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Improving Steam Temperature Control with Neural Networks

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Improving Steam Temperature Control with Neural Networks IMPROVING STEAM TEMPERATURE CONTROL WITH NEURAL NETWORKS by JACQUES FRANCOIS SMUTS THESIS submitted in partial fulfilment of the degree DOCTOR INGENERIAE in MECHANICAL ENGINEERING at the RAND AFRIKAANS UNIVERSITY SUPERVISOR: Prof. A.L. NEL JULY 1997 Summary The thesis describes the development, installation, and testing of a neural network-based steam • temperature controller for power plant boilers. Attention is focussed on the mechanical and thermodynamic aspects of the control problem, on the modelling and control aspects of the neural network solution, and on the practical and operational aspects of its implementation. A balance between theoretical and practical considerations is strived for. Experimental data is obtained from an operational coal fired power plant. As a starting point, the importance of good steam temperature control is motivated. The sensitivity of heated elements in boilers to changes in heat distribution is emphasized, and it is shown how various factors influence the heat distribution. The difficulties associated with steam temperature control are discussed, and an overview of developments in advanced steam temperature control on power plant boilers is given. The suitability of neural networks for process modelling and control are explored and the error backpropagation technique is shown to be well suited to the steam temperature control problem. A series of live plant tests to obtain modelling data is described and specific attention is given to discrepancies in the results. The prOcess of selecting the ideal network topology is covered and improvements in modelling accuracy by selecting different model output schemes are shown. The requirements for improving steam temperature control are listed and the philosophy of optimal heat distribution (OHD) control is introduced. Error backpropagation through the heat transfer model is utilized in an optimizer to calculate control actions to various fire-side elements. The scheme is implemented on a power boiler. It is shown that the optimizer manipulates control elements as expected. Problems with fuel-to- pressure oscillations and erroneous fuel flow measurement are discussed. Due to process oscillations caused by OHD control, a reduction in control quality is evident during mill trips and capability load runbacks. Substantial improvements over normal PID control however, are evident during load ramps. ii Opsomming Hierdie proefskrif beskryf die ontwikkelling, installasie, en toetsing van n neurale netwerk gebaseerde stoomtemperatuurbeheerder vir kragstasieketels. Aandag word gefokus op die meganiese en termodinamiese aspekte van die beheerprobleem, op die modellerings- en beheeraspekte van die neurale netwerk oplossing, en op praktiese- en bedryfsaspekte van die implementering. Daar word gepoog om 'n balans te handhaaf tussen teoretiese en praktiese oorwegings. Eksperimentele data word verkry vanaf 'n operasionele steenkool kragstasie. As beginpunt word die belangrikheid van goeie stoomtemperatuurbeheer gemotiveer. Verhitte elemente in stoomketels se sensitiwiteit vir veranderings in hitteoordragspatrone word beklemtoon, en daar word aangetoon hoe verskeie faktore die hittebalans beinvloed. Die moeilikhede wat gepaard gaan met stoomtemperatuurbeheer word bespreek, en 'n oorsig van ontwikkelinge in gevorderde stoomtemperatuurbeheer op kragstasieketels word gegee. Die toepaslikheid van neurale netwerke op prosesmodellering en -beheer word ondersoek en daar word getoon dat die tegniek van fout-terugpropagering gepas is vir stoomtemperatuurbeheer. 'n Reeks toetse wat gedoen is om modelleringsdata te bekom word beskryf, en aandag word spesifiek aan teenstrydighede in die resultate geskenk. Die keuse van 'n ideale netwerkuitleg word gedek en verbeteringe in die akuraatheid van modellering deur middel van verskillende uitsetskemas word getoon. Die vereistes vir die verbetering van stoomtemperatuurbeheer word genoem en die filosofie van optimale hitteverspreidingsbeheer (OHV beheer) word bekendgestel. Fout-terugpropagering deur die hitteoordragsmodel word gebruik in 'n optimiseerder om beheeraksies aan die vuur-kant te bereken. Die OHV algoritme word op 'n kragstasiestoomketel geimplementeer. Daar word aangedui dat die optimiseerder die beheerelemente na verwagting verstel. Probleme met brandstof-teenoor-druk ossillasies en foutiewe brandstofmeting word bespreek. As gevolg van prosesossillasies wat veroorsaak word deur OHV beheer, vind 'n daling in beheerkwaliteit plaas gedurende meulklinke en noodgedwonge vragvennindering. Noemenswaardige verbetering bo PID beheer is egter merkbaar gedurende vragveranderinge. iii Table of Contents Summary Opsomming ii Table of Contents iii List of Figures vi List of Tables List of Variables xi 1. Introduction 1 1.1 Power generation 1 1.2 A brief history of boiler control 2 1.3 The need for steam temperature regulation 5 1.4 Research hypothesis 6 1.5 Overview of thesis 6 The power plant boiler 9 2.1 Cycle description 9 2.2 Heat transfer theory 14 2.3 Steam generator design 19 Steam temperature control 30 3.1 Control elements for steam temperature regulation 30 3.2 Difficulties associated with steam temperature regulation 40 3.3 Temperature excursion study 47 3.4 Instrumentation and control configuration 55 iv 3.5 Developments in steam temperature control 61 4. Neural networks and process control 74 4.1 Description of a neural network 74 4.2 Selecting the size of a neural network 77 4.3 Training the network 78 4.4 Process modelling with neural networks 79 4.5 Process control with neural networks 81 5. Plant modelling 87 5.1 Desired model characteristics 87 5.2 Acquiring test data 89 5.3 Calculations and assumptions 98 5.4 Neural network model 120 Neural networks and steam temperature control 135 6.1 Requirements for improved steam temperature control 135 6.2 Optimal heat distribution control 139 6.3 Controller design 141 6.4 Expected results 155 Practical implementation and results 157 7.1 The PC as control platform 157 7.2 Interfacing to existing boiler controls 159 7.3 Steady state testing and optimization 165 7.4 Transient testing and optimization 167 7.5 Final results 185 Conclusion 190 8.1 Discussion 190 8.2 Return to research hypothesis 192 V 8.3 Future research 193 Bibliography 195 Appendix A. Heat distribution test programme 204 Appendix B. Variables recorded during heat distribution tests 210 Appendix C. Spreadsheet model 213 Appendix D. OHD graphic display 215 Appendix E. Selected test results 216 vi List of Figures 1.1 South African power demand through a typical day 2.1 Carnot cycle. 9 2.2 Carnot cycle T-S diagram. 9 2.3 Rankine cycle. 10 2.4 Rankine cycle T-S diagram. 11 2.5 Superheat cycle T-S diagram 12 2.6 Reheat cycle with economizer. 13 2.7 Reheat cycle with economizer T-S diagram 13 2.8 Fire-side components of a steam generator. 19 2.9 Different firing systems indicating fuel injection angle: 20 2.10 Diagrammatic view of the water & steam path through power plant components. 22 2.11 Typical steam temperature characteristics. 23 2.12 Heat rise in boiler elements vs. steam pressure 23 2.13 Different heat zones in a steam generator. 24 2.14 Typical location of steam generator elements 27 2.15 Layout of the Kendal boiler heat transfer elements 29 3.1 The effect of burner tilt angle on fireball elevation. 32 3.2 Effect of burner tilt angle on furnace exit temperatures. 33 3.3 Kendal superheater stages and desuperheater locations. 39 3.4 Reheater outlet temperature reacting to increased spray water flow. 42 3.5 Reheater outlet temperature response under two load conditions 44 3.6 Causes of temperature excursions at Kendal. 48 3.7 Main steam temperature deviations from setpoint caused by load variations 49 3.8 Temperature excursion caused by a mill shut down. 52 3.9 Basic temperature control loop. 55 3.10 Cascade control arrangement. 56 3.11 Feedforward control. 57 3.12 Combined feedback, feedforward and cascade control arrangement. 58 3.13 Multiple control elements with coupled control. 60 4.1 Schematic representation of a typical artificial neuron. 74 vii 4.2 Neuron transfer functions. 75 4.3 Feedforward_ neural network. 76 4.4 Backpropagation signal flow. 84 4.5 Feedforward and backpropagation modes. 85 5.1 Measurements on feed water system, economizer and evaporator. • 92 5.2 Measurements on superheater and reheater. 93 5.3 Correlation between fuel flow and total heat gain was obtained for all tests. 97 5.4 Heat shifts achieved during heat distribution tests. 98 5.5 Feed water heater. 103 5.6 Relation in pressure differential (DP) across superheater stages. 106 5.7 Variables for heat balance calculations 108 5.8 Superheater spray water flow measurement 111 5.9 Reheater spray water flow measurement. 111 5.10 Superheater spray and warmup flow. 111 5.11 Discrepancies between calculated and measured air flow ratio. 115 5.12 Correlation between fuel flow and generator load. 116 5.13 Air flow vs 02 in flue gas with fuel flow derived from generator load. 118 5.14 Normalized difference between LH and RH air flow measurements. 119 5.15 Furnace to boiler heat transfer mapping 120 5.16 Error on test data increases after many training runs. 122 5.17 7:50:3 neural network model output errors for all tests. 127 5.18 Absolute heat transfer rate model. 132 5.19 Relative heat transfer rate model errors. 132 5.20 Corrected relative heat transfer rate model errors. 132 6.1 Model-based predictive control. 136 6.2 Adaptive adjustment concept 137 6.3 Design heat transfer rates to maintain steam temperatures. 138 6.4 Signal flow to and from the optimal heat distribution controller. 140 6.5 Predictive calculation for error in heat manger. 143 6.6 Backpropagation of errors to obtain derivatives. 144 6.7 Bias development during an optimization run. 146 viii 6.8 Heat transfer errors during an optimization run. 146 6.9 Adjusting the design heat transfer to match plant conditions. 152 6.10 Adjusting the heat transfer model to match plant conditions. 154 7.1 Interface between PC and existing boiler control system. 159 7.2 Closed loop control signal flow diagram.
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