Welcome from the Computational Modelling Group

A picture showing several members of the CoMo Group

Welcome to the website of the CoMo Group. We develop and apply modern numerical methods to problems arising in Chemical Engineering. The overall aim is to shorten the development period from research bench to the industrial production stage by providing insight into the underlying physics and supporting the scale-up of processes to industrial level.

The group currently consists of 21 members from various backgrounds. We are keen to collaborate with people from both within industry and academia, so please get in touch if you think you have common interests.

The group's research divides naturally into two inter-related branches. The first of these is research into mathematical methods, which consists of the development of stochastic particle methods, computational fluid dynamics and quantum chemistry. The other branch consists of research into applications, using the methods we have developed in addition to well established techniques. The main application areas are reactive flow, combustion, engine modelling, extraction, nano particle synthesis and dynamics. This research is sponsored on various levels by the UK, EU, and industry.

Markus Kraft's Signature
Markus Kraft - Head of the CoMo Group

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Preprint 269 published

12 April 2021
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Preprint 269, "The role of oxygenated species in the growth of graphene, fullerenes and carbonaceous particles", has been published!


The growth of carbonaceous materials was studied using a Kinetic Monte Carlo model that captures the growth and oxidation of six-member and partially-embedded five-member rings. A novel algorithm was used to resolve the migration of partially-embedded five-member rings around the edges of molecules. Circumcoronene molecules were grown at 1500 K and 1 atm in the presence of varying mole fractions of atomic and molecular oxygen and constant mole fractions of hydrogen and acetylene. The parameter space of the study covered the mole fraction of atomic and molecular oxygen in the ranges: 10-8 ≤ XO ≤ 10-1 and 10-6 ≤ XO2 ≤ 10-1. Four regions of carbon growth associated with different carbonaceous products were identified. Graphene was formed in the presence of high mole fractions of atomic oxygen (10-4 < XO ≤ 10-2). Fullerenes were formed in the presence of low mole fractions of atomic oxygen and high mole fractions of molecular oxygen (XO ≤ 10-4 and 10-2 < XO2 ≤ 10-1). Low mole fractions of both atomic and molecular oxygen (XO ≤ 10-4 and XO2 ≤ 10-2) resulted in structures that became curved as time progressed. The highest mole fractions of atomic oxygen (XO > 10-2) produced small structures due to oxidation of the molecules. The production and consumption of partially-embedded five-member rings appear to explain the formation of the observed structures. The oxidation of partially-embedded five-member rings leaves behind armchair sites that grow to form large and flat structures that resemble graphene. Formation and subsequent embedding of partially-embedded five-member rings result in curved structures that resemble fullerenes.

Preprint 267 published

15 February 2021
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Preprint 267, "Self-assembly of curved aromatic molecules in nanoparticles", has been published!


The self-assembly and structure of nanoparticles containing curved polycyclic aromatic hydrocarbon molecules (cPAHs) are investigated using molecular modelling. These polar fullerene-like molecules are receiving increased attention recently due to the steric and electronic properties caused by the inclusion of five-membered ring(s) within their hexagonal lattice. In this work, the curPAHIP potential is extended to describe the interactions between large cPAHs. It is then used within molecular dynamics simulations to produce nanoparticles containing cPAHs. Structural and energetic metrics, including diameter, density, intermolecular spacing, coordination number, alignment angle, radial distance, and energy value, are used to analyse systems containing cPAHs of different sizes and ratios, and containing flat PAHs or ions. Homogeneous cPAH particles are more tightly packed than their flat PAH counterparts, with large cPAHs displaying stacked columnar configurations absent in nanoparticles containing small cPAHs. Mixing cPAHs of different sizes disrupts the ordered mesophase and forms a core-shell structure in which the larger molecules make up the core and the smaller molecules comprise the shell, although this partitioning is less distinct compared to flat PAHs. In addition, the presence of flat PAHs and ions within cPAH nanoparticles promotes distinct arrangements dominated by weak dispersive interactions and strong electrostatic interactions, respectively.

Preprint 266 published

25 January 2021
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Preprint 266, "A Question Answering System for Chemistry", has been published!


This paper describes the implementation and evaluation of a proof-of-concept Question Answering system for accessing chemical data from knowledge graphs which offer data from chemical kinetics to chemical and physical properties of species. We trained a question type classification model and an entity extraction model to interpret chemistry questions of interest. The system has a novel design which applies a topic model to identify the question-to-ontology affiliation. The topic model helps the system to provide more accurate answers. A new method that automatically generates training questions from ontologies is also implemented. The question set generated for training contains 80085 questions under 8 types. Such a training set has been proven to be effective for training both the question type classification model and the entity extraction model. We evaluated the system using the Google search engine as the baseline. We found that it can answer 114 questions of interest that Google or Wolfram alpha can not give a direct answer to. Moreover, the application of the topic model was found to increase the accuracy of constructing the correct queries.

Preprint 265 published

19 January 2021
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Preprint 265, "ElChemo: A Cross-Domain Interoperability in a Chemical Plant", has been published!


In this paper we propose a novel framework capable of establishing machine-to-machine (M2M) interactions between chemical and electrical systems in the industry. The semantic framework termed as ElChemo addresses the challenges in M2M interaction of entities from different silos, such as differences in the domains’ behaviour, the heterogeneities arising from different vocabularies and software. The OntoTwin ontology has been developed based on OntoPowSys and OntoEIP ontologies, which are parts of an intelligent platform called the "J-Park Simulator (JPS)". The ElChemo framework uses Description Logic (DL) and SPIN reasoning techniques to establish the interaction between the chemical and electrical systems in a plant. As use-case we study a depropaniser section of a chemical plant and its corresponding electrical system as a use case scenario to demonstrate the interoperability between the two silos within the ElChemo framework. The results indicate that the proposed approach can achieve significant economic benefits.

Preprint 264 published

18 December 2020
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Preprint 264, ''The National Digital Twin of the UK – a knowledge-graph approach'', has been published!

It is widely recognized that combatting climate change will require massive changes in infrastructure and energy systems. Digitalization will offer new ways to design and operate infrastructure and will form an important part of the response to this challenge. Digital twins composed of multiple interacting distributed entities that share data and combine to answer complex questions have been identified as a key aspect of this move towards digitalization. However, it is far from clear how to implement such a digital twin. This remains a critical area of research. In this paper we propose a solution to the implementation problem based on a dynamic knowledge graph developed as part of the World Avatar project. We present the concept and preliminary evidence from case studies in Singapore.

Preprint 263 published

12 November 2020
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Preprint 263, ''Carbonaceous nanoparticle formation in flames'', has been published!


The route by which gas-phase molecules in hydrocarbon flames form solid carbonaceous nanoparticles is reviewed. These products of incomplete combustion are introduced as particulates and materials revealing both their useful applications and unwanted impacts as pollutants. Significant advances in experimental techniques in the last decade have allowed the gas phase precursors and the transformation from molecules to nanoparticles to be directly observed. These measurements combined with computational techniques allow for various mechanisms known to date to be compared and explored. Questions remain surrounding the various mechanisms that lead to nanoparticle formation. Mechanisms combining physical and chemical routes, so-called physically stabilised soot inception, are highlighted as a possible "middle way" with reactive aromatics activated by hydrogen of particular interest.

Preprint 262 published

10 November 2020
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Preprint 262, ''Automated calibration of a poly(oxymethylene) dimethyl ether oxidation mechanism using knowledge-graph technology'', has been published!


In this paper, we develop a knowledge-graph based framework for the automated calibration of combustion reaction mechanisms and demonstrate its effectiveness on a case study of poly(oxymethylene) dimethyl ether (PODEn, where n=3) oxidation. We develop an ontological representation for combustion experiments, OntoChemExp, that allows for the semantic enrichment of experiments within the J-Park Simulator (JPS, theworldavatar.com), an existing cross-domain knowledge-graph. OntoChemExp is fully capable of supporting experimental results in the Process Informatics Model (PrIMe) database. Following this, a set of software agents are developed to perform experimental results retrieval, sensitivity analysis, and calibration tasks. The sensitivity analysis agent is used for both generic sensitivity analyses and reaction selection for subsequent calibration. The calibration process is performed as a sampling task followed by an optimisation task. The agents are designed for use with generic models but are demonstrated with ignition delay time and laminar flame speed simulations. We find that calibration times are reduced while accuracy is increased compared to manual calibration, achieving fittings 92% more accurate. Further, we demonstrate how this workflow is implemented as an extension of the JPS.

Another blockchain paper wins Highly Cited Paper Award

19 October 2020
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Applied Energy logo

Our paper

has been awarded a Highly Cited Paper Award by the journal Applied Energy.

Last time we won this award, it was also for a blockchain paper.

Supercharging decarbonisation through intelligent technologies

14 February 2020
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Integrating digital tools into the world’s energy systems could reduce carbon emissions by more than 50 per cent, a new review has found.

The review re-assesses the famous marginal abatement cost curve (MACC) popularised by McKinsey and finds that digitalisation of energy systems completely alters the curve, thanks to the creation of novel pathways for the transition to low-carbon energy. If cyber-physical systems are integrated into our energy systems, carbon abatement potential can be expected to increase by 20 per cent, rising to 30 per cent when artificial intelligence (AI) is included.

MACCs illustrate both the cost and potential of various carbon dioxide reduction strategies and are used by policy makers to assess which paths to pursue. The addition of cyber-physical systems –digital technologies that interact with the physical world – is a substantial update to the MACC and further establishes it as an indispensable tool for those working on decarbonisation.

Decarbonising the world’s energy systems is a crucial part of mitigating climate change through the reduction of greenhouse gas emissions. While decarbonisation is non-negotiable if climate breakdown is to be halted, it must be balanced with ensuring economic stability and a smooth transition to sustainable energy.

Digital technologies such as big data, machine learning and the Internet of Things hold immense potential to help us meet this challenge. Their applications range from helping to reduce our power bills by employing smart meters in the home, to assisting with peer-to-peer energy trading between power stations via blockchain.

An international team of researchers from Singapore, Switzerland, the UK and the US found that while existing digital technologies have numerous and effective applications when considered individually, the potential reduction of carbon emissions is multiplied when they are combined. Such combinations are called cyber-physical systems – interacting networks of physical infrastructure and computers that allow for smarter analysis, decision-making and optimisation of energy systems.

Introducing AI into these cyber-physical systems can lead to further carbon savings; up to 30 per cent more than without AI. This combination of technologies creates what is dubbed “intelligent cyber-physical systems”. Benefits include more resilient infrastructure and operational flexibility, among others.

Enhanced renewable energy forecasting is one good example of how an intelligent cyber-physical system can be applied. The wind and solar energy sectors have seen much growth and while the price of these technologies has come down, the intermittent nature of this type of power has limited its application. The integration of backup energy systems (natural gas plants, for example) or energy storage technologies is required. Intelligent cyber-physical technologies, in particular machine learning, could help with this integration through improved forecasting of solar and wind variability.

Other large energy systems such as power plants can also benefit. When applied to carbon capture and storage plants, for example, these technologies can convert operational data into actionable intelligence, thereby reducing costs and improving energy efficiency through improved processes.

Cyber-physical systems, especially those combined with AI, provide the much-needed boost required for countries to meet their decarbonisation and emissions targets. It is now up to policy makers to take this forward by incentivising the deployment of these technologies to combat climate change.

“The Impact of Intelligent Cyber-Physical Systems on the Decarbonization of Energy” (DOI: 10.1039/c9ee01919g) is published in Energy & Environmental Science by researchers from University of Cambridge, Cambridge Centre for Advanced Research and Education in Singapore Ltd, Swiss Academy of Sciences, Princeton University, National University of Singapore and Nanyang Technological University.

CoMo group open to Feodor Lynen Research Fellows

23 January 2020
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In 2016, Prof. Markus Kraft was awarded the Friedrich Wilhelm Bessel Award and is therefore eligible to host Feodor Lynen Research Fellows sponsored by the Alexander von Humboldt Foundation. The Feodor Lynen Research Fellowship covers the salary and travel expenses of researchers from Germany to work at the host institution for 6-24 months. In addition, the fellowship enables the successful candidate to apply for alumni sponsorship from the Humboldt Foundation after the end of the fellowship and become part of their international network of academics.

If you are interested in working at the University of Cambridge and in joining the CoMo group as a post-doctoral researcher, please check your eligibility on the official Feodor Lynen Research Fellowship website and familiarise yourself with the application procedure. You will need to write a research proposal that aligns with your professional expertise. The topic might be of computational or experimental nature but should lie within the research areas of the CoMo group.