Updating CRE: Implementing microkinetics Prof. Paolo Canu University of Padova - Italy Computer-Aided Chemical Reaction Engineering Course Graduate School in Chemical Engineering (GSCE) Åbo Akademi - POKE Researchers network May 2014
Contents 0. Perspective 1. Thermodynamic consistency 1.1 Equilibrium calculation 1.2 Software 1.3 Applications (Methane Steam Reforming) 2. Reactor models 2.1 Ideal 2.2 Ideal with heat- & mass transfer 2.3 Real reactors
Contents 3. Kinetics 3.1 Power law 3.2 LHHW 3.3 Detailed 4. Kinetic studies 5. Applications 5.1 Tuning of Sh(z) in a monolith (2.3 + 3.1) 5.2 CH4 combustion on Pt in a monolith (2.3 + 3.1) 5.3 CO combustion in an annular reactor (2.2 + 3.3) 5.4 CH4 partial oxidation on Rh foam (2.2 + 3.3) 5.5 CH4 partial oxidation on Pt monolith (2.3 + 3.3) 5.6 H2 oxidation on pure Pt in stagnation flow (2.3 + 3.3)
Chemical Reaction Engineering An overview Prof. Paolo Canu University of Padova - Italy Computer-Aided Chemical Reaction Engineering Course Graduate School in Chemical Engineering (GSCE) Åbo Akademi - POKE Researchers network May 2014
Personal introduction Prof. of Chemical Reaction Engineering University of Padua (NE of Italy) Industrial Engineering Dept. Chemical Engineering School: 3 y curricula (Chemical and Materials Eng.) 180 students enrolled 2 y Chemical Eng. Master 50 students/year 3 y Chemical Eng. PhD 5 students/year
Opportunities Through Erasmus, Students (incl. PhD) can stay for several months
Personal introduction Cultural background: 1. Modelling details later 2. Experiments reactors and granular 3. Industrial process eng.
Personal introduction Modelling background: 1. Chemometrics DoE (static/sequential) data manipulation (DAQ, Images, filtering,..) Data analysis (DFFT, ANOVA, PCA, Cluster analysis,..) System identification (Lin/Non-Lin models, multivariate) 2. Numerical Methods Population Balance Equations (PBEs) Methods of Weighted Residuals (MWRs) 3. CFD Modern Transport Phaenomena
Personal introduction Teaching 1. Chemical Reaction Engineering 2. Combustion 3. Transport phaenomena 4. Thermodynamics 5. Kinetics NO: Unit Operations, Control, Process Design, Industrial Chemistry
Personal introduction Teaching Fundamentals of CRE Emphasis on the physics and chemistry CRE = understanding a reaction to manipulate it
Personal introduction Research CRE (more details later) Particle Technology granular flow at high packing (mixing, shear flows,..) suspensions flow (colloids, blood,..)
Perspective The goal: (from laboratory to whatever) C measured C calculated [DoE/Optimization]
Perspective The basic paradigm: i-species material balance C t i + UC = D C + r ( ) ( ) i i i i in a single phase! r i = i-th species production rate per unit volume
Kinetics r i contains all the kinetics r = ν R local local R ( T, C, β ) e. g. R = kc C kc α α α j A B D β = [ AE,, AE,, α, α, α ] a a A B D A B D
Kinetics β = vector of size (NP x 1) For all the reactions we need: NP = NR x (2 + 2 +?) AE, AE, α a a NR can easily be 3 4!!
microkinetics reducing NP 1 Adopt elementary reactions: α = ν NP = NR x 4 2 - + Microscopic reversibility (TD) NP = NR x 2 caution: need for thermodynamics (H,S) of each species, K c K eq C i a i R(a)? High pressure applications (eg. ScCO 2 ) 3 - + Theory (energy surfaces/qm) NP = 0? perhaps simply good guesses
Partial conclusions (single phase) C local and T local must be known Large NP required detail in the mechanism
Standard problems 1. Design (i.e. Simulation) 2. Kinetics identification
1- Design (i.e. Simulation) MBi given ν and β (= r) C(t, x) Results: scale-up/down analysis of mechanisms ( reduction?)
1- Design (i.e. Simulation) Difficulties: MBi can be PDEs! (3D 1t) Options: 1. Ideal reactors 0D AEs (CSTR) 1D ODEs (PFR) 1t ODEs (batch) large Neqs (=NC) easy for ODEs, less for AEs 2. Approximations (QSSA, PE, RDS) very rough, to be forgotten! 3. Use of a unique numerical approach
1- Design (i.e. Simulation) - numerical solutions Ideal reactors: batch/pfr ODEs 1t or 1D easy CSTR AEs 0D multiple sols Departure from ideal reactors axial dispersion (d2/dx2) BVP 1D manageable non-isothermal ODEs 1D easy non-ideal flow/mixing/heating (QR is local!) PDEs 2 + D CFD Unsteady PDEs 1 + D +1t CFD
2 - Kinetics identification MBi C exp (t,x) ν and β (= r) Results: kinetic mechanism, including parameters Difficulties: difficult to automatize (..CRE is an art.. ) different hypotheses in ν different values of β estimates correct interpretation of r (local/global) and C exp (local/average)
Complication 1 non-isot ρh t htot tot p + ( ρuhtot ) = ( λ T) + + S t 1 2 = ht (, p) + U h h( T, p) 2 en = Thermal EoS One more PDE strongly coupled to MBi and Navier-Stokes (e.g. adiabatic combustion)
Complication 2 multiphase (mass+energy+momentum balances) in phase α + (mass+energy+momentum balances) in phase β + It requires interface fluxes (mass, energy, momentum )
Complication 2 multiphase Simplified approaches: transport coefficients + ideal reactors for each phase (transport analogies are very questionable with reaction!) Appropriate approach: CFD Difficulties: moving interfaces dispersed, irregular phases (e.g. particles) turbulent flow large number of species
Personal conclusions 1. Many levels of complexity in the approaches 2. Surprisingly, almost any model fits the data! 3. A few really give fundamental/intrinsic understanding To prove a simpler model is adequate a more complex model is required e.g. Ideal Gas Model, where a Complex EoS is required to allow its use
Personal conclusions An effort is required to develop a single framework for kinetic studies Ingredients: 1. Detailed chemistry + QM 2. Accurate measurements (well distributed in t or x) 3. Accurate flow simulation capabilities (i.e. CFD) 4. Optimization tools