1
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Srisuma P, Barbastathis G, Braatz RD. Real-time estimation of bound water concentration during lyophilization with temperature-based state observers. Int J Pharm 2024; 665:124693. [PMID: 39277151 DOI: 10.1016/j.ijpharm.2024.124693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 08/24/2024] [Accepted: 09/08/2024] [Indexed: 09/17/2024]
Abstract
Lyophilization (aka freeze drying) has been shown to provide long-term stability for many crucial biotherapeutics, e.g., mRNA vaccines for COVID-19, allowing for higher storage temperature. The final stage of lyophilization, namely secondary drying, entails bound water removal via desorption, in which accurate prediction of bound water concentration is vital to ensuring the quality of the lyophilized product. This article proposes a novel technique for real-time estimation of the bound water concentration during secondary drying in lyophilization. A state observer is employed, which combines temperature measurement and mechanistic understanding of heat transfer and desorption kinetics, without requiring any online concentration measurement. Results from both simulations and experimental data show that the observer can accurately estimate the concentration of bound water in real time for all possible concentration levels, operating conditions, and measurement noise. This framework can also be applied for monitoring and control of the residual moisture in other desorption-related processes.
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Affiliation(s)
- Prakitr Srisuma
- Center for Computational Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - George Barbastathis
- Center for Computational Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Richard D Braatz
- Center for Computational Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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2
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González-García AP, Díaz-Jiménez L, Padmadas PK, Carlos-Hernández S. Indirect Measurement of Variables in a Heterogeneous Reaction for Biodiesel Production. Methods Protoc 2024; 7:27. [PMID: 38668135 PMCID: PMC11054350 DOI: 10.3390/mps7020027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/07/2024] [Accepted: 03/21/2024] [Indexed: 04/29/2024] Open
Abstract
This research focuses on the development of a state observer for performing indirect measurements of the main variables involved in the soybean oil transesterification reaction with a guishe biochar-based heterogeneous catalyst; the studied reaction takes place in a batch reactor. The mathematical model required for the observer design includes the triglycerides' conversion rate, and the reaction temperature. Since these variables are represented by nonlinear differential equations, the model is linearized around an operation point; after that, the pole placement and linear quadratic regulator (LQR) methods are considered for calculating the observer gain vector L(x). Then, the estimation of the conversion rate and the reaction temperature provided by the observer are used to indirectly measure other variables such as esters, alcohol, and byproducts. The observer performance is evaluated with three error indexes considering initial condition variations up to 30%. With both methods, a fast convergence (less than 3 h in the worst case) of the observer is remarked.
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Affiliation(s)
| | | | | | - Salvador Carlos-Hernández
- Sustentabilidad de los Recursos Naturales y Energía, Cinvestav Saltillo, Ramos Arizpe 259000, Coahuila, Mexico; (A.P.G.-G.); (L.D.-J.); (P.K.P.)
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3
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Cegla M, Semrau R, Tamagnini F, Engell S. Flexible process operation for electrified chemical plants. Curr Opin Chem Eng 2023. [DOI: 10.1016/j.coche.2023.100898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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4
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Tuveri A, Nakama CS, Matias J, Holck HE, Jäschke J, Imsland L, Bar N. A regularized Moving Horizon Estimator for combined state and parameter estimation in a bioprocess experimental application. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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5
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Srisuma P, Pandit A, Zhang Q, Hong MS, Gamekkanda J, Fachin F, Moore N, Djordjevic D, Schwaerzler M, Oyetunde T, Tang W, Myerson AS, Barbastathis G, Braatz RD. Thermal imaging-based state estimation of a Stefan problem with application to cell thawing. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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6
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Müller DF, Wibbing D, Herwig C, Kager J. Simultaneous real-time estimation of maximum substrate uptake capacity and yield coefficient in induced microbial cultures. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
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7
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Ross E, Wagterveld R, Stigter J, Mayer M, Keesman K. Sensor data fusion in electrochemical applications: An overview and its application to electrochlorination monitoring. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2022.108128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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8
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López-Pérez PA, López-López M, Núñez-Colín CA, Mukhtar H, Aguilar-López R, Peña-Caballero V. A novel nonlinear sliding mode observer to estimate biomass for lactic acid production. CHEMICAL PRODUCT AND PROCESS MODELING 2022. [DOI: 10.1515/cppm-2021-0074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Abstract
This study deals with the problem of estimating the amount of biomass and lactic acid concentration in a lactic acid production process. A continuous stirred tank bioreactor was used for the culture of Lactobacillus helveticus. A nonlinear sliding mode observer is proposed and designed, which gives an estimate of both the biomass and lactic acid concentrations as a function of glucose uptake from the culture medium. Numerical results are given to illustrate the effectiveness of the proposed observer against a standard sliding-mode observer. It was found that the proposed observer worked very well for the benchmark bioreactor model. Also, the numerical results indicated that the proposed estimation methodology was robust to the uncertainties associated with un-modelled dynamics. These new sensing technologies, when coupled to software models, improve performance for smart process control, monitoring, and prediction.
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Affiliation(s)
- Pablo A. López-Pérez
- Escuela Superior de Apan, Universidad Autónoma del Estado de Hidalgo , Carretera Apan-Calpulalpan, Km.8., Chimalpa Tlalayote s/n, 43900, Colonia Chimalpa , Apan , Hgo. , Mexico
| | - Milagros López-López
- University of Guanajuato , Av. Ing. Barros Sierra No. 201 Ejido de Santa María del Refugio, C.P. 38140 Celaya , Guanajuato , Mexico
| | - Carlos A. Núñez-Colín
- University of Guanajuato , Av. Ing. Barros Sierra No. 201 Ejido de Santa María del Refugio, C.P. 38140 Celaya , Guanajuato , Mexico
| | - Hamid Mukhtar
- Institute of Industrial Biotechnology, Government College University , Katchery Road , Lahore 54000 , Pakistan
| | - Ricardo Aguilar-López
- Departamento de Biotecnología y Bioingeniería , CINVESTAV-IPN , Av. Instituto Politécnico Nacional No. 2508, Col. San Pedro Zacatenco, 07360 , México City , CDMX. , Mexico
| | - Vicente Peña-Caballero
- University of Guanajuato , Av. Ing. Barros Sierra No. 201 Ejido de Santa María del Refugio, C.P. 38140 Celaya , Guanajuato , Mexico
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9
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Stability Analysis of a Chemostat Model for Phenol and Sodium Salicylate Mixture Biodegradation. Processes (Basel) 2022. [DOI: 10.3390/pr10122571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
In this paper we consider a mathematical continuous-time model for biodegradation of phenol in the presence of sodium salicylate in a chemostat. The model is described by a system of three nonlinear ordinary differential equations. Based on the dynamical systems theory we provide mathematical investigations of the model including local and global analysis of the solutions. The local analysis consist in computation of two equilibrium points—one interior and one boundary (washout) equilibrium—in dependance of the dilution rate as a key model parameter. The local asymptotic stability of the equilibria is also presented. The global analysis of the model solutions comprises proving existence, uniqueness and uniform boundedness of positive solutions, as well as global asymptotic stabilizability of the dynamics. The theoretical investigations are illustrated by some numerical examples. The results in this study can be used in practice as a tool to control and optimize the chemostat performance of simultaneous biodegradation of mixed substrates in wastewater.
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10
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van der Hauwaert L, Regueira A, Selder L, Zeng AP, Mauricio-Iglesias M. Optimising bioreactor processes with in-situ product removal using mathematical programming: a case study for propionate production. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.108059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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11
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Comparative Analysis of a Family of Sliding Mode Observers under Real-Time Conditions for the Monitoring in the Bioethanol Production. FERMENTATION-BASEL 2022. [DOI: 10.3390/fermentation8090446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Online monitoring of fermentation processes is a necessary task to determine concentrations of key biochemical compounds, diagnose faults in process operations, and implement feedback controllers. However, obtaining the signals of all-important variables in a real process is a task that may be difficult and expensive due to the lack of adequate sensors, or simply because some variables cannot be directly measured. From the above, a model-based approach such as state observers may be a viable alternative to solve the estimation problem. This work shows a comparative analysis of the real-time performance of a family of sliding-mode observers for reconstructing key variables in a batch bioreactor for fermentative ethanol production. These observers were selected for their robust performance under model uncertainties and finite-time estimation convergence. The selected sliding-mode observers were the first-order sliding mode observer, the proportional sliding mode observer, and the high-order sliding mode observer. For estimation purposes, a power law kinetic model for ethanol production by Saccharomyces cerevisiae was performed. A hybrid methodology allows the kinetic parameters to be adjusted, and an approach based on inference diagrams allows the observability of the model to be determined. The experimental results reported here show that the observers under analysis were robust to modeling errors and measurement noise. Moreover, the proportional sliding-mode observer was the algorithm that exhibited the best performance.
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12
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Shen X, Budman H. Online estimation using dynamic flux balance model and multiparametric programming. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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13
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Sensor Data Fusion as an Alternative for Monitoring Chlorate in Electrochlorination Applications. SUSTAINABILITY 2022. [DOI: 10.3390/su14106119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
As chlorate concentrations have been found to be harmful to human and animal health, governments are increasingly demanding strict control of the chlorate concentration in drinking water. Since there are no chlorate sensors available, the current solution is sampling and laboratory analysis. This is costly and time consuming. The aim of this work was to investigate Sensor Data Fusion (SDF) as an alternative approach, with a focus on chlorate formation in the electrochlorination process, and design an observer for the real-time estimation of chlorate. The pH, temperature and UV-a absorption were measured in real time. A reduced-order nonlinear model was derived, and it was found to be detectable. An Extended Kalman Filter (EKF), based on this model, was then used to estimate the chlorate formation. The EKF algorithm was verified experimentally and was found to be capable of accurately estimating chlorate concentrations in real time. Electrochlorination is an emerging and efficient method of disinfecting drinking water. Soft sensing of chlorate concentrations, as proposed in this paper, may help to better control and manage the process of electrochlorination.
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14
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State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing. SUSTAINABILITY 2022. [DOI: 10.3390/su14063635] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in a dynamical system given the sensor measurements of related system states. They can be used to derive fast and accurate estimates of system variables that cannot be measured directly (`soft sensing’) or for which only noisy, intermittent, delayed, indirect, or unreliable measurements are available, perhaps from multiple sources (`sensor fusion’). In this paper, we introduce the concepts and main methods of state estimation and review recent applications in improving the sustainability of manufacturing processes across sectors including industrial robotics, material synthesis and processing, semiconductor, and additive manufacturing. It is shown that state estimation algorithms can play a key role in manufacturing systems for accurately monitoring and controlling processes to improve efficiencies, lower environmental impact, enhance product quality, improve the feasibility of processing more sustainable raw materials, and ensure safer working environments for humans. We discuss current and emerging trends in using state estimation as a framework for combining physical knowledge with other sources of data for monitoring and controlling distributed manufacturing systems.
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15
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Samandari Masooleh L, Arbogast JE, Seider WD, Oktem U, Soroush M. Distributed state estimation in large-scale processes decomposed into observable subsystems using community detection. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2021.107544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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16
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17
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Sinner P, Stiegler M, Goldbeck O, Seibold GM, Herwig C, Kager J. Online estimation of changing metabolic capacities in continuous Corynebacterium glutamicum cultivations growing on a complex sugar mixture. Biotechnol Bioeng 2021; 119:575-590. [PMID: 34821377 PMCID: PMC9299845 DOI: 10.1002/bit.28001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 10/06/2021] [Accepted: 11/12/2021] [Indexed: 01/16/2023]
Abstract
Model‐based state estimators enable online monitoring of bioprocesses and, thereby, quantitative process understanding during running operations. During prolonged continuous bioprocesses strain physiology is affected by selection pressure. This can cause time‐variable metabolic capacities that lead to a considerable model‐plant mismatch reducing monitoring performance if model parameters are not adapted accordingly. Variability of metabolic capacities therefore needs to be integrated in the in silico representation of a process using model‐based monitoring approaches. To enable online monitoring of multiple concentrations as well as metabolic capacities during continuous bioprocessing of spent sulfite liquor with Corynebacterium glutamicum, this study presents a particle filtering framework that takes account of parametric variability. Physiological parameters are continuously adapted by Bayesian inference, using noninvasive off‐gas measurements. Additional information on current parameter importance is derived from time‐resolved sensitivity analysis. Experimental results show that the presented framework enables accurate online monitoring of long‐term culture dynamics, whereas state estimation without parameter adaption failed to quantify substrate metabolization and growth capacities under conditions of high selection pressure. Online estimated metabolic capacities are further deployed for multiobjective optimization to identify time‐variable optimal operating points. Thereby, the presented monitoring system forms a basis for adaptive control during continuous bioprocessing of lignocellulosic by‐product streams.
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Affiliation(s)
- Peter Sinner
- Research Unit of Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Vienna, Austria
| | - Marlene Stiegler
- Research Unit of Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Vienna, Austria
| | - Oliver Goldbeck
- Institute of Microbiology and Biotechnology, University of Ulm, Ulm, Germany
| | - Gerd M Seibold
- Section for Synthetic Biology, Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Christoph Herwig
- Research Unit of Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Vienna, Austria
| | - Julian Kager
- Research Unit of Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Vienna, Austria.,Competence Center CHASE GmbH, Linz, Austria
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18
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Multi-Rate Data Fusion for State and Parameter Estimation in (Bio-)Chemical Process Engineering. Processes (Basel) 2021. [DOI: 10.3390/pr9111990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
For efficient operation, modern control approaches for biochemical process engineering require information on the states of the process such as temperature, humidity or chemical composition. Those measurement are gathered from a set of sensors which differ with respect to sampling rates and measurement quality. Furthermore, for biochemical processes in particular, analysis of physical samples is necessary, e.g., to infer cellular composition resulting in delayed information. As an alternative for the use of this delayed measurement for control, so-called soft-sensor approaches can be used to fuse delayed multirate measurements with the help of a mathematical process model and provide information on the current state of the process. In this manuscript we present a complete methodology based on cascaded unscented Kalman filters for state estimation from delayed and multi-rate measurements. The approach is demonstrated for two examples, an exothermic chemical reactor and a recently developed model for biopolymer production. The results indicate that the the current state of the systems can be accurately reconstructed and therefore represent a promising tool for further application in advanced model-based control not only of the considered processes but also of related processes.
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19
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Tupaz J, Asteasuain M, Sánchez M. Efficient and robust state estimation: Application to a copolymerization process. CAN J CHEM ENG 2021. [DOI: 10.1002/cjce.23976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jhovany Tupaz
- Departamento de Ingeniería Química Universidad Nacional del Sur (UNS) Bahía Blanca Argentina
| | - Mariano Asteasuain
- Departamento de Ingeniería Química Universidad Nacional del Sur (UNS) Bahía Blanca Argentina
- Planta Piloto de Ingeniería Química (PLAPIQUI), Universidad Nacional del Sur (UNS) – CONICET Bahía Blanca Argentina
| | - Mabel Sánchez
- Departamento de Ingeniería Química Universidad Nacional del Sur (UNS) Bahía Blanca Argentina
- Planta Piloto de Ingeniería Química (PLAPIQUI), Universidad Nacional del Sur (UNS) – CONICET Bahía Blanca Argentina
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20
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Pernsteiner D, Schirrer A, Kasper L, Hofmann R, Jakubek S. State estimation concept for a nonlinear melting/solidification problem of a latent heat thermal energy storage. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107444] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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21
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Abstract
Dynamic flux balance models (DFBM) are used in this study to infer metabolite concentrations that are difficult to measure online. The concentrations are estimated based on few available measurements. To account for uncertainty in initial conditions the DFBM is converted into a variable structure system based on a multiparametric linear programming (mpLP) where different regions of the state space are described by correspondingly different state space models. Using this variable structure system, a special set membership-based estimation approach is proposed to estimate unmeasured concentrations from few available measurements. For unobservable concentrations, upper and lower bounds are estimated. The proposed set membership estimation was applied to batch fermentation of E. coli based on DFBM.
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22
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Huang YS, Sheriff MZ, Bachawala S, Gonzalez M, Nagy ZK, Reklaitis GV. Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press. Processes (Basel) 2021; 9:10.3390/pr9091612. [PMID: 36776491 PMCID: PMC9912115 DOI: 10.3390/pr9091612] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects.
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Affiliation(s)
- Yan-Shu Huang
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - M Ziyan Sheriff
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Sunidhi Bachawala
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Marcial Gonzalez
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
- Ray W. Herrick Laboratories, Purdue University, West Lafayette, IN 47907, USA
| | - Zoltan K Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Gintaras V Reklaitis
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA
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23
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Abstract
Anaerobic digestion is associated with various crucial variables, such as biogas yield, chemical oxygen demand, and volatile fatty acid concentration. Real-time monitoring of these variables can not only reflect the process of anaerobic digestion directly but also accelerate the efficiency of resource conversion and improve the stability of the reaction process. However, the current real-time monitoring equipment on the market cannot be widely used in the industrial production process due to its defects such as expensive equipment, low accuracy, and lagging analysis. Therefore, it is essential to conduct soft sensor modeling for unmeasurable variables and use auxiliary variables to realize real-time monitoring, optimization, and control of the an-aerobic digestion process. In this paper, the basic principle and process flow of anaerobic digestion are first briefly introduced. Subsequently, the development history of the traditional soft sensor is systematically reviewed, the latest development of soft sensors was detailed, and the obstacles of the soft sensor in the industrial production process are discussed. Finally, the future development trend of deep learning in soft sensors is deeply discussed, and future research directions are provided.
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24
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Fernández MC, Pantano MN, Rodriguez L, Scaglia G. State estimation and nonlinear tracking control simulation approach. Application to a bioethanol production system. Bioprocess Biosyst Eng 2021; 44:1755-1768. [PMID: 33993385 DOI: 10.1007/s00449-021-02558-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 04/20/2020] [Accepted: 05/08/2020] [Indexed: 11/24/2022]
Abstract
Tracking control of specific variables is key to achieve a proper fermentation. This paper analyzes a fed-batch bioethanol production process. For this system, a controller design based on linear algebra is proposed. Moreover, to achieve a reliable control, on-line monitoring of certain variables is needed. In this sense, for unmeasurable variables, state estimators based on Gaussian processes are designed. Cell, ethanol and glycerol concentrations are predicted with only substrates measurement. Simulation results when the controller and estimators are coupled, are shown. Furthermore, the algorithms were tested with parametric uncertainties and disturbances in the control action, and are compared, in all cases, with neural networks estimators (previous work). Bayesian estimators show a performance improvement, which is reflected in a decrease of the total error. Proposed techniques give reliable monitoring and control tools, with a low computational and economic cost, and less mathematical complexity than neural network estimators.
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Affiliation(s)
- M Cecilia Fernández
- Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, J5400ARL, San Juan, Argentina.
| | - M Nadia Pantano
- Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, J5400ARL, San Juan, Argentina
| | - Leandro Rodriguez
- Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, J5400ARL, San Juan, Argentina
| | - Gustavo Scaglia
- Instituto de Ingeniería Química, Universidad Nacional de San Juan (UNSJ), CONICET, Av. Lib. San Martín Oeste 1109, J5400ARL, San Juan, Argentina
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25
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Abstract
Efficient monitoring is an open problem in the operation of anaerobic digestion processes, due to the lack of accurate, low-cost, and proper sensors for the on-line monitoring of key process variables. This paper presents two approaches for the indirect monitoring of the anaerobic digestion of cheese whey wastewater. First, the observability property is addressed using conventional and nonconventional techniques, including an observability index. Then, two model-based observer techniques, an extended Luenberger observer, a sliding mode observer, and a data-driven technique based on fractal analysis are formulated and discussed. The performance and capabilities of the proposed methodologies are illustrated on a validated model with experimental data of the anaerobic digestion of cheese whey. Experimental pH measurements are used for the data-driven approach based on fractal analysis. The experimental data sets correspond to experimental conditions (pH > 7.5 and temperature (T) = 40 °C) favoring volatile fatty acid (VFA) production (30 g/L) with simultaneously acceptable biogas production (3420 mL). Results also show that the proposed observers were able to predict satisfactory key process variables. On the other hand, the fractal analysis provides reliable qualitative trends of VFA production and chemical oxygen demand (COD) consumption.
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26
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Pauk JN, Raju Palanisamy J, Kager J, Koczka K, Berghammer G, Herwig C, Veiter L. Advances in monitoring and control of refolding kinetics combining PAT and modeling. Appl Microbiol Biotechnol 2021; 105:2243-2260. [PMID: 33598720 PMCID: PMC7954745 DOI: 10.1007/s00253-021-11151-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 01/19/2021] [Accepted: 01/27/2021] [Indexed: 12/21/2022]
Abstract
Overexpression of recombinant proteins in Escherichia coli results in misfolded and non-active protein aggregates in the cytoplasm, so-called inclusion bodies (IB). In recent years, a change in the mindset regarding IBs could be observed: IBs are no longer considered an unwanted waste product, but a valid alternative to produce a product with high yield, purity, and stability in short process times. However, solubilization of IBs and subsequent refolding is necessary to obtain a correctly folded and active product. This protein refolding process is a crucial downstream unit operation-commonly done as a dilution in batch or fed-batch mode. Drawbacks of the state-of-the-art include the following: the large volume of buffers and capacities of refolding tanks, issues with uniform mixing, challenging analytics at low protein concentrations, reaction kinetics in non-usable aggregates, and generally low re-folding yields. There is no generic platform procedure available and a lack of robust control strategies. The introduction of Quality by Design (QbD) is the method-of-choice to provide a controlled and reproducible refolding environment. However, reliable online monitoring techniques to describe the refolding kinetics in real-time are scarce. In our view, only monitoring and control of re-folding kinetics can ensure a productive, scalable, and versatile platform technology for re-folding processes. For this review, we screened the current literature for a combination of online process analytical technology (PAT) and modeling techniques to ensure a controlled refolding process. Based on our research, we propose an integrated approach based on the idea that all aspects that cannot be monitored directly are estimated via digital twins and used in real-time for process control. KEY POINTS: • Monitoring and a thorough understanding of refolding kinetics are essential for model-based control of refolding processes. • The introduction of Quality by Design combining Process Analytical Technology and modeling ensures a robust platform for inclusion body refolding.
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Affiliation(s)
- Jan Niklas Pauk
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Vienna University of Technology, Gumpendorferstrasse 1a/166, 1060, Vienna, Austria
- Competence Center CHASE GmbH, Altenbergerstraße 69, 4040, Linz, Austria
| | - Janani Raju Palanisamy
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Vienna University of Technology, Gumpendorferstrasse 1a/166, 1060, Vienna, Austria
| | - Julian Kager
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Vienna University of Technology, Gumpendorferstrasse 1a/166, 1060, Vienna, Austria
| | - Krisztina Koczka
- Bilfinger Industrietechnik Salzburg GmbH, Mooslackengasse 17, 1190, Vienna, Austria
| | - Gerald Berghammer
- Bilfinger Industrietechnik Salzburg GmbH, Mooslackengasse 17, 1190, Vienna, Austria
| | - Christoph Herwig
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Vienna University of Technology, Gumpendorferstrasse 1a/166, 1060, Vienna, Austria.
| | - Lukas Veiter
- Research Area Biochemical Engineering, Institute of Chemical, Environmental and Bioscience Engineering, Vienna University of Technology, Gumpendorferstrasse 1a/166, 1060, Vienna, Austria
- Competence Center CHASE GmbH, Altenbergerstraße 69, 4040, Linz, Austria
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Huang M, Zheng Y, Li S, Li M. Thermal Energy Correction Based Model Predictive Control for Fluid Catalytic Cracking Riser. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c03632] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Meng Huang
- Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yi Zheng
- Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shaoyuan Li
- Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Mengying Li
- Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China
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Rúa S, Vásquez RE, Crasta N, Zuluaga CA. Observability Analysis and Observer Design for a Nonlinear Three-Tank System: Theory and Experiments. SENSORS (BASEL, SWITZERLAND) 2020; 20:s20236738. [PMID: 33255678 PMCID: PMC7728094 DOI: 10.3390/s20236738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 10/18/2020] [Accepted: 10/19/2020] [Indexed: 05/16/2023]
Abstract
This paper addresses the observability analysis and observer design for a nonlinear interacting three-tank system. The plant configuration is first described using the process and instrumentation diagram (P&ID) and a state-space realization is derived; some insights about the behavior of the nonlinear system, considering equilibrium points and the phase portrait are provided. Then, observability in the Hermann-Krener sense is analyzed. A high-gain observer (HGO) is then designed, using the equivalence of the original state-space realization with its observability canonical form, in order to guarantee convergence of the state estimation. The performance was validated through simulation and experiments in a multipurpose plant equipped with real sensors; the HGO response was compared to a Luenberger observer (for a linear approximation of the plant) and the Extended Kalman Filter (for which convergence is not guaranteed), considering nonlinearities, interaction, disturbances and noise. Theoretical and experimental results show that the HGO can provide robust estimation and disturbance rejection, despite the sensitivity of HGOs to noisy variables in processes such as level of liquids.
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Affiliation(s)
- Santiago Rúa
- School of Engineering, Universidad Pontificia Bolivariana, Medellín 050031, Colombia; (S.R.); (C.A.Z.)
- Grupo de Investigación en Desarrollo Tecnológico GIDESTEC, Universidad Nacional Abierta y a Distancia, Carrera 45 # 55-19, Medellín 050012, Colombia
| | - Rafael E. Vásquez
- School of Engineering, Universidad Pontificia Bolivariana, Medellín 050031, Colombia; (S.R.); (C.A.Z.)
- Correspondence: ; Tel.: +57-(44)-488388 (ext. 14165)
| | - Naveen Crasta
- Institute for Systems and Robotics, Instituto Superior Técnico, 1049-001 Lisbon, Portugal;
| | - Carlos A. Zuluaga
- School of Engineering, Universidad Pontificia Bolivariana, Medellín 050031, Colombia; (S.R.); (C.A.Z.)
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Challenges and Opportunities on Nonlinear State Estimation of Chemical and Biochemical Processes. Processes (Basel) 2020. [DOI: 10.3390/pr8111462] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
This paper provides an overview of nonlinear state estimation techniques along with a discussion on the challenges and opportunities for future work in the field. Emphasis is given on Bayesian methods such as moving horizon estimation (MHE) and extended Kalman filter (EKF). A discussion on Bayesian, deterministic, and hybrid methods is provided and examples of each of these methods are listed. An approach for nonlinear state estimation design is included to guide the selection of the nonlinear estimator by the user/practitioner. Some of the current challenges in the field are discussed involving covariance estimation, uncertainty quantification, time-scale multiplicity, bioprocess monitoring, and online implementation. A case study in which MHE and EKF are applied to a batch reactor system is addressed to highlight the challenges of these technologies in terms of performance and computational time. This case study is followed by some possible opportunities for state estimation in the future including the incorporation of more efficient optimization techniques and development of heuristics to streamline the further adoption of MHE.
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Evolving granular control with high-gain observers for feedback linearizable nonlinear systems. EVOLVING SYSTEMS 2020. [DOI: 10.1007/s12530-020-09349-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Narayanan H, Behle L, Luna MF, Sokolov M, Guillén‐Gosálbez G, Morbidelli M, Butté A. Hybrid‐EKF: Hybrid model coupled with extended Kalman filter for real‐time monitoring and control of mammalian cell culture. Biotechnol Bioeng 2020; 117:2703-2714. [DOI: 10.1002/bit.27437] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Revised: 05/15/2020] [Accepted: 05/18/2020] [Indexed: 01/15/2023]
Affiliation(s)
- Harini Narayanan
- Department of Chemistry and Applied BiosciencesInstitute of Chemical and Bioengineering, ETH Zurich Zurich Switzerland
| | - Lars Behle
- Department of Chemistry and Applied BiosciencesInstitute of Chemical and Bioengineering, ETH Zurich Zurich Switzerland
| | - Martin F. Luna
- Department of Chemistry and Applied BiosciencesInstitute of Chemical and Bioengineering, ETH Zurich Zurich Switzerland
| | - Michael Sokolov
- Department of Chemistry and Applied BiosciencesInstitute of Chemical and Bioengineering, ETH Zurich Zurich Switzerland
- DataHow AG Zurich Switzerland
| | - Gonzalo Guillén‐Gosálbez
- Department of Chemistry and Applied BiosciencesInstitute of Chemical and Bioengineering, ETH Zurich Zurich Switzerland
| | - Massimo Morbidelli
- DataHow AG Zurich Switzerland
- Dipartimento di Chimica, Materiali e Ingegneria Chimica "Giulio Natta"Politecnico di Milano Milan Italy
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Md Nor N, Che Hassan CR, Hussain MA. A review of data-driven fault detection and diagnosis methods: applications in chemical process systems. REV CHEM ENG 2020. [DOI: 10.1515/revce-2017-0069] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
AbstractFault detection and diagnosis (FDD) systems are developed to characterize normal variations and detect abnormal changes in a process plant. It is always important for early detection and diagnosis, especially in chemical process systems to prevent process disruptions, shutdowns, or even process failures. However, there have been only limited reviews of data-driven FDD methods published in the literature. Therefore, the aim of this review is to provide the state-of-the-art reference for chemical engineers and to promote the application of data-driven FDD methods in chemical process systems. In general, there are two different groups of data-driven FDD methods: the multivariate statistical analysis and the machine learning approaches, which are widely accepted and applied in various industrial processes, including chemicals, pharmaceuticals, and polymers. Many different multivariate statistical analysis methods have been proposed in the literature, such as principal component analysis, partial least squares, independent component analysis, and Fisher discriminant analysis, while the machine learning approaches include artificial neural networks, neuro-fuzzy methods, support vector machine, Gaussian mixture model, K-nearest neighbor, and Bayesian network. In the first part, this review intends to provide a comprehensive literature review on applications of data-driven methods in FDD systems for chemical process systems. In addition, the hybrid FDD frameworks have also been reviewed by discussing the distinct advantages and various constraints, with some applications as examples. However, the choice for the data-driven FDD methods is not a straightforward issue. Thus, in the second part, this paper provides a guideline for selecting the best possible data-driven method for FDD systems based on their faults. Finally, future directions of data-driven FDD methods are summarized with the intent to expand the use for the process monitoring community.
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Affiliation(s)
- Norazwan Md Nor
- Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
- School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia
| | - Che Rosmani Che Hassan
- Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Mohd Azlan Hussain
- Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
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Kager J, Tuveri A, Ulonska S, Kroll P, Herwig C. Experimental verification and comparison of model predictive, PID and model inversion control in a Penicillium chrysogenum fed-batch process. Process Biochem 2020. [DOI: 10.1016/j.procbio.2019.11.023] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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Torgashov A, Skogestad S. The use of first principles model for evaluation of adaptive soft sensor for multicomponent distillation unit. Chem Eng Res Des 2019. [DOI: 10.1016/j.cherd.2019.08.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Narayanan H, Luna MF, Stosch M, Cruz Bournazou MN, Polotti G, Morbidelli M, Butté A, Sokolov M. Bioprocessing in the Digital Age: The Role of Process Models. Biotechnol J 2019; 15:e1900172. [DOI: 10.1002/biot.201900172] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 07/15/2019] [Indexed: 12/20/2022]
Affiliation(s)
- Harini Narayanan
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
| | - Martin F. Luna
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
| | | | - Mariano Nicolas Cruz Bournazou
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Gianmarco Polotti
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Massimo Morbidelli
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Alessandro Butté
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Michael Sokolov
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
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Boudjellal M, Illoul R. Design of a Robust Observer with Super-Twisting Algorithm for Simultaneous Concentration Estimation and Faults Reconstruction in a CSTR. INTERNATIONAL JOURNAL OF CHEMICAL REACTOR ENGINEERING 2019. [DOI: 10.1515/ijcre-2018-0073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
AbstractThis paper deals with the problem of simultaneous concentration and faults estimations of a continuous stirred tank reactor (CSTR) subject to unknown inputs. We propose a combination of two robust nonlinear observers for state estimation and fault reconstruction without any use of a linear approximation of the CSTR dynamic model. Based on the high-gain observer, the proposed scheme can guarantee the asymptotic estimation of the concentration inside the reactor, while, a robust term is added to the nominal plant on the basis of the super-twisting algorithm for fault reconstruction. The stability analysis is proved mathematically using the Lyapunov theory. The effectiveness and robustness of the proposed scheme are illustrated via simulations.
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Abstract
As the optimal linear filter and estimator, the Kalman filter has been extensively utilized for state estimation and prediction in the realm of lumped parameter systems. However, the dynamics of complex industrial systems often vary in both spatial and temporal domains, which take the forms of partial differential equations (PDEs) and/or delay equations. State estimation for these systems is quite challenging due to the mathematical complexity. This work addresses discrete-time Kalman filter design and realization for linear distributed parameter systems. In particular, the structural- and energy-preserving Crank–Nicolson framework is applied for model time discretization without spatial approximation or model order reduction. In order to ensure the time instance consistency in Kalman filter design, a new discrete model configuration is derived. To verify the feasibility of the proposed design, two widely-used PDEs models are considered, i.e., a pipeline hydraulic model and a 1D boundary damped wave equation.
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Khan MAI, Imtiaz SA, Khan F. Simultaneous Estimation of Hidden State and Unknown Input Using Expectation Maximization Algorithm. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.8b06091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Mohammad Aminul Islam Khan
- Safety and Risk Engineering Group of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada
| | - Syed Ahmad Imtiaz
- Safety and Risk Engineering Group of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada
| | - Faisal Khan
- Safety and Risk Engineering Group of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada
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Loskot P, Atitey K, Mihaylova L. Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks. Front Genet 2019; 10:549. [PMID: 31258548 PMCID: PMC6588029 DOI: 10.3389/fgene.2019.00549] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 05/24/2019] [Indexed: 01/30/2023] Open
Abstract
The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered-perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed.
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Affiliation(s)
- Pavel Loskot
- College of Engineering, Swansea University, Swansea, United Kingdom
| | - Komlan Atitey
- College of Engineering, Swansea University, Swansea, United Kingdom
| | - Lyudmila Mihaylova
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom
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Adegboye MA, Fung WK, Karnik A. Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches. SENSORS (BASEL, SWITZERLAND) 2019; 19:E2548. [PMID: 31167413 PMCID: PMC6603558 DOI: 10.3390/s19112548] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 05/24/2019] [Accepted: 05/27/2019] [Indexed: 11/18/2022]
Abstract
Pipelines are widely used for the transportation of hydrocarbon fluids over millions of miles all over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distribution depot. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses pipeline leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed.
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Affiliation(s)
- Mutiu Adesina Adegboye
- Communications and Autonomous Systems Group, Robert Gordon University, Aberdeen AB10 7GJ, UK.
- School of Engineering, Robert Gordon University, Aberdeen AB10 7GJ, UK.
| | - Wai-Keung Fung
- Communications and Autonomous Systems Group, Robert Gordon University, Aberdeen AB10 7GJ, UK.
- School of Engineering, Robert Gordon University, Aberdeen AB10 7GJ, UK.
| | - Aditya Karnik
- School of Engineering, Robert Gordon University, Aberdeen AB10 7GJ, UK.
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A hierarchical state estimation and control framework for monitoring and dissolved oxygen regulation in bioprocesses. Bioprocess Biosyst Eng 2019; 42:1467-1481. [DOI: 10.1007/s00449-019-02143-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 05/02/2019] [Indexed: 12/20/2022]
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Zhao L, Wang J, Yu T, Chen K, Su A. Incorporating delayed measurements in an improved high-degree cubature Kalman filter for the nonlinear state estimation of chemical processes. ISA TRANSACTIONS 2019; 86:122-133. [PMID: 30454950 DOI: 10.1016/j.isatra.2018.11.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 11/27/2017] [Accepted: 11/04/2018] [Indexed: 06/09/2023]
Abstract
The on-line estimation of process quality variables has a large impact on the advanced monitoring and control techniques of chemical processes. The present study offers an improved high-degree cubature Kalman filter (HCKF) to solve the nonlinear state estimation problem of high-dimensional chemical processes. We substituted the Cholesky decomposition in the HCKF filter with a diagonalization transformation of the matrix. In addition, we enhanced numerical stability and estimation accuracy. On this basis, we present one nonlinear state estimation method based on the sample-state augmentation and improved HCKF to handle issues with delayed measurements. Finally, we used the nonlinear state estimation experiments for the polymerization process to validate the proposed method. The numerical results indicated the achievement of state estimation with higher accuracy and better stability following the effective utilization of the delayed measurements for nonlinear chemical processes.
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Affiliation(s)
- Liqiang Zhao
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, PR China
| | - Jianlin Wang
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, PR China.
| | - Tao Yu
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, PR China
| | - Kunyun Chen
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, PR China
| | - Andong Su
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, PR China
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Modeling and Simulation Studies Analyzing the Pressure-Retarded Osmosis (PRO) and PRO-Hybridized Processes. ENERGIES 2019. [DOI: 10.3390/en12020243] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Pressure-retarded osmosis (PRO) is viewed as a highly promising renewable energy process that generates energy without carbon emissions in the age of the climate change regime. While many experimental studies have contributed to the quest for an efficiency that would make the PRO process commercially viable, computational modeling and simulation studies have played crucial roles in investigating the efficiency of PRO, particularly the concept of hybridizing the PRO process with reverse osmosis (RO). It is crucial for researchers to understand the implications of the simulation and modeling works in order to promote the further development of PRO. To that end, the authors collected many relevant papers and reorganized their important methodologies and results. This review, first of all, presents the mathematical derivation of the fundamental modeling theories regarding PRO including water flux and concentration polarization equations. After that, those theories and thermodynamic theories are then applied to depict the limitations of a stand-alone PRO process and the effectiveness of an RO-PRO hybridized process. Lastly, the review diagnoses the challenges facing PRO-basis processes which are insufficiently resolved by conventional engineering approaches and, in response, presents alternative modeling and simulation approaches as well as novel technologies.
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Biomass estimation of an industrial raceway photobioreactor using an extended Kalman filter and a dynamic model for microalgae production. ALGAL RES 2019. [DOI: 10.1016/j.algal.2018.11.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Kager J, Berezhinskiy V, Zimmerleiter R, Brandstetter M, Herwig C. Extension of a Particle Filter for Bioprocess State Estimation using Invasive and Non-Invasive IR Measurements. COMPUTER AIDED CHEMICAL ENGINEERING 2019. [DOI: 10.1016/b978-0-12-818634-3.50237-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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Lara-Cisneros G, Dochain D. Software Sensor for Online Estimation of the VFA’s Concentration in Anaerobic Digestion Processes via a High-Order Sliding Mode Observer. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b02607] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Gerardo Lara-Cisneros
- Mathematical Engineering Department, ICTEAM, Université Catholique de Louvain, 4-6 avenue G. Lemaître, 1348 Louvain-la-Neuve, Belgium
| | - Denis Dochain
- Mathematical Engineering Department, ICTEAM, Université Catholique de Louvain, 4-6 avenue G. Lemaître, 1348 Louvain-la-Neuve, Belgium
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