We continually review and update our portfolio of courses to ensure that they meet the needs of industry now and in the future. As part of this review the decision has been taken to withdraw the Energy Informatics MSc from our Energy and Power programme. The course has been replaced with Advanced Digital Energy Systems MSc, which incorporates digital tools such artificial intelligence (AI) and blockchain technology to reflect the energy sector’s move towards a sustainable, digital future.

Governments around the world have set ambitious targets to significantly reduce CO2 emissions. To achieve this, we need professionals with modern skills in data analytics and energy engineering. Energy informatics is an emerging discipline that utilises powerful tools data analytics tools to solve energy supply problems. Through this course, our dedicated state-of-the-art facilities, unique postgraduate-only environment and up-to-date course content you will develop the professional informatics skills required to be at the forefront of the industry and to support the net zero targets.


  • DurationOne year full-time, two-three years part-time
  • DeliveryTaught modules 40%, group project 20% (or dissertation for part-time students), and individual project 40%
  • QualificationMSc, PgDip, PgCert
  • Study typeFull-time / Part-time
  • CampusCranfield campus

Who is it for?

This course is suitable for computer science, mathematics, engineering and information technology graduates and practising IT engineers wishing to pursue a technical management career in the energy industry sector. It provides professional engineers and scientists with the multidisciplinary skills and ability to manage and analyse current and future energy engineering problems.

Your career

The international nature of this growing field allows Cranfield graduates to develop diverse and rewarding careers all over the world in industry, government or research.

Example careers include:

  • Energy Analyst – Data Science,
  • Offshore Energy Analyst,
  • Energy and Sustainability Analyst,
  • Research Analyst - Energy.

Cranfield Careers Service

Cranfield’s Careers Service is dedicated to helping you meet your career aspirations. You will have access to career coaching and advice, CV development, interview practice, access to hundreds of available jobs via our Symplicity platform and opportunities to meet recruiting employers at our careers fairs. We will also work with you to identify suitable opportunities and support you in the job application process for up to three years after graduation.

Why this course?

Energy supply is fundamentally important to our homes and workplaces. Future energy supply has to be stable, secure, and not only affordable but sustainable, which makes it a systems engineering problem. 

Due to the growth of sustainable and renewable energy production, energy informatics plays a significant role in managing the world's growing energy demand. Both developed and developing countries are facing great challenges in energy efficiency, reduction of greenhouse gas emissions and enlargements of renewable energy applications. 

The UK Government has set ambitious targets to decrease the greenhouse gas emissions to 80% of today’s by 2050; the China Government has also planned to significantly reduce CO2 emissions to a level of 5,000 million tons in 2050, which is half of current emissions.

  • You will develop professional informatics skills required in the growing energy sector, with essential abilities applicable in both the renewables industry (wind, geothermal and solar) and the traditional energy industry (oil and gas).
  • Students benefit from dedicated state-of-the-art facilities including unique engineering-scale facilities for the development of efficient technologies with low CO2 emissions.
  • The Management for Technology module is run by our world-renowned Cranfield School of Management.

Informed by industry

  • We have a world-class reputation for our industrial-scale research and pilot-scale demonstration programmes in the energy sector.
  • Close engagement with the energy and transport sectors over the last 20 years has produced long-standing strategic partnerships with the sectors most prominent players.
  • The strategic links with industry ensures that all of the material taught on the course is relevant, timely and meets the needs of organisations competing within the energy sector.
  • This industry-led education makes our graduates some of the most desirable in the world for energy companies to recruit.

Course details

The taught programme for the Energy Informatics master's is generally delivered from October to February and comprises eight modules. The modules are delivered over one week of intensive delivery with a second week being free from structured teaching to allow time for more independent learning and reflection.

Water course structure diagram

Students on the part-time programme will complete all of the modules based on a flexible schedule that will be agreed with the Course Director.

Course delivery

Taught modules 40%, group project 20% (or dissertation for part-time students), and individual project 40%

Group project

The group project is an applied, multidisciplinary, team-based activity. Often solving real-world, industry-based problems, students are provided with the opportunity to take responsibility for a consultancy-type project while working under academic supervision. Success is dependent on the integration of various activities and working within agreed objectives, deadlines and budgets. Transferable skills such as team work, self-reflection and clear communication are also developed.

Individual project

The individual project is the chance for students to focus on an area of particular interest to them and their future career.  Students select the individual project in consultation with the Thesis Co-ordinator, their allocated supervisor and their Course Director. These projects provide students with the opportunity to demonstrate their ability to carry out independent research, think and work in an original way, contribute to knowledge, and overcome genuine problems in the industry. Many of the projects are supported by external organisations.


Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff and, as a result, may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.

To give you a taster, we have listed the compulsory and elective (where applicable) modules which are currently affiliated with this course. All modules are indicative only, and may be subject to change for your year of entry.

Course modules

Compulsory modules
All the modules in the following list need to be taken as part of this course.

Risk and Reliability Engineering

Module Leader
  • Dr Dawid Hanak

    This module introduces you to the principles of risk and reliability engineering, and associated tools and methods to solve relevant engineering problems in industry.

    • Introduction and fundamentals of risk management and reliability engineering,
    • Failure distributions: how to analysis and interpret failure data, introduce the most commonly used discrete and continuous failure distributions (e.g. Poisson, Exponential, Weibull and Normal),
    • Risk management process: hazard identification, assessment, evaluation and mitigation (risk acceptance, reduction, ignorance, transfer),
    • Risk assessment techniques: risk matrix, Pareto analysis, fault tree analysis (FTA), event tree analysis (ETA), failure mode and effects analysis (FMEA), failure mode, effects and criticality analysis (FMECA), hazard and operability study (HAZOP),
    • Reliability and availability analysis: system duty cycle, breakdown/shutdown, MTTF/MTBF/MTTR, survival, failure/hazard rate,
    • Reliability analysis techniques: reliability block diagram (RBD), minimal cut-set (MCS), series and parallel configurations, k-out-of-n systems, active and passive redundancies,
    • Introduction to structural reliability analysis: stress strength interference and limit state function, first-order / second-order reliability method (FORM/SORM), damage accumulation and modelling of time-dependent reliability,
    • Identification of the role of inspection and structural health monitoring (SHM) in risk reduction and reliability improvement,
    • Introduction to maintainability and its various measures,
    • Workshops and case studies: work in groups to determine the risk and reliability of subsea production systems, power distribution networks, wind turbines, gas turbines, etc. 
Intended learning outcomes

On successful completion of this module you should be able to:

  • Identify and analyse the concepts and principals of risk and reliability engineering and their potential applications to different engineering problems,
  • Assess and analyse appropriate approaches to the collection and interpretation of data in the application of risk and reliability engineering methods,
  • Evaluate and select appropriate techniques and tools for qualitative and quantitative risk analysis and reliability assessment,
  • Analyse and evaluate failure distributions, failure likelihood and potential consequences, and develop solutions for control / mitigation of risks.

Informatics for the Energy Industry

Module Leader
  • Dr Gill Drew

    This module will introduce you to data and information methodologies to solve problems associated with the design and operation of industrial systems using operational data available.


    Data processing: data acquisition, data uncertainty, outliers and smoothing, statistical analysis,

    Data modelling: parametric and non-parametric modelling, linear and nonlinear modelling, static and dynamic modelling, meta modelling,

    Machine learning: artificial neural networks, feedforward and backpropagation, recurrent neural networks, deep learning,

    Case studies: examples will be chosen from a range of industrial systems including mechanical, chemical and fluid systems.

Intended learning outcomes

On successful completion of this module you should be able to:

  • critically evaluate and systematically select appropriate data processing techniques for industrial applications,
  • systematically design and implement modelling processes to solve energy and industrial problems,
  • comprehensively appraise the key machine learning tools and procedures for industrial applications.

Advanced Control Systems

Module Leader
  • Dr Liyun Lao

    This modules introduces you to the fundamental concepts, principles, methodologies, and application for the design of advanced control systems for industrial applications.

    • System dynamics:
      • modelling of typical physical systems, operating point, linearization, differential equation representation, state space representation of systems, laplace transforms, transfer functions, block diagrams, SISO and MIMO systems, time and frequency domain responses of systems,
    • Feedback control:
      • positive and negative feedback, stability, methods for stability analysis, closed loop performance specification, PID controllers, Ziegler-Nichols, self-tuning methods,
    • Enhanced controllers:
      • cascade control, feedforward control, control of non-linear systems, control of systems with delay,
    • Digital controllers:
      • effects of sampling, implementation of PID controller, stability and tuning,
    • Advanced control topics:
      • hierarchical control Kalman filter, system identification, model predictive control, statistical process control, the use of expert systems and neural networks in industrial control,
    • Design packages for process control systems:
      • examples including Simulink and MATLAB.
    • Case studies:
      • examples will be chosen from a range of industrial systems including mechanical, chemical and fluid systems.
Intended learning outcomes

On successful completion of this module you should be able to:

  • Evaluate and select appropriate modelling techniques for dynamic systems,
  • Formulate control methodologies in feedback, feedforward and cascade loops,
  • Recognise and critically appraise the key design tools and procedures for continuous and discrete controllers of dynamic systems.

Computational Fluid Dynamics for Industrial Processes

Module Leader
  • Dr Patrick Verdin

    This module introduces you to the CFD techniques and tools for modelling, simulating and analysing practical engineering problems with hands on experience using commercial software packages used in industry.

    • Introduction to CFD & thermo-fluids: introduction to the physics of thermo-fluids, governing equations (continuity, momentum, energy and species conservation) and state of the art computational fluid dynamics including modelling, grid generation, simulation, and high performance computing. Case study of industrial problems related to energy, process systems, offshore engineering, and the physical processes where CFD can be used,
    • Computational engineering exercise: specification for a CFD simulation, requirements for accurate analysis and validation for multi scale problems, introduction to turbulence & practical applications of turbulence models, introduction to turbulence and turbulent flows, traditional turbulence modelling,
    • Advanced turbulence modelling: introduction to Reynolds-averaged Navier Stokes (RANS) simulations and large-eddy simulation (LES),
    • Practical sessions: fluid process problems are solved employing the widely-used industrial flow solver software FLUENT. Lectures are followed by practical sessions on single/multiphase flows, heat transfer, to set up and simulate a problem incrementally.  Practical sessions cover the entire CFD process including geometric modelling, grid generation, flow solver, analysis, validation and visualisation.
Intended learning outcomes

On successful completion of this module you should be able to:

  • assemble and evaluate the different components of the CFD process,
  • explain the governing equations for fluid flows and how to solve them computationally,
  • compare and contrast various methods for simulating turbulent flows applicable to mechanical and process engineering,
  • set up simulations and evaluate a practical problem using a commercial CFD package,
  • design CFD modelling studies for use in industrial design of complex systems.

Process Measurement Systems

Module Leader
  • Dr Liyun Lao

    This module introduces you to a systematic approach to the design of measurement systems for industrial process applications. The fundamental concepts, key requirements, typical principles and key applications of the industrial process measurement technology and systems will be highlighted.

    Principles of Measurement System
    • Process monitoring requirements: operating conditions, range, static performance, dynamic performance,
    • Sensor technologies: resistive, capacitive, electromagnetic, ultrasonic, radiation, resonance,
    • Signal conditioning and conversion: amplifiers, filters, bridges, load effects, sampling theory, quantisation theory, A/D, D/A,
    • Data transmission and telemetry: analogue signal transmission, digital transmission, communication media, coding, modulation, multiplexing, communication strategies, communication topologies, communication standards, HART, Foundation Fieldbus, Profibus,
    • Smart and intelligent instrumentation. Soft sensors. Measurement error and uncertainty: systematic and random errors, estimating the uncertainty, effect of each uncertainty, combining uncertainties, use of Monte Carlo methods,
    • Calibration: importance of standards, traceability,
    • Safety aspects: intrinsic safety, zone definitions, isolation barriers,
    • Selection and maintenance of instrumentation.

    Principles of Process Measurement
    • Flow measurement: flow meter performance, flow profile, flow meter calibration; differential pressure flow meters, positive displacement flow meters, turbine, ultrasonic, electromagnetic, vortex, Coriolis flow meters,
    • Pressure measurement: pressure standards, Bourdon tubes, diaphragm gauges, bellows, strain gauges, capacitance, resonant gauges,
    • Temperature measurement: liquid-in-glass, liquid-in-metal, gas filled, thermocouple, resistance temperature detector, thermistor,
    • Level measurement: conductivity methods, capacitance methods, float switches, ultrasonic, microwave, radiation method,
    • Multiphase flow measurement: general features of vertical and horizontal multiphase flow, definition of parameters in multiphase flow, multiphase flow measurement strategies, water cut and composition measurement, velocity measurement, commercial multiphase flow meters, developments in multiphase flow metering,
    • Density and viscosity measurement,
    • Case study: flow assurance instrumentation/ environmental measurement/ measurement issues and challenges in CO2 transportation.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Critically assess the factors affecting the operation of a process sensor and the types and technologies of modern process sensors,
  • Examine the factors which have to be considered when designing a process measurement system,
  • Propose the most appropriate measurement system for a given process application.

Management for Technology

Module Leader
  • Dr Richard Adams

    The importance of technology leadership in driving the technical aspects of an organisation's products, innovation, programmes, operations and strategy is paramount, especially in today’s turbulent commercial environment with its unprecedented pace of technological development. As demand for ever more complex products and services has become the norm, one of the challenges for today’s manager is to deal with uncertainty, to allow technological innovation and change to flourish, whilst also remaining within planned parameters of performance. This module helps to develop your understanding of management processes within an organisational context, so that when you seek employment you are equipped with both the extensive subject/discipline knowledge and the ability to relate it to a management context.

    • Engineers and Technologists in organisations:
      • the role of organisations and the challenges facing engineers and technologies,
    • People management:
      • understanding you, understanding other people, working in teams and dealing with conflicts.
    • The Business Environment:
      • understanding the business environment; identifying key trends and their implications for the organisation.
    • Strategy and Marketing:
      • developing effective strategies, focusing on the customer, building competitive advantage, the role of strategic assets.
    • Finance:
      • profit and loss accounts, balance sheets, cash flow forecasting, project appraisal.
    • New product development:
      • commercialising technology, market drivers, time to market, focusing technology, concerns.
    • Business game:
      • Working in teams (companies), you will set up and run a technology company and make decisions on investment, R&D funding, operations, marketing and sales strategy,
    • Negotiation:
      • preparation for negotiations, negotiation process, win-win solutions.
    • Presentation skills:
      • understanding your audience, focusing your message, successful presentations, getting your message across.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Recognise the importance of teamwork in the performance and success of organisations with particular reference to commercialising technological innovation,
  • Operate as an effective team member, recognising the contribution of individuals within the team, and capable of developing team working skills in yourself and others to improve the overall performance of a team,
  • Compare and evaluate the impact of the key functional areas (strategy, marketing and finance) on the commercial performance of an organisation, relevant to the manufacture of a product or provision of a technical service,
  • Design and deliver an effective presentation that justifies and supports any decisions or recommendations made,
  • Argue and defend your judgements through constructive communication and negotiating skills.

Elective modules
A selection of modules from the following list need to be taken as part of this course

Process Design and Simulation 

Module Leader
  • Dr Dawid Hanak

    This module aims to introduce you to the modern techniques and computer aided engineering tools for the design, simulation and optimisation of process systems. Via a large share of process simulation and optimisation case studies, the module will enable you to gather the hands-on experience of using the commercial software.

    Process Design
    • Overview: Conceptual process design. Process flow-sheeting,
    • Process synthesis: Overview of a process system. Recycle structure of the flowsheet. Design of reaction and separation systems,
    • Process integration: Basic concepts of process integration for heat exchanger network design,
    • Process economic analysis: Equipment capital cost estimation. Process profitability analysis.
    Process Modelling, Simulation and Optimisation
    • Modelling and simulation: Basic concepts of process modelling. General concepts of simulation. Introduction to steady and dynamic process simulation. Introduction to commercial simulation software packages (i.e, Aspen HYSYS) for process flow-sheeting, design and analysis,
    • Process optimisation techniques: Basic principles of optimisation. Presentation of a number of industrial case studies (e.g., heat exchanges network synthesis).
    Case Studies (PC Lab and Demonstration Sessions)
    • A number of process simulation and optimisation case studies will be carried out using Aspen HYSYS and Aspen Plus.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Formulate strategies to carry out a process design and critically appraise the techniques and major commercial simulation tools for steady and dynamic process simulation,  
  • Competently apply the basic principles of process optimisation,
  • Design and analyse the performance of a process plant using simulation or optimisation tools.

Heat and Power Generation Systems

Module Leader
  • Dr Kumar Patchigolla
    This module provides you with an understanding of the fundamentals of operation, configuration and characteristics of thermal systems. During the module you will learn how to apply these for the design of energy-efficient furnaces and boilers, and the key implementation issues of various types of power plant.
    • Fuels and thermal conversion processes: primary solid and liquid fuels. Carbonisation of solid fuels. Thermodynamic equations. Dissociation and chemical equilibrium. Process efficiency, emission control and standards
    • Furnaces and boilers: types of furnaces and classification. Heat transfer in furnaces, efficient furnace and boiler design. Boiler efficiency and part-load operation and its maintenance.
    • Overview: World electricity demand and generation. Fuels. Environmental impacts.
    • Steam power plants: Thermodynamic principles. Fuels. Steam power generation cycles.
    • Gas turbine and combined-cycle power plants: Gas turbine engines and performance. Gas turbine cycles. Combined-cycle power plants.
    • Diesel- and gas-engine power plants: Diesel engines. Fuels. Emission control. Heat recovery systems.
    • Nuclear power generation: Basic nuclear physical processes (fission and fusion). Nuclear fuels. Types of reactors. Safety considerations in the nuclear industry. Developments in nuclear fusion. Decommissioning problems of nuclear sites. Nuclear‑waste disposal systems.
    • Fuel cells: Definition and principles of operation. Losses and efficiency. Possible fuels. Fuel-cell technologies and applications (alkaline fuel cells, molten carbonate fuel cells, phosphoric acid fuel cells, solid oxide fuel cells, and regenerative fuel cells).
    • CHP systems: CHP schemes (micro-scale CHP systems, small‑scale CHP systems, large‑scale CHP systems including district heating schemes). Application of CHP systems for the provision of heating, cooling and electric power. Selection criteria of CHP prime-movers. Integration of CHP systems into site services. Feasibility analysis of CHP schemes using spreadsheets/software tools. Case study (site appraisal for CHP scheme and evaluation of economic and environmental viability).
    • Advanced power plants: geothermal plants and its applications. Solar thermal enhanced designs and new materials. Innovative SCO2 cycles to operate at higher temperatures, bringing higher energy output.
Intended learning outcomes

On successful completion of this module you should be able to:

  • Critically evaluate the fundamentals and laws governing energy conversion and appraise various fuels and their characteristics.
  • Assess the operation of furnaces and boilers based on a fundamental understanding of the governing laws, and debate issues influencing the design/selection of furnaces and boilers and future trends.
  • Debate  issues related to the performance of conventional power-generation plants.
  • Propose appropriate  technologies  for improving energy-utilisation efficiency of power-generation plants.
  • Assess the need of a particular industrial/commercial site for a CHP system, identify the appropriate systems and undertake design, sizing and economic analyses.
  • Critically review technologies employed for advanced power generation systems (Geo-thermal, solar thermal, SCO2 cycle) and it's applications.

Advanced Optimisation of Process and Energy Systems


    This module will introduce you to the fundamental optimisation principles and tools for the design, analysis and optimisation of processes and operations in the energy and process industry.

    • Nonlinear unconstrained and constrained optimisation principles.
    • Linear programming.
    • Mixed integer programming.
    • Decision-making model building.
    • Introduction to multi-parametric programming.
    • Optimisation under uncertainty.
    • Introduction to general algebraic modelling languages (i.e., GAMS and AIMMS).
    • Several optimisation problems to be addressed, such as:
      • Heat exchanger networks design.
      • Process synthesis and optimal selection of processes.
      • Operational and cleaning planning of network of compressors.
      • Production scheduling and planning.
      • Energy planning of combined heat and power systems.
      • Supply chain operations in energy and process industries.
    • Presentation of real case studies from the energy and process industry.

Intended learning outcomes

On successful completion of this module you should be able to:

  • Appraise basic optimisation and model building principles, and a familiarity with optimisation tools.
  • Apply effectively state-of-the-art decision-making approaches in supply chain problems (process and energy systems).
  • Competently apply optimisation methods for problems related to energy and process industries.
  • Design and optimise the operational aspects of a process plant or energy system using optimisation tools. 

Teaching team

You will be taught by industry-active research academics from Cranfield with an established track record, supported by visiting lecturers from industry. To ensure the programme is aligned to industry needs, the course is directed by its own Industrial Advisory Committee.

How to apply

Online application form. UK students are normally expected to attend an interview and financial support is best discussed at this time. Overseas and EU students may be interviewed by telephone.