Dongda Zhang


Personal profile


Dr. Dongda Zhang is a University Lecturer at the Department of Chemical Engineering, the University of Manchester and an Honorary Research Fellow at the Centre for Process Systems Engineering, Imperial College London. His research group focuses on Digital Chemical Engineering, with primary interests in chemical and biochemical Process Systems Engineering, Reaction Engineering, Machine Learning, and Industrial Data Analytics. He is currently an Associate Editor for Digital Chemical Engineering, a member of Editorial Board of Biochemical Engineering Journal, a member of BBSRC Pool of Experts for Bioprocess Systems Engineering, and a member of the Industrial Management Board, Centre for Process Analytics and Control Technology (CPACT).

He holds BSc degree (2011) from Tianjin University and MSc (Distinction) degree (2013) from Imperial College London. He started his PhD research at the University of Cambridge in 2013, completed his research within 2 years, and graduated at the beginning of the third year after the University special approval for Thesis Early Submission. Upon the completion of his PhD in early 2016, he was invited by the Chinese Academy of Sciences and several universities for short research visits, and then moved to the Centre for Process Systems Engineering at Imperial College London as a postdoctoral research associate. In 2017, he was awarded the prestigious Leverhulme Early Career Fellowship at the University of Cambridge, followed by his appointment at the University of Manchester as a University Lecturer in the same year.

Research interests

My research group focuses on the development of methodologies and practical applications for industrially focused mathematical modelling and data analytical techniques, aimed at understanding and operating complex chemical and biochemical processes. We are interested in exploring how advanced 'digital technologies', particularly integrated through interpretable machine learning and data intelligence techniques, along with rigorous mathematical analysis and physical modelling approaches, can accelerate novel process development and automate industrial process manufacturing. The research activities of the group are multidisciplinary, combining Process Systems Engineering, Machine Learning, Reaction Engineering, Data Analytics, and Systems Biology. Our ongoing research projects include the kinetic modelling of catalytic reaction networks and metabolic reaction networks; real-time monitoring of batch processes, soft-sensing, online optimisation, and advanced control; industrial data analytics and visualisation; as well as multiscale modelling and upscaling of biochemical and formulation processes for biorenewable and fine chemicals production.

The three major research areas at this moment are:

  • Hybrid model based digital twins for bioprocess predictive modelling, optimisation, control, and metabolic network simulation.

Biomanufacturing process development and digital twin

  • Machine learning enabled data-driven techniques for industrial manufacturing process quality control and process flow diagram development.

Industrial Data Analytics

  • Interpretable AI based frameworks to discover physical knowledge and generate mechanistic expressions for reaction engineering applications.

Machine Learning for Reaction Engineering Applications


  • PhD in Chemical Engineering, University of Cambridge, 2013-2016
  • MSc in Advanced Chemical Engineering, Imperial College London, 2012-2013
  • BSc in Chemical Engineering and Technology, Tianjin University, 2007-2011

My group

We warmly welcome ambitious students who are interested in the fields of machine learning, reaction engineering, data analytics, process systems engineering, and biochemical engineering to apply for PhD positions. Currently, all our PhD students are fully sponsored by various scholarships. We are also actively seeking competitive undergraduate students who are enthusiastic about undertaking industrially focused research internship projects. If you are interested in these opportunities, please don't hesitate to contact me via email: [email protected].

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 4 - Quality Education
  • SDG 6 - Clean Water and Sanitation
  • SDG 7 - Affordable and Clean Energy
  • SDG 8 - Decent Work and Economic Growth
  • SDG 9 - Industry, Innovation, and Infrastructure
  • SDG 12 - Responsible Consumption and Production
  • SDG 13 - Climate Action

External positions

Member of Industrial Management Board, Centre for Process Analytics and Control Technology (CPACT)

2021 → …

Subject Editor for Digital Chemical Engineering Journal, Elsevier BV

2021 → …

Guest Editor for Bioengineering Special Issue 'Biological Process Modelling, Monitoring and Control in a Rapidly Changing World', MDPI AG

1 Jun 202031 Jan 2021

BBSRC Research Committee Pool of Experts, Biotechnology & Biological Sciences Research Council (BBSRC)


Member of Editorial Board for Biochemical Engineering Journal, Elsevier Ltd

2020 → …

Honorary Research Fellow, Department of Chemical Engineering, Imperial College London


UKRI Future Leaders Fellowships Peer Review College, Science & Technology Facilities Council (STFC)


Areas of expertise

  • TP Chemical technology
  • Process Systems Engineering
  • Biochemical Engineering
  • Machine Learning
  • Dynamic Modelling and Optimisation
  • Reaction Engineering
  • Complex Biosystems
  • Synthetic Biology
  • Fermentation Technology
  • Digital Economy
  • Data-driven Modelling
  • Computational Fluid Dynamics
  • Life Cycle Assessment
  • Renewable Energy

Research Beacons, Institutes and Platforms

  • Energy
  • Biotechnology
  • Digital Futures


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