Hybrid simulation-optimization based approach for the optimal design of single-product biotechnological processes

In this work, we present a systematic method for the optimal development of bioprocesses that relies on the combined use of simulation packages and optimization tools. One of the main advantages of our method is that it allows for the simultaneous optimization of all the individual components of a bioprocess, including the main upstream and downstream units. The design task is mathematically formulated as a mixed-integer dynamic optimization (MIDO) problem, which is solved by a decomposition method that iterates between primal and master sub-problems. The primal dynamic optimization problem optimizes the operating conditions, bioreactor kinetics and equipment sizes, whereas the master levels entails the solution of a tailored mixed-integer linear programming (MILP) model that decides on the values of the integer variables (i.e., number of equipments in parallel and topological decisions). The dynamic optimization primal sub-problems are solved via a sequential approach that integrates the process simulator SuperPro Designer® with an external NLP solver implemented in Matlab®. The capabilities of the proposed methodology are illustrated through its application to a typical fermentation process and to the production of the amino acid L-lysine.Support from the Spanish Ministry of Education and Science (projects DPI2008-04099 and CTQ2009-14420-C02) and the Spanish Ministry of External Affairs (projects A/023551/09, A/031707/10 and HS2007-0006)

Modelling of Biotechnological Processes - An approach based on Artificial Neural Networks

In this chapter we describe a software tool for modelling fermentation processes, the FerMoANN, which allows researchers in biology and biotechnology areas to access the potential of Artificial Neural Networks (ANNs) for this task. The FerMoANN is tested and validated using two fermentation processes, an Escherichia coli recombinant protein production and the production of a secreted protein with Saccharomyces cerevisiae in fed-batch reactors. The application to these two case studies, tested for different configurations of feedforward ANNs, illustrate the usefulness of these structures, when trained according to a supervised learning paradigm

Towards continuous biomanufacturing a computational approach for the intensification of monoclonal antibody production

Current industrial trends encourage the development of sustainable, environmentally friendly processes with reduced energy and raw material consumption. Meanwhile, the increasing market demand as well as the tight regulations in product quality, necessitate efficient operating procedures that guarantee products of high purity. In this direction, process intensification via continuous operation paves the way for the development of novel, eco-friendly processes, characterized by higher productivity compared to batch (Nicoud, 2014). The shift towards continuous operation could advance the market of high value biologics, such as monoclonal antibodies (mAbs), as it would lead to shorter production times, decreased costs, as well as significantly less energy consumption (Konstantinov and Cooney, 2015, Xenopoulos, 2015). In particular, mAb production comprises two main steps: the culturing of the cells (upstream) and the purification of the targeted product (downstream). Both processes are highly complex and their performance depends on various parameters. In particular, the efficiency of the upstream depends highly on cell growth and the longevity of the culture, while product quality can be jeopardized in case the culture is not terminated timely. Similarly, downstream processing, whose main step is the chromatographic separation, relies highly on the setup configuration, as well as on the composition of the upstream mixture. Therefore, it is necessary to understand and optimize both processes prior to their integration. In this direction, the design of intelligent computational tools becomes eminent. Such tools can form a solid basis for the: (i) execution of cost-free comparisons of various operating strategies, (ii) design of optimal operation profiles and (iii) development of advanced, intelligent control systems that can maintain the process under optimal operation, rejecting disturbances. In this context, this work focuses on the development of advanced computational tools for the improvement of the performance of: (a) chromatographic separation processes and (b) cell culture systems, following the systematic PAROC framework and software platform (Pistikopoulos et al., 2015). In particular we develop model-based controllers for single- and multi-column chromatographic setups based on the operating principles of an industrially relevant separation process. The presented strategies are immunized against variations in the feed stream and can successfully compensate for time delays caused due to the column residence time. Issues regarding the points of integration in multi-column systems are also discussed. Moreover, we design and test in silico model-based control strategies for a cell culture system, aiming to increase the culture productivity and drive the system towards continuous operation. Challenges and potential solutions for the seamless integration of the examined bioprocess are also investigated at the end of this thesis.Open Acces

Dynamic Simulation and Optimization for Arthrospira platensis Growth and C-Phycocyanin Production

This is the accepted manuscript. The final version is available at http://pubs.acs.org/doi/abs/10.1021/acs.iecr.5b03102.C-phycocyanin is a high-value bioproduct synthesized from cyanobacterium Arthrospira platensis. To facilitate its application, advanced dynamic models were built to simulate the complex effects of light intensity, light attenuation and nitrate concentration on cell growth and pigment production in the current research. By comparing these models against the experimental results, their accuracy was verified in both batch and fed-batch processes. Three key findings are presented in this work. First, a noticeable difference between the optimal light intensity for cell growth (282 μmol m-2 s-1) and phycocyanin synthesis (137 μmol m-2 s-1) is identified. Second, light attenuation is demonstrated to be the primary factor causing the decrease of intracellular phycocyanin content instead of nitrate concentration in the fed-batch process, while it has no significant effect on total phycocyanin production. Finally, although high nitrate concentration can enhance cell growth, it is demonstrated to suppress intracellular phycocyanin accumulation in a long-term operation.Author E. A. del Rio-Chanona is funded by CONACyT scholarship No. 522530 and the Secretariat of Public Education and the Mexican government. This work was also supported by the National High Technology Research and Development Program 863, China (No. 2014AA021701) and the National Marine Commonwealth Research Program, China (No. 201205020-2)