Style Editor Docs
     
  • Home
  • Products & Services
    • DataHowLab
    • Professional Services
    • Innovation Hub
  • Resources
    • Technology
    • Publications
    • Bioprocess Blog
  • Education & Events
    • Schedule events & trainings
    • Symposium
  • About
    • DataHow
    • Team
    • Careers
    • Contact
   info@datahow.ch
  • Home
  • Products & Services
    • DataHowLab
    • Professional Services
    • Innovation Hub
  • Resources
    • Technology
    • Publications
    • Bioprocess Blog
  • Education & Events
    • Schedule events & trainings
    • Symposium
  • About
    • DataHow
    • Team
    • Careers
    • Contact
    Bioprocess Blog  ·  02. March 2023

    Understanding hybrid models in biochemical process systems

    It's important to know what we know, and don't know

     

    The fundamental function of biochemical process systems has been studied for years, or better centuries, in the science and engineering fields. Much of the fundamental understanding is universally applicable and can be expressed in terms of material, population, energy, or other balances.

     

    The specifics of certain processes are much less understood, and experimentation along with more data-driven modeling approaches is required, e.g. to understand which temperatures, pH, etc. are ideal to maximize production. However, despite these areas of uncertainty, one should never forget about what is known about the fundamentals. For instance, if we have 1000 cells in 1 liter and we add 1 more liter or culture medium, you can determine the concentration directly without a need for a data-driven approach - so why disregard alike knowledge when applying design of experiment studies?

     

    Why use Hybrid models at all?

     

    While it might be clear that the of use purely mechanistic models leave degree of uncertainty remaining, much is written about the power of machine learning models and AI – so why not simply use these to solve for X? The reality is that machine learning models need data, and lots of it, before they enter their optimal sweet spot. More data, means more experiments which can be an excessively costly on time and resources. However, when combined with mechanistic models, which describe areas of knowledge, the unknowns are significantly reduced, and data requirements (and thus experimentation) becomes significantly lower.

     

    When and how to use a Hybrid model within process development?

     

    Aiming at a better understanding of how a process will evolve under varying conditions is a clear case for the application of hybrid models. Take for instance an upstream bioprocess, where a product is produced by cells. Applying the material balances for this process directly provides a valid frame against which any additional knowledge can be mapped. The only variables in these balances that are unknown are related to the biology - namely the specific rates and their dependence on changes in the process parameters such as temperature, or pH. Therefore, the only experimentation needed, when set up in an optimal fashion, are those which deliver this insight- read our latest publication on the comparison of strategies for iterative model-based upstream bioprocess development for further insight. Once set-up the hybrid model can then be used to simulate what would happen to the evolution of the process when changing, for example, temperature.

     

     

    Figure 1:  Material Balances

    Figure 2:  Simulation of process evolution with varying temperature

    Alternatively, the mode of operation could be changed. As the feeds are explicitly incorporated in the material balances, the impact of changing the feedrate or feed composition can directly be simulated, not requiring the execution of additional experiments, another potential use case. 

     

    Broadly applicable to a diverse range of process challenages

     

    These examples are just a few of the applications where hybrid models can drive significant efficiencies and deeper understanding. Their flexible nature is able to work through many process challenges across scale-up, process characterization, process validation etc. Crucially for industry, access to high performing hybrid models is becoming easier than ever before as service providers, such as DataHow, begin to integrate them as part of software solutions. 

     

    Hembarevskyy Serhiy

    Dr. Moritz von Stosch


    Chief Innovation Officer

    DataHow

     

     

    If you would like to know more about hybrid models and how they might be able to support you in your process operation, book a meeting with DataHow’s CIO, Dr. Moritz von Stosch.

    Book a meeting

    tagPlaceholderTags: bioprocesses, hybrid models, process development, lean operations

    Write a comment

    Comments: 0
    draggable-logo

    Hagenholzstrasse.111 / 8050 Zurich/ info@datahow.ch 


    Schedule a Demo

    Edit here your navigation button
    is-switcher admin-only
    is-switcher admin-only
    is-switcher admin-only

     

    Main colors
       bg-primary
       bg-primary-light
       bg-primary-dark
       bg-secondary
       bg-secondary-dark
    Template sections
       body
       top-header
       header
       content
    Footer Styles
       background
       text color
       link color
       horizontal line
    Buttons
       style 1
       style 2
       style 3
    Other elements
      social icons
      navigation color
      subnav background
    Mobile navigation
       background color
       navigation color
    Template configurations
    g-font
    navigation styles
    size-17 weight-400
    content styles
    form-white
    footer styles
    o-form color-white
    Typography
    Heading H1
    weight-600
    Heading H2
    weight-600
    Heading H3
    weight-600
    Buttons
    weight-600 is-uppercase
    Animations
    wow animated fadeInUp

    Note:
    All changes made here will be applied to your entire website.

    About | Privacy Policy | Cookie Policy | Sitemap
    © Copyright 2022 - DataHow - All rights reserved
    Log out | Edit
    • Scroll to top