Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.09.576
Hugo Jamet , Ahlem Sassi , Damien Ébérard , Michaël Di Loreto
In the present contribution, we address the existence and the design of a functional observer, for known bounded inputs and linear systems. Based on a characterization by means of invariants zeros given in the literature, the existence of a functional observer is revisited by means of observability matrix, as well as through a generalized Sylvester equation. The latter is then used to design a reduced functional observer, provided that the input signal is known and bounded.
{"title":"Existence and design of a functional observer for LTI systems with known bounded input","authors":"Hugo Jamet , Ahlem Sassi , Damien Ébérard , Michaël Di Loreto","doi":"10.1016/j.ifacol.2025.09.576","DOIUrl":"10.1016/j.ifacol.2025.09.576","url":null,"abstract":"<div><div>In the present contribution, we address the existence and the design of a functional observer, for known bounded inputs and linear systems. Based on a characterization by means of invariants zeros given in the literature, the existence of a functional observer is revisited by means of observability matrix, as well as through a generalized Sylvester equation. The latter is then used to design a reduced functional observer, provided that the input signal is known and bounded.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 12","pages":"Pages 109-114"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145242567","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.2025.10.063
D. Houjayri , S.E. Benattia , O. Sename
The identification of a turbomachine model is carried out within the linear parameter-varying framework with the aim of improving model precision and reliability, especially in large transients, thereby improving control performance in future work. An appropriate model structure and a new parameter dependency are selected and the recursive least squares algorithm is employed for determining the parameters to be estimated. A model is obtained for high transients. Validation results are carried out with a simulated high fidelity nonlinear model. The new model is compared to an existing gain-scheduling model, showing better precision and validating the approach. The proposed approach is detailed in this work and concluded with objectives and future perspectives.
{"title":"A Look Into LPV Modeling of Turbofan Engines","authors":"D. Houjayri , S.E. Benattia , O. Sename","doi":"10.1016/j.ifacol.2025.10.063","DOIUrl":"10.1016/j.ifacol.2025.10.063","url":null,"abstract":"<div><div>The identification of a turbomachine model is carried out within the linear parameter-varying framework with the aim of improving model precision and reliability, especially in large transients, thereby improving control performance in future work. An appropriate model structure and a new parameter dependency are selected and the recursive least squares algorithm is employed for determining the parameters to be estimated. A model is obtained for high transients. Validation results are carried out with a simulated high fidelity nonlinear model. The new model is compared to an existing gain-scheduling model, showing better precision and validating the approach. The proposed approach is detailed in this work and concluded with objectives and future perspectives.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 15","pages":"Pages 91-96"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145334496","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.2025.12.397
L. Auret , A.L. Haasbroek , T.M. Louw , S. Geldenhuys , J. Bezuidenhout
Dynamic flotation modelling is a valuable tool for designing process control and monitoring solutions for industrial application. However, flotation modelling is challenging, due to the complex interactions in the pulp and froth phases in a flotation cell, represented by hydrodynamic and kinetic relations. Dynamic flotation models published in literature are often not reproducible, due to a lack of sufficient detail (including parameter values and typical operating conditions), or do not consider specific relations of interest. In this work, a dynamic flotation model is developed for the purpose of rapid prototyping of industrial control and monitoring solutions, specifically such solutions that make use of pulp and froth sensors. These requirements determined the model complexity (e.g., including hydrodynamic and frother relations, while limiting the number of tunable parameters to allow easy calibration for new case studies). The goal is not a high-fidelity, high accuracy model, but a model that captures typical measurements, inputs, and interactions. A copper rougher case study with complete parameter and operating condition descriptions is provided, to promote reproducibility and further development of the model. Example applications of the dynamic flotation model to the design of advanced process control and frother advisor monitoring are also described.
{"title":"A dynamic flotation model for rapid prototyping of industrial control and monitoring solutions","authors":"L. Auret , A.L. Haasbroek , T.M. Louw , S. Geldenhuys , J. Bezuidenhout","doi":"10.1016/j.ifacol.2025.12.397","DOIUrl":"10.1016/j.ifacol.2025.12.397","url":null,"abstract":"<div><div>Dynamic flotation modelling is a valuable tool for designing process control and monitoring solutions for industrial application. However, flotation modelling is challenging, due to the complex interactions in the pulp and froth phases in a flotation cell, represented by hydrodynamic and kinetic relations. Dynamic flotation models published in literature are often not reproducible, due to a lack of sufficient detail (including parameter values and typical operating conditions), or do not consider specific relations of interest. In this work, a dynamic flotation model is developed for the purpose of rapid prototyping of industrial control and monitoring solutions, specifically such solutions that make use of pulp and froth sensors. These requirements determined the model complexity (e.g., including hydrodynamic and frother relations, while limiting the number of tunable parameters to allow easy calibration for new case studies). The goal is not a high-fidelity, high accuracy model, but a model that captures typical measurements, inputs, and interactions. A copper rougher case study with complete parameter and operating condition descriptions is provided, to promote reproducibility and further development of the model. Example applications of the dynamic flotation model to the design of advanced process control and frother advisor monitoring are also described.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 32","pages":"Pages 60-65"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145765916","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.2025.12.401
Francisco Diniz , Thomás Pinto , Saulo Matos , Eduardo Luz , Gustavo Pessin , Jó Ueyama
This study proposes a method to quantify uncertainty in soft sensors’ measurements for estimating ore mass flow rate on conveyor belts in mining. A linear regression model previously implemented in a PLC is extended with Conformal Prediction (CP) and a sliding window to generate adaptive prediction intervals. Residuals are updated incrementally for efficiency and adaptability. One-way ANOVA and Tukey’s HSD showed that window size significantly affects interval coverage and width. Larger windows (W100) yielded wider intervals (286.62 t/h) and higher coverage (95.3%), while smaller windows (W40) were narrower (243.97 t/h) with lower coverage (84.9%) but greater responsiveness. Processing time stayed under 0.1 seconds across all configurations, confirming suitability for real-time PLC use. The approach balances robustness and responsiveness, offering a lightweight, interpretable solution for uncertainty-aware control in industrial environments.
{"title":"Enhancing Operational Safety with Conformal Prediction in Soft Sensors","authors":"Francisco Diniz , Thomás Pinto , Saulo Matos , Eduardo Luz , Gustavo Pessin , Jó Ueyama","doi":"10.1016/j.ifacol.2025.12.401","DOIUrl":"10.1016/j.ifacol.2025.12.401","url":null,"abstract":"<div><div>This study proposes a method to quantify uncertainty in soft sensors’ measurements for estimating ore mass flow rate on conveyor belts in mining. A linear regression model previously implemented in a PLC is extended with Conformal Prediction (CP) and a sliding window to generate adaptive prediction intervals. Residuals are updated incrementally for efficiency and adaptability. One-way ANOVA and Tukey’s HSD showed that window size significantly affects interval coverage and width. Larger windows (W100) yielded wider intervals (286.62 t/h) and higher coverage (95.3%), while smaller windows (W40) were narrower (243.97 t/h) with lower coverage (84.9%) but greater responsiveness. Processing time stayed under 0.1 seconds across all configurations, confirming suitability for real-time PLC use. The approach balances robustness and responsiveness, offering a lightweight, interpretable solution for uncertainty-aware control in industrial environments.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 32","pages":"Pages 84-89"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145765920","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}
Effective material distribution among silos is essential in mineral processing to reduce wear and ensure continuous operation. This work proposes a control strategy to improve productivity and equipment integrity in screening by automatically deactivating lines when additional screening surface is not needed. The approach adjusts the number of active silos based on variables such as circulating load and silo levels, introducing the concept of available silos. Applied to an iron ore plant in Brazil, the strategy increased the average screening rate by 13.4% while deactivating lines 62.3% of the time, with no efficiency loss and potential equipment integrity gains.
{"title":"Enhancing Screening Performance Through Automatic Selection of Operating Lines","authors":"Alexandre Fonseca , Kaike Albuquerque , Robson Duarte , Nicolau Bylaard , Kennedy Luz , Thomás Pinto","doi":"10.1016/j.ifacol.2025.12.388","DOIUrl":"10.1016/j.ifacol.2025.12.388","url":null,"abstract":"<div><div>Effective material distribution among silos is essential in mineral processing to reduce wear and ensure continuous operation. This work proposes a control strategy to improve productivity and equipment integrity in screening by automatically deactivating lines when additional screening surface is not needed. The approach adjusts the number of active silos based on variables such as circulating load and silo levels, introducing the concept of available silos. Applied to an iron ore plant in Brazil, the strategy increased the average screening rate by 13.4% while deactivating lines 62.3% of the time, with no efficiency loss and potential equipment integrity gains.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 32","pages":"Pages 7-11"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145765996","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.2025.12.411
Thomás Pinto , Daniel Limon , Marcelo A. Santos , Guilherme V. Raffo
This work presents an Adaptive Learning-Based Model Predictive Control (ALB-MPC) framework for a thickening process characterized by high complexity and nonlinear dynamics. The approach leverages operational data to identify an accurate process model as a Nonlinear AutoRegressive eXogenous (NARX) structure, built using a learning method known as Lazily Adaptive Constant Kinky Inference (LACKI). Additionally, a neural network is employed as an online tuning mechanism to adapt the predictive controller parameters and enhance control performance. Simulation results indicate that the proposed control framework achieves performance comparable to or better than controllers with fixed parameters.
{"title":"Adaptive Learning-Based Model Predictive Control for Thickening Processes","authors":"Thomás Pinto , Daniel Limon , Marcelo A. Santos , Guilherme V. Raffo","doi":"10.1016/j.ifacol.2025.12.411","DOIUrl":"10.1016/j.ifacol.2025.12.411","url":null,"abstract":"<div><div>This work presents an Adaptive Learning-Based Model Predictive Control (ALB-MPC) framework for a thickening process characterized by high complexity and nonlinear dynamics. The approach leverages operational data to identify an accurate process model as a Nonlinear AutoRegressive eXogenous (NARX) structure, built using a learning method known as Lazily Adaptive Constant Kinky Inference (LACKI). Additionally, a neural network is employed as an online tuning mechanism to adapt the predictive controller parameters and enhance control performance. Simulation results indicate that the proposed control framework achieves performance comparable to or better than controllers with fixed parameters.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 32","pages":"Pages 144-149"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766002","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.2025.12.430
Yong Yuan , Xuan Liu , Lu Liu , Shengyuan Xie , Leilei Xu , Tao Liu , Ruizhe Yao
Data trading is a crucial component and decisive factor for the strategic development of data as a factor of production. Currently, data markets typically lack effective methods of mechanism design and decision-making, thus leading to significant issues in practices such as mis-matching market architectures, simple pricing mechanisms, as well as absence of incentive mechanisms. These issues might cause severe bottlenecks and obstacles in market entry, data pricing and establishing mutual trusts. Aiming at addressing these challenges, we proposed a novel research framework for blockchain-based data trading markets. Specifically, we discussed several key issues including the blockchain-based market architectures, auction-based data pricing mechanisms, and the incentive mechanism design. We also presented several open issues and research challenges awaiting future research efforts in this area. Our framework is aimed to help provide theoretical guidance and reference for data exchanges and the market ecosystem, which are currently in its exploratory and growth stages.
{"title":"Blockchain-based Data Trading Markets: A Research Framework on the Architecture and Mechanism Design","authors":"Yong Yuan , Xuan Liu , Lu Liu , Shengyuan Xie , Leilei Xu , Tao Liu , Ruizhe Yao","doi":"10.1016/j.ifacol.2025.12.430","DOIUrl":"10.1016/j.ifacol.2025.12.430","url":null,"abstract":"<div><div>Data trading is a crucial component and decisive factor for the strategic development of data as a factor of production. Currently, data markets typically lack effective methods of mechanism design and decision-making, thus leading to significant issues in practices such as mis-matching market architectures, simple pricing mechanisms, as well as absence of incentive mechanisms. These issues might cause severe bottlenecks and obstacles in market entry, data pricing and establishing mutual trusts. Aiming at addressing these challenges, we proposed a novel research framework for blockchain-based data trading markets. Specifically, we discussed several key issues including the blockchain-based market architectures, auction-based data pricing mechanisms, and the incentive mechanism design. We also presented several open issues and research challenges awaiting future research efforts in this area. Our framework is aimed to help provide theoretical guidance and reference for data exchanges and the market ecosystem, which are currently in its exploratory and growth stages.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 34","pages":"Pages 1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766005","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.2025.12.202
Abdelazim G. Hussien , Adrian Pop
Despite their widespread use, modeling and simulation tools still face limitations in scalability, particularly when executed in distributed computing environments.
In our prior research, we introduced a task-based library named ParModAuto, designed to enable the automatic parallelization of Modelica simulation models by supporting efficient representation, clustering, scheduling, profiling, and execution of complex, dependency-heavy equation systems, which employs heuristic-driven algorithms to address computationally challenging, NP-complete optimization problems.
This paper investigates whether further enhancements to the parallelization library can lead to greater speedups in simulation performance, as benchmark results indicate that additional gains are achievable.
{"title":"Improving ParModAuto for Equation Based Mathematical Modeling & Simulation: Metaheuristic Approach","authors":"Abdelazim G. Hussien , Adrian Pop","doi":"10.1016/j.ifacol.2025.12.202","DOIUrl":"10.1016/j.ifacol.2025.12.202","url":null,"abstract":"<div><div>Despite their widespread use, modeling and simulation tools still face limitations in scalability, particularly when executed in distributed computing environments.</div><div>In our prior research, we introduced a task-based library named ParModAuto, designed to enable the automatic parallelization of Modelica simulation models by supporting efficient representation, clustering, scheduling, profiling, and execution of complex, dependency-heavy equation systems, which employs heuristic-driven algorithms to address computationally challenging, NP-complete optimization problems.</div><div>This paper investigates whether further enhancements to the parallelization library can lead to greater speedups in simulation performance, as benchmark results indicate that additional gains are achievable.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 29","pages":"Pages 180-185"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760710","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.2025.12.180
Vahid Farokhi , Lars Erik Øi , Per Morten Hansen
Ammonia is recognized as a promising marine fuel due to its potential to reduce CO2 emissions serving both as an energy carrier and a clean fuel. In 2018, the International Maritime Organization (IMO) set targets to reduce greenhouse gas emissions from international shipping by at least 50% by 2050, aiming for complete elimination by 2100. In this study, a centralized ammonia cracking process was simulated using Aspen HYSYS V12. A base case was established, then other cases and a final version were simulated with the aim to maximize energy recovery from the waste heat of the cracker product. For the base case with no heat recovery, the produced H2/NH3 total feed on a kg/kg basis was calculated to 0.128 while for the final version it was calculated to 0.140. New in this work is to determine the most cost optimum case by calculating and comparing the Levelized Cost of Hydrogen (LCOH) for different alternatives. The final version, with the highest hydrogen production rate (586 kmol/h hydrogen from 500 kmol/h total ammonia feed) had the lowest LCOH (5.1 USD/kg H2). Recommendations for further work include reducing inherent uncertainties in the simulation like inclusion of catalyst data, defining other models than Gibbs equilibrium reactors and using data from the adsorption module of Aspen to precisely model the adsorption phenomena and conducting uncertainty analysis on the LCOH evaluations to obtain more reliable techno-economic analysis.
{"title":"Simulation and Cost optimization of Ammonia Cracker Process","authors":"Vahid Farokhi , Lars Erik Øi , Per Morten Hansen","doi":"10.1016/j.ifacol.2025.12.180","DOIUrl":"10.1016/j.ifacol.2025.12.180","url":null,"abstract":"<div><div>Ammonia is recognized as a promising marine fuel due to its potential to reduce CO<sub>2</sub> emissions serving both as an energy carrier and a clean fuel. In 2018, the International Maritime Organization (IMO) set targets to reduce greenhouse gas emissions from international shipping by at least 50% by 2050, aiming for complete elimination by 2100. In this study, a centralized ammonia cracking process was simulated using Aspen HYSYS V12. A base case was established, then other cases and a final version were simulated with the aim to maximize energy recovery from the waste heat of the cracker product. For the base case with no heat recovery, the produced H<sub>2</sub>/NH<sub>3</sub> total feed on a kg/kg basis was calculated to 0.128 while for the final version it was calculated to 0.140. New in this work is to determine the most cost optimum case by calculating and comparing the Levelized Cost of Hydrogen (LCOH) for different alternatives. The final version, with the highest hydrogen production rate (586 kmol/h hydrogen from 500 kmol/h total ammonia feed) had the lowest LCOH (5.1 USD/kg H<sub>2</sub>). Recommendations for further work include reducing inherent uncertainties in the simulation like inclusion of catalyst data, defining other models than Gibbs equilibrium reactors and using data from the adsorption module of Aspen to precisely model the adsorption phenomena and conducting uncertainty analysis on the LCOH evaluations to obtain more reliable techno-economic analysis.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 29","pages":"Pages 48-53"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760720","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.2025.12.181
Nawa U. Punabantu, Tobias M. Louw, Robert W.M. Pott, Johann Görgens
Simulated Moving Bed (SMB) chromatography is a continuous separation process widely adopted in pharmaceutical and sugar industries for high-resolution purification of binary and even ternary mixtures. Different SMB configurations must be investigated to optimise product grade and recovery but modelling such systems involves solving complex systems of partial differential equations, making optimization computationally intensive. The performance of the Constrained Multi-Objective Bayesian Optimization (CMOBO) algorithm was evaluated with limits on computational resources. A sparse Pareto front was generated using 20 iterations which closely followed the trend established by a more exhaustive 100-iteration optimisation run – preserving predominant trade-offs and insight into potential operating modes. The 100-iterations required ~74 CPU hours, while the 20-iterations required ~9 CPU hours on the institutional computing cluster. This comparison highlights the CMOBO’s strong sampling ability to maximize SMB performance parameters, even under a limited number of allowable function evaluations, enabling rapid prototyping of SMB configurations.
{"title":"Constrained Multi-Objective Bayesian Optimization of Simulated Moving Bed Chromatography","authors":"Nawa U. Punabantu, Tobias M. Louw, Robert W.M. Pott, Johann Görgens","doi":"10.1016/j.ifacol.2025.12.181","DOIUrl":"10.1016/j.ifacol.2025.12.181","url":null,"abstract":"<div><div>Simulated Moving Bed (SMB) chromatography is a continuous separation process widely adopted in pharmaceutical and sugar industries for high-resolution purification of binary and even ternary mixtures. Different SMB configurations must be investigated to optimise product grade and recovery but modelling such systems involves solving complex systems of partial differential equations, making optimization computationally intensive. The performance of the Constrained Multi-Objective Bayesian Optimization (CMOBO) algorithm was evaluated with limits on computational resources. A sparse Pareto front was generated using 20 iterations which closely followed the trend established by a more exhaustive 100-iteration optimisation run – preserving predominant trade-offs and insight into potential operating modes. The 100-iterations required ~74 CPU hours, while the 20-iterations required ~9 CPU hours on the institutional computing cluster. This comparison highlights the CMOBO’s strong sampling ability to maximize SMB performance parameters, even under a limited number of allowable function evaluations, enabling rapid prototyping of SMB configurations.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 29","pages":"Pages 54-59"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760721","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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