(Chemical) Industry Drivers Societal Needs & Wants Potential to make or lose $ Competition- increasing and global

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1 Why do simulations? Molecular Modelling Why Simulations are the only general method for solving complex (many-body) problems. Experiment is limited and expensive. Simulations can complement the experiment. They scale up with the computer power. (Chemical) Industry Drivers Societal eeds & Wants Potential to make or lose $ Competition- increasing and global Efficient i Management of Time Capital Knowledge Creativity Compression of R&D timescales Rapid & Accurate Evaluation of Complete Solutions Rapid & Parallel Development (e.g. Materials, Process, Environmental, & Economic) Go/oGo decisions Modeling & ptimization Steam cracking Industrially important & very large scale Main source of light olefins Feedstock: ethane, naphtha, gas oil 800 kton/yr ethene and 400 kton/yr propene per unit Complex radicalar gas phase chemistry (10 4 ) important reactions Many processes follow similar chemistry: partial oxidation, polymerisation, combustion 1 2

2 Steam cracking Feed: C 2,C 3,C 4 aphtha (C 5 -C 10 ) Vacuum Gas il (C 10 -C 30 ) T: K Pressure: 2-3 atm Steam: kg/kg gc Modelling aspects: oven Convection-section Reactor tubes Products: Gas-fired oven Modelling aspects Industrial process Modelling aspects: oven Unit perations Pressure Dependence Reactor Kinetic model Reaction network Transport Phenomena Reaction Engineering Kinetic and thermodynamic parameters Quantum chemistry Ref.: eynderickx et al.,aiche Journal 47, 388 (2001) 3 4

3 Modelling aspects: oven Temperature [K] Pressure [Pa] Modelling aspects Industrial process T ps Unit perations Pressure Dependence Reactor Kinetic model Reaction network Quantum chemistry Transport Phenomena Reaction Engineering Kinetic and thermodynamic parameters Modelling aspects: oven Reactor: Tube skin temperature Molecular Level Kinetic Model: Mechanism of elementary reactions Kinetic & thermodynamic data Classical kinetic study: empirical + chemical intuition Molecular level understanding of catalysis & green chemisty The ultimate goal for catalysis science in the 21st century is g y y to design the active sites to provide 100% selectivity for catalyst-based chemical processes Somorjai,

4 Modelling aspects: inside the reactor Fundamental Kinetic Model Independent of feedstock and process conditions Reaction etwork of elementary reactions Intrinsic kinetic parameters for every elementary reaction - Estimation through regression of available experimental data - Ab initio calculations ntial energy (kj/mol) Kinetic parameters: Energy profiles Poteand C-C distance (pm) ydrogen-abstraction bt ti Radical addition and -scission Kinetic Model: Reaction families Bond-scission and radical recombination R 1 R 2 R 1 + R 2 ature of the attacking radical R 1 R 4 R 6 R 2 C 3 + C 2 C 1 R 2 R 1 R 4 C 3 C 2 C 1 R 6 ydrogen abstraction ti R 1 + R 2 R 1 + R 2 Radical addition and -scission R 1 + R 2 R 3 R 1 R 2 R 3 Disproportionation and molecular addition R 1 + R 2 R 3 R 1 + R 2 R 3 R 3 methyl C 3 primary R-C 2 secondary R 1 -C -R 2 tertiary R 1 -C -R 2 R 3 R 5 R 7 R 3 R 5 R 7 alkylic allylic vinylic benzylic 2 C 2 C 2 C R 2 C 2 C R 1 C R 1 C 2 C 2 C C R C 2 R C C 6 5 R 2 R 1 C C

5 ature of the attacking radical Modelling: different levels Addition type, E a (kj/mol) -scission type, E a (kj/mol) Reactor ribution (kj/mol) Contr p, 24.3 allylic s, 22.6 benzylic alkylic vinylic t, 18.4 p, 14.7 s, 10.8 t, 10.5 Me, 0 p, -2.8 s, -7.5 t, p, s, Contr ribution (kj/mol) vinylic p, 26.1 alkylic benzylic s, 14.1 Me, 0 p, -8.2 s, t, p, s, t, p, allylic s, t, ydrogenation/dehydrogenation cyclic C6 ydrogenation/dehydrogenation cyclic C6 Industrially important: Diesel and gasoline production aphtha reforming/petroleum refining ydrocracking Model reactions Experimental studies Kinetic and spectroscopic Pt: very effective (de)hydrogenation catalyst Modelling: different levels Inside reactor 9 10

6 Modelling: different levels Catalyst particle A B 1. Transport of A from bulk to pellet surface 2. Transport of A to active site 3. Chemisorption of A 4. Surface reaction 5. Desorption of B 6T 6. Transport of fbt to pellet llt surface 7. Transport of B to bulk phase Reaction Mechanism oriuti-polanyi Mechanism: sequential addition of -atoms is there a dominant Reaction Path? is there a Rate Determining Step? is Dehydrogenation of benzene important? Modelling: different levels Molecular level Quantum Modeling Model for the catalyst (111); (110); (100); etc. Cluster, Periodic Slab, frozen/relaxed (Choice 4) 11 12

7 Drug Design Drug Discovery & Development Identify the site Identify disease Isolate protein involved in disease (2-5 years) Find a drug effective against disease protein (2-5 years) Preclinical testi ng (1-3 years) Formulation & Scale-up File ID uman clinical trials (2-10 years) File DA FDA approval (2-3 years) Estrogen Receptor Application to Drug Discovery 1: identify disease then the interactions 3: develop a drug to safely attack disease Chemistry Pharmacology Clinical Trials : Ring : Ring D/A 2: identify key disease proteins Biology Bioinformatics Chemoinformatics Comp. Chem. Molecular Modeling D/A 13 14

8 Rational Drug Design Successes While we are still waiting for a drug totally designed from scratch, many drugs have been developed with major contributions from computational methods F Et C 2 norfloxacin (1983) antibiotic first of the 6-fluoroquinolones QSAR studies Cl Bu Me Me donepezil (1996) Alzheimer's treatment acetylcholinesterase inhibitor shape analysis and docking studies losartan [Cozaar] (1995) angiotensin II antagonist anti-hypertensive Modeling Angiotensin II octapeptide S 2 S S 2 2 dorzolamide [Trusopt] (1994) glaucoma treatment carbonic anhydrase inhibitor SBLD and ab initio calcs Me 2 zolmatriptan [Zomig] T 1D agonist migraine treatment Molecular modeling Definitions: Molecular modelling is a collection of techniques to model or mimic the behaviour of molecules. The keywords: model and molecules Molecule defines the size and the scale of interest (i.e. eletrons-atoms) Model&Molecules define the ModMol as branch of Theoretical Chemistry Rational Drug Design Successes Two ways of Simulation IV-1 protease inhibitors Ph indinavir [Crixivan] (Merck, 1996) X-ray data from enzyme and molecular mechanics S Me Ph Ph ritonavir [orvir] (Abbott, 1995) peptidomimetic strategy S A. Give us the phenomena and invent a model to mimic the problem. The semi-empirical approach. Problem: one cannot reliably extrapolate the model away from the empirical data. Me SPh Ph C 2 B. Take a model (Maxwell, Boltzmann and Schödinger) and -numerically- solve the mathematical problem to determine the properties. The first principles or ab initio methods. nelfinivir [Viracept] (Agouron, 1996) saquinavir [Invirase, Fortovase] (Roche, 1990) transition state mimic of enzyme substrate 15 16

9 Challenges of Simulation Physical and mathematical underpinnings What approximations come in: Computer time is limited ïfew particles for short periods of time. (space-time is 4D. Moore s Law implies lengths and times will double every 6 years if ().) Systems with many particles and long time scales are problematical. amiltonian is unknown, until we solve the quantum many-body problem! ow do we estimate errors? Statistical and systematic. ow do we manage even more complex codes? Time year min. ms ierarchy of Models Quantitative Structure/Property Relationships (QSPR) & Theory Continuum ns CFD Mesoscale Mechanical Kinetic s Emag s 1Å Molecular Molecular Dynamics Monte Carlo Quantum 10Å 100Å 1 m 1cm km Macroscopic Plant Process Fab Distance Multi-scale Years Yards Seconds Inches Microseconds Microns Picoseconds anometers Femptoseconds Angstroms 2 F=ma = E ierarchical Simulations: Chemistry Genetic Engineering Pharmaceuticals Fossil Energy Fuel Cells Cancer anotechnology Research C 1 Chemistry Polymers rganelle Electronic Modeling & ptical Ceramics Specialty Materials Receptor Chemicals Modeling Metal & Catalysts Alloys Biochemistry Molecular Self-Assembly Catalysis Chemistry Equilibrium & Rate Constants Molecular Dynamics Force Fields QUATUM MECAICS Material Science Meso-scale Modeling Design Materials Molecules Atoms Electrons Molecular Modeling and Process Simulation: ext Generation Process Simulator Structure and Properties Quantum Chemistry F, MP2, MP4,.. DFT Force Field (MM) Reaction Mechanism Kinetics CAMD Computer-Aided Molecular Design Model validation Parameter Estimation Molecular Simulation MD / MC Process Simulation Database Statistical Thermodynamics Model Development (SAFT, LF-B, ) 17 18

10 Summary: Mol. Modeling & ChE Molecular Modeling (Computational Chemistry, Computational Materials Science, Theory, Modeling ) is the natural extension and limit of the reductionist approach to chemical engineering. MM is broadly applicable because everything is made of atoms and molecules. The power of MM is growing rapidly with the continuing development of computer power, new algorithms, and the availability of software. Today MM can provide useful estimates of the properties and behavior of materials- even before they have been synthesized. (materials design) Today MM can provide useful estimates of the parameters and behavior needed to do traditional chemical engineering process development & design. MM works best in partnership with experiments and with traditional estimation and design approaches. Thinking Like a Chemical Engineer time Processes Process Analysis ptimization Control Unit perations Petroleum Biological Systems Surface Science Semiconductors Production Specific Problem Food Energy Materials Design Environment Pharmaceuticals Polymers Transport Thermodynamics Kinetics Quantum, Classical & Statistical Mechanics application Particles Molecular Modeling and the Chemical Engineering Curriculum Modeling. Chemical Engineers should be aware of the possibility that t useful estimates of material properties can be calculated Chemical Engineers should have some familiarity with the techniques of MM and should be able to make informed guesses as to whether any given properties and materials are amenable to MM. MM techniques are wonderful pedagogical tools for understanding the fundamental physical processes which underlie thermodynamics, transport phenomena, and chemical kinetics. Biology & Biochemistry Material Science 19 20