Energy Informatics MSc

• Dr Mahmood Shafiee

To introduce 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/shudown, 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. 

On successful completion of this module a student 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.

• Dr Gill Drew

To introduce 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.

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

1. Critically evaluate and systematically select appropriate data processing techniques for industrial applications,
2. Systematically design and implement modelling processes to solve energy and industrial problems,
3. Comprehensively appraise the key machine learning tools and procedures for industrial applications.

• Dr Liyun Lao

To introduce 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.

On successful completion of this module a student 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.

• Dr Patrick Verdin

To introduce 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.

On successful completion of this module a student 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.

• Dr Liyun Lao

To introduce 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.

On successful completion of this module a student 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.

The importance of technology leadership in driving the technical aspects of an organisations products, innovation, programmes, operations and strategy is paramount, especially in today’s turbulent commercial environment with its unprecedented pace of technological development. Demand for ever more complex products and services has become the norm.  The challenge for today’s manager is to deal with uncertainty, to allow technological innovation and change to flourish but also to remain within planned parameters of performance.  Many organisations engaged with technological innovation struggle to find engineers with the right skills.  Specifically, engineers have extensive subject/discipline knowledge but do not understand management processes in organisational context.  In addition, STEM graduates often lack interpersonal skills.

• 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. 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), students 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.

On successful completion of this module a student 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 themselves 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 their judgements through constructive communication and negotiating skills.

• Dr Dawid Hanak

Process design, simulation and modelling are industrially-relevant tools to assess the techno-economic feasibility of complex engineering processes. These enable assessing the project feasibility and optimising the process plant design before the actual process plant is build. These tools are widely applied in the industry to assess a number of process variants and to select the most efficient and cost-effective option. This module aims to introduce the students 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 the students 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. 

On successful completion of this module a student 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. 
• Apply competently the basic principles of process optimisation.
• Design and analyse the performance of a process plant using simulation or optimisation tools.

• Dr Kumar Patchigolla

This module provides an understanding of the fundamentals of operation, configuration and characteristics of thermal systems. Students will also learn how to apply these for design of energy-efficient furnaces and boilers and 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.

On successful completion of this module a student 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
• Review critically technologies employed for advanced power generation systems (Geo-thermal, solar thermal, SCO2 cycle) and it's applications.

To introduce 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.

On successful completion of this module a student 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).
• Apply competently 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.