Fingerprint of a submerged-arc Furnace Optimising energy consumption through data mining, dynamic modelling and computational fluid dynamics Proefschrift ter verkrijging van de graad van doctor aan de Technische Universiteit Delft, op gezag van de Rector Magnificus prof.dr.ir. J.T.Fokkema, voorzitter van het College voor Promoties, in het openbaar te verdedigen op donderdag 26 juni 2008 om 15.00 uur door Emile SCHEEPERS Master of Science in Engineering Universiteit van Stellenbosch, Zuid Afrika geboren te Somerset West, Zuid Afrika Dit proefschrift is goedgekeurd door de promotor(en): Prof. Ph.D. Dr.Habil. D.Eng M.A.Reuter Prof.dr.ir. R.Boom Samenstelling promotiecommissie: Rector Magnificus, voorzitter Prof.Ph.D.Dr.Habil.D.Eng M.A.Reuter, Ausmelt Limited, Australia, promotor Prof.dr.ir. R.Boom, Technische Universiteit Delft, promotor Prof.ir. L.Katgerman, Technische Universiteit Delft Prof.D.Sc.(Tech.). J.H¨arkki, Oulun Yliopisto, Suomi Prof.dr.ir. G.A.Irons, McMaster University, Cananda Dr.ir. Y.Yang, Technische Universiteit Delft Dr.ir. R.de Ruiter, Thermphos International Prof.dr.ir. A.H.M. Verkooijen (reservelid), Technische Universiteit Delft The work described in this thesis was made possible through financial support from Thermphos International and SenterNovem. Copyright © 2008 by Emile Scheepers Publisher: Printpartners Ipskamp, Enschede, The Netherlands Cover design: Thank you Marco Scheepers and Giuditta Simeone No part of this book may be reproduced by print, photocopy, microfilm or any other means without written permission from the author. ISBN 978-90-9023183-9 Die boek dra ek onvoorwaardelik op aan my geliefde familie en my geliefde Eleonora iv Contents Executive Summary ix Samevatting xiii 1 Introduction 1 1.1 Researchmotivation ........................... 1 1.2 Theprocessinbrief ........................... 2 1.3 Researchfocusarea............................ 2 1.4 Researchobjectives............................ 3 1.5 Researchcontributions.. .. .. .. .. .. .. .. 4 1.6 Outlineofthethesis ........................... 7 I The PRODUCTION of PHOSPHORUS 9 2 The phosphorus production process description 11 2.1 Furnace modelling and control in the context of sustainable resource processingandrecovery . 12 2.2 Theimportanceofthereductionzone . 12 2.3 The concept of residence time and its role in furnace control..... 13 2.4 Introduction to the phosphorus producing process . ..... 15 2.5 The reduction of phosphorus according to the W¨ohler reaction . 17 2.6 Feedmaterial ............................... 18 2.7 Granulators, pellets and sinter plant . 21 2.8 The submerged arc furnace for phosphorus production . ..... 21 2.9 Gaseous product, liquid phases and downstream equipment ..... 23 2.10 Automated control of the furnace process . 27 2.11 Feedback operator-induced control of the furnace . ....... 30 2.12Conclusions ................................ 32 3 The symmetrical furnace and its asymmetrical character 33 3.1 Introduction................................ 34 3.2 The evidence of asymmetrical furnace behaviour . 34 3.3 The causes of asymmetrical furnace operation . 42 3.4 The impact of asymmetrical behaviour on phosphorus recovery . 45 3.5 The standard deviation in the P2O5(slag) composition . 46 vi CONTENTS 3.6 Conclusions ................................ 46 II The CONCEPTUAL FRAMEWORK of a DYNAMIC-CFD HYBRID MODEL 51 4 The conceptual framework of a dynamic-CFD hybrid model 53 4.1 Introduction................................ 53 4.2 Themodelstructure ........................... 53 4.3 The link between Dynamic and CFD data . 55 4.4 Modelpredictivecontrol . 56 4.5 Outlineoftherestofthestudy . 56 III The DYNAMIC MODEL 57 5 Industrial data investigation, data reconciliation and soft-sensoring 59 5.1 Introduction................................ 60 5.2 Raw industrial data investigation . 60 5.3 Data reconciliation . 66 5.4 Costanalysis ............................... 73 5.5 Conclusions ................................ 75 6 The fluctuating state of the furnace and its influence on data struc- turing and dynamic model selection 77 6.1 Introduction................................ 78 6.2 Thepredictionchallenge . 78 6.3 Choosingadynamicpredictionmodel . 79 6.4 The concepts of data, data sampling and sampling intervals . 81 6.5 Theconceptofafurnacestate . 82 6.6 Reconciled data structuring in view of choosing a dynamicmodel . 83 6.7 Phosphorus concentration in slag (P2O5(slag)) as model output variable 85 6.8 Linearmodelling ............................. 86 6.9 Non-linearmodelling........................... 87 6.10 Thefluctuatingstateofthefurnace. 87 6.11Conclusions ................................ 92 7 Linear, dynamic modelling results and the potential for predictive control 95 7.1 Introduction................................ 96 7.2 Thedynamicpredictionmodel . 96 7.3 Thedynamicpredictivecontrolmodel . 100 7.4 Conclusions ................................ 102 CONTENTS vii IV The CFD MODEL 105 8 CFD process modelling: Theory and methodology 107 8.1 Introduction................................ 107 8.2 Literature overview of electrical arc furnaces . ....... 108 8.3 FundamentalsofCFD .. .. .. .. .. .. .. .. 114 8.4 Numerical solution of a CFD problem . 123 8.5 BuildingupaCFDmodel . 127 9 CFD process modelling: Model development 133 9.1 Introduction................................ 134 9.2 Furnace operation data utilised for CFD model development . 134 9.3 Furnace dimensions, structure and computational grid . ....... 136 9.4 Furnace body, reactant and product material properties ....... 137 9.5 InputandBoundaryConditions. 143 9.6 PhysicalModels.............................. 148 9.7 User developed sub-model 1: Reaction Model . 149 9.8 User developed sub-model 2: Particle-particle radiation and effective thermalconductivitymodel . 157 9.9 Summaryofthemodelconfigurations . 158 9.10 Basecase modelresultsanddiscussion . 160 9.11 Modelvalidation ............................. 167 9.12 Conclusions ................................ 171 10 CFD modelling scenarios 173 10.1 Introduction................................ 174 10.2 Comparing CFD vs. actual values for the other four scenarios . 174 10.3 Sensitivity analyses of important process parameters ......... 176 10.4 The outlook for the Dynamic-CFD hybrid model . 194 10.5 Conclusions ................................ 198 V CONCLUSIONS AND RECOMMENDATIONS 201 11 Conclusions and Recommendations 203 11.1 Conclusions ................................ 204 11.2 Recommendations . .. .. .. .. .. .. .. .. 205 Bibliography 213 A Basic data reconciliation theory 219 B Data reconciliation and accompanying software development 221 C Basic theory of the ARX-type model 225 D Dynamic model and accompanying software development 227 viii CONTENTS E CFD model and accompanying software development 229 F CFD source code 245 G Acknowledgements 253 H Curriculum vitae 257 Executive Summary This study imparts a scientific perception of a phosphorous-producing submerged arc furnace never seen before; a proverbial fingerprint that can improve problem identification, disturbance diagnostics, process prediction, dynamic modelling and model predictive control of this type of furnace. It successfully incorporates accu- rate, multi-field thermodynamic-, kinetic- and industrial data with computational flow dynamic calculations; thus further unifying the sciences of kinetics and equilib- rium thermodynamics. The true power of this study is the extensive and methodical validation that ensures industrially endorsed results. To facilitate all this the author spent six uninterrupted months at an industrial plant (Thermphos International), twice walked inside a cold submerged-arc furnace, gathered and analysed more than thirty-four mineralogical samples, managed an extensive and insightful sampling campaign on the slag streams, performed feed material porosity tests and had thir- teen additional temperature probes installed inside the furnace lining. The author also scrutinised over years of industrial data, inspected many industrial drawing and partook in countless valuable conversations with industrial and technical experts to guarantee, not only a valuable scientific contribution, but one that is deep-rooted in authentic engineering principles. The process The process under investigation is the large-scale production of phos- phorus at Thermphos International. The main reaction is defined by the W¨ohler process, producing a calcium-silicate slag, calcium fluoride, carbon monoxide and the desired product, phosphorus gas. Gravity delivers the feed, consisting of pel- letised apatite, coke and silica (in the form of gravel), to a submerged arc furnace through ten, evenly distributed feed chutes ensuring a constant packed bed volume. The gaseous product leaves the furnace through two symmetrically spaced outlet vents situated above the ferrophosphorus tap hole in the roof of the furnace. The ferrophosphorus is tapped off, usually once per day. However, slag is continuously tapped through two alternating, water-cooled tapping holes located 400 mm above the furnace floor. Owing to the large production volume of slag, a seemingly small wt% of P2O5 in the slag (P2O5(slag)) results in substantial losses of unreduced, po- tential product. Control Controlling the process in order to keep the P2O5(slag) as low as possible, is therefore one of the top priorities. For this reason P2O5(slag) was chosen as the predicted output variable in subsequent models. The furnace process is controlled automatically though a constant-current principle that utilises changing electrode