Hector Budman - Academia.edu (original) (raw)

Papers by Hector Budman

Research paper thumbnail of Design and Optimization of a Penicillin Fed-Batch Reactor Based on a Deep Learning Fault Detection and Diagnostic Model

Industrial & Engineering Chemistry Research

Research paper thumbnail of Simultaneous model identification and optimization in presence of model-plant mismatch

In a standard optimization approach, the underlying process model is first identified at a given ... more In a standard optimization approach, the underlying process model is first identified at a given set of operating conditions and this updated model is, then, used to calculate the optimal conditions for the process. This two-step procedure can be repeated iteratively by conducting new experiments at optimal operating conditions, based on previous iterations, followed by re-identification and re-optimization until convergence is reached. However, when there is a model-plant mismatch, the set of parameter estimates that minimizes the prediction error in the identification problem may not predict the gradients of the optimization objective accurately. As a result, convergence of the two-step iterative approach to a process optimum cannot be guaranteed. This paper presents a new methodology where the model outputs are corrected explicitly for the mismatch such that, with the updated parameter estimates the identification and optimization objectives are properly reconciled. With the prop...

Research paper thumbnail of Identifying Frequency Domain Uncertainty Bounds for Robust Controller Design - Theory with Application to a Fixed-Bed Reactor

1989 American Control Conference, 1989

Christopher Webb Hector Bud Manfred Moraui Chemical Engineering, 210-41 Califormia institute of T... more Christopher Webb Hector Bud Manfred Moraui Chemical Engineering, 210-41 Califormia institute of Technology Pasadena, CA 91125 control problem, the control of a hotspot in a fixed-bed chemical ... A methodology for computing frequency domain uncertainty ... In sections 6 and 7, we ...

Research paper thumbnail of Fluorescence-based soft-sensor for monitoring beta-lactoglobulin and alpha-lactalbumin solubility during thermal aggregation

Biotechnology and bioengineering, 2008

A soft-sensor for monitoring solubility of native-like alpha-lactalbumin (alpha-LA) and beta-lact... more A soft-sensor for monitoring solubility of native-like alpha-lactalbumin (alpha-LA) and beta-lactoglobulin (beta-LG) and their aggregation behavior following heat treatment of mixtures under different treatment conditions was developed using fluorescence spectroscopy data regressed with a multivariate Partial Least Squares (PLS) regression algorithm. PLS regression was used to correlate the concentrations of alpha-LA and beta-LG to the fluorescence spectra obtained for their mixtures. Data for the calibration and validation of the soft sensor was derived from fluorescence spectra. The process of thermal induced aggregation of beta-LG and alpha-LA protein in mixtures, which involves the disappearance of native-like proteins, was studied under various treatment conditions including different temperatures, pH, total initial protein concentration and proportions of alpha-LA and beta-LG. It was demonstrated that the multivariate regression models used could effectively deconvolute multi-...

Research paper thumbnail of Author response for "Applications of Flow Cytometry Sorting in the Pharmaceutical Industry

Research paper thumbnail of A Type of Set Membership Estimation Designed for Dynamic Flux Balance Models

Processes, 2021

Dynamic flux balance models (DFBM) are used in this study to infer metabolite concentrations that... more 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.

Research paper thumbnail of Explainability: Relevance based dynamic deep learning algorithm for fault detection and diagnosis in chemical processes

Computers & Chemical Engineering, 2021

Research paper thumbnail of Applications of flow cytometry sorting in the pharmaceutical industry: A review

Biotechnology Progress, 2021

con respecto a la presencia/ausencia de frutos alternativos. Se concluye que la complejidad estru... more con respecto a la presencia/ausencia de frutos alternativos. Se concluye que la complejidad estructural de los cultivos funciona como un elemento integrador en la determinación del nivel de daño causado por las ardillas y define la idoneidad de su hábitat.

Research paper thumbnail of Assessing Observability using Supervised Autoencoders with Application to Tennessee Eastman Process

Research paper thumbnail of Development of new media formulations for cell culture operations based on regression models

Bioprocess and Biosystems Engineering, 2020

The paper discusses modelling and optimization of multi-component cell culture medium. The specif... more The paper discusses modelling and optimization of multi-component cell culture medium. The specific productivity (Qp) was considered a function of the medium components and possible interactions described by linear factors, two-way interactions and squared terms that results in a high dimensional problem where the number of variables p (represented by the medium components and their interactions) is much larger than the number of observations n. Principal Components Regression (PCR), Partial Least Squares (PLS), Lasso and Elastic Net regressions were compared as modelling tools to deal with a high dimensional n<p\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n<p$$\end{document} problem. PCR and PLS regression models resulted in better prediction results and were used for robust optimization of the medium composition by a nonlinear optimization. The case studies show that it is possible to formulate new media that result in higher Qp than the ones provided by the initial media experiments available. Also, the multivariate statistical approach permitted us to select media that is most informative about the optimum thus permitting modelling and optimization with a reduced set of initial experiments.

Research paper thumbnail of PLS-Based Robust Inferential Control for a Packed-Bed Reactor

1991 American Control Conference, Jun 1, 1991

This pae compas the performance of two different inferenti Khemm when applied to as experimestal ... more This pae compas the performance of two different inferenti Khemm when applied to as experimestal packd-bed reactor. The first sceme, proposed initiaBy by Broslow, is designed baed on Kalman fiter timation. Tie second lea traditional designan estimatk computed from the Partial Leat Squaw regressios method (PLS). The scoad approach na found to give superior performance when the nonear ystem under study is operated in wide rage of operatig points. Doe to te nonisearity of the system it is esential to addres the ise of robustass of the proposed schema. Thi is formally done in this work usig Structured Singular Value Thor. For the robustnes analysis it is crucial to develop & realistic but not overly conservative uncertainty description. Since the PLS estimator a large number of measremens a robust design basd on the uncertainty asocUed with each one of the measrements would be very consrvaive. To overcome tis probem a lumped uncertainty desciption is proposed which is identified directly from experiments.

Research paper thumbnail of On the use of physical boundary conditions for two-phase flow simulations: Integration of control feedback

Computers & Chemical Engineering, 2018

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights • The impact of inlet boundary conditions on two-phase flow CFD solutions is studied. • The two-phase upward flow in a vertical pipe is modeled using the Euler-Euler model. • Solutions using inlet pressure and velocity Dirichlet boundary conditions differ. • The gas phase distribution is strongly dependent on the inlet conditions. • A control scheme is used to enforce the Dirichlet pressure boundary and flow rates.

Research paper thumbnail of Robust Self-Tuning Control Design under Probabilistic Uncertainty using Polynomial Chaos Expansion-based Markov Models

IFAC-PapersOnLine, 2018

A robust adaptive controller is developed for a chemical process using a generalized Polynomial C... more A robust adaptive controller is developed for a chemical process using a generalized Polynomial Chaos (gPC) expansion-based Markov decision model, which can account for time-invariant probabilistic uncertainty and overcome computational challenge for building Markov models. To calculate the transition probability, a gPC model is used to iteratively predict probability density functions (PDFs) of system's states including controlled and manipulated variables. For controller tuning, these PDFs and controller parameters are discretized to a finite number of discrete states for building a Markov model. The key idea is to predict the transition probability of controlled and manipulated variables over a finite future control horizon, which can be further used to calculate an optimal sequence of control actions. This approach can be used to optimally tune a controller for set point tracking within a finite future control horizon. The proposed method is illustrated by a continuous stirred tank reactor (CSTR) system with stochastic perturbations in the inlet concentration. The efficiency of the proposed algorithm is quantified in terms of control performance and transient decay.

Research paper thumbnail of Experimental Design in Simultaneous Identification and Optimization of Batch Processes under Model-Plant Mismatch

IFAC-PapersOnLine, 2018

Model-plant mismatch commonly arises from simplifications and assumptions during the development ... more Model-plant mismatch commonly arises from simplifications and assumptions during the development of first-principles models. Hence, when employing such models in iterative optimization schemes, structural mismatch may lead to inaccurate prediction of the necessary conditions of optimality. This results in convergence to a predicted optimum which does not coincide with the actual process optimum. The method of simultaneous identification and optimization aims to correct for errors in the predicted gradients of the cost and constraints by adapting the model parameters. In a former implementation of this approach, the gradients have been corrected only locally at the current operating point. To achieve a better prediction of the cost function over a wider range of input conditions, we propose to consider cost measurements from previous batch experiments combined with an optimal experimental design of future experiments. Using this approach, it is possible to achieve a better prediction, especially around the optimum, and to make the gradient correction step less susceptible to uncertainty in local gradient measurements. The improvements are illustrated using a simulated run-to-run optimization study of a cell-culture process.

Research paper thumbnail of A simplified strategy to reduce the desorbent consumption and equipment installed in a three-zone simulated moving bed process for the separation of glucose and fructose

Chemical Engineering and Processing - Process Intensification, 2018

Abstract The operating strategy of the three-zone simulated moving bed aiming for decreasing the ... more Abstract The operating strategy of the three-zone simulated moving bed aiming for decreasing the desorbent consumption and the numbers of pump for the separation of glucose and fructose was proposed. Its principle was based on the port-relocation and port-closing/opening technique. The proposed strategy called PR-PCO-NFZIII was the rearrangement of the outlet port such that the raffinate and extract were alternately collected with no flow in zone III during raffinate collecting period. The adsorption model was first verified with experiments in the three-zone simulated moving bed operational mode (TZ-SMB). Then the model was used in the simulation of PR-PCO-NFZIII. The calculated performance parameters of the TZ-SMB, port-relocation, and PR-PCO-NFZIII were compared. The results revealed that PR-PCO-NFZIII outperformed the TZ-SMB mode in terms of solvent consumption, reduced number of pumps, and pressure drop. With this simplified approach, the purities and productivities of both extract and raffinate product were not compromised with adjustable product concentration as a function of extract collecting time to suit the specific demand. Furthermore, with the suitable choice of partial collecting/discarding strategy, the extract and raffinate were greatly concentrated.

Research paper thumbnail of Investigation of the Effects of Oxidative Stress Inducing Factors on Culturing and Productivity of Bordetella Pertussis

Biotechnology Progress, 2019

The stress response of Bordetella pertussis during fermentation was assessed by means of fluoresc... more The stress response of Bordetella pertussis during fermentation was assessed by means of fluorescence‐based techniques. During the manufacturing of vaccines, B. pertussis is subjected to stress during adaptation to a new environment and operating conditions in the bioreactor, which can have harmful consequences on growth and protein yield. In this study, stress was imposed by varying the percentage of dissolved oxygen (DO) and inoculum size, and by adding rotenone and hydrogen peroxide. In this study, fluorescence spectroscopy is used as a tool for measuring oxidative stress. High levels of DO during fed‐batch operation had no detrimental effect on growth, but the specific productivity of pertactin (PRN) decreased. Cultures that were started with an inoculum size that was 10 times smaller than the control resulted in significantly less PRN as compared to controls where reduction was more significant in flasks as compared to bioreactors. A comparison of filtered to heat‐sterilized media revealed that filtered media offered a protective effect against H2O2. Heat sterilization of the media might result in the destruction of components that offer protection against oxidative stress. Nonetheless, filter sterilization on its own would be insufficient for large‐scale manufacturing. It should be emphasized that the effects of these stressors while investigating for other microorganisms have not been studied for B. pertussis.

Research paper thumbnail of Impact of Oxidative Stress on Protein Production by Bordetella pertussis for Vaccine Production

Biochemical Engineering Journal, 2019

This research aims to determine the factors that influence the motivation of women entrepreneuria... more This research aims to determine the factors that influence the motivation of women entrepreneurial online in Jakarta. These factors include family environment, capital, and freedom of work. The data used is the primary data of the questionnaire to 80 women respondents spread over the Jakarta area. The results showed that the family environment, capital, and freedom of work influenced women's motivation for online entrepreneurship. The family environment is positively influence the motivation of women entrepreneurial online, capital is positively influence the motivation of women entrepreneurial online, and the freedom of work is positively influence the motivation of women entrepreneurial Online.

Research paper thumbnail of Robust economic model predictive control: disturbance rejection, robustness and periodic operation in chemical reactors

Engineering Optimization, 2018

ABSTRACTThis study investigates the properties of a robust economic model predictive control (REM... more ABSTRACTThis study investigates the properties of a robust economic model predictive control (REMPC) algorithm with respect to rejection of disturbances in initial conditions and non-stationary dis...

Research paper thumbnail of A systematic approach for finding the objective function and active constraints for dynamic flux balance analysis

Bioprocess and Biosystems Engineering, 2018

Dynamic flux balance analysis (DFBA) has become an instrumental modeling tool for describing the ... more Dynamic flux balance analysis (DFBA) has become an instrumental modeling tool for describing the dynamic behavior of bioprocesses. DFBA involves the maximization of a biologically meaningful objective subject to kinetic constraints on the rate of consumption/production of metabolites. In this paper, we propose a systematic data-based approach for finding both the biological objective function and a minimum set of active constraints necessary for matching the model predictions to the experimental data. The proposed algorithm accounts for the errors in the experiments and eliminates the need for ad hoc choices of objective function and constraints as done in previous studies. The method is illustrated for two cases: (1) for in silico (simulated) data generated by a mathematical model for Escherichia coli and (2) for actual experimental data collected from the batch fermentation of Bordetella Pertussis (whooping cough). Keywords Dynamic flux balance analysis • Flux balance analysis • Metabolic networks • Metabolic engineering • Bioprocess modeling Abbreviations S Stoichiometric matrix v k Vector of fluxes n Number of reactions k Time instance Concentration J Fluxes that satisfy tight constraints J Fluxes that satisfy relaxed constraints u Weight of sum of squared errors I Identity matrix w sc i Time-varying values of the weights for all the metabolites w u i Weight of upper bound W u Maximum allowable value for w sc i w l i Weight of lower bound N C Number of the objective functions' candidates n Estimated noise in the growth rate N SC Total number of metabolites N m Number of measured metabolites V i,max Maximum rate K i Half saturation concentration Measurement error X k The biomass value at time k w c i The weight coefficients of the objective function candidates

Research paper thumbnail of Comparison of stochastic fault detection and classification algorithms for nonlinear chemical processes

Computers & Chemical Engineering, 2017

This paper presents a comparative study of two methods to identify and classify intermittent stoc... more This paper presents a comparative study of two methods to identify and classify intermittent stochastic faults occurring in a dynamic nonlinear chemical process. The methods are based on two popular stochastic modelling techniques, i.e., generalized polynomial chaos expansion (gPC) and Gaussian Process (GP). The goal is to assess which method is more efficient for fault detection and diagnosis (FDD) when using models with parametric uncertainty, and to show the capabilities and drawbacks of each method. The first method is based on a firstprinciple model combined with a gPC expansion to represent the uncertainty. Resulting statistics such as probability density functions (PDFs) of the measured variables is further used to infer the intermittent faults. For the second method, a GP model is used to project multiple inputs into a univariate model response from which the fault can be identified based on a minimum distance criterion. The performance of the proposed FDD algorithms is illustrated through two examples: (i) a chemical process involving two continuous, stirred tank reactors (CSTRs) and a flash tank separator, and (ii) the Tennessee Eastman benchmark problem.

Research paper thumbnail of Design and Optimization of a Penicillin Fed-Batch Reactor Based on a Deep Learning Fault Detection and Diagnostic Model

Industrial & Engineering Chemistry Research

Research paper thumbnail of Simultaneous model identification and optimization in presence of model-plant mismatch

In a standard optimization approach, the underlying process model is first identified at a given ... more In a standard optimization approach, the underlying process model is first identified at a given set of operating conditions and this updated model is, then, used to calculate the optimal conditions for the process. This two-step procedure can be repeated iteratively by conducting new experiments at optimal operating conditions, based on previous iterations, followed by re-identification and re-optimization until convergence is reached. However, when there is a model-plant mismatch, the set of parameter estimates that minimizes the prediction error in the identification problem may not predict the gradients of the optimization objective accurately. As a result, convergence of the two-step iterative approach to a process optimum cannot be guaranteed. This paper presents a new methodology where the model outputs are corrected explicitly for the mismatch such that, with the updated parameter estimates the identification and optimization objectives are properly reconciled. With the prop...

Research paper thumbnail of Identifying Frequency Domain Uncertainty Bounds for Robust Controller Design - Theory with Application to a Fixed-Bed Reactor

1989 American Control Conference, 1989

Christopher Webb Hector Bud Manfred Moraui Chemical Engineering, 210-41 Califormia institute of T... more Christopher Webb Hector Bud Manfred Moraui Chemical Engineering, 210-41 Califormia institute of Technology Pasadena, CA 91125 control problem, the control of a hotspot in a fixed-bed chemical ... A methodology for computing frequency domain uncertainty ... In sections 6 and 7, we ...

Research paper thumbnail of Fluorescence-based soft-sensor for monitoring beta-lactoglobulin and alpha-lactalbumin solubility during thermal aggregation

Biotechnology and bioengineering, 2008

A soft-sensor for monitoring solubility of native-like alpha-lactalbumin (alpha-LA) and beta-lact... more A soft-sensor for monitoring solubility of native-like alpha-lactalbumin (alpha-LA) and beta-lactoglobulin (beta-LG) and their aggregation behavior following heat treatment of mixtures under different treatment conditions was developed using fluorescence spectroscopy data regressed with a multivariate Partial Least Squares (PLS) regression algorithm. PLS regression was used to correlate the concentrations of alpha-LA and beta-LG to the fluorescence spectra obtained for their mixtures. Data for the calibration and validation of the soft sensor was derived from fluorescence spectra. The process of thermal induced aggregation of beta-LG and alpha-LA protein in mixtures, which involves the disappearance of native-like proteins, was studied under various treatment conditions including different temperatures, pH, total initial protein concentration and proportions of alpha-LA and beta-LG. It was demonstrated that the multivariate regression models used could effectively deconvolute multi-...

Research paper thumbnail of Author response for "Applications of Flow Cytometry Sorting in the Pharmaceutical Industry

Research paper thumbnail of A Type of Set Membership Estimation Designed for Dynamic Flux Balance Models

Processes, 2021

Dynamic flux balance models (DFBM) are used in this study to infer metabolite concentrations that... more 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.

Research paper thumbnail of Explainability: Relevance based dynamic deep learning algorithm for fault detection and diagnosis in chemical processes

Computers & Chemical Engineering, 2021

Research paper thumbnail of Applications of flow cytometry sorting in the pharmaceutical industry: A review

Biotechnology Progress, 2021

con respecto a la presencia/ausencia de frutos alternativos. Se concluye que la complejidad estru... more con respecto a la presencia/ausencia de frutos alternativos. Se concluye que la complejidad estructural de los cultivos funciona como un elemento integrador en la determinación del nivel de daño causado por las ardillas y define la idoneidad de su hábitat.

Research paper thumbnail of Assessing Observability using Supervised Autoencoders with Application to Tennessee Eastman Process

Research paper thumbnail of Development of new media formulations for cell culture operations based on regression models

Bioprocess and Biosystems Engineering, 2020

The paper discusses modelling and optimization of multi-component cell culture medium. The specif... more The paper discusses modelling and optimization of multi-component cell culture medium. The specific productivity (Qp) was considered a function of the medium components and possible interactions described by linear factors, two-way interactions and squared terms that results in a high dimensional problem where the number of variables p (represented by the medium components and their interactions) is much larger than the number of observations n. Principal Components Regression (PCR), Partial Least Squares (PLS), Lasso and Elastic Net regressions were compared as modelling tools to deal with a high dimensional n<p\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n<p$$\end{document} problem. PCR and PLS regression models resulted in better prediction results and were used for robust optimization of the medium composition by a nonlinear optimization. The case studies show that it is possible to formulate new media that result in higher Qp than the ones provided by the initial media experiments available. Also, the multivariate statistical approach permitted us to select media that is most informative about the optimum thus permitting modelling and optimization with a reduced set of initial experiments.

Research paper thumbnail of PLS-Based Robust Inferential Control for a Packed-Bed Reactor

1991 American Control Conference, Jun 1, 1991

This pae compas the performance of two different inferenti Khemm when applied to as experimestal ... more This pae compas the performance of two different inferenti Khemm when applied to as experimestal packd-bed reactor. The first sceme, proposed initiaBy by Broslow, is designed baed on Kalman fiter timation. Tie second lea traditional designan estimatk computed from the Partial Leat Squaw regressios method (PLS). The scoad approach na found to give superior performance when the nonear ystem under study is operated in wide rage of operatig points. Doe to te nonisearity of the system it is esential to addres the ise of robustass of the proposed schema. Thi is formally done in this work usig Structured Singular Value Thor. For the robustnes analysis it is crucial to develop & realistic but not overly conservative uncertainty description. Since the PLS estimator a large number of measremens a robust design basd on the uncertainty asocUed with each one of the measrements would be very consrvaive. To overcome tis probem a lumped uncertainty desciption is proposed which is identified directly from experiments.

Research paper thumbnail of On the use of physical boundary conditions for two-phase flow simulations: Integration of control feedback

Computers & Chemical Engineering, 2018

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights • The impact of inlet boundary conditions on two-phase flow CFD solutions is studied. • The two-phase upward flow in a vertical pipe is modeled using the Euler-Euler model. • Solutions using inlet pressure and velocity Dirichlet boundary conditions differ. • The gas phase distribution is strongly dependent on the inlet conditions. • A control scheme is used to enforce the Dirichlet pressure boundary and flow rates.

Research paper thumbnail of Robust Self-Tuning Control Design under Probabilistic Uncertainty using Polynomial Chaos Expansion-based Markov Models

IFAC-PapersOnLine, 2018

A robust adaptive controller is developed for a chemical process using a generalized Polynomial C... more A robust adaptive controller is developed for a chemical process using a generalized Polynomial Chaos (gPC) expansion-based Markov decision model, which can account for time-invariant probabilistic uncertainty and overcome computational challenge for building Markov models. To calculate the transition probability, a gPC model is used to iteratively predict probability density functions (PDFs) of system's states including controlled and manipulated variables. For controller tuning, these PDFs and controller parameters are discretized to a finite number of discrete states for building a Markov model. The key idea is to predict the transition probability of controlled and manipulated variables over a finite future control horizon, which can be further used to calculate an optimal sequence of control actions. This approach can be used to optimally tune a controller for set point tracking within a finite future control horizon. The proposed method is illustrated by a continuous stirred tank reactor (CSTR) system with stochastic perturbations in the inlet concentration. The efficiency of the proposed algorithm is quantified in terms of control performance and transient decay.

Research paper thumbnail of Experimental Design in Simultaneous Identification and Optimization of Batch Processes under Model-Plant Mismatch

IFAC-PapersOnLine, 2018

Model-plant mismatch commonly arises from simplifications and assumptions during the development ... more Model-plant mismatch commonly arises from simplifications and assumptions during the development of first-principles models. Hence, when employing such models in iterative optimization schemes, structural mismatch may lead to inaccurate prediction of the necessary conditions of optimality. This results in convergence to a predicted optimum which does not coincide with the actual process optimum. The method of simultaneous identification and optimization aims to correct for errors in the predicted gradients of the cost and constraints by adapting the model parameters. In a former implementation of this approach, the gradients have been corrected only locally at the current operating point. To achieve a better prediction of the cost function over a wider range of input conditions, we propose to consider cost measurements from previous batch experiments combined with an optimal experimental design of future experiments. Using this approach, it is possible to achieve a better prediction, especially around the optimum, and to make the gradient correction step less susceptible to uncertainty in local gradient measurements. The improvements are illustrated using a simulated run-to-run optimization study of a cell-culture process.

Research paper thumbnail of A simplified strategy to reduce the desorbent consumption and equipment installed in a three-zone simulated moving bed process for the separation of glucose and fructose

Chemical Engineering and Processing - Process Intensification, 2018

Abstract The operating strategy of the three-zone simulated moving bed aiming for decreasing the ... more Abstract The operating strategy of the three-zone simulated moving bed aiming for decreasing the desorbent consumption and the numbers of pump for the separation of glucose and fructose was proposed. Its principle was based on the port-relocation and port-closing/opening technique. The proposed strategy called PR-PCO-NFZIII was the rearrangement of the outlet port such that the raffinate and extract were alternately collected with no flow in zone III during raffinate collecting period. The adsorption model was first verified with experiments in the three-zone simulated moving bed operational mode (TZ-SMB). Then the model was used in the simulation of PR-PCO-NFZIII. The calculated performance parameters of the TZ-SMB, port-relocation, and PR-PCO-NFZIII were compared. The results revealed that PR-PCO-NFZIII outperformed the TZ-SMB mode in terms of solvent consumption, reduced number of pumps, and pressure drop. With this simplified approach, the purities and productivities of both extract and raffinate product were not compromised with adjustable product concentration as a function of extract collecting time to suit the specific demand. Furthermore, with the suitable choice of partial collecting/discarding strategy, the extract and raffinate were greatly concentrated.

Research paper thumbnail of Investigation of the Effects of Oxidative Stress Inducing Factors on Culturing and Productivity of Bordetella Pertussis

Biotechnology Progress, 2019

The stress response of Bordetella pertussis during fermentation was assessed by means of fluoresc... more The stress response of Bordetella pertussis during fermentation was assessed by means of fluorescence‐based techniques. During the manufacturing of vaccines, B. pertussis is subjected to stress during adaptation to a new environment and operating conditions in the bioreactor, which can have harmful consequences on growth and protein yield. In this study, stress was imposed by varying the percentage of dissolved oxygen (DO) and inoculum size, and by adding rotenone and hydrogen peroxide. In this study, fluorescence spectroscopy is used as a tool for measuring oxidative stress. High levels of DO during fed‐batch operation had no detrimental effect on growth, but the specific productivity of pertactin (PRN) decreased. Cultures that were started with an inoculum size that was 10 times smaller than the control resulted in significantly less PRN as compared to controls where reduction was more significant in flasks as compared to bioreactors. A comparison of filtered to heat‐sterilized media revealed that filtered media offered a protective effect against H2O2. Heat sterilization of the media might result in the destruction of components that offer protection against oxidative stress. Nonetheless, filter sterilization on its own would be insufficient for large‐scale manufacturing. It should be emphasized that the effects of these stressors while investigating for other microorganisms have not been studied for B. pertussis.

Research paper thumbnail of Impact of Oxidative Stress on Protein Production by Bordetella pertussis for Vaccine Production

Biochemical Engineering Journal, 2019

This research aims to determine the factors that influence the motivation of women entrepreneuria... more This research aims to determine the factors that influence the motivation of women entrepreneurial online in Jakarta. These factors include family environment, capital, and freedom of work. The data used is the primary data of the questionnaire to 80 women respondents spread over the Jakarta area. The results showed that the family environment, capital, and freedom of work influenced women's motivation for online entrepreneurship. The family environment is positively influence the motivation of women entrepreneurial online, capital is positively influence the motivation of women entrepreneurial online, and the freedom of work is positively influence the motivation of women entrepreneurial Online.

Research paper thumbnail of Robust economic model predictive control: disturbance rejection, robustness and periodic operation in chemical reactors

Engineering Optimization, 2018

ABSTRACTThis study investigates the properties of a robust economic model predictive control (REM... more ABSTRACTThis study investigates the properties of a robust economic model predictive control (REMPC) algorithm with respect to rejection of disturbances in initial conditions and non-stationary dis...

Research paper thumbnail of A systematic approach for finding the objective function and active constraints for dynamic flux balance analysis

Bioprocess and Biosystems Engineering, 2018

Dynamic flux balance analysis (DFBA) has become an instrumental modeling tool for describing the ... more Dynamic flux balance analysis (DFBA) has become an instrumental modeling tool for describing the dynamic behavior of bioprocesses. DFBA involves the maximization of a biologically meaningful objective subject to kinetic constraints on the rate of consumption/production of metabolites. In this paper, we propose a systematic data-based approach for finding both the biological objective function and a minimum set of active constraints necessary for matching the model predictions to the experimental data. The proposed algorithm accounts for the errors in the experiments and eliminates the need for ad hoc choices of objective function and constraints as done in previous studies. The method is illustrated for two cases: (1) for in silico (simulated) data generated by a mathematical model for Escherichia coli and (2) for actual experimental data collected from the batch fermentation of Bordetella Pertussis (whooping cough). Keywords Dynamic flux balance analysis • Flux balance analysis • Metabolic networks • Metabolic engineering • Bioprocess modeling Abbreviations S Stoichiometric matrix v k Vector of fluxes n Number of reactions k Time instance Concentration J Fluxes that satisfy tight constraints J Fluxes that satisfy relaxed constraints u Weight of sum of squared errors I Identity matrix w sc i Time-varying values of the weights for all the metabolites w u i Weight of upper bound W u Maximum allowable value for w sc i w l i Weight of lower bound N C Number of the objective functions' candidates n Estimated noise in the growth rate N SC Total number of metabolites N m Number of measured metabolites V i,max Maximum rate K i Half saturation concentration Measurement error X k The biomass value at time k w c i The weight coefficients of the objective function candidates

Research paper thumbnail of Comparison of stochastic fault detection and classification algorithms for nonlinear chemical processes

Computers & Chemical Engineering, 2017

This paper presents a comparative study of two methods to identify and classify intermittent stoc... more This paper presents a comparative study of two methods to identify and classify intermittent stochastic faults occurring in a dynamic nonlinear chemical process. The methods are based on two popular stochastic modelling techniques, i.e., generalized polynomial chaos expansion (gPC) and Gaussian Process (GP). The goal is to assess which method is more efficient for fault detection and diagnosis (FDD) when using models with parametric uncertainty, and to show the capabilities and drawbacks of each method. The first method is based on a firstprinciple model combined with a gPC expansion to represent the uncertainty. Resulting statistics such as probability density functions (PDFs) of the measured variables is further used to infer the intermittent faults. For the second method, a GP model is used to project multiple inputs into a univariate model response from which the fault can be identified based on a minimum distance criterion. The performance of the proposed FDD algorithms is illustrated through two examples: (i) a chemical process involving two continuous, stirred tank reactors (CSTRs) and a flash tank separator, and (ii) the Tennessee Eastman benchmark problem.