Run To Run Optimization Of Biochemical Batch Processes In The Presence Of Model Plant Mismatch
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Run To Run Optimization Of Biochemical Batch Processes In The Presence Of Model Plant Mismatch
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Author : Rubin Hille
language : en
Publisher:
Release Date : 2018
Run To Run Optimization Of Biochemical Batch Processes In The Presence Of Model Plant Mismatch written by Rubin Hille and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Biochemical engineering categories.
An increased demand for novel pharmaceuticals such as recombinant proteins with therapeutic potential has lead to significant advances in the operation of biotechnological processes. In general, biochemical processes are characterized by nonlinear behavior and a sensitivity to environmental conditions. Furthermore, due to their complex operation, exposure to contamination and the low volume of the obtained product, these processes are generally still frequently operated in batch or fed-batch reactors. The repetitive nature of batch processes motivates the use of previous experimental effort to improve the performance of future batch operations. In this way, a so-called run-to-run optimization can be performed where the measurements of the current batch-run are utilized to determine the input for the next experiment. To conduct this step in a systematic and reliable manner, fundamental process models can be used for prediction and optimization purposes. This way, it is possible to determine the input for the next iteration from the predicted optimum obtained by calibrating a model based on measurements from the current batch-run. Fundamental models are typically derived from the underlying physical phenomena of the process. However, to make these models useful and tractable, it is common to make assumptions and simplifications during the model development. As a result, there often exists mismatch between the model and process under study. In the presence of model-plant mismatch, the set of model parameter estimates, which satisfy an identification objective, may not result in an accurate prediction of the gradients of the cost-function and constraints, which are essential for optimization. To still ensure convergence to the optimum, the method of simultaneous identification and optimization aims at forcing the predicted gradients to match the measured gradients by adapting the model parameters. At the same time, a correction factor is introduced into the model output so that the previously achieved fitting accuracy can be maintained. This results in a set of model parameters that reconcile the objectives of identification and optimization in presence of model-plant mismatch. Although the method provides the potential for dealing with structural error in iterative optimization schemes, there exist several challenges that have to be addressed before it is applicable to more complex systems. For example, when dealing with models containing a large number of parameters, it is unclear which parameters should be selected for calibration and adaptation. Since updating all available parameters is impractical due to estimability problems and over-fitting, there is a motivation for adapting only a subset of parameters. Furthermore, for this method to be more efficient under uncertainty, it is necessary to introduce additional robustness to uncertainty in initial conditions and gradient measurements. Finally, it is essential to develop experimental design criteria that will provide the user with more informative experiments to speed up convergence to the optimum and to calibrate the model with better accuracy. Following the above, this work presents the following new contributions: (i) An algorithmic approach to select a subset of parameters based on the sensitivities of the model outputs as well as of the cost function and constraint gradients. (ii) A run-to-run optimization formulation that is robust to uncertainties in initial batch conditions based on polynomial chaos expansions that are used to quantify the uncertainty and to propagate it onto the optimization cost. (iii) A modified parameter identification objective based on the minimization of the ratio of the sum of squared prediction errors to a parametric sensitivity measure to speed up convergence of the run-to-run procedure. (iv) The use of uncertainty bounds on the predicted trajectories to ensure model accuracy while solving the parameter identification problem described in item (iii) and to determine whether a model update is necessary at any given run. (v) The use of a design of experiments approach within the run-to-run optimization procedure to optimally complement the cost gradient information that is already available from previous batch experiments. The presented methods are shown to be efficient and facilitate the use of complex models for run-to-run optimization of batch processes. Several case studies of cell culture processes are presented to illustrate the improvements in robustness and performance. These case studies involve batch, fed-batch and perfusion operations. A part of this work has been developed in collaboration with an industrial partner whose main line of business is the development of perfusion growth media for mammalian cell culture operations.
Real Time Optimization
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Author : Dominique Bonvin
language : en
Publisher: MDPI
Release Date : 2018-07-05
Real Time Optimization written by Dominique Bonvin and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-05 with Electronic book categories.
This book is a printed edition of the Special Issue "Real-Time Optimization" that was published in Processes
Nonlinear Model Predictive Control
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Author : Frank Allgöwer
language : en
Publisher: Birkhäuser
Release Date : 2012-12-06
Nonlinear Model Predictive Control written by Frank Allgöwer and has been published by Birkhäuser this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
During the past decade model predictive control (MPC), also referred to as receding horizon control or moving horizon control, has become the preferred control strategy for quite a number of industrial processes. There have been many significant advances in this area over the past years, one of the most important ones being its extension to nonlinear systems. This book gives an up-to-date assessment of the current state of the art in the new field of nonlinear model predictive control (NMPC). The main topic areas that appear to be of central importance for NMPC are covered, namely receding horizon control theory, modeling for NMPC, computational aspects of on-line optimization and application issues. The book consists of selected papers presented at the International Symposium on Nonlinear Model Predictive Control – Assessment and Future Directions, which took place from June 3 to 5, 1998, in Ascona, Switzerland. The book is geared towards researchers and practitioners in the area of control engineering and control theory. It is also suited for postgraduate students as the book contains several overview articles that give a tutorial introduction into the various aspects of nonlinear model predictive control, including systems theory, computations, modeling and applications.
Chemical Engineering Abstracts
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Author :
language : en
Publisher:
Release Date : 1987
Chemical Engineering Abstracts written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987 with Chemical engineering categories.
Model Predictive Control In The Process Industry
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Author : Eduardo F. Camacho
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Model Predictive Control In The Process Industry written by Eduardo F. Camacho and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.
Process Modelling And Simulation
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Author : César de Prada
language : en
Publisher: MDPI
Release Date : 2019-09-23
Process Modelling And Simulation written by César de Prada and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-23 with Technology & Engineering categories.
Since process models are nowadays ubiquitous in many applications, the challenges and alternatives related to their development, validation, and efficient use have become more apparent. In addition, the massive amounts of both offline and online data available today open the door for new applications and solutions. However, transforming data into useful models and information in the context of the process industry or of bio-systems requires specific approaches and considerations such as new modelling methodologies incorporating the complex, stochastic, hybrid and distributed nature of many processes in particular. The same can be said about the tools and software environments used to describe, code, and solve such models for their further exploitation. Going well beyond mere simulation tools, these advanced tools offer a software suite built around the models, facilitating tasks such as experiment design, parameter estimation, model initialization, validation, analysis, size reduction, discretization, optimization, distributed computation, co-simulation, etc. This Special Issue collects novel developments in these topics in order to address the challenges brought by the use of models in their different facets, and to reflect state of the art developments in methods, tools and industrial applications.
Dynamical Modelling Estimation In Wastewater Treatment Processes
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Author : D. Dochain
language : en
Publisher: IWA Publishing
Release Date : 2001-12-01
Dynamical Modelling Estimation In Wastewater Treatment Processes written by D. Dochain and has been published by IWA Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-12-01 with Science categories.
Environmental quality is becoming an increasing concern in our society. In that context, waste and wastewater treatment, and more specifically biological wastewater treatment processes play an important role. In this book, we concentrate on the mathematical modelling of these processes. The main purpose is to provide the increasing number of professionals who are using models to design, optimise and control wastewater treatment processes with the necessary background for their activities of model building, selection and calibration. The book deals specifically with dynamic models because they allow us to describe the behaviour of treatment plants under the highly dynamic conditions that we want them to operate (e.g. Sequencing Batch Reactors) or we have to operate them (e.g. storm conditions, spills). Further extension is provided to new reactor systems for which partial differential equation descriptions are necessary to account for their distributed parameter nature (e.g. settlers, fixed bed reactors). The model building exercise is introduced as a step-wise activity that, in this book, starts from mass balancing principles. In many cases, different hypotheses and their corresponding models can be proposed for a particular process. It is therefore essential to be able to select from these candidate models in an objective manner. To this end, structure characterisation methods are introduced. Important sections of the book deal with the collection of high quality data using optimal experimental design, parameter estimation techniques for calibration and the on-line use of models in state and parameter estimators. Contents Dynamical Modelling Dynamical Mass Balance Model Building and Analysis Structure Characterisation (SC) Structural Identifiability Practical Identifiability and Optimal Experiment Design for Parameter Estimation (OED/PE) Estimation of Model Parameters Recursive State and Parameter Estimation Glossary Nomenclature
Practical Grey Box Process Identification
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Author : Torsten P. Bohlin
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-09-07
Practical Grey Box Process Identification written by Torsten P. Bohlin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-09-07 with Technology & Engineering categories.
This book reviews the theoretical fundamentals of grey-box identification and puts the spotlight on MoCaVa, a MATLAB-compatible software tool, for facilitating the procedure of effective grey-box identification. It demonstrates the application of MoCaVa using two case studies drawn from the paper and steel industries. In addition, the book answers common questions which will help in building accurate models for systems with unknown inputs.
Cell Culture Bioprocess Engineering Second Edition
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Author : Wei-Shou Hu
language : en
Publisher: CRC Press
Release Date : 2020-03-06
Cell Culture Bioprocess Engineering Second Edition written by Wei-Shou Hu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-06 with Science categories.
This book is the culmination of three decades of accumulated experience in teaching biotechnology professionals. It distills the fundamental principles and essential knowledge of cell culture processes from across many different disciplines and presents them in a series of easy-to-follow, comprehensive chapters. Practicality, including technological advances and best practices, is emphasized. This second edition consists of major updates to all relevant topics contained within this work. The previous edition has been successfully used in training courses on cell culture bioprocessing over the past seven years. The format of the book is well-suited to fast-paced learning, such as is found in the intensive short course, since the key take-home messages are prominently highlighted in panels. The book is also well-suited to act as a reference guide for experienced industrial practitioners of mammalian cell cultivation for the production of biologics.
Dynamic Process Modeling
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Author :
language : en
Publisher: John Wiley & Sons
Release Date : 2013-10-02
Dynamic Process Modeling written by and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-02 with Technology & Engineering categories.
Inspired by the leading authority in the field, the Centre for Process Systems Engineering at Imperial College London, this book includes theoretical developments, algorithms, methodologies and tools in process systems engineering and applications from the chemical, energy, molecular, biomedical and other areas. It spans a whole range of length scales seen in manufacturing industries, from molecular and nanoscale phenomena to enterprise-wide optimization and control. As such, this will appeal to a broad readership, since the topic applies not only to all technical processes but also due to the interdisciplinary expertise required to solve the challenge. The ultimate reference work for years to come.