Enzymatic Synthesis of Ampicillin: Nonlinear Modeling, Kinetics Estimation, and Adaptive Control

Nowadays, the use of advanced control strategies in biotechnology is quite low. A main reason is the lack of quality of the data, and the fact that more sophisticated control strategies must be based on a model of the dynamics of bioprocesses. The nonlinearity of the bioprocesses and the absence of cheap and reliable instrumentation require an enhanced modeling effort and identification strategies for the kinetics. The present work approaches modeling and control strategies for the enzymatic synthesis of ampicillin that is carried out inside a fed-batch bioreactor. First, a nonlinear dynamical model of this bioprocess is obtained by using a novel modeling procedure for biotechnology: the bond graph methodology. Second, a high gain observer is designed for the estimation of the imprecisely known kinetics of the synthesis process. Third, by combining an exact linearizing control law with the on-line estimation kinetics algorithm, a nonlinear adaptive control law is designed. The case study discussed shows that a nonlinear feedback control strategy applied to the ampicillin synthesis bioprocess can cope with disturbances, noisy measurements, and parametric uncertainties. Numerical simulations performed with MATLAB environment are included in order to test the behavior and the performances of the proposed estimation and control strategies

Novel strategies for process control based on hybrid semi-parametric mathematical systems

Tese de doutoramento. Engenharia Química. Universidade do Porto. Faculdade de Engenharia. 201

Current State and Perspective in the Models Applicable to Oenology

Modeling, simulation, and control of the alcoholic fermentation of grape juice into wine are still not a totally resolved problem. A model that makes it possible to predict alcoholic fermentation development would be a valuable instrument to its technical and economical implications. Considering the field of bioprocess used in food production at the industrial scale, the chapter will be centered on models applicable to oenology. On the first part, the chapter proposes the different approaches that have been taken: “knowledge-based” models, non-physiological mathematical descriptions, behavior prediction models, and empirical models. The second part will deal with a nonlinear model for white wine alcoholic fermentation process which, besides the detailed kinetic model, involves equations corresponding to the physiological phases of yeast cells, the inhibitory effect of ethanol, heat transfer equations, and the dependence of kinetic parameters on temperature

Multivariable Tracking Control of a Bioethanol Process under Uncertainties

Bioethanol is one of the most studied alternative fuels nowadays. Due to its production process complexity and the low quality of the mathematical models that describe it, a reliable controller is needed to maximize the fuel production and minimize its environmental impact, even in the presence of uncertainty. Here, a controller for tracking optimal profiles considering model errors and external perturbations is proposed. This work is an improvement of a previously presented technique. To reduce the earlier mentioned uncertainties' effect during the fermentation, some tracking error integrators are added in the control action calculation. This simple modification ensures the tracking error convergence to zero, even in the presence of uncertainties (demonstration available). Different tests are carried out and a performance comparison with the original controller is shown to highlight improvements in the tracking error of up to 98% when integrators are incorporated. Furthermore, a classical PI controller is contrasted with the proposed techniques.Fil: Fernández Puchol, María Cecilia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Pantano, Maria Nadia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Serrano, Mario Emanuel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

Production of L-carnitine by secondary metabolism of bacteria

The increasing commercial demand for L-carnitine has led to a multiplication of efforts to improve its production with bacteria. The use of different cell environments, such as growing, resting, permeabilized, dried, osmotically stressed, freely suspended and immobilized cells, to maintain enzymes sufficiently active for L-carnitine production is discussed in the text. The different cell states of enterobacteria, such as Escherichia coli and Proteus sp., which can be used to produce L-carnitine from crotonobetaine or D-carnitine as substrate, are analyzed. Moreover, the combined application of both bioprocess and metabolic engineering has allowed a deeper understanding of the main factors controlling the production process, such as energy depletion and the alteration of the acetyl-CoA/CoA ratio which are coupled to the end of the biotransformation. Furthermore, the profiles of key central metabolic activities such as the TCA cycle, the glyoxylate shunt and the acetate metabolism are seen to be closely interrelated and affect the biotransformation efficiency. Although genetically modified strains have been obtained, new strain improvement strategies are still needed, especially in Escherichia coli as a model organism for molecular biology studies. This review aims to summarize and update the state of the art in L-carnitine production using E. coli and Proteus sp, emphasizing the importance of proper reactor design and operation strategies, together with metabolic engineering aspects and the need for feed-back between wet and in silico work to optimize this biotransformation

Production of indigo by recombinant bacteria

Indigo is an economically important dye, especially for the textile industry and the dyeing of denim fabrics for jeans and garments. Around 80,000 tonnes of indigo are chemically produced each year with the use of non-renewable petrochemicals and the use and generation of toxic compounds. As many microorganisms and their enzymes are able to synthesise indigo after the expression of specific oxygenases and hydroxylases, microbial fermentation could offer a more sustainable and environmentally friendly manufacturing platform. Although multiple small-scale studies have been performed, several existing research gaps still hinder the effective translation of these biochemical approaches. No article has evaluated the feasibility and relevance of the current understanding and development of indigo biocatalysis for real-life industrial applications. There is no record of either established or practically tested large-scale bioprocess for the biosynthesis of indigo. To address this, upstream and downstream processing considerations were carried out for indigo biosynthesis. 5 classes of potential biocatalysts were identified, and 2 possible bioprocess flowsheets were designed that facilitate generating either a pre-reduced dye solution or a dry powder product. Furthermore, considering the publicly available data on the development of relevant technology and common bioprocess facilities, possible platform and process values were estimated, including titre, DSP yield, potential plant capacities, fermenter size and batch schedule. This allowed us to project the realistic annual output of a potential indigo biosynthesis platform as 540 tonnes. This was interpreted as an industrially relevant quantity, sufficient to provide an annual dye supply to a single industrial-size denim dyeing plant. The conducted sensitivity analysis showed that this anticipated output is most sensitive to changes in the reaction titer, which can bring a 27.8% increase or a 94.4% drop. Thus, although such a biological platform would require careful consideration, fine-tuning and optimization before real-life implementation, the recombinant indigo biosynthesis was found as already attractive for business exploitation for both, luxury segment customers and mass-producers of denim garments

Novel analysis and modelling methodologies applied to pultrusion and other processes

Often a manufacturing process may be a bottleneck or critical to a business. This thesis focuses on the analysis and modelling of such processest, to both better understand them, and to support the enhancement of quality or output capability of the process. The main thrusts of this thesis therefore are: To model inter-process physics, inter-relationships, and complex processes in a manner that enables re-exploitation, re-interpretation and reuse of this knowledge and generic elements e.g. using Object Oriented (00) & Qualitative Modelling (QM) techniques. This involves the development of superior process models to capture process complexity and reuse any generic elements; To demonstrate advanced modelling and simulation techniques (e.g. Artificial Neural Networks(ANN), Rule-Based-Systems (RBS), and statistical modelling) on a number of complex manufacturing case studies; To gain a better understanding of the physics and process inter-relationships exhibited in a number of complex manufacturing processes (e.g. pultrusion, bioprocess, and logistics) using analysis and modelling. To these ends, both a novel Object Oriented Qualitative (Problem) Analysis (OOQA) methodology, and a novel Artificial Neural Network Process Modelling (ANNPM) methodology were developed and applied to a number of complex manufacturing case studies- thermoset and thermoplastic pultrusion, bioprocess reactor, and a logistics supply chain. It has been shown that these methodologies and the models developed support capture of complex process inter-relationships, enable reuse of generic elements, support effective variable selection for ANN models, and perform well as a predictor of process properties. In particular the ANN pultrusion models, using laboratory data from IKV, Aachen and Pera, Melton Mowbray, predicted product properties very well

Bioprocess Monitoring and Control

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