Dongda Zhang

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University Lecturer

The University of Manchester

United Kingdom

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Dongda Zhang is a University Lecturer at the University of Manchester, UK. His research areas include catalytic reaction mechanism analysis, bioprocesses optimization using reinforcement learning, and hybrid modeling frameworks for chemical reaction kinetics. His recent articles focus on developing innovative methodologies for accelerating bioprocess model construction and understanding the relationship between macroscopic kinetics and surface coverages.

Recent Grants

Grant: Close

Algal Biorefinery for Sustainable Biofuels and High-value Bioproducts Production

Open Date: 2017-10-01

Close Date: 2020-09-01

Grant: Close

Sustainable Algal Biofuel Production Process Optimisation and Scale-up

Open Date: 2017-10-01

Close Date: 2019-09-01

Grant: Close

Sustainable Biopolymer Production

Open Date: 2016-10-01

Close Date: 2017-09-01

Articles (24)

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2021

Collaborators (7)

Ehecatl Antonio del Rio Chanona

Lecturer

Imperial College London

UNITED KINGDOM

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Fernando Almeida-Trasvina

Teaching Assistant

The University of Manchester

UNITED KINGDOM

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Max Mowbray

The University of Manchester

UNITED KINGDOM

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King Kuok (Mimi) Hii

Imperial College London

UNITED KINGDOM

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Klaus Hellgardt

Lecturer and Senior Lecturer

Loughborough University

UNITED KINGDOM

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Philip A. Martin

The University of Manchester

UNITED KINGDOM

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Alan J. Dickson

The University of Manchester

UNITED KINGDOM

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Dongda Zhang

Research Interests

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About

Recent Grants

Articles (24)

Extracting microkinetic insights from macroscopic measurements: A power law modelling framework for catalytic reaction mechanism analysis

Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocesses

Interpretable‐AI‐Based Model Structural Transfer Learning to Accelerate Bioprocess Model Construction

Developing a Hybrid Modeling Framework for Enhanced Prediction in Chemical Reaction Kinetics

Integrating feature attribution and symbolic regression for automatic model structure identification and strategic sampling

A novel approach to identify optimal and flexible operational spaces for product quality control

Accelerating bioprocess digital twin development by integrating hybrid modelling with transfer learning

Investigating the reliability and interpretability of machine learning frameworks for chemical retrosynthesis

The automated discovery of kinetic rate models – methodological frameworks

Constructing a Symbolic Regression-Based Interpretable Soft Sensor for Industrial Data Analytics and Product Quality Control

Integrating knowledge-guided symbolic regression and model-based design of experiments to automate process flow diagram development

Cracking the physical insight of power law models: Bridging the gap between macroscopic kinetics and surface coverages

Integrating transfer learning within data-driven soft sensor design to accelerate product quality control

A multiscale hybrid modelling methodology for cell cultures enabled by enzyme-constrained dynamic metabolic flux analysis under uncertainty

A reinforcement learning‐based hybrid modeling framework for bioprocess kinetics identification

Industrial data science – a review of machine learning applications for chemical and process industries

Integrating Autoencoder and Heteroscedastic Noise Neural Networks for the Batch Process Soft-Sensor Design

Probabilistic machine learning based soft-sensors for product quality prediction in batch processes

An MIQP framework for metabolic pathways optimisation and dynamic flux analysis

A transfer learning approach for predictive modeling of bioprocesses using small data

An integrated dimensionality reduction and surrogate optimization approach for plant‐wide chemical process operation

A two-step multivariate statistical learning approach for batch process soft sensing

Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty

Dynamic modelling of Rhodopseudomonas palustris biohydrogen production: Perturbation analysis and photobioreactor upscaling

Collaborators (7)

Ehecatl Antonio del Rio Chanona

Fernando Almeida-Trasvina

Max Mowbray

King Kuok (Mimi) Hii

Klaus Hellgardt

Philip A. Martin

Alan J. Dickson

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