Advanced Process Engineering MSc

• 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. 

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.

• 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.

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.

• 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.

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.

• Dr Dawid Hanak

Environmental impact assessment and economic feasibility assessment are important tools for detailed evaluation of complex engineering processes and innovative process concepts, which aim to tackle global challenges. The tools and concepts taught in this module will enable process engineers to assess the sustainability of the process plant designs from environmental and economic standpoints. Tools such as net present value (NPV) assessment and life cycle assessment (LCA) are becoming widely applied in the industry to ensure sustainability of their assets over their lifetime. This module aims to introduce you to the modern techniques for economic and environmental assessment of sustainable process systems. It comprises several hands-on case studies (microprojects) that enable you to develop relevant economic and environmental impact assessment competencies via hands-on experience using commercial software. You will also work on an industrially relevant case study that will take you through the entire process assessment process. You will also learn how to account for uncertainty and apply reduced-order models.  

Economic appraisal of engineering projects

• Introduction to economic assessment and its role in engineering decision making,
• Project cost estimation (capital and operating costs),
• Identification of key performance indicators (benefit: cost ratio, net present value, payback period, and internal rate of return); selection of parameters framework for economic assessment (net present value), levelised costs, and sensitivity analysis,
• Introduction to Monte Carlo simulation; surrogate modelling using machine learning (artificial neural networks/machine learning),
• Case studies for power and industrial processes. 

Environmental impact assessment and sustainability

• Sustainability and business models,
• Net-zero technologies for sustainable development,
• Life cycle analysis and environmental impact assessment.

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

• Critically assess the emissions and waste production throughout the lifecycle of the process,
• Design and implement a strategy to assess the economic and environmental sustainability of a process,
• Critically evaluate different economic and environmental appraisal metrics,
• Evaluate the effect of uncertainty on the economic and environmental feasibility using stochastic models and formulate a set of recommendations.

• 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.

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.

• Dr Stephanie Hussels

In this world of downsizing, restructuring and technological change, notions of traditional careers and ways of creating value have all been challenged. People are depending more upon their own initiative to realise success. Never, it seems, have more people been starting their own companies than now, particularly to exploit the World Wide Web. There’s no single Government (in either the developed or the developing world), which is not paying at least lip service to enterprise development. The aim of this module is to provide you with knowledge and skills relevant for starting and managing new ventures across the entrepreneurial life cycle. Moreover, it will prepare you on how to prepare a business pitch to an investor.

• Entrepreneurial risk, performance and environment,
• Business planning techniques and their application in entrepreneurial ventures
• Venture strategy in dynamic markets
• Start-up and resources to exploit a profit opportunity
• The evolution of the venture and managing growth
• Protecting and securing intellectual capital: IPR and antitrust law
• Financial management for new ventures: financing a start-up 
• The entrepreneurial financing process: buying and selling a venture.

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

• Assess the impact of the business environment on entrepreneurial opportunity identification and exploitation.
• Critically apply the theoretical underpinning of entrepreneurship to the process of managing risk in new ventures and supporting their development.
• Compare and contrast how managerial challenges vary across the life cycle of an entrepreneurial venture.
• Assess the likely financial needs of a new venture and pitch for finance.
• Develop and write a credible business plan for a new venture.

• Dr Liyun Lao

This module introduces a systematic approach to the design of measurement and control systems for industrial process applications. The fundamental concepts, key requirements, typical principles and key applications of the industrial process measurement and control 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, and radiation method,
• Multiphase measurement and monitoring: general features of vertical and horizontal multiphase flow, definition of parameters in multiphase flow, multiphase flow measurement strategies, Content and composition measurement, velocity measurement, commercial multiphase flow meters, developments in multiphase flow metering.

Practical of Process Control

• Case study 1: Cooling System Control,
• Case study 2: Flow Assurance Control System.

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 and control system,
• Propose the most appropriate measurement system for a given process control application.

• Dr Gill Drew

This module will provide you with experience of scoping and designing a research project.  This requires a thorough understanding of the background literature, as well as qualitative and quantitative analysis techniques. The module provides sessions on project scoping and planning, including project risk management and resource allocation.  A key part of this module is the consideration of ethics, professional conduct and the role of an engineer within the wider industry context.

Research methods:

• Literature reviews,
• Qualitative analysis methods (surveys, SWOT, PESTLE),
• Quantitative analysis methods (hypothesis testing, statistics and regression.

Project management:

• Project scoping and definition,
• Project planning,
• Project risk assessment and mitigation,
• Resource planning and allocation.

Ethics and the role of the engineer:

• Expert witness case study

Professional code of conduct (in line with the code of conduct defined by the Engineering Council, IMechE, IChemE and Energy Institute).  

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

• Undertake a systematic literature review in order to identify the key gaps in knowledge for a particular research topic,
• Design a data analysis method appropriate to their chosen research topic (either quantitative or qualitative),
• Design and scope a research project, including identification of research methods, resources required and risk management approaches,
• Evaluate ethical dilemmas and the role of the engineer within the context of their chosen industry.