School of Chemical Technology Degree Programme of Chemical Technology Igor Saavedra MODEL-BASED OPTIMIZATION OF A COMPACTCOOKING G2 DIGESTING PROCESS STAGE Master’s thesis for the degree of Master of Science in Technology submitted for inspection, Espoo, 31 December 2015. Supervisor Prof. Sirkka-Liisa Jämsä-Jounela Instructors Dr. Ing. Aldo Cipriano D.Sc. Olli Joutsimo ABSTRACT OF MASTER’S THESIS Author Igor Saavedra Thesis title Model-based Optimization of a CompactCooking G2 Digesting Process Stage Department Biotechnology and Chemical Technology Abstract A CompactCooking™ G2 (Valmet) digesting system represents a challenging process stage to be optimized in the context of a kraft pulp mill. Its highly non-linear behavior due to liquor recycling and heat integration poses a barrier to traditional trial-and-error optimization conducted by physical lab-scale simulation. Hence, this thesis aims to design a solution based on numerical simulation and mathematical optimization, whose results can be directly applied on industrial-scale as computed optimal set-points for the supervisory control. Based on published, first-principles, pulp digester models, a customized dynamic model was developed in Matlab/Simulink to simulate a complete CompactCooking™ G2 stage. The process model is founded on Purdue wood reaction kinetics and Härkönen chips bed compaction models, and it seamlessly takes into account process characteristics mentioned above. The non-linear model was validated by comparison against historical data of an industrial unit (200 h), and then employed in the design of a steady-state optimizer for this process stage by means of linear programming. Simulation results showed very good agreement in terms of liquors residual alkali, weak black liquor solids, and blowline kappa, despite high uncertainty on disturbances data and model simplifications. However, simulated kappa showed higher sensitivity to temperature fluctuations than the plant signal, likely indicating the need for more detail when modelling heat transfer phenomena. As to the optimization goal, a base case scenario (plant steady-state) was identified from industrial data to attempt process economics optimization. The results showed a potential for increasing profit or reducing variable costs in at least 2 USD/ADt, which for a modern pulp mill represents annual benefits between 1 – 2 million USD depending on production rate and mill availability. Further, the simulation model showed remarkable results when used in a novel process analysis technique, called here simulated contribution, letting to explain the variability of blowline kappa in terms of multiple-time-scale process dynamics. In conclusion, a model-based optimization method has been successfully designed for the CompactCooking™ G2 system, and potential economic benefits should encourage industrial testing and further work to develop a real-time optimizer software technology. Professorship Professorship code Process Control KE-90 Thesis supervisor Pages Prof. Sirkka-Liisa Jämsä-Jounela 157+18 Thesis advisors Language Dr. Ing. Aldo Cipriano English D.Sc. Olli Joutsimo Keywords Date pulp digester, CompactCooking G2, dynamic simulation, 31.12.2015 model-based optimization, model-based process analysis Preface I have been studying kraft pulping processes since I entered the South American pulp industry as a trainee research engineer four years ago. A modern kraft pulp mill represents a challenging and interesting process system to learn about for a young chemical engineer. Unfortunately, although pulp production is an important economic activity for South America, I was not able to find the specialized knowledge I needed at home. I came to know about Finland as a forerunner in the pulp and paper industry through my job, from where I was given the support to study there, along with other colleagues, in order to learn as much as I could about related fields to the kraft pulp mill process. Thus, I began studies in Åbo Akademi compulsively consuming knowledge about wood and fiber chemistry, biomass combustion and ash chemistry, and the new trends of transforming kraft pulp mill into future biorefineries. After that, I moved to Aalto-yliopisto attracted by the idea of acquiring knowledge on process automation, including subjects such as modelling, simulation, control, optimization, and monitoring of chemical processes; topics that may be the fundamentals to the process systems engineering discipline. This thesis hence crystallizes a bunch of ideas related to the above matters, and although it is certainly not the most advanced work on its field, it aims to be useful for anyone interested in modelling, simulation and optimization of pulp digesters. The code itself is indebted to the original spirit of Castro and Doyle’s Pulp Mill Benchmark Model from which I learned a lot. Open innovation is certainly the best way to innovate and probably the most natural one, as sharing words and ideas should be in our genetic code. I am greatly indebted to the Finnish higher education system. Thus I am thankful to all lecturers and professors from whom I learned during my time in Finland. Special acknowledgments to Prof. Sirkka-Liisa Jämsä-Jounela and Alexey Zakharov from Aalto-yliopisto, who taught me most of the mathematical techniques found here; and to Jan Gustafsson, Anna Sundberg, and Pedro Fardim from Åbo Akademi for their teaching in wood and paper chemistry subjects. Also, I wish to acknowledge Aldo Cipriano for his feedback and comments, and Olli Joutsimo for his patience and support. Future, experts say, is all about knowledge-, bio- and digital-economy. South American pulp industry is blessed regarding its forest resources but is (light) years behind forerunners in terms of advancing process technologies, likely, due to shortsighted decision-making, poor collaboration between industry and local universities, and low investment in R&D. Hopefully, the effort to bring new knowledge to this industry will not be a vain attempt. Finally, I would like to express my gratitude to my beloved ones, to Loreto and Alex that even travelled to share some time along, and to my colleagues with whom we kill freezing cold times. My special gratitude also to Onu, Evi, Leila, Sanna, Anni, Aleksi, Vahid, Moses, José, Vincent, Anatoly and all the Axelbandet members. I know I am the worst at keeping in touch, but I keep the best memories of all of you… after all, life may not be more than a bunch of memories. Igor Saavedra Confidential thanks to Nueva Aldea Pulp Mill and Biocel Pulping Research Center. Table of Contents 1 Introduction 1 1.1 Problem statement 1 1.2 Goals and scope 2 1.3 Methodology and contribution 3 1.4 Structure of the thesis 3 LITERATURE PART 4 2 The Kraft Pulp Mill 4 2.1 Industry facts and terms 4 2.2 Mill-wide process description 7 2.2.1 Wood line 8 2.2.2 Fiber line 9 2.2.3 Recovery line 11 3 Pulp Digesting Stage 13 3.1 Woodchips as raw material 13 3.2 Chemistry of kraft cooking 17 3.3 Equipment and instrumentation 22 3.4 Control and optimization 24 4 Mathematical Models on Pulp Digesters 27 4.1 Wood reaction kinetics 31 4.1.1 Purdue model 32 4.1.2 Gustafson model 33 4.2 Chips bed compaction 34 4.2.1 Härkönen correlations 35 4.2.2 Compacting pressure models 36 4.3 Chip size phenomena (mass transfer) 36 4.3.1 Diffusion and chip size 36 4.3.2 Liquor penetration 37 4.4 Pulp quality variables 38 4.4.1 Yield, kappa and screen reject 38 4.4.2 DP and intrinsic viscosity 39 4.4.3 Z-span, tensile and tear indices 41 4.5 Applications on control and optimization 42 EXPERIMENTAL PART 44 5 Methods 44 6 Process Description 46 7 Simulator Design 53 7.1 Simulation model architecture 55 7.1.1 Vessel submodel architecture 61 7.2 Mathematical modelling 64 7.2.1 Main assumptions 64 7.2.2 Vessels 66 7.2.2.1 Bed compaction 67 7.2.2.2 Woodchips degradation 74 7.2.2.3 Quality variables (depolymerization) 82 7.2.2.4 Top feeding 94 7.2.2.5 Digester wash zone 99 7.2.2.6 Levels 101 7.2.3 Heat-exchangers 103 7.2.4 Mill controlled variables 103 8 Simulation Results 106 8.1 Parameters and data acquisition 107 8.2 Testing and validation 115 8.3 Steady-state identification 123 9 Optimizer Design 126 9.1 Economic and operating models 128 9.1.1 Objective functions 129 9.1.2 Steady-state model and constraints 130 9.2 Linear programming approach 132 10 Optimization Results 137 10.1 Heuristic optima 137 10.2 Economic assessment 141 11 Conclusions 145 References 147 Appendices 158 A Model-based Process Analysis 158 Algebraic Notation Simulation Part J 푅 8.314 푔 Universal gas constant mol∙K m 9.81 푔 Gravity acceleration s2 퐴 Cross-sectional area m2 cm2 퐷 Diffusivity coefficient min 푇 Temperature K 푃 Pressure Pa m3 퐹 Volumetric flow rate min kg 퐹̃ Mass flow rate min kg 휌 Density m3 푠 Rotational speed rpm 푧 Axial space dimension m 푡 Time dimension min 휏 Retention time min g 푤 Molar mass mol kg of 푖 휌푖 Basic concentration of wood components m3sub kg of 푗 퐶푗 Mass concentration of liquor components m3e/f kmol of 푗 퐶̂푗 Molar concentration of liquor components m3e/f 퐶 Mass-specific heat capacity J 푃 kg∙K 퐶̅ Volume-specific heat capacity J 푃 m3K ℎ Mass-specific enthalpy J kg 퐻 Mass-specific heat of reaction J 푅 kg 퐸 Activation energy J 푎 mol ad hoc 푘 Pre-exponential factor 0 min 푘 Reaction rate constant ad hoc min 푈 Overall heat transfer coefficient J min∙m2K 푅 Volume-specific reaction rate of wood components kg of 푖 푖 m3sub∙min 푅 Volume-specific reaction rate of liquor components kg of 푗 푗 m3e/f∙min m 푢 Superficial velocity min 푣 Interstitial velocity idem ̂