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Introduction to API Process Simulation. Pharmaceutical API Process Development and Design. Module Structure. Process modeling basics Model applications Model types Modeling procedure Simulation packages DynoChem Examples Heat transfer Batch reactor with accumulation effects.

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introduction to api process simulation

Introduction to API Process Simulation

Pharmaceutical API Process Development and Design

module structure
Module Structure
  • Process modeling basics
    • Model applications
    • Model types
    • Modeling procedure
  • Simulation packages
    • DynoChem
  • Examples
    • Heat transfer
    • Batch reactor with accumulation effects
model applications
Model Applications
  • Effects of process parameter changes
  • Optimal operating policies for batch operations
    • Compare different reactant or solvent feed strategies
    • Maximization of yield in crystallization
    • Minimize side-product formation in batch reaction
  • Safety
    • Loss of cooling
model types
Model Types
  • Mechanistic (white box)
  • Empirical (black box)
  • Combined models (grey box)
  • Lumped parameter
  • Distributed parameter
  • Continuous
  • Discrete
  • Hybrid discrete/continuous
modeling procedure
Modeling Procedure
  • Problem definition
    • Level of detail
    • Inputs and outputs
  • Identify controlling mechanisms
  • Evaluate problem data
    • Measured data
    • Parameter values
  • Construct model
  • Solve model
controlling mechanisms
Controlling Mechanisms
  • Chemical reaction
  • Mass transfer
    • Diffusion
    • Boundary layer
  • Heat transfer
    • Conduction
    • Convective
    • Radiation
  • Fluid flow
  • Mixing
  • Evaporation
model construction
Model Construction
  • System boundary and balance volumes
  • Characterizing variables
  • Balance equations
  • Transfer rate specifications
  • Property relations
model components
Model Components
  • Model equations and variables
    • Overall and component mass balances
    • Energy balance
    • Momentum balance
    • Transfer rates
    • Physical properties
  • Initial conditions
  • Parameters
software packages
Software Packages
  • Examples
    • gPROMS, DynoChem, Daesim Studio, MATLAB
  • Desired features
    • Solution of differential algebraic equation systems
    • Parameter estimation
    • Optimization
    • Model templates, physical properties estimation
  • Software used for examples in this module
    • DynoChem
dynochem features
DynoChem Features
  • Tools for simulation, optimization and fitting
  • Excel spreadsheets for data entry and utility calculations
  • Model library
    • Templates for common API Unit Operations
  • Utilities for physical properties, vessel characterization
dynochem model structure
DynoChem Model Structure
  • Component Definitions
    • Name, molecular weight, functional groups for property calculations
  • Process Definition
    • Statements
  • Scenarios
    • Initial values, parameters
  • Data sheets
    • Profiles for measured variables
  • Phase
    • Represents vessel (e.g. header tank, condenser, receiving vessel) or compartment (e.g. headspace)
    • Solid, liquid, gas
  • Flow
    • Transfer, feed, remove
  • Reactions
    • Take place in phases or flows
statements contd
Statements (contd.)
  • Heat transfer
    • Heat or cool a phase with a jacket (flow)
    • Heat exchange between phases
    • Heat duty
  • Mass transfer
    • Liquid-liquid (transfer between immiscible phases)
    • Gas-liquid (e.g. hydrogen into solvent)
    • Solid-liquid (e.g. dissolution)
statements contd14
Statements (contd.)
  • Condense
    • V-L phase equilibrium (Antoine eqn)
  • Calculate
    • Set up user defined equations
  • Integrate
    • Integrate variables during a simulation
  • Solver
    • Solution method, accuracy
balance volumes
Balance Volumes
  • Bulk liquid
  • Heating fluid
assumptions and controlling mechanisms
Assumptions and Controlling Mechanisms
  • Assumptions
    • Neglect agitator work
    • Neglect heat losses to environment
    • Neglect evaporation
    • Constant properties
  • Controlling Mechanisms
    • Flow of heating liquid
    • Heat transfer between jacket and tank
    • Perfect mixing
model variables
Model Variables

Bulk mass

Bulk specific heat

Bulk temperature

Jacket mass flow rate

Jacket specific heat

Jacket inlet temperature

Jacket outlet temperature

model objectives
Model Objectives
  • Determine UA by fitting experimental data
  • Estimate time to heat bulk liquid to boiling point for different jacket temperatures
dynochem model summary
DynoChem Model Summary
  • Components
    • solvent (methanol), htfluid
  • Process definition (statements)
    • Phase bulk liquid
    • Heat bulk liquid with jacket
  • Scenarios (initial values and parameters)
    • Bulk liquid: Initial temperature, solvent mass, specific heat
    • Jacket: Inlet temperature, flow, specific heat
    • UA (to be determined by fitting data)
simulation tool
Simulation Tool
  • Requires UA value
  • Obtain by fitting simulated temperature profile to plant data
fitting tool
Fitting Tool
  • Least squares fitting (Levenberg-Marquardt)
  • Compare heating time with different jacket parameters
example 2 fed batch reaction with safety constraint

Example 2: Fed-batch reaction with safety constraint

(see handout for detailed process description)

balance volumes27
Balance Volumes
  • Bulk liquid
  • Heating fluid
  • Header tank
process description
Process Description
  • Exothermic reaction
    • substrate + reagent → product
  • Isothermal operation, fed-batch
  • Objective
    • Minimize time to produce given amount of product
  • Manipulated variable
    • Feed rate of reagent
model variables29
Model Variables

concentration of species X in reactor;

volume of material in reactor;

maximum volume;

feed rate;

concentration of X in header tank;

kinetic rate constant;

reactor temperature (normal process operation);

Maximum temperature of synthetic reaction

(temperature attained after cooling failure);

maximum allowable temperature;

heat of reaction;

Reaction heat generation;


heat capacity of material in reactor

safety constraint
Safety Constraint
  • MTSR (maximum temperature of synthetic reaction)
safety constraint31
Safety Constraint
  • Cooling failure → Stop feed→ Reaction continues till unreacted components are exhausted
  • Maximum attainable temperature
  • Without safety constraint, batch operation (add all B at t=0) is optimal

extent of reaction after feed is stopped

Srinivasan et al., (2003), Computers and Chemical Engineering, 27(2003) 1-26

feed profile


Feed Profile
  • Max flow (1, 3): Volume and safety constraints are inactive
  • Controlled flow (2): Safety constraint is active
  • No flow (4): Volume at maximum value

Srinivasan et al., (2003), Computers and Chemical Engineering, 27(2003) 1-26

reaction equations
Reaction Equations

Heat transfer equations as in Example 1

dynochem model summary34
DynoChem Model Summary
  • Components
    • solvent, coolant, reagent, substrate, product
  • Process definition (statements)
    • Phase bulk liquid
    • Heat bulk liquid with jacket
    • Phase header tank
    • Transfer to bulk liquid from header tank
    • Reactions in bulk liquid
    • Calculate MTSR
dynochem model summary35
DynoChem Model Summary
  • Scenarios (initial values and parameters)
    • Bulk liquid: Initial temperature, solvent mass, specific heat, substrate moles, reagent moles
    • Header tank: Temperature, solvent mass, reagent moles
    • Jacket: Inlet temperature, flow, specific heat, UA
data sheet for simulation
Data Sheet for Simulation
  • Adjust feed profile to satisfy MTSR and volume constraints
  • Isothermal temperature profile is imposed through data sheet (DynoChem calculates required jacket temperature internally)
simulation results38

Safety constraint active

Volume constraint active

Safety and volume constraints inactive

Simulation Results

Volume constraint no longer active

  • Increase reactor volume, reduce cycle time
  • Katalin Hangos and Ian Cameron, Process Modeling and Model Analysis, Academic Press, 2001, London.
  • P.E. Burke, Experiences in Heat-Flow Calorimetry and Thermal Analysis, in W. Hoyle (ed), Pilot Plants and Scale-Up of Chemical Processes, Royal Society of Chemistry, 1997, Cambridge.