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Our Research Themes
Our Research Themes
At the Cordiner Group, we drive continual improvement in industrial performance by deploying cutting-edge mechanistic and data-driven modeling. Our mission is to unpack the complex physical properties and operational behaviors of real-world systems, turning those insights into predictive tools that make industries safer, smarter, and inherently sustainable.
1. Process Safety & National Resilience
1. Process Safety & National Resilience
2. Complex Process and Property Modelling (Mechanistic and Machine Learning)
2. Complex Process and Property Modelling (Mechanistic and Machine Learning)
3. Sustainability & Green Systems Design
3. Sustainability & Green Systems Design
1. Process Safety & National Resilience
1. Process Safety & National Resilience
Traditional safety metrics are often retrospective. Our framework treats safety and operational resilience as two sides of the same coin, designing systems that are mathematically optimized to anticipate, survive, adapt to, and recover from unexpected disruptions.
Key Focus Areas
Key Focus Areas
Next-Gen Risk Frameworks: Integrating quantitative resilience principles directly into standard industrial Hazard and Operability (HAZOP) studies during early-stage plant design.
Net-Zero & Hydrogen Infrastructure: Modelling risk and quantitative safety parameters for hydrogen blending and transportation networks (e.g., via natural gas pipelines).
National Security Risk Registers: Developing early-design process resilience models to insulate critical national infrastructure against extreme environmental, geopolitical, or systemic shocks.
2. Complex Process and Property Modelling (Mechanistic and Machine Learning)
2. Complex Process and Property Modelling (Mechanistic and Machine Learning)
As manufacturing enters the digital era, the nature of industrial failures becomes more complex. We fuse physics-based engineering insights with advanced machine learning to predict behavior before failures happen.
Our research into Knowledge-Informed Machine Learning (KIML) directly powered the launch of our university spinout, Kausalyze. By embedding physical laws into data-driven algorithms, the platform delivers ahead-of-time predictive maintenance and root-cause analysis for high-value manufacturing assets, moving beyond traditional statistical anomalies.
Key Focus Areas
Key Focus Areas
Physical Property, Process and Formulation Prediction: Utilizing molecular and mixture modeling to accurately forecast the behavior of complex chemical components, shifting industry practices away from slow, empirical experimentation.
Bioprocess Scaling (mRNA Vaccines): Building mechanistic and kinetic models to optimize the formulation design, stability prediction, and commercial scale-up of nucleic acid therapeutics and mRNA vaccines.
Particle & Granular Processes: Modeling crystallization, fluidization, and structural behavior in complex material systems.
3. Sustainability & Green Systems Design
3. Sustainability & Green Systems Design
True sustainability cannot be retrofitted onto an inefficient design; it must be built into the financial and structural blueprints of a process from day one. We create multi-objective decision frameworks that prove green engineering is also economically resilient.
Integrated Metrics (S2R2): We pioneered the S2R2 framework—an augmented return on investment metric that quantifies Safety, Sustainability, Reliability, and Resilience into standard techno-economic analyses. This gives corporate decision-makers concrete data to justify green process routes over legacy systems.
Key Focus Areas
Key Focus Areas
Decarbonization Pathways: Formulating mathematical decision-making models that allow industrial facilities to select, cost, and execute optimal pathways toward net-zero.
Circular Material Strategy: Leading national-level research into material circularity—such as the recovery, recycling, and remanufacturing of rare-earth neodymium magnets salvaged from decommissioned wind turbines.
The University of SheffieldEnergy & Carbon Ledger Optimization: Engineering adaptive frameworks that optimize corporate purchasing models for energy and carbon credits based on fluctuating real-time grid demands.
Current and previous grants
Current and previous grants
2023-2028 UK-HyRES: Hub for Research Challenges in Hydrogen and Alternative Liquid Fuels ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL https://ukhyres.co.uk/
2023-2024 183358 Wellcome LEAP - SoW4
18/10/2023 181009 Modelling of adsorption in tritium separations for materials selection and inventory minimisation UNITED KINGDOM ATOMIC ENERGY AUTHORITY / UKAEA / UK ATOMIC ENERGY AUTHORITY
180925 UK-ASEAN+ Vaccine Manufacturing Research Hub (EPSRC funded)ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL / EPSRC
UK-ASEAN+ Vaccine Manufacturing Research Hub (DHSC funded) DEPARTMENT OF HEALTH AND SOCIAL CARE / DHSC
20/12/2021- 2024 173553 Digitised, small-scale and high-throughput process for distributed RNA production for therapy and pandemic preparedness WELLCOME LEAP INC
20/05/2022 -2025 172093 Sustainable microwave manufacturing ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC)
PhD research projects
PhD research projects
2025 - Hazard assessment 2nd Supervisor with Motaba Hoseini leading
2024 Shady Hamed- mRNA modelling of purification - joint supervision with Zoltan Kis leading
2024 Fawad Ahsan- Mathematical Modelling for Next Gen Formulated Products- joint supervision with Mo Zandi leading
2024- Yipei Zhao - Machine learning prediction of emulsion concentrate formulations- funded by Syngenta iCase.
2023 - Joseph Middleton- mRNA Process Modelling
2022 - Kesler Isoko -mRNA connected systems and control. Co Supervised with Dr Peyman Moghadan
PASSED VIVA 2022 - Mahdi Ahmed - Framework modelling and optimisation of industry towards UK Net Zero
PASSED VIVA MPhil 2022- Hengxin Wu Batch reactor modelling .
COMPLETED 2021 -Louis Allen- Prescriptive Maintenance Methodologies in the Chemical Process Industries.
COMPLETED Viva: Ana Franco-3d Printing and growing neurons on silk- Co-supervised with Prof Ipsita Roy
COMPLETED 2020 Viva - Lawson T. Glasby metal-organic frameworks, novel materials in the field of gas adsorption, sensing, and separation alongside catalysis, drug-delivery, and quantum applications. Co Supervised with Dr Peyman Moghadan now of UCL
COMPLETED 2020- 2024 - Rama Oktavian - Computational screening on Metal-organic frameworks (MOFs) and novel materials in the field of gas adsorption. Co Supervised with Dr Peyman Moghadan now of UCL
COMPLETED viva 2019 - Victoria Shields - Separation of rate earth metals co supervised with Dr Mark Ogden
COMPLETED 2018-2022- Aaron Yeardley- Main Supervisor Prof Sol Brown.
Post Doc research Projects
Post Doc research Projects
UK Hyres Hydrogen Safety- Rob Pilling, Neil Lowrie, Mohsen Hosini (2024) and Harmanpreet Sihgn (2025)
CEPI MRna Vaccine in a Box- Post doc Dec 2024- Jeramiah Corrigan
Former Post Doc Research
Seyed Mojtaba Hoseyni
Development of advanced computational methods for resilience assessment in process safety
Hydrogen Safety
Min Tao and Ehsan Nourafkan
mRNA Process Modelling (Wellcome Leap funded) - Co-Supervised with Prof Sol Brown and Dr Zoltan Kis
Royce Copley
mRNA Process Modelling (Wellcome Leap funded) - Co-Supervised with Dr Peyman Moghadan (UCL) and Dr Zoltan Kis
Mohsen Hosini- UK-Hyres
Wiz dashboard interface developed in Siemens’ MindSphere: (a) 5-dimensional plot showing conditions within the twin screw granulator, (b) real-time monitoring of the humidity in the fluidised bed dryer, (c) gauge plot showing dryer temperature as a key performance indicator (KPI) showing how the data is trending, and (d) scatter plot visualisation of local outlier factor (LOF) unsupervised machine learning algorithm showing outlier data detection.
Data Visualisation for Process Insight and Decision Making
The proliferation of data owing to the onset of Industry 4.0 (I4.0) has led to many traditional data analysis approaches becoming redundant. Novel and innovative solutions are required to facilitate the new era of data-driven manufacturing characteristics of I4.0. This work demonstrates one such approach in the formation of a bespoke web-based visualisation and machine learning analytics platform, designed to bridge the gap between the old ways and the new. Our unique I4.0 data analytics platform, called Wiz 4.0, enables advanced big data analytics in conjunction with user-friendly features and multivariate data visualisations. This allows for both a holistic overview of manufacturing processes as well as a detailed analysis of data. Wiz 4.0 lays the foundations of an industry-defining software to grant deep insight into the inner relationships between process variables to the everyday user. The software provides the ability to analyse data using a variety of machine learning algorithms and plot the data in high dimensional space through the innovative no-code platform hosted on the Siemens MindSphere. This approach is set to revolutionise the value creation of data in the new IoT and smart factory paradigms emerging from the transition towards I4.0.
Predictive Maintenance in Industry 4.0 using Machine Learning
Poor maintenance regimes often contribute to unplanned downtime, quality defects and accidents, thus it is crucial to apply an effective maintenance strategy to achieve an efficient and safe process. Industry 4.0 has brought about a proliferation of digital data and with it, new opportunities to advance and improve the way maintenance activities are planned. Here, we propose a novel methodology that utilises machine learning to predict the maintenance of both faults and the repair time and uses this to underpin the scheduling of maintenance activities. This can be used to plan maintenance, optimising the schedule for cost within the constraints of labour availability and plant layout. When applied to simulated data, using a simulated Fischertechnik (FT) model, this methodology reduces overall plant maintenance costs by reducing unplanned downtime and increasing maintenance efficiency. This work provides a promising first step toward improving the way maintenance tasks are approached in Industry 4.0.
A flowchart of an ensemble of machine learning techniques used to produce an optimum maintenance schedule
Find out more about our research themes below!
Find out more about our research themes below!
Shows schematic of ensemble machine learning and optimisation used to extract relevant compositional information from raw data, predict Jominy End Quench Test results, and suggest most optimal compositional arrangement based on historical market data for the prices of alloying elements.
Physical Property Prediction in the Steel Industry
Prediction of relevant mechanical properties is vital for the steel industry and for materials engineering. This work investigates a novel method to predict the Jominy hardness curve of a single grade of steel based solely on the composition of the alloying elements. Further consideration is given to the optimisation of the chemical profile of the alloying elements with respect to the overall cost. The ultimate aim of the work is to be able to reduce spending on alloying elements by offering cheaper methods of achieving the same Jominy profile based upon accurate prediction. A feature auto-encoder neural network is employed to reduce the dimensionality of 19 alloying elements from some 1000 steel samples to extract relevant property information. Gaussian Process (GP) regression is used to predict the Jominy hardness profile of the steel up to a depth of 10mm. Results showed that a GP fed with extracted features was able to predict Jominy hardness profiles with high accuracy (RMSEunseen= 1.35, MAPE = 3.3%). Constrained optimisation using the GP model, and historical market data for the price of alloying elements showed that an average cost reduction of 18% on alloying element spend is possible. This work shows not only that novel machine learning approaches can be used to accurately predict mechanical properties of a given grade of steel, but crucially that use of these in conjunction with optimisation can generate considerable financial savings.
Past grants as an Industrialist working with Academics
Past grants as an Industrialist working with Academics
EP/R008027/1 From Kinetic Theory to Hydrodynamics: re-imagining two-fluid models of particle-laden flows
EP/N034066/1 A new multi-scale paradigm for particulate flow with Prof R Ocone
European 6th Framework grants & Danish Grants with CAPEC consortia and Prof R Gani and Prof SB Jørgensen
EPSRC Grant with Prof CS Adjiman, Prof A Galindo, Prof G Jackson SAFT approach for surfactants
EPSRC Grant with Prof CS Adjiman Solvent selection to improve reaction rates 2002-2005
Various funding for research from ICI/ Astra Zeneca/ Syngenta as the Industrial Collaborator
GR/T17595/01 Demonstration of the commercial potential of advanced molecular thermodynamics software Prof CS Adjiman, Prof C Pantelides, Prof G Jackson Prof A Galindo
GR/L63020/01 Batch Route Innovation Technology Evaluation and Selection Techniques (BRITEST) also DTI Funding
Case Award with Prof CS Adjiman as a new academic
EPSRC Changing the Culture: To report for the EPSRC on the Strategy and Structure of Chemical Engineering Research in the USA July 1999 C Axon, Prof Howell, JL Cordiner et al.
Your Essential Thesis Guide
The attached file (Link) is your go-to resource for understanding and executing various aspects of your thesis work. For a strong beginning, I recommend that you start by reading Section 1, which covers "What is a Literature Review and How to Conduct It." In this section, you will find detailed explanations, useful tips, and links to external websites and videos that will aid in your literature review process. I encourage you to visit these websites and watch the videos as they provide valuable insights.
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