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Chemistry
Phone:
07805807508
Email:
M.G.HassanSayed@soton.ac.uk

Dr Mohamed Galal Hassan Sayed  PhD, MSc, BSc

Associate Professor of Chemical and Sustainable Engineering

Dr Mohamed Galal Hassan Sayed 's photo

Experienced academic with a demonstrated history of working in higher education and industry for over 20 years. Skilled in Chemical and Petroleum systems, a Chartered Energy Engineer and a Chartered Engineer. Strong engineering professional with extensive publications and several book contributions. Holds a PhD in Chemical Engineering from Loughborough University.

Chemical Engineering at Southampton is a “comprehensive chemical engineering course that is very much focused on the industries of the future”

Qualifications

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Research interests

Sustainable energy engineering in the form of:

  • Reactions and catalysis of algae, biofuels, biomass, reforming 
biofuels (biodiesel, pyrolysis oil upgrading), and heavy oil
  • Gas production and storage reservoirs
  • Multi-criteria decision making based on value of information using artificial intelligence and machine learning for thermodynamic predictions

Research group

Functional Inorganic, Materials and Supramolecular Chemistry

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Undergraduate Director of Programmes in Chemical Engineering

Engaged in teaching modules on:

  • Thermofields,
  • Mass and heat transfer,
  • Principles of Chemical Engineering.

Challenges in technical and economical modelling & simulation of shale gas reservoirs

Gas adsorption-desorption is frequently practised for the measurement of surface area, pore size, pore size distribution, and porosity. These are important for optimizing their use in the practical applications of nanomembranes based upon polymer blends, as well as understanding of the adsorption mechanism of fluids in materials with highly ordered pore structures. 

The Energy Information Administration (EIA) estimated that globally 30% of the total natural gas is found in shale formation. The USA is now the world’s largest producer of gas, with 20% of the world’s total supply, 40% of which is derived from shale. The British Geological Survey (BGS) assessment shows that in northern England alone there are about 40 trillion cubic metres of shale gas. By tapping only 10% of the UK reserves, the country could be powered for the following 50 years.

Shale gas exploration has played an important role in providing an alternate energy source to our world. Previously thought unexplorable due to the technical challenges in producing it from the tight permeability zones, advancement in technology such as hydraulic fracturing has allowed large production of gas, thereby helping to reduce overall dependence on conventional oil and gas resources.

Despite the advances made in producing from shale, large quantities of the gas still remain unrecovered. This is due to the fracturing mechanism targeted towards the compressed gas stored within the shale matrix. These compressed gases make up about 15-20% of the total gas storage in shale. The rest is made up entirely of adsorbed gas. Therefore, production of gas through hydraulic fracturing tends to under-produce the vast amount of gas in shale, with most shale gas experiencing very fast decline once it is put on production.

To improve on the production of gas through shale and ultimately maximise the benefit for society (ensuring a secure energy supply to society and consequently lower energy prices), whilst also ensuring that environmental impacts are minimised,  my research aims to provide a framework to further explore the concept of improving gas production by:

  • Examining numerical studies that involve the use of thermal technology as a heat source. Temperature-dependent adsorption models are used in place of pressure-dependent adsorption models, such Langmuir isotherms, in numerical studies to allow for theoretical investigation of thermal application in shale gas exploration.
  • Developing novel methods to quantify Total Organic Carbon (TOC), which is an essential property needed to have a productive shale gas reservoir; accurate characterisation of TOC is critical in evaluating the potential of shale gas reservoirs because it closely relates to the amount of kerogen and therefore total gas content.
  • Assessing Estimated Ultimate Recoverable (EUR) values for the shale formations using empirical decline curve methods like the Arp's hyperbolic, modified Arp's hyperbolic and Doung's method, therefore overcoming the uncertainty regarding the accurate determination of EUR in shale gas reservoirs. In addition, we are investigating the economic viability of wells over time.
  • Using Artificial intelligence and machine learning to predict and develop the geometry of fracture and its propagation from the geo-chemical and geo-mechanical properties of the geological formation.

Intelligent prediction, monitoring and decision-making in the chemical and energy industries

The research aim is to create a decision-making system to investigate ways to alleviate risks in Chemical and Energy Industries.  A predictive system based on trend analysis of historic data to determine the probable future condition of trait ‘states’ and so predict potential situations that may lead to catastrophe.

The system will use the -as is- model to develop Multi Criteria/ Machine Learning Algorithms in combination with an Ensemble model to predict the probable future condition of trait ‘states’ and so predict potential situations that may lead to catastrophe based on the following factors:

  • Work is required to develop methods and tools to suit operational decision-making in the chemical and energy industry. Practical guidance for operational and emergency decision-making is needed.
  • There is a need to focus on the expression of risk types, to review and find suitable and robust risk measures to express each risk type, especially the ones that current risk measures are not suitable for in terms of expected loss.
  • A systematic risk-based approach to determine the threshold values for sensor function groups needs to be developed Further study is needed on sensor fault diagnosis to enhance the ability of the first-level of the Intelligent Alarm Management Framework. More cases need to be studied, especially in real petrochemical plants, to validate and optimize proposed strategies.
  • Further study in the area of sensor network fusion is needed, extending the multi-agent architecture to support redundancy to achieve more reliability and robustness of an intelligent alarm system. This will also highlight the ability of agents to learn from interaction with users.
  • Further investigation is necessary to understand the reasons and risk factors behind why various challenges exist inside the construction organization, particularly between senior management and labourers
  • There is a need to discuss why the importance of Human Factors (HF) work has not been sufficiently prioritized in practice from safety authorities, management and engineering and how to mitigate this technology slant to support a more balanced collaboration between technology/automation and HF.
Dr Mohamed Galal Hassan Sayed
Student Office, Building 59, Room1201, University of Southampton, Southampton SO17 1BJ

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