Advanced sensors and smart controls for coal-fired power plant Toby Lockwood CCC/251 – June 2015 © IEA Clean Coal Centre Advanced sensors and smart controls for coal-fired power plant Author: Toby Lockwood IEACCC Ref: CCC/251 ISBN 978–92–9029–573-0 Copyright: © IEA Clean Coal Centre Published Date: June 2015 IEA Clean Coal Centre 14 Northfields London SW18 1DD United Kingdom Telephone: +44(0)20 8877 6280 www.iea-coal.org IEA Clean Coal Centre – Advanced sensors and smart controls for coal-fired power plant 2 Preface This draft report has been produced by IEA Clean Coal Centre and is based on a survey and analysis of published literature, and on information gathered in discussions with interested organisations and individuals. Their assistance is gratefully acknowledged. It should be understood that the views expressed in this report are our own, and are not necessarily shared by those who supplied the information, nor by our member countries. IEA Clean Coal Centre is an organisation set up under the auspices of the International Energy Agency (IEA) which was itself founded in 1974 by member countries of the Organisation for Economic Co-operation and Development (OECD). The purpose of the IEA is to explore means by which countries interested in minimising their dependence on imported oil can co-operate. In the field of Research, Development and Demonstration over fifty individual projects have been established in partnership between member countries of the IEA. IEA Clean Coal Centre began in 1975 and has contracting parties and sponsors from: Australia, Austria, China, the European Commission, Germany, India, Italy, Japan, New Zealand, Poland Russia, South Africa, Thailand, the UK and the USA. The Centre provides information and assessments on all aspects of coal from supply and transport, through markets and end-use technologies, to environmental issues and waste utilisation. Neither IEA Clean Coal Centre nor any of its employees nor any supporting country or organisation, nor any employee or contractor of IEA Clean Coal Centre, makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness or usefulness of any information, apparatus, product or process disclosed, or represents that its use would not infringe privately-owned rights. IEA Clean Coal Centre – Advanced sensors and smart controls for coal-fired power plant 3 Abstract Coal power plant control systems have progressively evolved to meet the growing demand for efficient and flexible power generation whilst maintaining low emissions. In particular, optimisation of the combustion process has required increased use of online monitoring technologies and the replacement of standard control loops with more advanced algorithms capable of handling multivariable systems. Improved stoichiometric control can be achieved with coal and air flow sensors or imaging and spectral analysis of the flame itself, whilst in situ laser absorption spectroscopy provides a means of mapping CO and O2 distribution in hot regions of the furnace. Modern plant control systems are able to draw on a range of computational techniques to determine the appropriate control response, including artificial intelligence which mimics the actions of expert operators and uses complex empirical models built from operational data. New sensor technologies are also being researched to further improve control and to withstand the high temperature and corrosive environments of advanced coal plant and gasifiers. Increased use of optical technologies is of particular interest, with sensors based on optical fibres able to perform low noise, highly sensitive, and distributed measurements at high temperatures. Microelectronic fabrication techniques and newly developed high temperature materials are also being combined to develop miniaturised devices which provide a robust and low cost solution for in situ monitoring of gases and other parameters. These new sensors can be integrated with wireless communication technology and self-powering systems to facilitate the deployment of distributed sensor networks and monitoring of inaccessible locations. Using principles of self-organisation to optimise their output, such networks may play a growing role in future control systems. IEA Clean Coal Centre – Advanced sensors and smart controls for coal-fired power plant 4 Acronyms and abbreviations APC advanced process control CCTV closed-circuit television CCD charge coupled device CLD chemiluminescence detector CMOS complementary metal-oxide semiconductor CV controlled variable DCS distributed control system EPRI Electric Power Research Institute EEGT economiser exit gas temperature FEGT furnace exit gas temperature FD forced draught (fan) FPI Fabry-Pérot Interferometer FTIR Fourier transform infrared GC gas chromatography GIF graded index fibre HCF hollow core fibre HMI human machine interface ID induced draught (fan) IGCC integrated gasification combined cycle LIBS laser induced breakdown spectroscopy LVDT linear variable differential transformer MEMS microelectromechanical systems MIMO multi input-multi output MPC model predictive control MV manipulated variable NETL US National Energy Technology Laboratory NDIR non-dispersive infrared OFA overfire air PCF photonic crystal fibre PDC polymer-derived ceramic PLC programmable logic controller PGNAA prompt gamma neutron activation analysis RTD resistance temperature detector UV ultraviolet vis visible light SCR selective catalytic reduction SISO single input-single output SMF single mode fibre SNCR selective non-catalytic reduction TDLAS tunable diode laser absorption spectroscopy IEA Clean Coal Centre – Advanced sensors and smart controls for coal-fired power plant 5 Contents Preface 3 Abstract 4 Acronyms and abbreviations 5 Contents 6 List of Figures 8 List of Tables 10 1 Introduction 11 2 Overview of coal plant control 13 2.1 Sensors and actuators 13 2.1.1 Smart sensors 14 2.2 Control loops 15 2.3 Condition monitoring 17 2.4 Distributed control systems 17 2.5 Process networking 19 2.6 Data historians 20 3 Advanced process control 21 3.1 Artificial intelligence 22 3.1.1 Fuzzy logic 22 3.1.2 Expert systems 23 3.2 Model-based optimisation 23 3.2.1 Neural networks 24 3.2.2 Model predictive control 25 3.3 Model-free adaptive control 27 4 Combustion optimisation 28 4.1 Sensors used in combustion optimisation 29 4.1.1 Oxygen 30 4.1.2 CO 32 4.1.3 NOx 33 4.1.4 Gas sensor arrays 34 4.1.5 Temperature 35 4.1.6 Tunable diode laser spectroscopy 36 4.1.7 Carbon-in-ash 39 4.1.8 Coal flow 40 4.1.9 Air flow 41 4.1.10 Flame detectors 42 4.2 Advanced process control in combustion optimisation 43 4.2.1 NeuCo CombustionOpt 44 4.2.2 ABB Predict & Control 46 4.2.3 Emerson SmartProcess 47 4.2.4 Invensys Connoisseur 47 4.2.5 Siemens SPPA-P3000 47 4.2.6 EUtech EUcontrol 48 4.2.7 STEAG Powitec PiT Navigator 48 4.3 Smart sootblowing 49 4.3.1 NeuCo SootOpt 50 4.3.2 Taber Knowledge-Based Sootblowing 51 4.3.3 Siemens SPPA-P3000 52 4.4 Online monitoring of corrosive species 52 4.4.1 Metso Corrored 52 4.4.2 ZoloSALT 53 5 Advanced optical sensors and imaging 54 5.1 Optical fibre sensors 54 5.1.1 Fabry-Perot interferometer sensors 55 IEA Clean Coal Centre – Advanced sensors and smart controls for coal-fired power plant 6 5.1.2 Fibre Bragg grating sensors 57 5.1.3 Gas sensors using microstructured fibres 59 5.1.4 Distributed optical fibre sensing 61 5.1.5 Embedded optical fibre sensors 63 5.2 Advanced spectroscopic techniques 64 5.2.1 TDLAS for gasifiers 65 5.2.2 Raman spectroscopy 66 5.3 Flame imaging 67 6 High temperature microsensors 69 6.1 High temperature gas sensors 69 6.1.1 Solid electrolyte-based sensors 70 6.1.2 Conductometric sensors 73 6.1.3 Microresonant sensors 76 6.2 High temperature silicon-based sensors 76 6.2.1 Temperature and pressure sensors 77 6.2.2 Field effect transistor gas sensors 78 6.3 Metallic thin film sensors 79 7 Wireless sensor networks 80 7.1 Considerations for wireless networking 80 7.2 Wireless power supply 82 7.2.1 Radio frequency-powered sensors 82 7.3 Distributed sensor networks 86 7.4 Optimising sensor placement 87 8 Conclusions 88 9 References 91 IEA Clean Coal Centre – Advanced sensors and smart controls for coal-fired power plant 7 List of Figures Figure 1 A smart sensor integrated with signal processing and digital networking capability (ADC, analogue to digital converter) 15 Figure 2 a) Open loop, b) feedforward and c) closed loop (feedback) control 16 Figure 3 A typical DCS architecture 18 Figure 4 An example of two interacting control loops 21 Figure 5 Example of fuzzy logic membership sets 23 Figure 6 Training a neural network by backpropagation. Weights are adjusted to minimise model error 25 Figure 7 Illustration of model predictive control (MV, manipulated variable; CV, controlled variable) 26 Figure 8 A single loop model-free adaptive control system, where y(t) is the process variable, r(t) the set point, e(t) the set point error, u(t) the control action generated by the MFA neural network, and d(t) are system disturbances 27 Figure 9 The variation of key combustion parameters with air-fuel ratio, showing the potential efficiency and emissions improvements achievable with optimised combustion 28 Figure 10 A schematic of a typical zirconia-based oxygen sensor 31 Figure 11 The grid placement of O2/CO sensors in Delta CCM 34 Figure 12 Spectral regions for the detection of various combustion gases, including the near IR region addressed by tunable diode laser absorption spectroscopy 36 Figure 13 2D profiles