In the manufacture of bio-therapeutics the importance of a well-known process is key for a high product titer and low batch to batch variations. Soft sensors are based on the concept that online sensor signals can be used as inputs to mathematical models to derive new valuable process information. This information could then be used for better monitoring and control of the bioprocess. The aim of the present thesis has been to develop soft sensor solutions for upstream bioprocessing and demonstrate their usefulness in improving robustness and increase the batch-to-batch reproducibility in bioprocesses. The thesis reviews the potential and possibilities with soft sensors for use in production of bio-therapeutics to realize FDA´s process analytical technology (PAT) initiative. Modelling and hardware sensor alternatives which could be used in a soft sensor setup are described and critically analyzed. Different soft sensor approaches to control glucose feeding in fed-batch cultures of Escherichia coli are described. Measurements of metabolic fluxes and specific carbon dioxide production was used as control parameters to increase product yield and decrease the variability of produced recombinant proteins. Metabolic heat signals were used in uninduced cultures to estimate and control the specific growth rate at a desired level and thereby also estimate the biomass concentration online. The introduction of sequential filtering of the signal enabled this method to be used in a down-scaled system. The risk and high impact of contaminations in cell cultures are also described. An in situ microscope (ISM) was used as an online tool to estimate cell concentration and also to determine cell diameter size which enabled the detection of contaminant cells at an early stage. The work presented in this thesis supports the idea that soft sensors can be a useful tool in the strive towards robust and reliable bioprocesses, to ensure high product quality and increased economic profit.

Closes the gap between bioscience and mathematics-based process engineering This book presents the most commonly employed approaches in the control of bioprocesses. It discusses the role that control theory plays in understanding the mechanisms of cellular and metabolic processes, and presents key results in various fields such as dynamic modeling, dynamic properties of bioprocess models, software sensors designed for the online estimation of parameters and state variables, and control and supervision of bioprocesses Control in Bioengineering and Bioprocessing: Modeling, Estimation and the Use of Sensors is divided into three sections. Part I, Mathematical preliminaries and overview of the control and monitoring of bioprocess, provides a general overview of the control and monitoring of bioprocesses, and introduces the mathematical framework necessary for the analysis and characterization of bioprocess dynamics. Part II, Observability and control concepts, presents the observability concepts which form the basis of design online estimation algorithms (software sensor) for bioprocesses, and reviews controllability of these concepts, including automatic feedback control systems. Part III, Software sensors and observer-based control schemes for bioprocesses, features six application cases including dynamic behavior of 3-dimensional continuous bioreactors; observability analysis applied to 2D and 3D bioreactors with inhibitory and non-inhibitory models; and regulation of a continuously stirred bioreactor via modeling error compensation. Applicable across all areas of bioprocess engineering, including food and beverages, biofuels and renewable energy, pharmaceuticals and nutraceuticals, fermentation systems, product separation technologies, wastewater and solid-waste treatment technology, and bioremediation Provides a clear explanation of the mass-balance–based mathematical modelling of bioprocesses and the main tools for its dynamic analysis Offers industry-based applications on: myco-diesel for implementing "quality" of observability; developing a virtual sensor based on the Just-In-Time Model to monitor biological control systems; and virtual sensor design for state estimation in a photocatalytic bioreactor for hydrogen production Control in Bioengineering and Bioprocessing is intended as a foundational text for graduate level students in bioengineering, as well as a reference text for researchers, engineers, and other practitioners interested in the field of estimation and control of bioprocesses.

This volume presents the reader with an overview of current chemical sensor technology and outlines a framework relating industrial bioprocess monitoring to modern process control technology. It deals with conventional multivariable control technology, focusing on bioprocess applications.

This book reviews current design paths for soft sensors, and guides readers in evaluating different choices. The book presents case studies resulting from collaborations between the authors and industrial partners. The solutions presented, some of which are implemented on-line in industrial plants, are designed to cope with a wide range of applications from measuring system backup and what-if analysis through real-time prediction for plant control to sensor diagnosis and validation.

Automated Measurement and Monitoring of Bioprocesses: Key Elements of the M3C Strategy, by Bernhard Sonnleitner Automatic Control of Bioprocesses, by Marc Stanke, Bernd Hitzmann An Advanced Monitoring Platform for Rational Design of Recombinant Processes, by G. Striedner, K. Bayer Modelling Approaches for Bio-Manufacturing Operations, by Sunil Chhatre Extreme Scale-Down Approaches for Rapid Chromatography Column Design and Scale-Up During Bioprocess Development, by Sunil Chhatre Applying Mechanistic Models in Bioprocess Development, by Rita Lencastre Fernandes, Vijaya Krishna Bodla, Magnus Carlquist, Anna-Lena Heins, Anna Eliasson Lantz, Gürkan Sin and Krist V. Gernaey Multivariate Data Analysis for Advancing the Interpretation of Bioprocess Measurement and Monitoring Data, by Jarka Glassey Design of Pathway-Level Bioprocess Monitoring and Control Strategies Supported by Metabolic Networks, by Inês A. Isidro, Ana R. Ferreira, João J. Clemente, António E. Cunha, João M. L. Dias, Rui Oliveira Knowledge Management and Process Monitoring of Pharmaceutical Processes in the Quality by Design Paradigm, by Anurag S Rathore, Anshuman Bansal, Jaspinder Hans The Choice of Suitable Online Analytical Techniques and Data Processing for Monitoring of Bioprocesses, by Ian Marison, Siobhán Hennessy, Róisín Foley, Moira Schuler, Senthilkumar Sivaprakasam, Brian Freeland

Computational Intelligence (CI) and Bioprocess are well-established research areas which have much to offer each other. Under the perspective of the CI area, Biop- cess can be considered a vast application area with a growing number of complex and challenging tasks to be dealt with, whose solutions can contribute to boosting the development of new intelligent techniques as well as to help the refinement and s- cialization of many of the already existing techniques. Under the perspective of the Bioprocess area, CI can be considered a useful repertoire of theories, methods and techniques that can contribute and offer interesting alternative approaches for solving many of its problems, particularly those hard to solve using conventional techniques. Although throughout the past years CI and Bioprocess areas have accumulated substantial specific knowledge and progress has been quick and with a high degree of success, we believe there is still a long way to go in order to use the potentialities of the available CI techniques and knowledge at their full extent, as tools for supporting problem solving in bioprocesses. One of the reasons is the fact that both areas have progressed steadily and have been continuously accumulating and refining specific knowledge; another reason is the high level of technical expertise demanded by each of them. The acquisition of technical skills, experience and good insights in either of the two areas is very demanding and a hard task to be accomplished by any professional.

The goal of this work was to develop monitoring techniques for use during the production of monoclonal antibodies (Mabs) in Chinese hamster ovary cell cultures. Such monitoring would enable real-time screening and control of key process variables both upstream and downstream so as to guarantee product quality and process consistency. The measurement techniques that are currently available are time and labor intensive and in some cases require frequent maintenance. Thus, they are not suitable for fast online monitoring of bioprocesses. Thus, with a goal of future real-time implementation, data-driven (empirical models) and model-driven (mechanistic models) soft sensors were developed. The bioreactor is the key component of the upstream manufacturing phase. Continuous monitoring and control of this unit is critical in order to maximize production of the Mab with a desired quality (i.e. glycosylation pattern). Data-driven soft sensors were developed using intrinsic multi-wavelength fluorescence spectra of the culture broth in combination with partial least square regression (PLSR) for tracking viable cell, dead cell, recombinant protein, glucose, and ammonia concentrations. To better elucidate the relationship between the fluorescence spectra and process operating conditions, trajectories of fluorophore-peaks over the course of the culture were investigated and compared to changes in key process variables prior to model development. The proposed soft sensors were capable of predicting the aforementioned process variables with high accuracy. To enhance the extrapolation accuracy of the data-driven soft sensor outside of the region of operating conditions used for model calibration and to better track the dynamics of the culture, an extended Kalman filter (EKF) was developed based on a combination of mechanistic and empirical models. To address the structural and parameter uncertainty of the models, non-stationary disturbances were introduced to the model through parameter adaptation. The resulting EKF-based soft sensor's predictions surpassed the accuracy of a standalone fluorescence based soft sensor and was capable of tracking process dynamics in between sampling instances with high precision. N-linked glycosylation has a significant impact on the therapeutic properties of Mab and is an important quality attribute that is associated with the extracellular metabolic state of the culture. Based on the primary investigation it was revealed that the fluorescence spectroscopy is not capable of accurately tracking the glycosylation profile of the Mab. Thus, to enhance the controllability of the glycoprofile, a novel dynamic model was developed that relates the extracellular culture conditions to the accumulated glycosylation pattern of Mab produced through the production of nucleotide sugars required for N-linked glycosylation in the Golgi apparatus. The model parameters were estimated using the experimental data. The resulting model was capable of accurately predicting the glycosylation extent in the form of a galactosylation index as well as individual glycan structures. Another area of application of fluorescence was for monitoring protein aggregation. During downstream processing proteins are exposed to stress factors such as changes in temperature, pH, or shear stress that can increase the propensity of Mab to aggregate. Aggregation can trigger undesirable impacts including an increased immunogenicity response in the patient. Therefore, developing an in situ technique for fast quality and quantity control of protein aggregation is of great industrial interest. Fluorescence-based soft sensors, in conjugation with PLSR, were developed for quality control (product classification) and for quantitative monitoring (Mab monomer concentration) at different process conditions that typically occur in different stages of the purification process. To better elucidate the impact of stress factors on the degree of aggregation and identify the operating conditions for which the propensity to aggregate is minimal, a surface response model was fitted to the data prior to soft sensor development. The soft sensors were capable of accurately predicting monomer concentration of samples exposed to different levels of stress factors as well as for classifying the final product into different groupings according to their relative aggregation levels.

Process monitoring and control are fundamental to all processes; this holds especially for bioprocesses, due to their complex nature. Usually, bioprocesses deal with living cells, which have their own regulatory systems. It helps to adjust the cell to its environmental condition. This must not be the optimal condition that the cell needs to produce whatever is desired. Therefore, a close monitoring of the cell and its environment is essential to provide optimal conditions for production. Without measurement, no information of the current process state is obtained. In this book, methods and techniques are provided for the monitoring and control of bioprocesses. From new developments for sensors, the application of spectroscopy and modelling approaches, the estimation and observer implementation for ethanol production and the development and scale-up of various bioprocesses and their closed loop control information are presented. The processes discussed here are very diverse. The major applications are cultivation processes, where microorganisms were grown, but also an incubation process of bird's eggs, as well as an indoor climate control for humans, will be discussed. Altogether, in 12 chapters, nine original research papers and three reviews are presented.

Written for industrial and academic researchers and development scientists in the life sciences industry, Bioprocessing Technology for Production of Biopharmaceuticals and Bioproducts is a guide to the tools, approaches, and useful developments in bioprocessing. This important guide: • Summarizes state-of-the-art bioprocessing methods and reviews applications in life science industries • Includes illustrative case studies that review six milestone bio-products • Discuses a wide selection of host strain types and disruptive bioprocess technologies

Process Control, Intensification, and Digitalisation in Continuous Biomanufacturing Explore new trends in continuous biomanufacturing with contributions from leading practitioners in the field With the increasingly widespread acceptance and investment in the ??technology, the last decade has demonstrated the utility of continuous ??processing in the pharmaceutical industry. In Process Control, Intensification, and Digitalisation in Continuous Biomanufacturing, distinguished biotechnologist Dr. Ganapathy Subramanian delivers a comprehensive exploration of the potential of the continuous processing of biological products and discussions of future directions in advancing continuous processing to meet new challenges and demands in the manufacture of therapeutic products. A stand-alone follow-up to the editor’s Continuous Biomanufacturing: Innovative Technologies and Methods published in 2017, this new edited volume focuses on critical aspects of process intensification, process control, and the digital transformation of biopharmaceutical processes. In addition to topics like the use of multivariant data analysis, regulatory concerns, and automation processes, the book also includes: Thorough introductions to capacitance sensors to control feeding strategies and the continuous production of viral vaccines Comprehensive explorations of strategies for the continuous upstream processing of induced microbial systems Practical discussions of preparative hydrophobic interaction chromatography and the design of modern protein-A-resins for continuous biomanufacturing In-depth examinations of bioprocess intensification approaches and the benefits of single use for process intensification Perfect for biotechnologists, bioengineers, pharmaceutical engineers, and process engineers, Process Control, Intensification, and Digitalisation in Continuous Biomanufacturing is also an indispensable resource for chemical engineers seeking a one-stop reference on continuous biomanufacturing.