The document describes the IMPRESS industrial modeling and presolving system. IMPRESS provides optimization and estimation capabilities for planning, scheduling, data reconciliation, and real-time control problems. It includes components for modeling, presolving, interfacing with solvers, and interactive use via programming languages. IMPRESS supports linear, nonlinear, discrete, and mixed-integer formulations.

1. industrIALgorithms
IMPRESS
Industrial Modeling & Presolving System
Are you ready to get IMPRESSed?
Brenno, December, 2012
12/10/2012

2. Commercial Scheduling Tools
industrIALgorithms
Simulation based Simulation based Simulation based Optimization based Optimization based
Suite of tools Suite of tools
Suite of tools (PIMS, Suite of tools (RPMS,
(SIMTO Sched, (Flower, Suite of tools
APS, MBO, PS, BLEND,
MBlend, Dock Compass, (IMPRESS)
IMOS) SAND)
Sol) Optimix)
Scheduling Blend: Scheduling Blend: Scheduling Blend: Scheduling Blend: Scheduling Blend:
MBO SIMTO M-Blend Optimix BLEND CBS, PBS-IMF
Easy interface Easy interface Interface less
Installed in the
Installed in the Installed in the interactive No User-Interface
machine
machine machine Access by intranet
Integration by Data Integration by Web Integration by File,
Integration by XML
Base Service Excel, API’s
BP
VALERO PETROCHINA
CONOCO
CHEVRON INDIAN OIL
SHELL

3. Types of Optimization & Estimation
 IMPRESS is designed both for industrial decision-
making & data-mining problems deployed off-line,
in-line & on-line such as:
 Planning & Scheduling Optimization (active).
 Data Reconciliation & Regression Estimation (passive).
 Real-Time Control & Optimization (active).
 Monitoring, Tracking & Tracing (passive).
 The terms active & passive imply the "degree of causality".
Active models must be "causal" (cause & effect amongst
variables) & passive models may or may not be causal (no
cause & effect required) also known as "observational".

7. Advanced Modeling System: IMPRESS
 Problems are configured either interfacing with a
flat-file language (IML = Industrial Modeling
Language) or interactively using a programming
language such as Python, Java, C#, C++, C or
Fortran (IPL = Industrial Programming Language).
 Currently have bindings to several linear and
nonlinear programming solvers such as COINMP,
GLPK, LPSOLVE, SCIP, XPRESS, XPRESS-SLP,
CONOPT, IPOPT, KNITRO, NOVA & SLPQPE, etc.

8. System Architecture: SIIMPL
 SIIMPL = Server, Interfacer, Interacter, Modeler,
Presolver Libraries.
 There are five DLL components in IMPRESS:
 Server = data modeling & presolving routines.
 Interfacer = parsing for the language.
 Interacter = inserting, updating & viewing routines
for the interchange.
 Modeler = formulating of the variables, constraints,
derivatives & expressions including
"dependent" sets, lists & parameters.
 Presolver = bindings for 3rd-party solving-systems.

9. Types of Variables (Phenomena)
Q=quantity, L=logic, q=quality
Q=flow
L=setup, startup, switchover, shutdown
“unit-operation”
“out-port” “out-port-to-in-port” stream, path, etc.
“in-port”
Q=flow (rate, yield, holdup)
q=densities, components, properties, conditions
rate = flow / (time-period-duration)
yield = (port-flow) / (unit-operation-flow)
holdup = flow[t] – flow[t-1]
Q=flow (rate, holdup)
q=conditions
L=setup, startup, switchover, shutdown

10. Time Model
• Logistics (Quantity*Logic (proy’d-quality), MILP):
– “discrete-time” where each time-period has the same duration.
– Time-periods may be “small-buckets” (un-ary) or “big-buckets”
(N-ary):
• If un-ary then only one activity per time-period (scheduling) but if N-ary
Planuling = Planning + Scheduling
then multiple activities per time-period where a “time-portion” variable
for each operation is applied (planning).
• Quality (Quantity*quality (fixed-logic), NLP):
– “distributed-time” where each time-period may have a different
duration (global/common time-grid).
– Same as logistics.
• All input data is entered in “continuous-time” (begin, end-
times) and digitized i.e., discretized or distributed
accordingly.

11. Scheduling
without the blender scheduling details
planning
crude
Solution: duration of each blender grade
with time-period
Scheduling only for the blender:
Including setup, startup,
switchover, shutdown

12. Prod A Prod A
Prod B Prod B
Prod C Prod C
Prod D Prod D
Crude A
Prod E Prod E
Crude B
Prod F Prod F
Crude C Prod G Prod G
Crude D Prod H Prod H
Crude Recipe Refining Units Products Recipes Products
Operational Modes Scheduling
Crude Scheduling Products Planning
resulting targets to
Including setup, startup, switchover, shutdown
blender Scheduling/RTO
1st Planuling (big-buckets) 2nd Scheduling
(small-buckets)

13. User/Adhoc Linear Constraints
• There are 2 ways to add user, adhoc or custom
linear/logistics constraints or formulations:
– Inside IML/IPL using special frames with access to UOPSS
flow, holdup, setup, startup, etc. variables i.e.,
• UOLConstraint-&sUnit,&sOperation,&sName,&iBegin,&iEnd,@sType,@rValue,@rWeight
– Import a “foreign” LP (CPLEX) file generated by GAMS,
AMPL, MOSEL, etc.
• Variables are referenced as “Xnnn” and constraints by
“Fmmm” where if “nnn” corresponds to an IMPRESS generated
variable index number then the IMPRESS variable will be used
instead. All “mmm” constraint index numbers correspond to
new constraint instances (or cuts/valid inequalities).

14. User/Adhoc Nonlinear Constraints
• There are 3 ways to add user, adhoc or custom
nonlinear/quality constraints or formulations:
– Single-value formula using conditions on “black-box”
subtype unit-operations to calculate port flows, rates,
yields, densities, components, etc.
– Single-value functions for “coefficients” as functions of
conditions on “black-box” subtype unit-operations and
written in C, C++ or Fortran i.e., useful for physical-
properties such as enthalpy, entropy, fugacity, etc.
– Multi-value functions on “black-blox” subtype unit-
operations written in C, C++ or Fortran.

15. User/Adhoc Nonlinear Constraints
Multi-value functions on “black-blox” subtype unit-operations written in C,
C++ or Fortran.
function UOfunction(numtpp, numtpf,
& durtpp, durtpf,
& numip, numop,
& numdns, numcmp, numprp, numcnd,
& varipf, varopf,
& varipdns, varopdns,
& varipcmp, varopcmp,
& varipprp, varopprp,
& varipcnd, varopcnd,
& varuof, varuocnd,
& numcon, typcon,
& numnzder, rownder, colnzder,
& valipf, valopf,
& valipdns, valopdns,
& valipcmp, valopcmp,
& valipprp, valopprp,
& valipcnd, valopcnd,
& valuof, valuocnd,
& valcon,
& flag)

16. Formula Intrinsic/Extrinsic Functions
• Unary intrinsic functions supported are:
– ABS, SQRT, LN, LOG, EXP, SIN, COS, TAN, DEG, RAD, INT,
ROUND, FLOOR, CEILING, FACT, IF, NOT and URN, NRN
(uniform and normal random numbers).
• Binary and N-ary intrinsic functions are:
– EQ, NE, LE, LT, GE, GT, MIN, MAX and
– MNL, MXL, KIP, LIP, SIP, KIP2, LIP2, SIP2 (monotonic spline
using piecewise Hermite polynomials).
• Extrinsic unary, binary and N-ary functions are:
– XFU1, XFU2, XFU3, XFB1, XFB2, XFB3 & XFN1, XFN2, XFN3

17. Solving via SLPQPE (not SQP, SLQP, SQLP)
• What is SLPQPE?
– It is identical to SLP (Zhang et. al., 1985) except that at each major iteration of the
nonlinear program a QP is called instead of the LP if there are quadratic terms in the
objective function.
J. Zhang, et. al., “An Improved Successive Linear Programming Algorithm”,
Management Science, 1985.
• Where is it useful?
– When the objective function is a mix of linear (1-norm) and quadratic (2-norm) terms such
as found in industrial planning, scheduling, control & reconciliation problems, then SLPQPE
has been found to be extremely effective in practice.
• Where is it usually not useful?
– When the objective function has an indefinite Hessian (+/- eigenvalues) i.e., +/- diagonal
quadratic terms.
– However, industrial optimization & estimation problems do not usually have an indefinite
objective function Hessian although the Hessian of the Lagrangian most likely will be.

18. Other Techniques found in IMPRESS
 Differentiation Engine:
 All 1st-order partial derivatives are either supplied
analytically or computed numerically of analytical quality
using complex-step differencing & graph-colouring. *
The modeler or user is not responsible for providing
derivatives.
 Diagnostic Engine (Work-in-Progress ...):
 Experience says that over 95% of IOP's have infeasibilities
that occur in the linear part of the model where these can
be quickly identified in LP primal presolve. The rest
require artificial or what we call excursion variables in
both the integer and nonlinear parts of the model and are
generated automatically.

19. IMPRESS.EXE (Console)
• impress.exe -feed=“yourmodelhere.iml"
-form=sparsic/symbolic
-fit=discrete/distribute
-filter=quantity/logistics/quality
-foreign=*.ilp
-focus=optimization/estimation
-factor=1
-factorizer=y12m/ysmp/nspiv/pardiso
-fork=lpsolve/xpress/ipopt/slpqpe/secqpe_sorve
-flashback=flatfile
-fanfare=1073741823

20. IMPRESS Components
• IMPRSserver.dll, IMPRSinteracter.dll,
IMPRSinterfacer.dll, IMPRSmodeler.dll,
IMPRSpresolver.dll, IMPRESS.EXE
• Microsoft Visual Studio 10 and Intel
Fortran/MKL Redistributables (free).
• IAConstructer.py (2.3) (Flowsheet),
UOPSS.sheet, unit_perimeter.shape, etc.
• IAViewer.py (2.7) (Gantt/Trend Charts)
• Python 2.3 and Python 2.7 (coexist)
• Dia 0.97.2 (Gnome)