removes the need to update parameters at every prediction step. Although the proposed formulation increases the computational load slightly, it provides stronger disturbance rejection and improved constraint handling. Experiments on a coupled-tank setup demonstrate that the method is both effective and practical for real-time implementation.

针对受有界扰动和时变但可测量的调度参数影响的准线性变参系统，提出了一种基于管的模型预测控制（MPC）方案。控制器采用多面体模型和增益调度反馈律来保持对参数变化和外部干扰的鲁棒性。为了更灵活地描述终端区域，引入了与参数相关的终端成本。此外，一个辅助的代价函数，只在多面体的顶点处计算，消除了在每个预测步骤更新参数的需要。虽然提出的公式稍微增加了计算量，但它提供了更强的抗干扰性和改进的约束处理。在一个耦合槽装置上的实验证明了该方法的有效性和实时性。

引用次数: 0

A physics-guided hybrid model for calendering width prediction in rubber tire manufacturing 橡胶轮胎压延宽度预测的物理导向混合模型

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

Journal of Process Control

Pub Date : 2026-01-04 DOI: 10.1016/j.jprocont.2025.103612

Shaoyuan Li, Haolei Yin, Xiaohong Yin, Wenjian Cai

The width in rubber extrusion-calendering is a crucial process parameter in the rubber production workflow, as it directly influences both the quality and performance of rubber products, as well as overall production efficiency. However, the rubber extrusion-calendering process involves strong coupling among multiple parameters, with operating condition variations and significant external disturbances, leading to complex dynamic characteristics such as nonlinearity and time delays, which severely impact the accuracy of width prediction. To address these challenges, a hybrid modeling approach that integrates physical mechanisms with data-driven methods has been proposed within the framework of Physics-Informed Neural Networks (PINN). Firstly, a data-driven prediction model for calendering width was developed using a combination of a Temporal Convolutional Network and a Bidirectional Long Short-Term Memory network (TCN-BiLSTM). Secondly, an analysis of the physical mechanism underlying the extrusion-calendering process was conducted based on the power-law constitutive relationship to provide essential physical constraints for the prediction model. Furthermore, a dynamically adaptive weighting strategy was proposed to effectively reconcile conflicts between physical constraints and data fitting in the PINN model. Validation experiments demonstrate that this hybrid modeling approach can sustain high prediction accuracy even when faced with limited training data, noise interference, and varying operating conditions.

橡胶挤出压延宽度是橡胶生产流程中一个至关重要的工艺参数，它直接影响到橡胶制品的质量、性能和整体生产效率。然而，橡胶挤出-压延过程是一个多参数强耦合的过程，操作条件变化大，外部干扰大，导致非线性和时滞等复杂动态特性，严重影响宽度预测的准确性。为了应对这些挑战，在物理信息神经网络（PINN）框架内提出了一种将物理机制与数据驱动方法相结合的混合建模方法。首先，将时序卷积网络与双向长短期记忆网络（TCN-BiLSTM）相结合，建立了一种数据驱动的压延宽度预测模型；其次，基于幂律本构关系分析了挤压压延过程的物理机制，为预测模型提供了必要的物理约束条件。在此基础上，提出了一种动态自适应加权策略，有效地解决了PINN模型中物理约束与数据拟合之间的冲突。验证实验表明，即使面对有限的训练数据、噪声干扰和变化的操作条件，这种混合建模方法也能保持较高的预测精度。

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引用次数: 0

Correcting batch effects in fermentation processes using empirical Bayesian approach 利用经验贝叶斯方法修正发酵过程中的批效应

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

Journal of Process Control

Pub Date : 2026-01-03 DOI: 10.1016/j.jprocont.2025.103616

Kaiqiang Lou, Shunyi Zhao, Xiaoli Luan, Fei Liu

Modeling fermentation processes is challenging due to their nonlinear dynamics, time-dependent behavior, and inherent system uncertainties. Data-driven approaches, including black-box and gray-box models, are widely used in practice, but their performance relies heavily on the consistency and reliability of input data. A common issue affecting fermentation datasets is the presence of batch effects, which refer to systematic differences between datasets collected from separate fermentation runs conducted under similar conditions. These differences reduce data comparability and hinder reliable modeling. To address this problem, this study proposes an empirical Bayes-based method for fermentation datasets. A key component of the proposed approach is an unsupervised batch clustering strategy that enables more stable parameter estimation in the absence of within-batch replicates. The clustering-assisted ComBat method is applied to two representative cases: penicillin fermentation and Saccharomyces cerevisiae yeast fermentation. On the penicillin dataset (20 batches), the results demonstrate that the method effectively reduces batch-to-batch variability by 70.3% (median standard deviation) and improves data consistency by 74.4% (median coefficient of variation). Evaluation using the median absolute deviation confirms its advantage over conventional correction methods, resulting in a 64.4% reduction relative to the raw data. Additional tests on larger datasets further support its robustness and practical applicability.

由于发酵过程的非线性动力学、时变行为和固有的系统不确定性，建模是具有挑战性的。数据驱动方法，包括黑盒模型和灰盒模型，在实践中得到了广泛的应用，但它们的性能严重依赖于输入数据的一致性和可靠性。影响发酵数据集的一个常见问题是批次效应的存在，这是指在相似条件下进行的单独发酵运行收集的数据集之间的系统差异。这些差异降低了数据的可比性，阻碍了可靠的建模。为了解决这个问题，本研究提出了一种基于经验贝叶斯的发酵数据集方法。该方法的一个关键组成部分是无监督批聚类策略，该策略在没有批内重复的情况下实现更稳定的参数估计。将聚类辅助战斗方法应用于青霉素发酵和酿酒酵母发酵两个典型案例。在青霉素数据集（20批次）上，结果表明，该方法有效地将批间变异性（中位标准差）降低了70.3%，将数据一致性（中位变异系数）提高了74.4%。使用中位数绝对偏差的评估证实了它比传统校正方法的优势，相对于原始数据减少了64.4%。对更大数据集的额外测试进一步支持其鲁棒性和实际适用性。

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引用次数: 0

Dry biomass estimation in production of insects larvae using Interconnected Generalized Super-Twisting Observer 利用互联广义超扭观测器估算昆虫幼虫生产中的干生物量

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

Journal of Process Control

Pub Date : 2026-01-01 DOI: 10.1016/j.jprocont.2025.103615

Rania Tafat , Jaime A. Moreno , Stefan Streif

Alternative protein sources are becoming essential for achieving a sustainable food system. The Black Soldier Fly larvae (BSFL), a protein-rich insect capable of feeding on a wide range of organic materials, shows immense potential for use in bio-conversion. It is already being used in poultry and fish aquaculture and is currently under evaluation for human consumption. Consequently, the farming of this insect is of great interest, and advanced control methods could significantly optimize the process and improve resource efficiency. One of the main challenges in applying these advanced techniques is the lack of information about certain critical system states, particularly the estimation of dry biomass weight. Measuring the dry biomass weight of the larvae is a destructive process that can only be performed at the beginning and end of the cycle. This low sampling frequency is insufficient for the application of advanced control strategies. Thus, a non-invasive estimation method is required. This work addresses the observer design problem for estimating the dry biomass weight of BSFL. The objective is to obtain an online estimation of this weight before the larvae reach maturity. To achieve this, a reduced version of the existing BSFL full-fledged model is proposed, based on specific assumptions. A subsystem is extracted from this BSFL reduced model, for which, a necessary and sufficient condition is provided for its global strong observability. Moreover, an interconnection of Generalized Super-Twisting Observers is designed, and a comparison is made between this method and the high-gain observer.

替代蛋白质来源对于实现可持续粮食系统至关重要。黑兵蝇幼虫（BSFL）是一种富含蛋白质的昆虫，能够以多种有机材料为食，在生物转化方面显示出巨大的潜力。它已用于家禽和鱼类水产养殖，目前正在评估是否供人食用。因此，这种昆虫的养殖具有重要意义，先进的控制方法可以显着优化过程并提高资源效率。应用这些先进技术的主要挑战之一是缺乏关于某些关键系统状态的信息，特别是对干生物量重量的估计。测量幼虫的干生物量重量是一个破坏性的过程，只能在周期的开始和结束时进行。这种低采样频率不足以应用先进的控制策略。因此，需要一种非侵入性的估计方法。这项工作解决了估计BSFL干生物量重量的观察者设计问题。目的是在幼虫成熟之前获得该重量的在线估计。为了实现这一目标，基于特定的假设，提出了现有BSFL全功能模型的简化版本。从该BSFL简化模型中提取了一个子系统，并给出了其全局强可观测性的充分必要条件。设计了一种广义超扭观测器的互连方法，并与高增益观测器进行了比较。

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引用次数: 0

Real-time physical activity detection module during sensor augmented insulin pump therapy 传感器增强胰岛素泵治疗过程中的实时身体活动检测模块

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

Journal of Process Control

Pub Date : 2026-01-01 DOI: 10.1016/j.jprocont.2025.103608

Eleonora Manzoni , Emilia Fushimi , Eleonora M. Aiello , Zoey Li , Robin Gal , Corby K. Martin , Susana R. Patton , Simone Del Favero , Francis J. Doyle III

Individuals living with type 1 diabetes (T1D) face important challenges when engaging in physical activity (PA), as it necessitates careful management of blood glucose levels often through insulin adjustments and carbohydrate intake. Integrating PA detection into sensor augmented insulin pumps (SAP) is a promising strategy to enhance glycemic control by suggesting basal insulin reduction or carbohydrate ingestion in the critical 24 h after the PA detection.

We have developed a real-time, model-based module for PA detection based solely on measured glucose levels, insulin infusion rates, and carbohydrate intake. The approach is based on the monitoring of the magnitude as well as various statistical properties of the prediction residuals, i.e., the discrepancies between actual sensor-measured glucose levels and model-predicted levels.

We tested our algorithm on the Type 1 Diabetes and Exercise Initiative (T1DEXI) dataset, which includes structured sessions of aerobic, resistance, and interval exercises. In a dataset containing all three activity types, the detection approach based on the median of the prediction residuals successfully detected an average of 59% of PA instances, while keeping the false alarms to 3.5 per considered timeframe, when considering models tailored to each participant. When using a population model identified on in-silico data from the UVa/Padova T1D simulator, the approach successfully detected 62% of PAs, while keeping the false alarms to 3.6 per considered timeframe.

These encouraging findings open the possibility of integrating PA detection into SAP systems without the need for additional physiological signals, thus enabling improved glucose management.

1型糖尿病（T1D）患者在进行体育活动（PA）时面临着重要的挑战，因为它需要经常通过胰岛素调节和碳水化合物摄入来仔细管理血糖水平。将PA检测整合到传感器增强胰岛素泵（SAP）中是一种很有前景的策略，通过提示在PA检测后关键的24小时内基础胰岛素减少或碳水化合物摄入来加强血糖控制。我们开发了一个实时的、基于模型的模块，用于仅根据测量的葡萄糖水平、胰岛素输注率和碳水化合物摄入量检测PA。该方法基于对预测残差的大小和各种统计特性的监测，即传感器实际测量的葡萄糖水平与模型预测的水平之间的差异。我们在1型糖尿病和运动倡议（T1DEXI）数据集上测试了我们的算法，该数据集包括有氧、阻力和间歇运动的结构化会话。在包含所有三种活动类型的数据集中，基于预测残差中位数的检测方法成功地检测了平均59%的PA实例，同时在考虑为每个参与者定制的模型时，将每个考虑的时间框架的假警报保持在3.5。当使用来自UVa/Padova T1D模拟器的计算机数据识别的种群模型时，该方法成功检测到62%的pa，同时将每个考虑的时间范围内的假警报保持在3.6。这些令人鼓舞的发现开启了将PA检测整合到SAP系统的可能性，而不需要额外的生理信号，从而改善葡萄糖管理。

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引用次数: 0