Dr Dongda Zhang PhD (Cantab), DIC

• PhD in Chemical Engineering, The University of Cambridge, UK, 2016
• MSc in Chemical Engineering, Imperial College London, UK, 2013
• BSc in Chemical Engineering and Technology, Tianjin University, China, 2011

Dr Dongda Zhang is a researcher working at the intersection of artificial intelligence, process systems engineering and chemical and biochemical manufacturing. He completed his PhD research at the University of Cambridge within two years, receiving special approval from the University for early thesis submission, before joining Imperial College London as a Postdoctoral Research Associate in 2016. 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 an Assistant Professor (University Lecturer) in the same year. In 2024, he was awarded the Royal Academy of Engineering Industrial Fellowship, working with Unilever on AI-driven digital manufacturing. He joined the University of Birmingham in 2026 as a 125^th Anniversary Fellow (Associate Professor) through the University’s strategic academic recruitment scheme.

Dr Zhang’s research focuses on developing innovative AI and digital technologies to accelerate scientific discovery, process development and industrial manufacturing. His work spans interpretable and scientific machine learning, hybrid modelling, predictive digital twins, transfer and active learning, and generative AI, with applications ranging from catalytical reaction kinetics and molecular discovery to process scale-up, optimisation and autonomous manufacturing. His research has contributed to advancing fundamental understanding of complex chemical and biochemical systems while translating scientific innovations into practical industrial solutions.

Dr Zhang has established extensive industrial collaborations with Unilever, Johnson Matthey, Croda, BP and other leading chemical and biochemical companies, alongside active engagement with the European industrial reaction kinetics consortium (Eurokin). His research has enabled the global deployment of AI-driven soft-sensors in the formulated products industry for real-time product quality monitoring, delivering substantial annual cost savings while reducing manufacturing waste and energy consumption. Other industrial achievements include developing AI-enabled technologies for process design and scale-up, digital twins for specialty chemical manufacturing, and hybrid modelling approaches for reaction kinetics prediction and reactor optimisation. His recent collaborations also extend to generative AI for molecular and formulation design, supporting the development of innovative ingredients and sustainable formulated products.

Dr Zhang’s academic achievements have received international recognition. He received the Biochemical Engineering Journal Young Investigator Award in 2024, becoming the first Europe-based recipient since the award’s establishment in 2009, in recognition of his contributions to hybrid modelling and bioprocess systems engineering. He was also awarded the IChemE Hutchison Medal in 2023 for his research in AI-enabled industrial process analytics and product quality control, and received a Highly Commended distinction for the IChemE Nicklin Medal in 2022.

Dr Zhang serves as an Associate Editor of Digital Chemical Engineering, an Editorial Board Member of Biochemical Engineering Journal, and a member of the BBSRC Pool of Experts. He is also the Academic Lead of the Process Modelling, Control and Optimisation Focus Group at the Centre for Process Analytics and Control Technology (CPACT), a leading UK industrial-academic network involving over 40 members from the chemical, pharmaceutical and related process industries. Through this role, he advises industrial partners on applications of artificial intelligence, machine learning and digital technologies in process monitoring, optimisation and control.

Dr Dongda Zhang has an established track record in supervising PhD students in chemical and biochemical engineering, particularly in artificial intelligence, machine learning, process systems engineering and reaction engineering. His PhD students have received competitive research awards and scholarships, including the Society of Chemical Industry (SCI) PhD Scholarship, SCI Sydney Andrew Scholarship, and CPACT Malcolm McIvor Prize.

His graduated PhD students have progressed to successful careers in academia and industry, including recipients of prestigious independent research fellowships such as the EPSRC Doctoral Prize Fellowship and Royal Society Career Development Fellowship, as well as senior engineering roles specialising in the modelling and optimisation of chemical and biochemical processes.

Dr Zhang welcomes enquiries from prospective PhD students interested in artificial intelligence, machine learning, process systems engineering, reaction engineering and their applications in chemical and biochemical manufacturing. Applicants are encouraged to explore his research profile for further information on current research interests and potential PhD topics before contacting him directly. Most of his PhD projects are conducted in close collaboration with industrial partners, covering a broad spectrum of research from fundamental scientific discovery to solving real-world industrial challenges. Funded PhD studentships are available for UK home students only.

Dr Dongda Zhang leads the Intelligent Chemical Systems Engineering (ICSE) Research Group, focusing on integrating data intelligence, machine learning and fundamental engineering knowledge to advance scientific discovery, intelligent process development and sustainable chemical and biochemical manufacturing.

His group’s research covers six principal areas:

1. Scientific AI and Digital Twins: Developing physics-informed machine learning, hybrid modelling and digital twin technologies to enable predictive simulation, intelligent process development and scale-up of complex chemical and biochemical systems.

2. Interpretable AI and Knowledge Discovery: Integrating explainable AI, symbolic regression and scientific machine learning to discover governing equations, reaction kinetics and interpretable scientific knowledge from experimental and industrial data.

3. Industrial Analytics and Process Monitoring: Developing advanced industrial data analytics, soft-sensors, fault detection and diagnosis technologies for real-time process monitoring, predictive maintenance and product quality assurance.

4. Process Optimisation and Intelligent Control: Applying reinforcement learning, Bayesian optimisation and uncertainty-aware decision-making to achieve robust process optimisation, autonomous control and efficient industrial manufacturing.

5. Bioprocess Engineering and Scale-up: Combining mechanistic modelling, machine learning and transfer learning to accelerate bioprocess development, improve predictive performance across different scales and support sustainable biomanufacturing.

6. Generative AI for Molecular and Formulation Design: Developing generative AI and foundation models for molecular discovery, inverse ingredient design, formulation optimisation and the development of novel materials and formulated products.

Further information about these research areas, ongoing projects, publications and industrial collaborations can be found on the ICSE Research Group website.