Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.07.145
Rastislav Fáber , Marco Vaccari , Riccardo Bacci di Capaci , Karol Ľubušký , Gabriele Pannocchia , Radoslav Paulen
We study real-time process monitoring, where employed online sensors yield inaccurate information. A multi-fidelity (MF) modeling approach is adopted that integrates dynamic information from online, low-fidelity (LF) data with infrequent, high-fidelity (HF) laboratory measurements. The proposed methodology is demonstrated on a composition monitoring problem derived from real oil refinery operations. The developed MF model exhibits a significant improvement in accuracy with respect to both LF data (online sensor) and the HF model (standard soft sensor). The results highlight the potential of MF modeling for improving process monitoring and control through the integration of diverse data sources.
{"title":"Improving Process Monitoring via Dynamic Multi-Fidelity Modeling","authors":"Rastislav Fáber , Marco Vaccari , Riccardo Bacci di Capaci , Karol Ľubušký , Gabriele Pannocchia , Radoslav Paulen","doi":"10.1016/j.ifacol.2025.07.145","DOIUrl":"10.1016/j.ifacol.2025.07.145","url":null,"abstract":"<div><div>We study real-time process monitoring, where employed online sensors yield inaccurate information. A multi-fidelity (MF) modeling approach is adopted that integrates dynamic information from online, low-fidelity (LF) data with infrequent, high-fidelity (HF) laboratory measurements. The proposed methodology is demonstrated on a composition monitoring problem derived from real oil refinery operations. The developed MF model exhibits a significant improvement in accuracy with respect to both LF data (online sensor) and the HF model (standard soft sensor). The results highlight the potential of MF modeling for improving process monitoring and control through the integration of diverse data sources.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 6","pages":"Pages 199-204"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.07.147
Michal Darowski , Muhammad Faisal Aftab , David Walker , Hongyu Li , Christian W. Omlin
Bonnet polishing is an ultra-precision polishing technique used for manufacturing components utilized in optics, electronics, and Scientific instrumentation, where sub-nanometer accuracy is required. However, the process is not fully deterministic and requires multiple process-metrology iterations. In modern computer numerically controlled (CNC) machines, polishing is performed by moderating the bonnet tool dwell time at each location based on the input parameters and material removal rate (MRR). While the MRR is typically treated as constant once established, it continuously evolves due to the process’s dynamic nature and changing conditions. This variability in MRR impacts the convergence of the polishing process, necessitating repeated surface processing and resulting in increased manufacturing time and cost. In this work, we present a data-driven approach to estimate the amount of material removed during the pre-polishing routine in bonnet polishing. The estimations are based on the force exerted by the bonnet tool on a polished surface along the three dimensions. Measurements were obtained using a bespoke force table with load sensors across three axes, mounted on the Zeeko IRP600 machine table. The results demonstrate the Effectiveness of this data-driven approach for estimating MRR, achieving a mean absolute error of 0.0541 µm and a mean absolute percentage error of 5.89% across the test set.
{"title":"Data-driven material removal rate estimation in bonnet polishing process","authors":"Michal Darowski , Muhammad Faisal Aftab , David Walker , Hongyu Li , Christian W. Omlin","doi":"10.1016/j.ifacol.2025.07.147","DOIUrl":"10.1016/j.ifacol.2025.07.147","url":null,"abstract":"<div><div>Bonnet polishing is an ultra-precision polishing technique used for manufacturing components utilized in optics, electronics, and Scientific instrumentation, where sub-nanometer accuracy is required. However, the process is not fully deterministic and requires multiple process-metrology iterations. In modern computer numerically controlled (CNC) machines, polishing is performed by moderating the bonnet tool dwell time at each location based on the input parameters and material removal rate (MRR). While the MRR is typically treated as constant once established, it continuously evolves due to the process’s dynamic nature and changing conditions. This variability in MRR impacts the convergence of the polishing process, necessitating repeated surface processing and resulting in increased manufacturing time and cost. In this work, we present a data-driven approach to estimate the amount of material removed during the pre-polishing routine in bonnet polishing. The estimations are based on the force exerted by the bonnet tool on a polished surface along the three dimensions. Measurements were obtained using a bespoke force table with load sensors across three axes, mounted on the Zeeko IRP600 machine table. The results demonstrate the Effectiveness of this data-driven approach for estimating MRR, achieving a mean absolute error of 0.0541 µm and a mean absolute percentage error of 5.89% across the test set.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 6","pages":"Pages 211-216"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.07.148
Jiaorao Wang , Lishuai Li , S. Joe Qin
Multimode characteristics commonly exist in modern industrial processes. Previous multi-model approaches treat steady states and transitions separately. However, identifying each mode is often tedious, generally achieved through clustering, requiring operators to tune hyperparameters extensively. As practitioners prefer a concise and easily implemented approach for multimode dynamic process monitoring, we initially propose a hierarchical scheme to simplify the modeling process while enhancing monitoring performance. Our method iteratively constructs dynamic models in a hierarchical, monitoring-oriented manner without mode partition. It offers three advantages. Firstly, modeling is directly conducted following a hierarchical structure driven by monitoring indexes, which is more concise and ensures monitoring performance. Secondly, by eliminating mode partition, only three hyperparameters, such as model order and the termination condition, need to be decided by humans. This significantly reduces human labour and facilitates the applicability of the proposed method across various processes. Lastly, by focusing on dynamic characteristics rather than steady-state and transitional modes, our method reduces the number of required models for a given process, resulting in a simpler multi-model structure that still ensures monitoring performance.
{"title":"A hierarchical multimode dynamic process monitoring scheme and its application to the Tennessee Eastman process⁎","authors":"Jiaorao Wang , Lishuai Li , S. Joe Qin","doi":"10.1016/j.ifacol.2025.07.148","DOIUrl":"10.1016/j.ifacol.2025.07.148","url":null,"abstract":"<div><div>Multimode characteristics commonly exist in modern industrial processes. Previous multi-model approaches treat steady states and transitions separately. However, identifying each mode is often tedious, generally achieved through clustering, requiring operators to tune hyperparameters extensively. As practitioners prefer a concise and easily implemented approach for multimode dynamic process monitoring, we initially propose a hierarchical scheme to simplify the modeling process while enhancing monitoring performance. Our method iteratively constructs dynamic models in a hierarchical, monitoring-oriented manner without mode partition. It offers three advantages. Firstly, modeling is directly conducted following a hierarchical structure driven by monitoring indexes, which is more concise and ensures monitoring performance. Secondly, by eliminating mode partition, only three hyperparameters, such as model order and the termination condition, need to be decided by humans. This significantly reduces human labour and facilitates the applicability of the proposed method across various processes. Lastly, by focusing on dynamic characteristics rather than steady-state and transitional modes, our method reduces the number of required models for a given process, resulting in a simpler multi-model structure that still ensures monitoring performance.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 6","pages":"Pages 217-222"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.07.197
Brijesh Kumar , Mani Bhushan
In this work, we propose a Symmetric Kullback Leibler divergence (SKLD)-based approach for optimal Design of Experiments (DOE) along with estimation of unspecified values in the design of experiments data matrix. Using SKLD as optimality criteria as opposed to various existing alphabetic optimality criteria, facilitates the incorporation of end-user desired performance of estimates. For the case when experimental noise is Gaussian and uncorrelated, the proposed approach results in a Mixed Integer Non-Linear Programming (MINLP) problem. This problem is NP-hard to solve. Hence, a novel heuristic solution strategy is also proposed which solves the proposed problem iteratively and sequentially. In particular, the MINLP problem is split into two sub-problems: (i) Non-Linear Programming (NLP) problem: to estimate optimal unspecified values, and (ii) Non-Linear Integer Programming (IP) problem: to obtain optimal DOE. These two subproblems are solved sequentially and iteratively until convergence is reached. The proposed solution strategy guarantees the decreasing behaviour of SKLD value. The efficacy of the proposed solution strategy is tested on an illustrative example and a Material synthesis problem, and performance is compared with Fedorov exchange algorithm, Forward Greedy search algorithm, and some of the popular MINLP solvers available in GAMS environment. Results demonstrate that the proposed solution approach outperforms most other methods.
{"title":"Symmetric Kullback Leibler divergence-based design of experiments with estimation of unspecified values","authors":"Brijesh Kumar , Mani Bhushan","doi":"10.1016/j.ifacol.2025.07.197","DOIUrl":"10.1016/j.ifacol.2025.07.197","url":null,"abstract":"<div><div>In this work, we propose a Symmetric Kullback Leibler divergence (SKLD)-based approach for optimal Design of Experiments (DOE) along with estimation of unspecified values in the design of experiments data matrix. Using SKLD as optimality criteria as opposed to various existing alphabetic optimality criteria, facilitates the incorporation of end-user desired performance of estimates. For the case when experimental noise is Gaussian and uncorrelated, the proposed approach results in a Mixed Integer Non-Linear Programming (MINLP) problem. This problem is NP-hard to solve. Hence, a novel heuristic solution strategy is also proposed which solves the proposed problem iteratively and sequentially. In particular, the MINLP problem is split into two sub-problems: (i) Non-Linear Programming (NLP) problem: to estimate optimal unspecified values, and (ii) Non-Linear Integer Programming (IP) problem: to obtain optimal DOE. These two subproblems are solved sequentially and iteratively until convergence is reached. The proposed solution strategy guarantees the decreasing behaviour of SKLD value. The efficacy of the proposed solution strategy is tested on an illustrative example and a Material synthesis problem, and performance is compared with Fedorov exchange algorithm, Forward Greedy search algorithm, and some of the popular MINLP solvers available in GAMS environment. Results demonstrate that the proposed solution approach outperforms most other methods.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 6","pages":"Pages 510-515"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.07.115
Tobias Brockhoff , Moritz Heinlein , Georg Hubmann , Stephan Lütz , Sergio Lucia
Model predictive control (MPC) is an advanced control strategy that can deal with general nonlinear systems and constraints but relies on accurate predictions given by a dynamic model. To satisfy constraints and improve performance despite imperfect models, robust MPC methods can be formulated. Multi-stage MPC is a robust MPC method based on the formulation of scenario trees. The resulting optimization problems can be large, as the number of scenarios considered in the tree results from the combinations of all possible uncertainties. For systems with many uncertainties, as it is the case in bioprocesses, the optimization problems become rapidly intractable. To solve this issue, heuristics are typically used to select the most relevant uncertain parameters and their range of uncertainty. In this paper, we propose a two-step approach to obtain a systematic design of multi-stage MPC controllers: First, the key uncertain parameters are extracted based on the parametric sensitivities. Second, Bayesian optimization is employed for tuning of the range of uncertainties. The approach is applied to a bioreactor simulation study. The proposed approach can avoid constraint violations that are otherwise obtained by standard MPC while being less conservative than a manually-tuned robust controller.
{"title":"Automatic design of robust model predictive control of a bioreactor via Bayesian optimization⁎","authors":"Tobias Brockhoff , Moritz Heinlein , Georg Hubmann , Stephan Lütz , Sergio Lucia","doi":"10.1016/j.ifacol.2025.07.115","DOIUrl":"10.1016/j.ifacol.2025.07.115","url":null,"abstract":"<div><div>Model predictive control (MPC) is an advanced control strategy that can deal with general nonlinear systems and constraints but relies on accurate predictions given by a dynamic model. To satisfy constraints and improve performance despite imperfect models, robust MPC methods can be formulated. Multi-stage MPC is a robust MPC method based on the formulation of scenario trees. The resulting optimization problems can be large, as the number of scenarios considered in the tree results from the combinations of all possible uncertainties. For systems with many uncertainties, as it is the case in bioprocesses, the optimization problems become rapidly intractable. To solve this issue, heuristics are typically used to select the most relevant uncertain parameters and their range of uncertainty. In this paper, we propose a two-step approach to obtain a systematic design of multi-stage MPC controllers: First, the key uncertain parameters are extracted based on the parametric sensitivities. Second, Bayesian optimization is employed for tuning of the range of uncertainties. The approach is applied to a bioreactor simulation study. The proposed approach can avoid constraint violations that are otherwise obtained by standard MPC while being less conservative than a manually-tuned robust controller.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 6","pages":"Pages 19-24"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.07.169
Bo Song , Tao Liu , Mingyan Zhao , Yan Cui , Yuanjun Li
To describe a quantitative relationship between the operating conditions of cooling crystallization process and product crystal size distribution (CSD), a surrogate modelling method based on the Gaussian process regression (GPR) is proposed by using only experimental data of batch crystallization process of β form L-glutamic acid (LGA). A modified design of experiments (DoE) is presented to reduce the number of batch crystallization experiments. Based on the surrogate model, an objective function reflecting the concentration of product CSD and desired yield is introduced to optimize these operating conditions. Experiments on the seeded cooling crystallization process of β-LGA are conducted to verify the effectiveness and advantage of the proposed method.
{"title":"Surrogate modeling and control optimization of batch crystallization process of β form LGA","authors":"Bo Song , Tao Liu , Mingyan Zhao , Yan Cui , Yuanjun Li","doi":"10.1016/j.ifacol.2025.07.169","DOIUrl":"10.1016/j.ifacol.2025.07.169","url":null,"abstract":"<div><div>To describe a quantitative relationship between the operating conditions of cooling crystallization process and product crystal size distribution (CSD), a surrogate modelling method based on the Gaussian process regression (GPR) is proposed by using only experimental data of batch crystallization process of <em>β</em> form L-glutamic acid (LGA). A modified design of experiments (DoE) is presented to reduce the number of batch crystallization experiments. Based on the surrogate model, an objective function reflecting the concentration of product CSD and desired yield is introduced to optimize these operating conditions. Experiments on the seeded cooling crystallization process of <em>β</em>-LGA are conducted to verify the effectiveness and advantage of the proposed method.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 6","pages":"Pages 343-348"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.07.172
Sebastian Leonow, Qi Zhang, Martin Mönnigmann
Flow rate soft sensors have become an important alternative for costly hardware flow meters, as they can estimate the flow rate with sufficient precision from easily measurable variables by using models and state estimation algorithms. This paper addresses the fundamental challenge that arises from ambiguous estimation problems, where the measured variable corresponds to two or more possible flow rate values. We develop and implement a decision algorithm that yields correct results in an industrial setup with substantial measurement noise. The results demonstrate a reliable flow rate estimation, providing a viable solution for real-time flow monitoring in centrifugal pumps with complex characteristics.
{"title":"A flow rate soft sensor for pumps with complex characteristics","authors":"Sebastian Leonow, Qi Zhang, Martin Mönnigmann","doi":"10.1016/j.ifacol.2025.07.172","DOIUrl":"10.1016/j.ifacol.2025.07.172","url":null,"abstract":"<div><div>Flow rate soft sensors have become an important alternative for costly hardware flow meters, as they can estimate the flow rate with sufficient precision from easily measurable variables by using models and state estimation algorithms. This paper addresses the fundamental challenge that arises from ambiguous estimation problems, where the measured variable corresponds to two or more possible flow rate values. We develop and implement a decision algorithm that yields correct results in an industrial setup with substantial measurement noise. The results demonstrate a reliable flow rate estimation, providing a viable solution for real-time flow monitoring in centrifugal pumps with complex characteristics.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 6","pages":"Pages 361-366"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.06.009
Mohammad Ahmadasas , Mate Siket , Mudassir M. Rashid , Ali Cinar , Mustafa Bilgic
Unannounced meals introduce substantial disturbances, causing large deviations in blood glucose concentrations from the desired range. Accurate estimation of meal timing and size is crucial for precise state estimation in a Kalman filter. Achieving accurate meal estimation remains a challenging task for fully-automated insulin delivery systems. This paper proposes incorporating a correction mechanism for the estimated states, where missed meals are detected by a neural network. Additionally, a Bayesian network is utilized to forecast timing probabilities of the next meal. Our proposed stochastic model predictive controller (SMPC) incorporates predicted meal scenarios. We evaluate the controller performance with respect to the stochasticity of the dietary patterns; the results illustrate that integrating the most likely meal scenarios into SMPC decision-making enhances both robustness and performance.
{"title":"Stochastic Model Predictive Control of Blood Glucose Levels using Probabilistic Meal Anticipation⁎","authors":"Mohammad Ahmadasas , Mate Siket , Mudassir M. Rashid , Ali Cinar , Mustafa Bilgic","doi":"10.1016/j.ifacol.2025.06.009","DOIUrl":"10.1016/j.ifacol.2025.06.009","url":null,"abstract":"<div><div>Unannounced meals introduce substantial disturbances, causing large deviations in blood glucose concentrations from the desired range. Accurate estimation of meal timing and size is crucial for precise state estimation in a Kalman filter. Achieving accurate meal estimation remains a challenging task for fully-automated insulin delivery systems. This paper proposes incorporating a correction mechanism for the estimated states, where missed meals are detected by a neural network. Additionally, a Bayesian network is utilized to forecast timing probabilities of the next meal. Our proposed stochastic model predictive controller (SMPC) incorporates predicted meal scenarios. We evaluate the controller performance with respect to the stochasticity of the dietary patterns; the results illustrate that integrating the most likely meal scenarios into SMPC decision-making enhances both robustness and performance.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 2","pages":"Pages 49-54"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.06.022
María F. Villa-Tamayo (Ms) , Jacopo Pavan (PhD) , Marc Breton (PhD)
Automated insulin delivery (AID) systems have shown significant potential in managing type 1 diabetes (T1D), yet personalizing therapy parameters remains challenging. This study advances the optimization of AID therapy profiles through an updated decision support system (DSS) leveraging the University of Virginia Replay Simulator (UVA-RS). The DSS employs a personalized glucose-insulin dynamics model to simulate glucose response to therapy adjustments and an optimization algorithm to determine therapy parameters that improves overall glycemic control. We evaluated the system’s performance through three in-silico scenarios, focusing on recommendation reliability, constraint impact, robustness to metabolic and behavioral variability, and performance over five-month simulated use. Results indicate improved therapy personalization and glycemic control, supporting the potential for DSS to enhance AID system efficacy.
{"title":"In-Silico Validation of Parameter Optimization Strategies for Automated Insulin Delivery Systems using the UVA Replay Simulation Technology","authors":"María F. Villa-Tamayo (Ms) , Jacopo Pavan (PhD) , Marc Breton (PhD)","doi":"10.1016/j.ifacol.2025.06.022","DOIUrl":"10.1016/j.ifacol.2025.06.022","url":null,"abstract":"<div><div>Automated insulin delivery (AID) systems have shown significant potential in managing type 1 diabetes (T1D), yet personalizing therapy parameters remains challenging. This study advances the optimization of AID therapy profiles through an updated decision support system (DSS) leveraging the University of Virginia Replay Simulator (UVA-RS). The DSS employs a personalized glucose-insulin dynamics model to simulate glucose response to therapy adjustments and an optimization algorithm to determine therapy parameters that improves overall glycemic control. We evaluated the system’s performance through three in-silico scenarios, focusing on recommendation reliability, constraint impact, robustness to metabolic and behavioral variability, and performance over five-month simulated use. Results indicate improved therapy personalization and glycemic control, supporting the potential for DSS to enhance AID system efficacy.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 2","pages":"Pages 127-132"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.06.023
Dinesh Krishnamoorthy , Francis J. Doyle (III)
Type 1 diabetes management requires compensating carbohydrate intake with bolus insulin matched to the meal size. Recent clinical studies revealed diurnal variations in insulin sensitivity (SI) in patients with type 1 diabetes, where the insulin resistance varies over the day. Diurnal variations in insulin sensitivity requires different bolus insulin dose for the same meal size depending on the time of the meal. Standard bolus calculators that use patient-specific parameters such as insulin-carb-ratio (CR) and correction factors (CF), however do not account for such diurnal variations. To address this gap, this paper proposes a fully data-driven safe and personalized bolus calculator that explicitly accounts for the diurnal variations. The proposed algorithm safely learns the optimum bolus needs tailored to each patient without the need for any patient-specific parameters such as carb-ratio, correction factor, insulin sensitivity etc., nor any historical clinical data. The proposed algorithm is tested and verified on the 10-adult cohort of the FDA-accepted UVA/Padova T1DM simulator.
{"title":"Personalized Meal Bolus Calculator for Type-1 Diabetes Accounting for Diurnal Effects","authors":"Dinesh Krishnamoorthy , Francis J. Doyle (III)","doi":"10.1016/j.ifacol.2025.06.023","DOIUrl":"10.1016/j.ifacol.2025.06.023","url":null,"abstract":"<div><div>Type 1 diabetes management requires compensating carbohydrate intake with bolus insulin matched to the meal size. Recent clinical studies revealed diurnal variations in insulin sensitivity (SI) in patients with type 1 diabetes, where the insulin resistance varies over the day. Diurnal variations in insulin sensitivity requires different bolus insulin dose for the same meal size depending on the time of the meal. Standard bolus calculators that use patient-specific parameters such as insulin-carb-ratio (CR) and correction factors (CF), however do not account for such diurnal variations. To address this gap, this paper proposes a fully data-driven safe and personalized bolus calculator that explicitly accounts for the diurnal variations. The proposed algorithm safely learns the optimum bolus needs tailored to each patient without the need for any patient-specific parameters such as carb-ratio, correction factor, insulin sensitivity etc., nor any historical clinical data. The proposed algorithm is tested and verified on the 10-adult cohort of the FDA-accepted UVA/Padova T1DM simulator.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 2","pages":"Pages 133-138"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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