Pub Date : 2025-12-01Epub Date: 2025-10-28DOI: 10.1016/j.jprocont.2025.103577
Jiamin Xu , Nazli Demirer , Vy Pho , Kaixiao Tian , He Zhang , Ketan Bhaidasna , Robert Darbe , Dongmei Chen
This paper presents a multi-input, multi-output (MIMO) economic model predictive control (MPC) approach for directional drilling using an efficient model with state and parameter estimation using sensor fusion. The MPC framework coordinates weight-on-bit (WOB) and pad force to ensure the bit follows the planned well trajectory while maintaining high WOB, implying a high rate of penetration (ROP). The simulation studies, conducted under scenarios with initial bit positions both ahead of and behind the well plan, demonstrate the robustness and effectiveness of the proposed MPC strategy. The results show that the controller can maintain the bit on the well plan despite various disturbances and noise, indicating its potential for practical application in the field.
{"title":"Real time multi-inputs multi-outputs economic model predictive control for directional drilling based on fast modeling and sensor fusion","authors":"Jiamin Xu , Nazli Demirer , Vy Pho , Kaixiao Tian , He Zhang , Ketan Bhaidasna , Robert Darbe , Dongmei Chen","doi":"10.1016/j.jprocont.2025.103577","DOIUrl":"10.1016/j.jprocont.2025.103577","url":null,"abstract":"<div><div>This paper presents a multi-input, multi-output (MIMO) economic model predictive control (MPC) approach for directional drilling using an efficient model with state and parameter estimation using sensor fusion. The MPC framework coordinates weight-on-bit (WOB) and pad force to ensure the bit follows the planned well trajectory while maintaining high WOB, implying a high rate of penetration (ROP). The simulation studies, conducted under scenarios with initial bit positions both ahead of and behind the well plan, demonstrate the robustness and effectiveness of the proposed MPC strategy. The results show that the controller can maintain the bit on the well plan despite various disturbances and noise, indicating its potential for practical application in the field.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103577"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145371409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-11-25DOI: 10.1016/j.jprocont.2025.103591
Abhilash Dev, Sharad Bhartiya, Sachin Patwardhan
Sample-based nonlinear state estimation is an intensive problem to solve and significantly contributes to the computation delay in real-time applications. This paper explores a novel neural network (NN) based state estimation method in order to reduce the computation time required by sample-based state estimators during online deployment. The proposed method obtains estimates for the predicted as well as the filtered states, wherein the NN is trained using data obtained from simulations of constrained state estimators. The efficacy of the method is demonstrated by learning constrained Unscented Kalman Filter (UKF) on a six-state benchmark Williams Otto reactor with four measurements using a deep feedforward NN. The NN is then used as the proposal density in lieu of UKF for a Particle Filter (PF) implementation. Another example consists of exploring the role of the trained NN as a state estimator in a nonlinear internal model control (NIMC) application for tracking of economic setpoints in the benchmark Willaims-Otto reactor. Both these applications show that the proposed approach significantly outperforms the sample-based estimator in terms of the computation time while matching its performance, thereby reducing the latency related to sample-based state estimators.
{"title":"Efficient computation for sample-based state estimators using deep neural networks","authors":"Abhilash Dev, Sharad Bhartiya, Sachin Patwardhan","doi":"10.1016/j.jprocont.2025.103591","DOIUrl":"10.1016/j.jprocont.2025.103591","url":null,"abstract":"<div><div>Sample-based nonlinear state estimation is an intensive problem to solve and significantly contributes to the computation delay in real-time applications. This paper explores a novel neural network (NN) based state estimation method in order to reduce the computation time required by sample-based state estimators during online deployment. The proposed method obtains estimates for the predicted as well as the filtered states, wherein the NN is trained using data obtained from simulations of constrained state estimators. The efficacy of the method is demonstrated by learning constrained Unscented Kalman Filter (UKF) on a six-state benchmark Williams Otto reactor with four measurements using a deep feedforward NN. The NN is then used as the proposal density in lieu of UKF for a Particle Filter (PF) implementation. Another example consists of exploring the role of the trained NN as a state estimator in a nonlinear internal model control (NIMC) application for tracking of economic setpoints in the benchmark Willaims-Otto reactor. Both these applications show that the proposed approach significantly outperforms the sample-based estimator in terms of the computation time while matching its performance, thereby reducing the latency related to sample-based state estimators.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103591"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-11-04DOI: 10.1016/j.jprocont.2025.103570
Simen Bjorvand, Johannes Jäschke
This work addresses the challenge of computational complexity in Multistage Model Predictive Control (MPC). Multistage MPC is a robust control algorithm where uncertainty is accounted for by constructing a finite set of scenarios for different realizations of uncertainty. The resulting Multistage MPC problem becomes large and computationally expensive to solve. Several strategies have been proposed to reduce computation time and to make real-time implementation possible. A popular class of methods is decomposition methods, in which the Multistage MPC problem is decomposed into several smaller parametric subproblems that can be solved in parallel. The subproblems are repeatedly solved while a coordinator algorithm adjusts a parameter to recover the solution of the full problem. The main computational cost in this approach comes from (1) the coordinator algorithm requiring many iterations to converge, resulting in many resolves of the subproblems, and (2) the computational time to solve the subproblems. In this paper we propose a Predictor–Corrector based path-following algorithm to reduce the solution time of the subproblems for a primal-decomposition algorithm for Multistage MPC. A new Predictor–Corrector methodology based on nonsmooth equation solving is proposed with local superlinear/quadratic convergence. The algorithm path-follows along the parameter path given by the coordinator algorithm. Our path-following algorithm is combined with the Extended Newton algorithm from Bjorvand and Jäschke (2023) for reducing number of coordinator steps. The proposed algorithm is applied to a gas-lift system where the total number of iterations of the algorithm are significantly reduced compared to the standard primal decomposition algorithm for Multistage MPC.
{"title":"Fast decentralized multi-stage model predictive control using sensitivity-based path-following and a nonsmooth Newton method","authors":"Simen Bjorvand, Johannes Jäschke","doi":"10.1016/j.jprocont.2025.103570","DOIUrl":"10.1016/j.jprocont.2025.103570","url":null,"abstract":"<div><div>This work addresses the challenge of computational complexity in Multistage Model Predictive Control (MPC). Multistage MPC is a robust control algorithm where uncertainty is accounted for by constructing a finite set of scenarios for different realizations of uncertainty. The resulting Multistage MPC problem becomes large and computationally expensive to solve. Several strategies have been proposed to reduce computation time and to make real-time implementation possible. A popular class of methods is decomposition methods, in which the Multistage MPC problem is decomposed into several smaller parametric subproblems that can be solved in parallel. The subproblems are repeatedly solved while a coordinator algorithm adjusts a parameter to recover the solution of the full problem. The main computational cost in this approach comes from (1) the coordinator algorithm requiring many iterations to converge, resulting in many resolves of the subproblems, and (2) the computational time to solve the subproblems. In this paper we propose a Predictor–Corrector based path-following algorithm to reduce the solution time of the subproblems for a primal-decomposition algorithm for Multistage MPC. A new Predictor–Corrector methodology based on nonsmooth equation solving is proposed with local superlinear/quadratic convergence. The algorithm path-follows along the parameter path given by the coordinator algorithm. Our path-following algorithm is combined with the Extended Newton algorithm from Bjorvand and Jäschke (2023) for reducing number of coordinator steps. The proposed algorithm is applied to a gas-lift system where the total number of iterations of the algorithm are significantly reduced compared to the standard primal decomposition algorithm for Multistage MPC.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103570"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-11-11DOI: 10.1016/j.jprocont.2025.103588
Ming Lu , Yingcong Li , Xianke He , Lei He , Ying Zou , Zunhui Yi , Pei Li
In the flotation cell, the flotation process involves complex physicochemical reactions and exhibits unknown, intricate nonlinear dynamic characteristics, making it difficult to establish an accurate model or predict froth flotation grade. To address this issue, this paper proposes a mechanism-based modeling approach for predicting froth flotation grade. First, the working mechanism of the flotation cell is analyzed, the concentrate grade is redefined, and a simplified mechanistic model is derived. Next, preprocessed flotation process data is integrated with an intelligent optimization algorithm to conduct mechanism-guided data-driven modeling. This identifies the optimal system matrix parameters in the mechanistic model under current working conditions, thereby enhancing its adaptability to the actual flotation environment. Finally, the optimized system matrix parameters are incorporated into the mechanistic flotation grade model to construct an online prediction model. The predicted values are used to guide parameter settings during the flotation process. Experiments show that this method has high measurement accuracy, good generalization performance, and strong robustness.
{"title":"The flotation grade prediction model based on mechanism-guided and data-driven approaches","authors":"Ming Lu , Yingcong Li , Xianke He , Lei He , Ying Zou , Zunhui Yi , Pei Li","doi":"10.1016/j.jprocont.2025.103588","DOIUrl":"10.1016/j.jprocont.2025.103588","url":null,"abstract":"<div><div>In the flotation cell, the flotation process involves complex physicochemical reactions and exhibits unknown, intricate nonlinear dynamic characteristics, making it difficult to establish an accurate model or predict froth flotation grade. To address this issue, this paper proposes a mechanism-based modeling approach for predicting froth flotation grade. First, the working mechanism of the flotation cell is analyzed, the concentrate grade is redefined, and a simplified mechanistic model is derived. Next, preprocessed flotation process data is integrated with an intelligent optimization algorithm to conduct mechanism-guided data-driven modeling. This identifies the optimal system matrix parameters in the mechanistic model under current working conditions, thereby enhancing its adaptability to the actual flotation environment. Finally, the optimized system matrix parameters are incorporated into the mechanistic flotation grade model to construct an online prediction model. The predicted values are used to guide parameter settings during the flotation process. Experiments show that this method has high measurement accuracy, good generalization performance, and strong robustness.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103588"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Large-scale industrial processes produce process data that have the characteristics of high dimensionality, nonlinearity, and strong coupling. Process monitoring plays a vital role in ensuring production safety and product quality. At present, for quality-related fault detection, the existing methods have the problems of weak robustness for the extracted features, large model parameters, and black-box modeling. To address these problems, this paper proposes the layer-wise information-aggregation-decoupled convolutional self-attention network (LIA-DCSA). First, a one-dimensional decoupled convolutional self-attention network (DCSA) is constructed to explicitly extract complex features between variables guided by process knowledge. Second, the rules of relationship inheritance and relationship elimination are proposed to construct different levels of quality-related variable relationship graphs (QR-VRGs). The QR-VRGs and DCSA are combined to achieve effective extraction and layer-by-layer aggregation of quality-related features. Then, based on Kullback–Leibler (KL) divergence, a distribution-constrained regression layer is designed to regularize the quality-related features gathered in neurons. Finally, the Tennessee Eastman process and the Cranfield multiphase flow process are used to show the effectiveness of the proposed method. The experimental results show that compared with the other methods, this method has the best fault monitoring performance while effectively reducing the number of model parameters. Furthermore, the quality-related features extracted by LIA-DCSA show the best robustness in the presence of interference from Gaussian noise and impulse noise.
{"title":"Layer-wise information-aggregation-decoupled convolutional self-attention network guided by process knowledge for quality-related process monitoring","authors":"Yuguo Yang, Hongbo Shi, Bing Song, Yang Tao, Keyu Yao, Hongyu Tian","doi":"10.1016/j.jprocont.2025.103580","DOIUrl":"10.1016/j.jprocont.2025.103580","url":null,"abstract":"<div><div>Large-scale industrial processes produce process data that have the characteristics of high dimensionality, nonlinearity, and strong coupling. Process monitoring plays a vital role in ensuring production safety and product quality. At present, for quality-related fault detection, the existing methods have the problems of weak robustness for the extracted features, large model parameters, and black-box modeling. To address these problems, this paper proposes the layer-wise information-aggregation-decoupled convolutional self-attention network (LIA-DCSA). First, a one-dimensional decoupled convolutional self-attention network (DCSA) is constructed to explicitly extract complex features between variables guided by process knowledge. Second, the rules of relationship inheritance and relationship elimination are proposed to construct different levels of quality-related variable relationship graphs (QR-VRGs). The QR-VRGs and DCSA are combined to achieve effective extraction and layer-by-layer aggregation of quality-related features. Then, based on Kullback–Leibler (KL) divergence, a distribution-constrained regression layer is designed to regularize the quality-related features gathered in neurons. Finally, the Tennessee Eastman process and the Cranfield multiphase flow process are used to show the effectiveness of the proposed method. The experimental results show that compared with the other methods, this method has the best fault monitoring performance while effectively reducing the number of model parameters. Furthermore, the quality-related features extracted by LIA-DCSA show the best robustness in the presence of interference from Gaussian noise and impulse noise.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103580"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-11-24DOI: 10.1016/j.jprocont.2025.103596
Akhtar Jan , Rehan Ali Shah , Ebraheem Alzahrani , Zehba Raizah
<div><div>Enzymatic reactions operate under a variety of biochemical responses in cellular systems and are frequently affected by intrinsic variations imposed by molecular uncertainty. This study examined both deterministic and stochastic models for a two-step enzymatic process in which the primary substrate <span><math><mrow><msub><mrow><mi>S</mi></mrow><mrow><mn>1</mn></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> and the inhibitory substrate <span><math><mrow><msub><mrow><mi>S</mi></mrow><mrow><mn>2</mn></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> collaborate to generate intermediate complexes <span><math><mrow><msub><mrow><mi>C</mi></mrow><mrow><mn>1</mn></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> and <span><math><mrow><msub><mrow><mi>C</mi></mrow><mrow><mn>2</mn></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span>. The overall dynamics of the reaction are represented by combining the enzyme–substrate kinetics and the resulting product production. The invariant region of the system, the existence and non-negativity of solutions, the classification of equilibrium states and their stability characteristics are the main issues of a qualitative investigation. For both the models, the threshold parameter <span><math><msub><mrow><mi>T</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> is determined. The system’s sensitivity to stochastic influences is shown by the comparison analysis, which reveals that random perturbations typically lower the system’s effective activation threshold by <strong>25–35%</strong> when compared to the deterministic calculation. Additionally, sensitivity studies showed that stochastic noise parameters (<span><math><mrow><msub><mrow><mi>σ</mi></mrow><mrow><mn>3</mn></mrow></msub><mo>=</mo><msub><mrow><mi>σ</mi></mrow><mrow><mn>4</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>1</mn></mrow></math></span>) generate around <strong>15–25%</strong> variation in the rates of complex formation; however, altering the initial enzyme level (<span><math><mrow><msub><mrow><mi>E</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>3</mn></mrow></math></span>) impacts the reaction progress by about <strong>40%</strong>. Numerical simulations, performed over <span><math><mrow><mi>t</mi><mo>=</mo><mn>150</mn></mrow></math></span> with rate constants <span><math><mrow><msub><mrow><mi>k</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>3</mn></mrow></math></span>, <span><math><mrow><msub><mrow><mi>k</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>4</mn></mrow></math></span>, <span><math><mrow><msub><mrow><mi>k</mi></mrow><mrow><mn>3</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>2</mn></mrow></math></span>, and <span><math><mrow><msub><mrow><mi>k</mi></mrow><mrow><mn>4</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>5</mn></
{"title":"Mathematical modeling of stability in enzyme-catalyzed cooperative reactions through deterministic and stochastic approaches","authors":"Akhtar Jan , Rehan Ali Shah , Ebraheem Alzahrani , Zehba Raizah","doi":"10.1016/j.jprocont.2025.103596","DOIUrl":"10.1016/j.jprocont.2025.103596","url":null,"abstract":"<div><div>Enzymatic reactions operate under a variety of biochemical responses in cellular systems and are frequently affected by intrinsic variations imposed by molecular uncertainty. This study examined both deterministic and stochastic models for a two-step enzymatic process in which the primary substrate <span><math><mrow><msub><mrow><mi>S</mi></mrow><mrow><mn>1</mn></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> and the inhibitory substrate <span><math><mrow><msub><mrow><mi>S</mi></mrow><mrow><mn>2</mn></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> collaborate to generate intermediate complexes <span><math><mrow><msub><mrow><mi>C</mi></mrow><mrow><mn>1</mn></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> and <span><math><mrow><msub><mrow><mi>C</mi></mrow><mrow><mn>2</mn></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span>. The overall dynamics of the reaction are represented by combining the enzyme–substrate kinetics and the resulting product production. The invariant region of the system, the existence and non-negativity of solutions, the classification of equilibrium states and their stability characteristics are the main issues of a qualitative investigation. For both the models, the threshold parameter <span><math><msub><mrow><mi>T</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> is determined. The system’s sensitivity to stochastic influences is shown by the comparison analysis, which reveals that random perturbations typically lower the system’s effective activation threshold by <strong>25–35%</strong> when compared to the deterministic calculation. Additionally, sensitivity studies showed that stochastic noise parameters (<span><math><mrow><msub><mrow><mi>σ</mi></mrow><mrow><mn>3</mn></mrow></msub><mo>=</mo><msub><mrow><mi>σ</mi></mrow><mrow><mn>4</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>1</mn></mrow></math></span>) generate around <strong>15–25%</strong> variation in the rates of complex formation; however, altering the initial enzyme level (<span><math><mrow><msub><mrow><mi>E</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>3</mn></mrow></math></span>) impacts the reaction progress by about <strong>40%</strong>. Numerical simulations, performed over <span><math><mrow><mi>t</mi><mo>=</mo><mn>150</mn></mrow></math></span> with rate constants <span><math><mrow><msub><mrow><mi>k</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>3</mn></mrow></math></span>, <span><math><mrow><msub><mrow><mi>k</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>4</mn></mrow></math></span>, <span><math><mrow><msub><mrow><mi>k</mi></mrow><mrow><mn>3</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>2</mn></mrow></math></span>, and <span><math><mrow><msub><mrow><mi>k</mi></mrow><mrow><mn>4</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>5</mn></","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103596"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-30DOI: 10.1016/j.jprocont.2025.103571
Estefania Yap , Viet Huynh , Calvin Vong , Peter Vogel , Viv Louzado , Thomas Barnes , Buser Say , Michael Burke , Dana Kulić , Aldeida Aleti
The automation of liquid handling has become integral in speeding up pharmaceutical development for faster drug development and more affordable treatments. However, the optimal parameters which define the aspirate and dispense procedures vary between liquids and liquid volumes, limiting transfer accuracy and precision. Even state-of-the-art liquid handling devices offer predefined parameters for only a handful of liquids and volumes, resulting in novel parameter sets being defined via a manual, time-consuming process. In this study, we propose an experimental framework for automating the optimisation of liquid class parameters for arbitrary liquids. Within our framework, we propose an optimisation and segmentation algorithm, OptAndSeg, to identify the optimal parameters by automatically grouping volumes into volume ranges and optimising parameters for these volume range subsets. Our method was validated on three live experiments: glycerol, a solution of 25% purified human serum albumin, and human serum. The results showed that OptAndSeg outperformed existing benchmarks for glycerol and human serum. By optimising in non-overlapping volume range segments, we were also able to increase the accuracy and precision of liquid transfer for the 25% purified human serum albumin solution and human serum, achieving relative errors of 5% and 6% or less for volumes as small as 30 L. This methodology can be rapidly applied to any arbitrary liquid, therefore enhancing efficiency and throughput of liquid handling in research and development settings.
{"title":"A Bayesian Optimisation with segmentation approach to optimising liquid handling parameters","authors":"Estefania Yap , Viet Huynh , Calvin Vong , Peter Vogel , Viv Louzado , Thomas Barnes , Buser Say , Michael Burke , Dana Kulić , Aldeida Aleti","doi":"10.1016/j.jprocont.2025.103571","DOIUrl":"10.1016/j.jprocont.2025.103571","url":null,"abstract":"<div><div>The automation of liquid handling has become integral in speeding up pharmaceutical development for faster drug development and more affordable treatments. However, the optimal parameters which define the aspirate and dispense procedures vary between liquids and liquid volumes, limiting transfer accuracy and precision. Even state-of-the-art liquid handling devices offer predefined parameters for only a handful of liquids and volumes, resulting in novel parameter sets being defined via a manual, time-consuming process. In this study, we propose an experimental framework for automating the optimisation of liquid class parameters for arbitrary liquids. Within our framework, we propose an optimisation and segmentation algorithm, OptAndSeg, to identify the optimal parameters by automatically grouping volumes into volume ranges and optimising parameters for these volume range subsets. Our method was validated on three live experiments: glycerol, a solution of 25% purified human serum albumin, and human serum. The results showed that OptAndSeg outperformed existing benchmarks for glycerol and human serum. By optimising in non-overlapping volume range segments, we were also able to increase the accuracy and precision of liquid transfer for the 25% purified human serum albumin solution and human serum, achieving relative errors of 5% and 6% or less for volumes as small as 30 <span><math><mi>μ</mi></math></span>L. This methodology can be rapidly applied to any arbitrary liquid, therefore enhancing efficiency and throughput of liquid handling in research and development settings.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103571"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Agitation intensity exerts a decisive influence on product yield in microbial fermentation, yet most published work treats mixing effects qualitatively and lacks a quantitative link between stirring conditions and product synthesis. This study develops a distributed parameter system (DPS) model that couples partial differential equations (PDEs) with ordinary differential equations (ODEs) to capture how agitation shapes axial substrate gradients and product formation. Building on the Birol kinetics, the model introduces an axial diffusion coefficient (m2 s$-1$) to translate stirring speed into mixing efficiency, and a product-inhibition constant to represent feedback from local product accumulation on microbial metabolism. Theoretical analysis establishes the well-posedness of the model, and numerical simulations illustrate that insufficient agitation leads to strong substrate gradients and reduced productivity, whereas stronger agitation promotes uniform mixing and higher yields. In addition, a laboratory visualization experiment confirmed the agitation-dependent attenuation of gradients, providing further support for the proposed model. Together, these results highlight the DPS model’s potential to guide the design of effective agitation strategies and improve the efficiency of large-scale fermentation processes.
{"title":"Capturing stirring–diffusion and product inhibition in fed-batch fermentation process: A distributed parameter modeling framework","authors":"Bingke Zhou, Shunyi Zhao, Runsheng Guo, Fei Liu, Xiaoli Luan","doi":"10.1016/j.jprocont.2025.103587","DOIUrl":"10.1016/j.jprocont.2025.103587","url":null,"abstract":"<div><div>Agitation intensity exerts a decisive influence on product yield in microbial fermentation, yet most published work treats mixing effects qualitatively and lacks a quantitative link between stirring conditions and product synthesis. This study develops a distributed parameter system (DPS) model that couples partial differential equations (PDEs) with ordinary differential equations (ODEs) to capture how agitation shapes axial substrate gradients and product formation. Building on the Birol kinetics, the model introduces an axial diffusion coefficient <span><math><msub><mrow><mi>D</mi></mrow><mrow><mi>Z</mi></mrow></msub></math></span> (m<sup>2</sup> <!-->s<sup>$-1$</sup>) to translate stirring speed into mixing efficiency, and a product-inhibition constant to represent feedback from local product accumulation on microbial metabolism. Theoretical analysis establishes the well-posedness of the model, and numerical simulations illustrate that insufficient agitation leads to strong substrate gradients and reduced productivity, whereas stronger agitation promotes uniform mixing and higher yields. In addition, a laboratory visualization experiment confirmed the agitation-dependent attenuation of gradients, providing further support for the proposed model. Together, these results highlight the DPS model’s potential to guide the design of effective agitation strategies and improve the efficiency of large-scale fermentation processes.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103587"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-11-04DOI: 10.1016/j.jprocont.2025.103578
Dazi Li, Ziqi Yang
The optimization and control of modern industrial processes are increasingly complex, often necessitating fine trade-offs among multiple conflicting objectives. Multi-Objective Reinforcement Learning (MORL) offers a highly promising paradigm for addressing such problems. However, existing MORL methods commonly face challenges in effectively integrating domain prior knowledge and exhibit low data efficiency. To tackle these challenges, a novel MORL framework named Pri-AG is proposed in this paper. Pri-AG utilizes Graph Convolutional Networks (GCNs) to effectively integrate an initial graph topology derived from domain knowledge. Furthermore, a hypernetwork is employed to dynamically adjust graph edge weights according to preferences, thereby enhancing learning efficiency and improving model interpretability. The proposed Pri-AG framework was systematically validated on a typical lysine fed-batch fermentation process simulation. Extensive experimental results demonstrate that Pri-AG enhances sample utilization efficiency, accelerates policy convergence, and surpasses other MORL benchmark algorithms across most performance metrics.
{"title":"Prior-informed adaptive multi-objective graph reinforcement learning for lysine fed-batch fermentation process","authors":"Dazi Li, Ziqi Yang","doi":"10.1016/j.jprocont.2025.103578","DOIUrl":"10.1016/j.jprocont.2025.103578","url":null,"abstract":"<div><div>The optimization and control of modern industrial processes are increasingly complex, often necessitating fine trade-offs among multiple conflicting objectives. Multi-Objective Reinforcement Learning (MORL) offers a highly promising paradigm for addressing such problems. However, existing MORL methods commonly face challenges in effectively integrating domain prior knowledge and exhibit low data efficiency. To tackle these challenges, a novel MORL framework named Pri-AG is proposed in this paper. Pri-AG utilizes Graph Convolutional Networks (GCNs) to effectively integrate an initial graph topology derived from domain knowledge. Furthermore, a hypernetwork is employed to dynamically adjust graph edge weights according to preferences, thereby enhancing learning efficiency and improving model interpretability. The proposed Pri-AG framework was systematically validated on a typical lysine fed-batch fermentation process simulation. Extensive experimental results demonstrate that Pri-AG enhances sample utilization efficiency, accelerates policy convergence, and surpasses other MORL benchmark algorithms across most performance metrics.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103578"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-30DOI: 10.1016/j.jprocont.2025.103576
Qiang Zhu, Zhonggai Zhao, Fei Liu
Ensuring on-spec product quality that satisfies both customer and regulatory requirements is a fundamental objective in batch manufacturing. Over the years, various data-driven strategies have been proposed for batch quality control, involving batch-to-batch and within-batch approaches. While the former is often implemented using offline optimization, maintaining consistent product quality within a batch remains challenging due to unanticipated disturbances that can lead to off-spec products. Existing within-batch strategies, such as latent-variable-based tracking control, mainly address disturbances that affect batch trajectories, potentially overlooking quality-related variations that do not manifest in the trajectory. To address this gap, we proposed a new within-batch control strategy, quality-by-design latent-variable model predictive control (QbD-LV-MPC), which extends the conventional LV-MPC framework. This strategy dynamically updates the reference trajectories within a predefined design space (DS), ensuring all adjustments remain quality-compliant. Two latent variable models, namely principal component analysis and partial least-squares, are calibrated in parallel to construct the LV-MPC and calculate the DS. Upon detecting quality-related disturbances, QbD-LV-MPC promptly adjusts the reference profiles within the DS and computes optimal inputs using LV-MPC. By confining control actions to the DS, the strategy ensures product quality and enhances process flexibility. The proposed strategy has been validated using a benchmark simulator, IndPensim, and the case study results show that it outperforms the conventional LV-MPC in reducing quality deviations.
{"title":"Implementing quality-by-design latent-variable model predictive control (QbD-LV-MPC) for batch processes: An updating policy for batch profiles","authors":"Qiang Zhu, Zhonggai Zhao, Fei Liu","doi":"10.1016/j.jprocont.2025.103576","DOIUrl":"10.1016/j.jprocont.2025.103576","url":null,"abstract":"<div><div>Ensuring on-spec product quality that satisfies both customer and regulatory requirements is a fundamental objective in batch manufacturing. Over the years, various data-driven strategies have been proposed for batch quality control, involving batch-to-batch and within-batch approaches. While the former is often implemented using offline optimization, maintaining consistent product quality within a batch remains challenging due to unanticipated disturbances that can lead to off-spec products. Existing within-batch strategies, such as latent-variable-based tracking control, mainly address disturbances that affect batch trajectories, potentially overlooking quality-related variations that do not manifest in the trajectory. To address this gap, we proposed a new within-batch control strategy, quality-by-design latent-variable model predictive control (QbD-LV-MPC), which extends the conventional LV-MPC framework. This strategy dynamically updates the reference trajectories within a predefined design space (DS), ensuring all adjustments remain quality-compliant. Two latent variable models, namely principal component analysis and partial least-squares, are calibrated in parallel to construct the LV-MPC and calculate the DS. Upon detecting quality-related disturbances, QbD-LV-MPC promptly adjusts the reference profiles within the DS and computes optimal inputs using LV-MPC. By confining control actions to the DS, the strategy ensures product quality and enhances process flexibility. The proposed strategy has been validated using a benchmark simulator, IndPensim, and the case study results show that it outperforms the conventional LV-MPC in reducing quality deviations.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"156 ","pages":"Article 103576"},"PeriodicalIF":3.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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