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Vacancies
For all vacancies please email Professor Joan Cordiner (j.cordiner@sheffield.ac.uk)
Current PhD and postdoctoral positions available
We are currently recruiting for the following open Postdoctoral Research Associate positions:
We are currently recruiting for the following open PhD positions:
Self-funded or to apply for university or other scholarships
Resilience for UK manufacturing. (Self funded)
Hydrogen Process Safety for New Processes for a sustainable world
Automating a New Model for Risk Management for Industry. (Self Funded)
Additional PhD and postdoctoral positions are available
We are always keen to support people who are passionate about the development of novel modelling methodologies to improve industry. Prospective PhD and postdoctoral researchers are encouraged to send an expression of interest along with their CV to Professor Joan Cordiner (j.cordiner@sheffield.ac.uk).
There are a number of scholarship schemes through which prospective PhD researchers can apply for PhD funding, as well as schemes to support postdoctoral and research fellows. We will gladly support talented individuals in these applications for projects which align with the broad research themes below.
Prospective PhD and postdoctoral researchers who have already secured funding are also very welcome to propose their own research ideas
Available Masters Projects:
Process Safety and Loss Prevention in Hydrogen Production, Storage, and Transportation for the Net Zero Energy Economy
The transition to a net-zero energy economy requires a fundamental shift towards cleaner energy sources, where hydrogen plays a pivotal role. Hydrogen is a promising energy carrier, but its safe production, storage, and transportation are critical factors in achieving sustainability and mitigating risks. This MSc thesis aims to investigate and improve process safety aspects in hydrogen-related processes. The research will encompass the following key areas:
1.Hydrogen Production: Analysing the safety measures and protocols in hydrogen production processes, with a focus on electrolysis and reforming methods, to ensure safe and efficient hydrogen generation.
2.Hydrogen Storage: Evaluating various hydrogen storage methods, including gaseous, liquid, and solid-state storage, to identify potential safety risks and develop strategies for their mitigation.
3.Hydrogen Transportation: Investigating the safe transportation of hydrogen, including pipelines, cryogenic carriers, and hydrogen-powered vehicles, to ensure the secure distribution of hydrogen within the net-zero energy economy.
This research will include a combination of literature review, safety assessment, and the development of safety-enhancing protocols and technologies, all within the context of the emerging net-zero energy paradigm.
Enhancing Safety in Nuclear-Powered Green Hydrogen Production: A Process Safety Approach
This project is aimed at addressing critical safety concerns associated with the production of green hydrogen powered by nuclear electricity, a cutting-edge technology with the potential to revolutionize clean energy production. Students engaging in this project will delve into various aspects of safety engineering and risk management within the context of nuclear-powered hydrogen generation.
Risk Interconnections in Engineering Systems
The impacts of emerging hazards like climate change on engineering systems are multifaceted and interconnected, posing a growing challenge for risk management and resilience. This MSc thesis research seeks to investigate the intricate interdependence of risks within engineering systems. The study will encompass the following key aspects:
Risk Assessment: Analysing the evolving risks associated with different hazards including climate change, extreme weather events, rising temperatures, and sea-level changes, and how these risks affect engineering systems.
Interconnected Systems: Investigating how risks propagate through interconnected engineering systems, such as energy, transportation, and water supply networks, and identifying potential cascading effects.
Resilience Strategies: Developing strategies to enhance the resilience of engineering systems by considering the interdependence of risks, including the integration of adaptive technologies, policies, and risk-based decision-making.
The research will combine empirical data analysis, modelling, and simulation to explore the complex dynamics of risk interdependence.
Methods for the evaluation and optimization of the resilience in process industry
The uncertain nature and extent of hazards emerging in climate change and post-pandemic era highlights, once again, the required attention to the properties of resilience of systems, in the face of large uncertainties. In contrast to the concept of risk, resilience is focused also on the ability to prepare and recover quickly from an accident or disruptive event, which may be known or unknown. Managing for resilience, then, requires ensuring a system's ability to plan and prepare for the potential occurrence of accidents and disruptive events, and then absorb, recover, and adapt in case of occurrence.
This research aims at proposing innovative resilience assessment methodologies that account for the uncertainty. The objective of the research is to study and develop advance metrics, methods and frameworks for the balanced resilience and efficiency optimization of process plants, considering the large uncertainties. The resilience of the process plants can be quantified considering different disruptive events including natural hazards, pandemic, etc.
Resilience Assessment of Process Industries to NaTech Events in the Face of Climate Change
The increasing frequency and intensity of climate change-related events pose significant challenges to industrial facilities located in vulnerable areas. Natural and Technological (NaTech) events, such as floods, storms, heatwaves, and wildfires, can lead to hazardous situations, including the release of toxic substances, explosions, and fires. Assessing and enhancing the resilience of process facilities against NaTech events in the context of climate change is crucial for ensuring the safety of both workers and the surrounding environment. This master thesis aims to investigate the resilience of process facilities to NaTech events in the face of climate change and develop an assessment framework to guide decision-making and risk management strategies.
Risk Assessment of High-Impact, Low-Probability Extreme Weather Events in the Face of Climate Change
Climate change has led to an increase in the frequency and intensity of extreme weather events worldwide. While some of these events occur regularly, others, known as high-impact, low-probability events, have severe consequences but are infrequent. Understanding the risks associated with these events is crucial for effective disaster preparedness and climate change adaptation strategies. This research proposal aims to investigate the risk assessment of high-impact, low-probability extreme weather events in the context of climate change.
Application of Dynamic Bayesian Network in HAZOP Study: Enhancing Hazard Identification and Risk Assessment
HAZOP study is a widely used technique for identifying potential hazards and risks in industrial processes. However, HAZOP study is often limited by the complexity and uncertainty of the process variables and their interactions, which can make the analysis challenging and prone to errors. To address these challenges, Dynamic Bayesian Network (DBN) has been proposed as a powerful tool for probabilistic modeling and risk analysis. The purpose of this research is to investigate the application of DBN in HAZOP study and evaluate its effectiveness in enhancing hazard identification and risk assessment.
HAZOP Using Multi-criteria Decision Making (MCDM) and Fuzzy Logic
HAZOP (Hazard and Operability) is a widely used technique for identifying and evaluating potential hazards in industrial processes. However, traditional HAZOP analysis is limited in its ability to consider multiple criteria simultaneously and often relies on expert judgment. To overcome these limitations, the proposed research aims to investigate the potential use of Multi-Criteria Decision Making (MCDM) and Fuzzy logic to enhance the HAZOP methodology.
Application of Natural Language Processing and Machine Learning to Reverse HAZOP and Validate Existing Study
HAZOP (Hazard and Operability) study is a widely used method for identifying and assessing potential hazards and operability issues in industrial processes. The study involves a team of experts who analyse the process design, identify potential deviations from the intended operation, and assess their consequences. HAZOP study is a critical step in ensuring the safety and reliability of industrial processes. However, the HAZOP study process is complex and may not always identify all potential hazards and operability issues. Natural Language Processing (NLP) and Machine Learning (ML) can be used to reverse the HAZOP study process and validate the existing study by identifying potential deviations that were not previously identified.
Incorporating Resilience into HAZOP: Enhancing Process Safety in Industrial Systems
Industrial processes play a crucial role in our modern society, but they also pose significant risks if not adequately managed. Hazard and Operability (HAZOP) analysis is a widely adopted technique for identifying and mitigating process-related hazards. However, traditional HAZOP methods primarily focus on identifying failures and their consequences, often overlooking the aspect of system resilience. This thesis proposal aims to explore the integration of resilience concepts into the HAZOP framework to enhance process safety and operational performance.
MEng Machine learning and structural activity relationship modelling of EC formulations with Joan Cordiner and Syngenta
The proposal is to model mixtures that can form stable Emulsion Concentrate (EC) formulations using machine learning from a dataset produced by Syngenta using Artemus (Robot). The ability to model this complex multi-dimensional problem cannot be currently completely solely using mechanistic models alone. This proposal would take our machine learning methodologies, expertise and state of the art from the literature and adapt them to model the data of a designed experimental dataset of formulation mixtures that cover the breadth of EC mixtures of interest to Syngenta. This currently has not been done to date however we have had success with for example metal organic frameworks in eutectic mixtures and many other datasets there is scope to discover how we can use machine learning in development of formulation design. The plan is to start with EC's as a simple formulation type to demonstrate the technology and to learn how best to adapt the methodology. You would need to be able to learn programming and be interested in learning machine learning techniques. Has the possibility of becoming an iCase fully funded funded PhD in collaboration with Syngenta
MEng Climate Risk and Vulnerability Assessment (CRVA) for resilient process industry (Framework development not modelling) with Joan Cordiner and Seyed Mojtaba Hosenyi
Due to the climate change, extremes in weather and more frequent natural hazards are imposing every industry to added risks. Consequently, the communities and industries are and will be facing severe and more frequent hazards such as floods, wildfires, droughts and heat waves while they are not well prepared to plan and adjust for the potential occurrence, and then absorb, recover, and adapt. Therefore, resilient process plants should be designed to withstand current and future extreme environmental (climate) conditions. In particular, there is a need to proactively identify procedures that systematically consider different climate conditions into the risk and vulnerability assessments in process industry trying to make them more resilient to increasing climate hazards. This research aims at proposing innovative framework for Climate Risk and Vulnerability Assessment (CRVA) in process industry. The framework helps to document climate hazards that could harm the safety of process plants, decide which situations to avoid, and come up with feasible solutions to reduce climate-related risks and increase resiliency. The research framework includes five steps to reach resilience by conducting climate & risk vulnerability assessment. These steps are: • Explore hazards • Assess vulnerabilities and risk • Investigate options• Prioritize and plan
MEng Predictive maintenance for chemical plants using machine learning with Joan Cordiner and Louis Allen
This project looks at quantitatively assessing the risk of missed maintenance activities on plant performance, plant health, and safety for process manufacturing facilities. Often, in planned maintenance periods there is insufficient time or labour availability to complete all the planned activities, which begs the question which actions must be done, and which can be missed. This project looks to directly answer this question by simulating different scenarios to forecast the impact of missing planned activities. The ultimate goal is to assign a priority to each maintenance task such that plant health and operator wellbeing are maintained, even when under time pressures to bring the plant back online.
Requirements for student: - Knowledge of Python programming language including standard frameworks (Numpy, Pandas, Scipy) - Ideally someone with experience with machine learning, or someone open to learning.
MEng or MSc Methods for the evaluation and optimization of the resilience in process industry with Joan Cordiner and Seyed Mojtaba Hosenyi
The uncertain nature and extent of hazards emerging in climate change and post-pandemic era highlights, once again, the required attention to the properties of resilience of systems, in the face of large uncertainties. In contrast to the concept of risk, resilience is focused also on the ability to prepare and recover quickly from an accident or disruptive event, which may be known or un-known. Managing for resilience, then, requires ensuring a system's ability to plan and prepare for the potential occurrence of accidents and disruptive events, and then absorb, recover, and adapt in case of occurrence. This research aims at proposing innovative resilience assessment methodologies that account for the uncertainty. The objective of the research is to study and develop advance metrics, methods and frameworks for the balanced resilience and efficiency optimization of process plants, considering the large uncertainties. The resilience of the process plants can be quantified considering different disruptive events including natural hazards, pandemic, etc. Objective of the research: Literature review, methodology investigation, development, and case study examination, with software implementation of the method explored. Knowledge of Python or MATLAB programming language is required.
MEng: Climate change impact on the risk assessment of process plants with Joan Cordiner and Seyed Mojtaba Hosenyi
Systems, Structures and Components (SSCs) of process plants should be designed to withstand current and future environmental (climate) conditions. With the impacts of climate change on creating more frequent extreme events, there is a need to proactively identify procedures that systematically consider different climate conditions into the risk assessments of process plants, because these will affect not only their efficiency but also their safety. This research aims at proposing innovative risk assessment methodologies that account for the uncertainty affecting the climate projections models for different natural hazards, such as the increase of air temperature, sea level, wind speed, etc. We aim at i) assessing the reliance on the prediction models and ii) exploring ways of combining model uncertainty to gather confidence in the climate projections and in the ultimate risk assessment. The challenges arising from the activity will be tackled for solving the risk assessment problem under climate change of a process safety plant connected with and dependent on the renewable plants, that are also to be modelled. Objective of the research: Literature review, methodology investigation, development, and case study examination, with software implementation of the method explored. Knowledge of Python or MATLAB programming language is required.
MSc Reverse Hazop or Hazid. With Joan Cordiner and Seyed Mojtaba Hosenyi Automated hazop or hazid is very helpful for inexperienced teams and to be a quality check on a team. Therefore a reverse hazid is the ability to look at cases in an existing hazid and give a quality evaluation and look for scenarios not picked up. This will be building on work by previous students who have developed some automated scenarios and mitigations and a tool from the American Institute of Chemical Engineering and Dow partnership. A framework can then be developed for this to be automated and for recommendations for mitigations that would be recommended. This can be done with modelling or a written framework perspective dependent on the interested candidate.
MSc Predicting Safety Critical Properties Many critical safety properties are not available and need measurement. In addition mixtures can have a huge impact on properties. This project will be to look at what properties can be predicted in the literature. Then look at what else could be predicted, mixtures or analogous materials. This will use property prediction software develop by Joan and her collaborators.
MSc Lithium battery recycling : Reviewing the different processes for recycling of lithium ion batteries following on from a previous student . Process safety issues with recycling of lithium ion batteries and potential solutions/ alternatives for process safety improvement/ mitigations.
MSc Process Safety improvement through potential problem prevention with Data analysis Supervisor: Prof Joan Cordiner Project Description: Using plant data to spot where there are trends, issues that could lead to a process safety issue and provide solutions. Program process knowledge, machine learning and decision making into a tool to analyse data from the pilot plant and produce real time catch of issues and proposed solutions. Learning outcomes: Learn about analysig data to look for trends, problems. Connecting process safety issues to plant data, machine
MSc Process Safety -Building in Resilience Supervisor: Prof Joan Cordiner and Seyed Mojtaba Hosenit- Project Description: Looking at resilience work that has been done in my group and externally- develop a toolkit/ methodology :to help process development & process safety personnel to aid decision making in building in resilience ( from Net Zero, CCS, Pandemics, natural disasters- 1 or more of these). Learning outcomes: Learning about resilience, How to impact improved PS due major changes/ challenges the world will be facing over the next decade or two.