Dr Dongda Zhang Cantab, DIC, AMIChemE, AMRSC

Lecturer in Process Systems Engineering and Machine Learning

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Research interests

My group focuses on the theory development and applications of industrially focused mathematical modelling tools and data analytical techniques to digitalise and operate complex chemical and biochemical systems. These research particularly aims to explore how ‘digital technologies’, such as mechanistic and hybrid modelling, data mining and soft-sensing, machine learning, process analytical technology (PAT), and automation, can be used to accelerate process development and enhance commercial scale manufacturing operations. Many of my studies lie at the interface between Process Systems Engineering, Machine Learning, Reaction Engineering, and Systems Biology, where state-of-art modelling techniques are investigated and integrated to enable the real-time visualisation, online optimisation and control, and scale-up and smarter design of biomanufacturing processes for sustainable biofuels and high-value biorenewables production. This has resulted in the highest productivities ever reported for several commercial compounds in both photo-production and fermentation processes at different scales (from lab to industrial systems). I am also interested in transferring technology from academia to industry to advance development of industrially-desired microorganism mutants and high efficiency bioreaction systems at an early research and commercial stage. These techniques have also been transferred to further assist design and integration of traditional chemical processes. In addition, I am currently collaborating with both chemical and biochemical companies (Unilever, ProAIM etc) to develop data-driven soft-sensors and online predictive modelling tools for industrial batch/continuous processes monitoring, fault detection, and predictive maintenance.

My current research themes are summarised in the diagram:

Current Research ThemesCurrent Research Themes


There are a number of ongoing activities intensively conducted in my group to facilitate digital manufacturing and autonomous decision-making of the next generation industrial systems from different aspects (illustrated below), ranging from microscale metabolic reaction network simulation, to macroscale process dynamic modelling and optimisation, and to large scale facilities design and visualisation. These projects include:

Biomanufacturing process development and digital twin

Biomanufacturing Process Design and Digitalisation

  • Hybrid modelling (physical & data-driven) for industrial system digitalisation
  • Machine learning based process simulation and prediction under uncertainty
  • Surrogate modelling and optimisation of complex (bio)chemical systems
  • Deep learning based multiscale visualisation techniques for unit operation design
  • Hybrid data-driven based process analytical technology (PAT)
  • Online monitoring and optimal control of (bio)manufacturing processes
  • Design of industrially-desired mutants and biorefinery systems for sustainable production

Most of these projects are carried out under intense collaborations with multidisciplinary research groups from both the UK and overseas universities, mainly including:

Academic groups from the University of Manchester:

  • Professor Robin Smith (process integration)
  • Professor Philip Martin (process analysis and control)
  • Dr. Richard Allmendinger (data science and business)

Academic groups from external UK universities:

  • Professor Klaus Hellgardt (reaction engineering and separation, Imperial College London)
  • Dr. Ehecatl Antonio del Rio-Chanona (process systems engineering, Imperial College London)
  • Dr. David Lea-Smith (microbiology and biochemistry, University of East Anglia)
  • Dr. Jonathan Wagner (catalytic engineering, Loughborough University)

Academic groups from overseas universities:

  • Professor Keju Jing (industrial biotechnology, Xiamen University, China)
  • Dr. Robert Pott (process engineering, Stellenbosch University, South Africa)

A short description of my ongoing projects can be found in the "Opportunities" page.



Research and projects

No current projects are available for public display