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Developing the next generation of Real Time Optimization Technologies (Blend Optimization)


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Developing the next generation of Real Time Optimization Technologies (Blend Optimization)

  1. 1. Real-­‐Time  Blend  Optimization   Industrial  Modeling  Framework  (RTBO-­‐IMF)     i  n  d  u  s  t  r  IAL  g  o  r  i  t  h  m  s  LLC.  (IAL)   June  2013     Introduction  to  Real-­‐Time  Blend  Optimization,  UOPSS  and  QLQP     Presented  in  this  short  document  is  a  description  of  what  is  typically  known  as  on-­‐line  or  real-­‐time   "multi-­‐process",  "multi-­‐pool",  "multi-­‐product",  "multi-­‐property"  and  "multi-­‐period"  blend   optimization.    This  kind  of  processing  is  found  in  all  petroleum  refineries  where  the  blending   process  mixes  diverse  refinery  rundown  streams  or  components  into  various  types  and  grades  of   gasoline,  jet  fuel,  diesel  and  heating  oil.    Figure  1  depicts  these  four  types  of  blended  products  with   shared  components  resources  including  their  inventory  such  as  cracked  naphtha,  kerosene,  etc.   configured  in  our  unit-­‐operation-­‐port-­‐state  superstructure  (UOPSS)  (Kelly,  2004b,  2005,  and   Zyngier  and  Kelly,  2012).             Figure  1.  Gasoline,  Jet  Fuel,  Diesel  &  Heating  Oil  Blending  Flowsheet  Example.    
  2. 2. The  CTank's  and  PTank's  (triangle  shapes)  in  Figure  1  represent  component  and  product  tanks  or   pools  where  the  small  circle  shapes  define  what  we  call  inlet  and  outlet  (with  "x")  ports  and  are   only  found  in  our  UOPSS.    The  Blender's  (rectangle  shapes  with  "x")  are  controlled  mixers  in  the   sense  that  component  flows  into  the  blenders  can  be  regulated  and  are  sometimes  referred  to  as   pools  with  no  inventory  and  maybe  either  in  continuous  or  semi-­‐continuous  operation.    The   diamond  shapes  are  called  perimeters  and  are  the  usual  source  and  sink  nodes  found  in  other  types   of  network  flow  representations.     On-­‐line  analyzers  or  instruments  are  usually  available  to  measure  the  intensive  property   specifications  of  the  material  such  as  octane,  cetane,  sulfur,  viscosity,  density,  vapor  pressure,   distillation  temperature,  flash  point,  cloud  point,  etc.  just  to  name  a  few.    The  other  type  of   continuous  process  configured  is  a  Hydrotreater  which  reacts  hydrogen  with  the  virgin  diesel   stream  (VDiesel)  in  the  presence  of  a  catalyst  at  high  pressure  to  reduce  its  sulfur  content  i.e.,   HDiesel  will  have  a  very  low  sulfur  concentration.    The  "severity"  (i.e.,  its  process/operating   condition)  of  the  hydrotreater  is  also  modeled  in  order  to  be  able  to  manipulate  or  optimize  the   degree  or  extent  to  which  the  virgin  diesel  is  desulfurized.    The  quantity  (flows  and  inventories)   and  quality  (properties  and  conditions)  aspects  of  the  problem  as  well  as  its  logic  attributes  (Kelly,   2006)  define  what  we  call  the  quantity-­‐logic-­‐quality  phenomena  (QLQP)  where  more  details   around  the  blending  process  modeling  and  its  planning  and  scheduling  can  also  be  found  in  Kelly   (2004a)  and  Castillo,  Kelly  and  Mahalec  (2013).    Another  important  issue  is  the  handling  feedback   especially  when  controlling  flows,  inventories  and  properties  in  real-­‐time  or  closed-­‐loop.    This  is   addressed  using  our  state-­‐of-­‐the-­‐art  dynamic  and  nonlinear  data  reconciliation  and  regression   technology  (Kelly,  1998  and  2004c)  implemented  inside  a  "moving  horizon  estimation"  (MHE)   framework  (Kelly  and  Zyngier,  2008).     What  makes  this  blending  configuration  interesting  is  the  modeling  of  all  four  products  together   into  a  single  blending  optimization  problem.    Due  to  the  sharing  of  rundown  components  between   one  or  more  blenders  at  different  times,  there  is  tremendous  opportunity  to  produce  on-­‐ specification  product  using  the  lowest  cost  and  most  available  components.    Existing  blend  control   and  optimization  software  only  manage  one  blender  at  a  time  with  no  other  pools  such  as  tanks   included,  and  they  look  out  no  further  than  the  current  blend  (mono-­‐period).    In  our  formulation   we  look  out  over  multiple  blends  of  product  over  multiple  blenders  considering  multiple  periods  or   time-­‐intervals  into  the  future  where  these  time-­‐periods  can  be  either  of  equal  or  unequal  duration.     In  addition  and  unique  to  our  formulation,  we  also  allow  the  integration  of  other  types  of  processes   (not  only  hydrotreaters)  such  as  crude  distillation  units,  catalytic  reformers,  fluidized  catalytic   converters,  hydrocrackers  and  alkylation  units.    This  allows  for  upstream  manipulations  of   process/operating  conditions  to  produce  more  appropriate  component  rundown  properties  before   they  even  enter  the  blending  area.  This  alleviates  possible  quantity  and/or  quality  bottlenecks  (long   and  shorts  of  material)  that  may  arise  during  the  blending  operation  avoiding  off-­‐specification   events  as  well  as  minimizing  over  and  under-­‐use  of  high-­‐octane,  high-­‐cetane,  low-­‐sulfur  and/or   low-­‐viscosity  component  rundowns.     Benefits  for  such  a  RTBO  application  can  be  in  the  millions  of  dollars  and  are  comparable  to  the   benefits  defined  by  Kelly  and  Mann  (2003)  for  crude-­‐oil  blend  optimization.    More  specifically,  a   similar  installation  of  this  technology  and  its  approach  installed  at  a  major  oil  company's  refinery  in   Europe  quoted  a  payback  period  of  only  two-­‐weeks!         Industrial  Modeling  Framework  (IMF),  IMPRESS  and  SIIMPLE    
  3. 3. To  implement  the  mathematical  formulation  of  this  and  other  systems,  IAL  offers  a  unique   approach  and  is  incorporated  into  our  Industrial  Modeling  and  Pre-­‐Solving  System  we  call   IMPRESS.    IMPRESS  has  its  own  modeling  language  called  IML  (short  for  Industrial  Modeling   Language)  which  is  a  flat  or  text-­‐file  interface  as  well  as  a  set  of  API's  which  can  be  called  from  any   computer  programming  language  such  as  C,  C++,  Fortran,  Java  (SWIG),  C#  or  Python  (CTYPES)   called  IPL  (short  for  Industrial  Programming  Language)  to  both  build  the  model  and  to  view  the   solution.    Models  can  be  a  mix  of  linear,  mixed-­‐integer  and  nonlinear  variables  and  constraints  and   are  solved  using  a  combination  of  LP,  QP,  MILP  and  NLP  solvers  such  as  COINMP,  GLPK,  LPSOLVE,   SCIP,  CPLEX,  GUROBI,  LINDO,  XPRESS,  CONOPT,  IPOPT  and  KNITRO  as  well  as  our  own   implementation  of  SLP  called  SLPQPE  (successive  linear  &  quadratic  programming  engine)  which  is   a  very  competitive  alternative  to  the  other  nonlinear  solvers  and  embeds  all  available  LP  and  QP   solvers.     The  underlying  system  architecture  of  IMPRESS  is  called  SIIMPLE  (we  hope  literally)  which  is  short   for  Server,  Interacter  (IPL),  Interfacer  (IML),  Modeler,  Presolver  Libraries  and  Executable.    The   Server,  Presolver  and  Executable  are  primarily  model  or  problem-­‐independent  whereas  the   Interacter,  Interfacer  and  Modeler  are  typically  domain-­‐specific  i.e.,  model  or  problem-­‐dependent.     Fortunately,  for  most  industrial  planning,  scheduling,  optimization,  control  and  monitoring   problems  found  in  the  process  industries,  IMPRESS's  standard  Interacter,  Interfacer  and  Modeler   are  well-­‐suited  and  comprehensive  to  model  the  most  difficult  of  production  and  process   complexities  allowing  for  the  formulations  of  straightforward  coefficient  equations,  ubiquitous   conservation  laws,  rigorous  constitutive  relations,  empirical  correlative  expressions  and  other   necessary  side  constraints.     User,  custom,  adhoc  or  external  constraints  can  be  augmented  or  appended  to  IMPRESS  when   necessary  in  several  ways.    For  MILP  or  logistics  problems  we  offer  user-­‐defined  constraints   configurable  from  the  IML  file  or  the  IPL  code  where  the  variables  and  constraints  are  referenced   using  unit-­‐operation-­‐port-­‐state  names  and  the  quantity-­‐logic  variable  types.    It  is  also  possible  to   import  a  foreign  LP  file  (row-­‐based  MPS  file)  which  can  be  generated  by  any  algebraic  modeling   language  or  matrix  generator.    This  file  is  read  just  prior  to  generating  the  matrix  and  before   exporting  to  the  LP,  QP  or  MILP  solver.    For  NLP  or  quality  problems  we  offer  user-­‐defined  formula   configuration  in  the  IML  file  and  single-­‐value  and  multi-­‐value  function  blocks  writable  in  C,  C++  or   Fortran.    The  nonlinear  formulas  may  include  intrinsic  functions  such  as  EXP,  LN,  LOG,  SIN,  COS,   TAN,  MIN,  MAX,  IF,  LE,  GE  and  KIP,  LIP,  SIP  (constant,  linear  and  monotonic  spline  interpolation)  as   well  as  user-­‐written  extrinsic  functions.     Industrial  modeling  frameworks  or  IMF's  are  intended  to  provide  a  jump-­‐start  to  an  industrial   project  implementation  i.e.,  a  pre-­‐project  if  you  will,  whereby  pre-­‐configured  IML  files  and/or  IPL   code  are  available  specific  to  your  problem  at  hand.    The  IML  files  and/or  IPL  code  can  be  easily   enhanced,  extended,  customized,  modified,  etc.  to  meet  the  diverse  needs  of  your  project  and  as  it   evolves  over  time  and  use.    IMF's  also  provide  graphical  user  interface  prototypes  for  drawing  the   flowsheet  as  in  Figure  1  and  typical  Gantt  charts  and  trend  plots  to  view  the  solution  of  quantity,   logic  and  quality  time-­‐profiles.    Current  developments  use  Python  2.3  and  2.7  integrated  with  open-­‐ source  Dia  and  Matplotlib  modules  respectively  but  other  prototypes  embedded  within  Microsoft   Excel/VBA  for  example  can  be  created  in  a  straightforward  manner.     However,  the  primary  purpose  of  the  IMF's  is  to  provide  a  timely,  cost-­‐effective,  manageable  and   maintainable  deployment  of  IMPRESS  to  formulate  and  optimize  complex  industrial  manufacturing   systems  in  either  off-­‐line  or  on-­‐line  environments.    Using  IMPRESS  alone  would  be  somewhat   similar  (but  not  as  bad)  to  learning  the  syntax  and  semantics  of  an  AML  as  well  as  having  to  code  all  
  4. 4. of  the  necessary  mathematical  representations  of  the  problem  including  the  details  of  digitizing   your  data  into  time-­‐points  and  periods,  demarcating  past,  present  and  future  time-­‐horizons,   defining  sets,  index-­‐sets,  compound-­‐sets  to  traverse  the  network  or  topology,  calculating   independent  and  dependent  parameters  to  be  used  as  coefficients  and  bounds  and  finally  creating   all  of  the  necessary  variables  and  constraints  to  model  the  complex  details  of  logistics  and  quality   industrial  optimization  problems.    Instead,  IMF's  and  IMPRESS  provide,  in  our  opinion,  a  more   elegant  and  structured  approach  to  industrial  modeling  and  solving  so  that  you  can  capture  the   benefits  of  advanced  decision-­‐making  faster,  better  and  cheaper.     References     Kelly,  J.D.,  "A  regularization  approach  to  the  reconciliation  of  constrained  data  sets",  Computers  &   Chemical  Engineering,  1771,  (1998).     Kelly,  J.D.,  Mann,  J.M.,  "Crude-­‐oil  blend  scheduling  optimization:  an  application  with  multi-­‐million   dollar  benefits",  Hydrocarbon  Processing,  June,  47,  July,  72,  (2003).     Kelly,  J.D.,  "Formulating  production  planning  models",  Chemical  Engineering  Progress,  January,  43,   (2004a).     Kelly,  J.D.,  "Production  modeling  for  multimodal  operations",  Chemical  Engineering  Progress,   February,  44,  (2004b).     Kelly,  J.D.,  "Techniques  for  solving  industrial  nonlinear  data  reconciliation  problems",  Computers  &   Chemical  Engineering,  2837,  (2004c).     Kelly,  J.D.,  "The  unit-­‐operation-­‐stock  superstructure  (UOSS)  and  the  quantity-­‐logic-­‐quality   paradigm  (QLQP)  for  production  scheduling  in  the  process  industries",  In:  MISTA  2005  Conference   Proceedings,  327,  (2005).     Kelly,  J.D.,  "Logistics:  the  missing  link  in  blend  scheduling  optimization",  Hydrocarbon  Processing,   June,  45,  (2006).     Kelly,  J.D.,  Zyngier,  D.,  "Continuously  improve  planning  and  scheduling  models  with  parameter   feedback",  FOCAPO  2008,  July,  (2008).       Zyngier,  D.,  Kelly,  J.D.,  "UOPSS:  a  new  paradigm  for  modeling  production  planning  and  scheduling   systems",  ESCAPE  22,  June,  (2012).     Castillo,  P.A.,  Kelly,  J.D.,  Mahalec,  V.,  "Inventory  pinch  analysis  for  gasoline  blend  planning",  AIChE  J.,   June,  (2013).