SlideShare una empresa de Scribd logo
1 de 13
Descargar para leer sin conexión
Advanced Production Accounting of a Flotation Plant
Industrial Modeling Framework (APA-FP-IMF)
i n d u s t r IAL g o r i t h m s LLC. (IAL)
www.industrialgorithms.com
August 2014
Introduction to Advanced Production Accounting, UOPSS and QLQP
Presented in this short document is a description of what we call "Advanced" Production
Accounting (APA) applied to a small Tin-Iron Flotation Plant found in Woollacott and Stange
(1987) where their “smoothing” algorithm used can be partially found in Hodouin (2010). APA is
the term given to the technique of vetting, screening or cleaning the past production data using
statistical data reconciliation and regression (DRR) when continuous-processes are assumed to
be at steady-state (Kelly and Hedengren, 2013) i.e., there is no significant material
accumulation. For this case, the model and data define a simultaneous quantity and quality
bilinear DRR problem (Kelly, 2004b).
Figure 1a shows the Flotation Plant using names for the nodes and simple number indices for
its streams where Figure 1b depicts the same problem configured in our unit-operation-port-
state superstructure (UOPSS) (Kelly, 2004a, 2005; Zyngier and Kelly, 2012).
Figure 1a. Flotation Plant Flowsheet (Woollacott and Stange, 1987).
Figure 1b. Flotation Plant UOPSS Flowsheet.
The diamond shapes or objects are the sources and sinks known as perimeters, the rectangle
shapes with the cross-hairs are continuous-process units and as mentioned these units should
have a steady-state detection algorithm (SSD) installed to determine if the units are steady or
stationary. The circle shapes with no cross-hairs are in-ports which can accept one or more
inlet flows and are considered to be simple or uncontrolled mixers. The cross-haired circles are
out-ports which can allow one or more outlet flows and are considered to be simple or
uncontrolled splitters. The lines, arcs or edges in between the various shapes are known as
internal and external streams and represent in this context the flows or transfers of materials
from one shape to another. This example and its flow and assay data are taken directly from
Woollacott and Stange (1987) as mentioned but is mapped to our UOPSS modeling framework
which includes only one time-period typically defined for one business or calendar day.
For this problem the configuration is as follows. There are nine (9) mass flows, six (6)
components in mass percent (representing the size distributions of >44.3, >30.2, >22.3, >15.8,
>12.4 and <12.4 micro-meters) and two (2) properties (tin and iron) in mass percent. The
components and properties are called assays in metallurgical accounting applications. Each
continuous-process unit-operation is configured as a blackbox subtype with a “%” character
suffixed (or prefixed) indicating that an overall quantity balance is applied (see Appendix A) and
each quality also has a “%” character suffixed (or prefixed) indicating that an overall quality
balance is to applied (see Appendix B). More details and discussion on these types of bilinear
data reconciliation problems can be found in Kelly (2004b).
Industrial Modeling Framework (IMF), IMPL and SSIIMPLE
To implement the mathematical formulation of this and other systems, IAL offers a unique
approach and is incorporated into our Industrial Modeling Programming Language we call IMPL.
IMPL 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, C#, VBA, Java (SWIG), Python (CTYPES)
and/or Julia (CCALL) 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, KNITRO and WORHP 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.
In addition and specific to DRR problems, we also have a special solver called SECQPE
standing for Sequential Equality-Constrained QP Engine which computes the least-squares
solution and a post-solver called SORVE standing for Supplemental Observability, Redundancy
and Variability Estimator to estimate the usual DRR statistics. SECQPE also includes a
Levenberg-Marquardt regularization method for nonlinear data regression problems and can be
presolved using SLPQPE i.e., SLPQPE warm-starts SECQPE. SORVE is run after the
SECQPE solver and also computes the well-known "maximum-power" gross-error statistics
(measurement and nodal/constraint tests) to help locate outliers, defects and/or faults i.e., mal-
functions in the measurement system and mis-specifications in the logging system.
The underlying system architecture of IMPL is called SSIIMPLE (we hope literally) which is short
for Server, Solvers, Interfacer (IML), Interacter (IPL), Modeler, Presolver Libraries and
Executable. The Server, Solvers, Presolver and Executable are primarily model or problem-
independent whereas the Interfacer, Interacter 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, IMPL's standard
Interfacer, Interacter 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 IMPL 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 *.ILP 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, NOT, EQ, NE, LE, LT, GE, GT and CIP,
LIP, SIP and KIP (constant, linear and monotonic spline interpolations) as well as user-written
extrinsic functions (XFCN). It is also possible to import another type of foreign file called the
*.INL file where both linear and nonlinear constraints can be added easily using new or existing
IMPL variables.
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 Gnome 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 IMPL to formulate and optimize complex industrial
manufacturing systems in either off-line or on-line environments. Using IMPL 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 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 IMPL 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.
Flotation Plant "Advanced" Production Accounting Synopsis
At this point we explore further the purpose of "advanced" production accounting in terms of its
diagnostic capability of aiding in the detection, identification and elimination of "bad" production
data where "bad" really implies inconsistent data. The major advantage of DRR is its ability to
use redundant data which is sometimes referred to as over-determined or over-specified
problems. The redundancy primarily occurs because of the inclusion of a model i.e., equations
or equality constraints relating all of the quantity and quality variables together including the
laws of conservation of matter (i.e., a mass, volume or mole basis). It interesting to quote a
recent trade magazine article on “metal accounting risks” (Lothian, 2012) regarding assessing
the accuracy of metallurgical balances: “attempting to achieve this without a statistical data
reconciliation engine is impossible”.
From our Table 1, taken directly from Woollacott and Stange (1987)’s Table I, all 9 stream
assays are measured where we use their Error Model 1 which applies a 10% standard error
uncertainty to all components and properties (i.e., the standard-deviation equals 0.10 times its
actual, raw or measured value). In addition, shown in their Table II are the measured flowrates
with estimates of their uncertainty.
Table 1. Flotation Plant’s Measurements (Woollacott and Stange, 1987).
After modeling and solving this bilinear DRR problem in twenty-five (25) iterations using IMPL’s
SECQPE, we arrive at a solution with an objective function of 8.92 which has a Hotelling
statistic of 21.66 at 95% confidence and 36 – 2 = 34 degrees-of-freedom indicating that no
gross-errors are present. There are thirty-six (36) equations (i.e., 4 nodes times (1 quantity + 8
qualities)), seventy-nine (79) measured variables of which all are declared to be redundant and
the two (2) unmeasured quantity or flow variables are declared to be observable for a total of
eighty-one (81) variables.
Unfortunately Woollacott and Stange (1987) do not report the objective function value of their
“smoothed” algorithm, which is somewhat similar to other well-known data reconciliation
algorithms except that it uses a hierarchical solution strategy briefly described in Hodouin
(2010). However, they present the solutions for several Error Models (except Error Model 1
which is the one we use) in their Table VII and our Table 2. Fortunately, our reconciled results
found in our Table 3 using Error Model 1 are relatively close to their smoothed results using
Error Models 2, 3, 4 and 5 which confirms that our model and data are consistent.
Table 2. Flotation Plant’s “Smoothed” Flow Results from Woollacott and Stange (1987).
Table 3. Flotation Plant’s Reconciled Flow Results from IMPL-SECQPE.
1. Feed 6.47
2. PyriteConcentrate 0.61
3. PyriteTailings 5.86
4. RougherFeed 11.48
5. ScavengerConcentrate 1.70
6. RougherConcentrate 4.03
7. FinalTailings 5.74
8. CleanerTailings 3.91
9. FinalConcentrate 0.12
In summary, we have highlighted the advanced production accounting of a small Flotation Plant
with both mass quantity and quality data and no statistically detectable gross-errors are present.
References
Woollacott, L.C., Stange, W., “Guidelines for the derivation of reliable material balances from
plant data”, Journal of South Africa Institute of Mining and Metallurgy, 87, 207-217, (1987).
Kelly, J.D., "Production modeling for multimodal operations", Chemical Engineering Progress,
February, 44, (2004a).
Kelly, J.D., "Formulating large-scale quantity-quality bilinear data reconciliation problems",
Computers & Chemical Engineering, 357-362, (2004b).
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).
Hodouin, “Process observers and data reconciliation using mass and energy balance
equations”, Chapter 2 in Advanced Control and Supervision of Mineral Processing Plants,
editors Sbarbaro, D., del Villar, R., Springer, (2010).
Zyngier, D., Kelly, J.D., "UOPSS: a new paradigm for modeling production planning and
scheduling systems", ESCAPE 22, June, (2012).
Lothian, K., “Is your business safe from metal accounting risks”, Global Mining,
www.energydigital.com, November, (2012).
Kelly, J.D., Hedengren, J.D., "A steady-state detection (SDD) algorithm to detect non-stationary
drifts in processes", Journal of Process Control, 23, 326, (2013).
Appendix A – APA-FP-IMF.UPS (UOPSS) File
i M P l (c)
Copyright and Property of i n d u s t r I A L g o r i t h m s LLC.
checksum,80
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Unit-Operation-Port-State-Superstructure (UOPSS) *.UPS File.
! (This file is automatically generated from the Python program IALConstructer.py)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sUnit,&sOperation,@sType,@sSubtype,@sUse
Cleaners,Recleaners,processc,blackbox%,
Conditioner,,processc,blackbox%,
Feed,,perimeter,,
FinalConc,,perimeter,,
FinalTails,,perimeter,,
PyriteCircuit,,processc,blackbox%,
PyriteConc,,perimeter,,
Roughers,Scavengers,processc,blackbox%,
&sUnit,&sOperation,@sType,@sSubtype,@sUse
! Number of UO shapes = 8
&sAlias,&sUnit,&sOperation
ALLPARTS,Cleaners,Recleaners
ALLPARTS,Conditioner,
ALLPARTS,Feed,
ALLPARTS,FinalConc,
ALLPARTS,FinalTails,
ALLPARTS,PyriteCircuit,
ALLPARTS,PyriteConc,
ALLPARTS,Roughers,Scavengers
&sAlias,&sUnit,&sOperation
&sUnit,&sOperation,&sPort,&sState,@sType,@sSubtype
Cleaners,Recleaners,6,,in,
Cleaners,Recleaners,8,CleanerTails,out,
Cleaners,Recleaners,9,,out,
Conditioner,,3,,in,
Conditioner,,4,RougherFeed,out,
Conditioner,,5,,in,
Conditioner,,8,,in,
Feed,,1,,out,
FinalConc,,9,,in,
FinalTails,,7,,in,
PyriteCircuit,,1,,in,
PyriteCircuit,,2,,out,
PyriteCircuit,,3,PyriteTails,out,
PyriteConc,,2,,in,
Roughers,Scavengers,4,,in,
Roughers,Scavengers,5,ScavengerConc,out,
Roughers,Scavengers,6,RougherConc,out,
Roughers,Scavengers,7,,out,
&sUnit,&sOperation,&sPort,&sState,@sType,@sSubtype
! Number of UOPS shapes = 18
&sAlias,&sUnit,&sOperation,&sPort,&sState
ALLINPORTS,Cleaners,Recleaners,6,
ALLINPORTS,Conditioner,,3,
ALLINPORTS,Conditioner,,5,
ALLINPORTS,Conditioner,,8,
ALLINPORTS,FinalConc,,9,
ALLINPORTS,FinalTails,,7,
ALLINPORTS,PyriteCircuit,,1,
ALLINPORTS,PyriteConc,,2,
ALLINPORTS,Roughers,Scavengers,4,
ALLOUTPORTS,Cleaners,Recleaners,8,CleanerTails
ALLOUTPORTS,Cleaners,Recleaners,9,
ALLOUTPORTS,Conditioner,,4,RougherFeed
ALLOUTPORTS,Feed,,1,
ALLOUTPORTS,PyriteCircuit,,2,
ALLOUTPORTS,PyriteCircuit,,3,PyriteTails
ALLOUTPORTS,Roughers,Scavengers,5,ScavengerConc
ALLOUTPORTS,Roughers,Scavengers,6,RougherConc
ALLOUTPORTS,Roughers,Scavengers,7,
&sAlias,&sUnit,&sOperation,&sPort,&sState
&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState
Cleaners,Recleaners,8,CleanerTails,Conditioner,,8,
Cleaners,Recleaners,9,,FinalConc,,9,
Conditioner,,4,RougherFeed,Roughers,Scavengers,4,
Feed,,1,,PyriteCircuit,,1,
PyriteCircuit,,2,,PyriteConc,,2,
PyriteCircuit,,3,PyriteTails,Conditioner,,3,
Roughers,Scavengers,5,ScavengerConc,Conditioner,,5,
Roughers,Scavengers,6,RougherConc,Cleaners,Recleaners,6,
Roughers,Scavengers,7,,FinalTails,,7,
&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState
! Number of UOPSPSUO shapes = 9
&sAlias,&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState
ALLPATHS,Roughers,Scavengers,6,RougherConc,Cleaners,Recleaners,6,
ALLPATHS,PyriteCircuit,,3,PyriteTails,Conditioner,,3,
ALLPATHS,Roughers,Scavengers,5,ScavengerConc,Conditioner,,5,
ALLPATHS,Cleaners,Recleaners,8,CleanerTails,Conditioner,,8,
ALLPATHS,Cleaners,Recleaners,9,,FinalConc,,9,
ALLPATHS,Roughers,Scavengers,7,,FinalTails,,7,
ALLPATHS,Feed,,1,,PyriteCircuit,,1,
ALLPATHS,PyriteCircuit,,2,,PyriteConc,,2,
ALLPATHS,Conditioner,,4,RougherFeed,Roughers,Scavengers,4,
&sAlias,&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState
Appendix B – APA-FP-IMF.IML File
i M P l (c)
Copyright and Property of i n d u s t r I A L g o r i t h m s LLC.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Calculation Data (Parameters)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sCalc,@sValue
!Miscellaneous Constants.
NNON,-99999
LARGEBOUND,1E+3
!Horizon and Period Times.
START,-1.0
BEGIN,0.0
END,1.0
PERIOD,1.0
! Measured Quality Tolerance Fraction (Error Model 1)
TOL,0.10
!Measured Stream Flows.
F1_V,NNON
F2_V,0.61
F3_V,6.94
F4_V,NNON
F5_V,1.7
F6_V,4.14
F7_V,5.75
F8_V,3.42
F9_V,0.11
F1_L,0.0
F1_U,LARGEBOUND
F1_T,F1_V
F1_W,0.0
F2_L,0.0
F2_U,LARGEBOUND
F2_T,F2_V
F2_W,NE(F2_V;NNON)* 1.0/(0.02)^2
F3_L,0.0
F3_U,LARGEBOUND
F3_T,F3_V
F3_W,NE(F3_V;NNON)* 1.0/(2.12)^2
F4_L,0.0
F4_U,LARGEBOUND
F4_T,F4_V
F4_W,0.0
F5_L,0.0
F5_U,LARGEBOUND
F5_T,F5_V
F5_W,NE(F5_V;NNON)* 1.0/(0.10)^2
F6_L,0.0
F6_U,LARGEBOUND
F6_T,F6_V
F6_W,NE(F6_V;NNON)* 1.0/(2.0)^2
F7_L,0.0
F7_U,LARGEBOUND
F7_T,F7_V
F7_W,NE(F7_V;NNON)* 1.0/(0.18)^2
F8_L,0.0
F8_U,LARGEBOUND
F8_T,F8_V
F8_W,NE(F8_V;NNON)* 1.0/(2.14)^2
F9_L,0.0
F9_U,LARGEBOUND
F9_T,F9_V
F9_W,NE(F9_V;NNON)* 1.0/(0.03)^2
&sCalc,@sValue
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Constant Data (Parameters)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sData,@sValue
! Measured Stream Component Assays in %mass (>44.3, >30.2, >22.3, >15.8, >12.4, <12.4 microns).
C1,2.6
,5.2
,2.4
,1.9
,1.7
,1.2
,1.9
,1.1
,2.3
C2,15.9
,15.4
,17
,11.3
,6.8
,6.3
,17.1
,6.8
,4.5
C3,29.6
,26
,29.7
,21
,16.3
,15.9
,30.8
,16.2
,14.8
C4,22.3
,22
,21.2
,21.3
,20.9
,22.4
,22.4
,21.5
,27.4
C5,7.5
,7.6
,6.9
,8.4
,9
,9.9
,7.1
,9.6
,12.7
C6,22.1
,23.8
,22.8
,36.1
,45.3
,44.4
,20.7
,44.8
,38.3
! Measured Stream Property Assays in %mass (Tin, Iron).
P1,0.795
,1.095
,0.78
,2.455
,3.18
,5.26
,0.26
,4.70
,25.35
P2,5.5
,11.87
,4.61
,6.79
,9.69
,10.78
,3.88
,10.58
,19.66
&sData,@sValue
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Chronological Data (Periods)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
@rPastTHD,@rFutureTHD,@rTPD
START,END,PERIOD
@rPastTHD,@rFutureTHD,@rTPD
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Construction Data (Pointers)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Include-@sFile_Name
APA-FP-IMF.ups
Include-@sFile_Name
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Capacity Data (Prototypes)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sUnit,&sOperation,@rRate_Lower,@rRate_Upper
ALLPARTS,0.0,LARGEBOUND
&sUnit,&sOperation,@rRate_Lower,@rRate_Upper
&sUnit,&sOperation,&sPort,&sState,@rTeeRate_Lower,@rTeeRate_Upper
ALLINPORTS,0.0,LARGEBOUND
ALLOUTPORTS,0.0,LARGEBOUND
&sUnit,&sOperation,&sPort,&sState,@rTeeRate_Lower,@rTeeRate_Upper
&sUnit,&sOperation,&sPort,&sState,@rTotalRate_Lower,@rTotalRate_Upper
ALLINPORTS,0.0,LARGEBOUND
ALLOUTPORTS,0.0,LARGEBOUND
&sUnit,&sOperation,&sPort,&sState,@rTotalRate_Lower,@rTotalRate_Upper
&sUnit,&sOperation,&sPort,&sState,@rYield_Lower,@rYield_Upper,@rYield_Fixed
ALLINPORTS,0.0,LARGEBOUND
ALLOUTPORTS,0.0,LARGEBOUND
&sUnit,&sOperation,&sPort,&sState,@rYield_Lower,@rYield_Upper,@rYield_Fixed
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Constituent Data (Properties)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sComponent
C1
C2
C3
C4
C5
C6
&sComponent
&sTemplate,&sComponent,@rComponent_Lower,@rComponent_Upper,@rComponent_Target
CT,C1,0.0,100.0,100.0
,C2,0.0,100.0,100.0
,C3,0.0,100.0,100.0
,C4,0.0,100.0,100.0
,C5,0.0,100.0,100.0
,C6,0.0,100.0,100.0
&sTemplate,&sComponent,@rComponent_Lower,@rComponent_Upper,@rComponent_Target
&sUnit,&sOperation,&sPort,&sState,&sComponent,@rComponent_Lower,@rComponent_Upper,@rComponent_Target
ALLINPORTS,CT%
ALLOUTPORTS,CT%
&sUnit,&sOperation,&sPort,&sState,&sComponent,@rComponent_Lower,@rComponent_Upper,@rComponent_Target
&sProperty
P1
P2
&sProperty
&sTemplate,&sProperty,@rProperty_Lower,@rProperty_Upper,@rProperty_Target
PT,P1,0.0,100.0,100.0
,P2,0.0,100.0,100.0
&sTemplate,&sProperty,@rProperty_Lower,@rProperty_Upper,@rProperty_Target
&sUnit,&sOperation,&sPort,&sState,&sProperty,@rProperty_Lower,@rProperty_Upper,@rProperty_Target
ALLINPORTS,PT%
ALLOUTPORTS,PT%
&sUnit,&sOperation,&sPort,&sState,&sProperty,@rProperty_Lower,@rProperty_Upper,@rProperty_Target
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Cost Data (Pricing)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState,
@rFlowPro_Weight,@rFlowPer1_Weight,@rFlowPer2_Weight,@rFlowPen_Weight
Cleaners,Recleaners,8,CleanerTails,Conditioner,,8,,,,F8_W,
Cleaners,Recleaners,9,,FinalConc,,9,,,,F9_W,
Conditioner,,4,RougherFeed,Roughers,Scavengers,4,,,,F4_W,
Feed,,1,,PyriteCircuit,,1,,,,F1_W,
PyriteCircuit,,2,,PyriteConc,,2,,,,F2_W,
PyriteCircuit,,3,PyriteTails,Conditioner,,3,,,,F3_W,
Roughers,Scavengers,5,ScavengerConc,Conditioner,,5,,,,F5_W,
Roughers,Scavengers,6,RougherConc,Cleaners,Recleaners,6,,,,F6_W,
Roughers,Scavengers,7,,FinalTails,,7,,,,F7_W,
&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState,
@rFlowPro_Weight,@rFlowPer1_Weight,@rFlowPer2_Weight,@rFlowPen_Weight
&sUnit,&sOperation,&sPort,&sState,&sComponent,
@rComponentPro_Weight,@rComponentPer1_Weight,@rComponentPer2_Weight,@rComponentPen_Weight
Cleaners,Recleaners,8,CleanerTails,C1,,,1.0/(C1[8]*TOL)^2.0,
,,,,C2,,,1.0/(C2[8]*TOL)^2.0,
,,,,C3,,,1.0/(C3[8]*TOL)^2.0,
,,,,C4,,,1.0/(C4[8]*TOL)^2.0,
,,,,C5,,,1.0/(C5[8]*TOL)^2.0,
,,,,C6,,,1.0/(C6[8]*TOL)^2.0,
Cleaners,Recleaners,9,,C1,,,1.0/(C1[9]*TOL)^2.0,
,,,,C2,,,1.0/(C2[9]*TOL)^2.0,
,,,,C3,,,1.0/(C3[9]*TOL)^2.0,
,,,,C4,,,1.0/(C4[9]*TOL)^2.0,
,,,,C5,,,1.0/(C5[9]*TOL)^2.0,
,,,,C6,,,1.0/(C6[9]*TOL)^2.0,
Conditioner,,4,RougherFeed,C1,,,1.0/(C1[4]*TOL)^2.0,
,,,,C2,,,1.0/(C2[4]*TOL)^2.0,
,,,,C3,,,1.0/(C3[4]*TOL)^2.0,
,,,,C4,,,1.0/(C4[4]*TOL)^2.0,
,,,,C5,,,1.0/(C5[4]*TOL)^2.0,
,,,,C6,,,1.0/(C6[4]*TOL)^2.0,
Feed,,1,,C1,,,1.0/(C1[1]*TOL)^2.0,
,,,,C2,,,1.0/(C2[1]*TOL)^2.0,
,,,,C3,,,1.0/(C3[1]*TOL)^2.0,
,,,,C4,,,1.0/(C4[1]*TOL)^2.0,
,,,,C5,,,1.0/(C5[1]*TOL)^2.0,
,,,,C6,,,1.0/(C6[1]*TOL)^2.0,
PyriteCircuit,,2,,C1,,,1.0/(C1[2]*TOL)^2.0,
,,,,C2,,,1.0/(C2[2]*TOL)^2.0,
,,,,C3,,,1.0/(C3[2]*TOL)^2.0,
,,,,C4,,,1.0/(C4[2]*TOL)^2.0,
,,,,C5,,,1.0/(C5[2]*TOL)^2.0,
,,,,C6,,,1.0/(C6[2]*TOL)^2.0,
PyriteCircuit,,3,PyriteTails,C1,,,1.0/(C1[3]*TOL)^2.0,
,,,,C2,,,1.0/(C2[3]*TOL)^2.0,
,,,,C3,,,1.0/(C3[3]*TOL)^2.0,
,,,,C4,,,1.0/(C4[3]*TOL)^2.0,
,,,,C5,,,1.0/(C5[3]*TOL)^2.0,
,,,,C6,,,1.0/(C6[3]*TOL)^2.0,
Roughers,Scavengers,5,ScavengerConc,C1,,,1.0/(C1[5]*TOL)^2.0,
,,,,C2,,,1.0/(C2[5]*TOL)^2.0,
,,,,C3,,,1.0/(C3[5]*TOL)^2.0,
,,,,C4,,,1.0/(C4[5]*TOL)^2.0,
,,,,C5,,,1.0/(C5[5]*TOL)^2.0,
,,,,C6,,,1.0/(C6[5]*TOL)^2.0,
Roughers,Scavengers,6,RougherConc,C1,,,1.0/(C1[6]*TOL)^2.0,
,,,,C2,,,1.0/(C2[6]*TOL)^2.0,
,,,,C3,,,1.0/(C3[6]*TOL)^2.0,
,,,,C4,,,1.0/(C4[6]*TOL)^2.0,
,,,,C5,,,1.0/(C5[6]*TOL)^2.0,
,,,,C6,,,1.0/(C6[6]*TOL)^2.0,
Roughers,Scavengers,7,,C1,,,1.0/(C1[7]*TOL)^2.0,
,,,,C2,,,1.0/(C2[7]*TOL)^2.0,
,,,,C3,,,1.0/(C3[7]*TOL)^2.0,
,,,,C4,,,1.0/(C4[7]*TOL)^2.0,
,,,,C5,,,1.0/(C5[7]*TOL)^2.0,
,,,,C6,,,1.0/(C6[7]*TOL)^2.0,
&sUnit,&sOperation,&sPort,&sState,&sComponent,
@rComponentPro_Weight,@rComponentPer1_Weight,@rComponentPer2_Weight,@rComponentPen_Weight
&sUnit,&sOperation,&sPort,&sState,&sProperty,
@rPropertyPro_Weight,@rPropertyPer1_Weight,@rPropertyPer2_Weight,@rPropertyPen_Weight
Cleaners,Recleaners,8,CleanerTails,P1,,,1.0/(P1[8]*TOL)^2.0,
,,,,P2,,,1.0/(P2[8]*TOL)^2.0,
Cleaners,Recleaners,9,,P1,,,1.0/(P1[9]*TOL)^2.0,
,,,,P2,,,1.0/(P2[9]*TOL)^2.0,
Conditioner,,4,RougherFeed,P1,,,1.0/(P1[4]*TOL)^2.0,
,,,,P2,,,1.0/(P2[4]*TOL)^2.0,
Feed,,1,,P1,,,1.0/(P1[1]*TOL)^2.0,
,,,,P2,,,1.0/(P2[1]*TOL)^2.0,
PyriteCircuit,,2,,P1,,,1.0/(P1[2]*TOL)^2.0,
,,,,P2,,,1.0/(P2[2]*TOL)^2.0,
PyriteCircuit,,3,PyriteTails,P1,,,1.0/(P1[3]*TOL)^2.0,
,,,,P2,,,1.0/(P2[3]*TOL)^2.0,
Roughers,Scavengers,5,ScavengerConc,P1,,,1.0/(P1[5]*TOL)^2.0,
,,,,P2,,,1.0/(P2[5]*TOL)^2.0,
Roughers,Scavengers,6,RougherConc,P1,,,1.0/(P1[6]*TOL)^2.0,
,,,,P2,,,1.0/(P2[6]*TOL)^2.0,
Roughers,Scavengers,7,,P1,,,1.0/(P1[7]*TOL)^2.0,
,,,,P2,,,1.0/(P2[7]*TOL)^2.0,
&sUnit,&sOperation,&sPort,&sState,&sProperty,
@rPropertyPro_Weight,@rPropertyPer1_Weight,@rPropertyPer2_Weight,@rPropertyPen_Weight
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Content Data (Past, Present Provisos)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Command Data (Future Provisos)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
&sUnit,&sOperation,@rSetup_Lower,@rSetup_Upper,@rBegin_Time,@rEnd_Time
ALLPARTS,1,1,BEGIN,END
&sUnit,&sOperation,@rSetup_Lower,@rSetup_Upper,@rBegin_Time,@rEnd_Time
&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState,@rSetup_Lower,@rSetup_Upper,@rBegin_Time,@rEnd_Time
ALLPATHS,1,1,BEGIN,END
&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState,@rSetup_Lower,@rSetup_Upper,@rBegin_Time,@rEnd_Time
&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState,
@rRate_Lower,@rRate_Upper,@rRate_Target,@rBegin_Time,@rEnd_Time
Cleaners,Recleaners,8,CleanerTails,Conditioner,,8,,F8_L,F8_U,F8_T,BEGIN,END
Cleaners,Recleaners,9,,FinalConc,,9,,F9_L,F9_U,F9_T,BEGIN,END
Conditioner,,4,RougherFeed,Roughers,Scavengers,4,,F4_L,F4_U,F4_T,BEGIN,END
Feed,,1,,PyriteCircuit,,1,,F1_L,F1_U,F1_T,BEGIN,END
PyriteCircuit,,2,,PyriteConc,,2,,F2_L,F2_U,F2_T,BEGIN,END
PyriteCircuit,,3,PyriteTails,Conditioner,,3,,F3_L,F3_U,F3_T,BEGIN,END
Roughers,Scavengers,5,ScavengerConc,Conditioner,,5,,F5_L,F5_U,F5_T,BEGIN,END
Roughers,Scavengers,6,RougherConc,Cleaners,Recleaners,6,,F6_L,F6_U,F6_T,BEGIN,END
Roughers,Scavengers,7,,FinalTails,,7,,F7_L,F7_U,F7_T,BEGIN,END
&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState,
@rRate_Lower,@rRate_Upper,@rRate_Target,@rBegin_Time,@rEnd_Time
&sUnit,&sOperation,&sPort,&sState,&sComponent,@rComponent_Lower,@rComponent_Upper,@rComponent_Target,@rBegin_Time,@rEnd_Time
Cleaners,Recleaners,8,CleanerTails,C1,0.0,100.0,C1[8],BEGIN,END
,,,,C2,0.0,100.0,C2[8],BEGIN,END
,,,,C3,0.0,100.0,C3[8],BEGIN,END
,,,,C4,0.0,100.0,C4[8],BEGIN,END
,,,,C5,0.0,100.0,C5[8],BEGIN,END
,,,,C6,0.0,100.0,C6[8],BEGIN,END
Cleaners,Recleaners,9,,C1,0.0,100.0,C1[9],BEGIN,END
,,,,C2,0.0,100.0,C2[9],BEGIN,END
,,,,C3,0.0,100.0,C3[9],BEGIN,END
,,,,C4,0.0,100.0,C4[9],BEGIN,END
,,,,C5,0.0,100.0,C5[9],BEGIN,END
,,,,C6,0.0,100.0,C6[9],BEGIN,END
Conditioner,,4,RougherFeed,C1,0.0,100.0,C1[4],BEGIN,END
,,,,C2,0.0,100.0,C2[4],BEGIN,END
,,,,C3,0.0,100.0,C3[4],BEGIN,END
,,,,C4,0.0,100.0,C4[4],BEGIN,END
,,,,C5,0.0,100.0,C5[4],BEGIN,END
,,,,C6,0.0,100.0,C6[4],BEGIN,END
Feed,,1,,C1,0.0,100.0,C1[1],BEGIN,END
,,,,C2,0.0,100.0,C2[1],BEGIN,END
,,,,C3,0.0,100.0,C3[1],BEGIN,END
,,,,C4,0.0,100.0,C4[1],BEGIN,END
,,,,C5,0.0,100.0,C5[1],BEGIN,END
,,,,C6,0.0,100.0,C6[1],BEGIN,END
PyriteCircuit,,2,,C1,0.0,100.0,C1[2],BEGIN,END
,,,,C2,0.0,100.0,C2[2],BEGIN,END
,,,,C3,0.0,100.0,C3[2],BEGIN,END
,,,,C4,0.0,100.0,C4[2],BEGIN,END
,,,,C5,0.0,100.0,C5[2],BEGIN,END
,,,,C6,0.0,100.0,C6[2],BEGIN,END
PyriteCircuit,,3,PyriteTails,C1,0.0,100.0,C1[3],BEGIN,END
,,,,C2,0.0,100.0,C2[3],BEGIN,END
,,,,C3,0.0,100.0,C3[3],BEGIN,END
,,,,C4,0.0,100.0,C4[3],BEGIN,END
,,,,C5,0.0,100.0,C5[3],BEGIN,END
,,,,C6,0.0,100.0,C6[3],BEGIN,END
Roughers,Scavengers,5,ScavengerConc,C1,0.0,100.0,C1[5],BEGIN,END
,,,,C2,0.0,100.0,C2[5],BEGIN,END
,,,,C3,0.0,100.0,C3[5],BEGIN,END
,,,,C4,0.0,100.0,C4[5],BEGIN,END
,,,,C5,0.0,100.0,C5[5],BEGIN,END
,,,,C6,0.0,100.0,C6[5],BEGIN,END
Roughers,Scavengers,6,RougherConc,C1,0.0,100.0,C1[6],BEGIN,END
,,,,C2,0.0,100.0,C2[6],BEGIN,END
,,,,C3,0.0,100.0,C3[6],BEGIN,END
,,,,C4,0.0,100.0,C4[6],BEGIN,END
,,,,C5,0.0,100.0,C5[6],BEGIN,END
,,,,C6,0.0,100.0,C6[6],BEGIN,END
Roughers,Scavengers,7,,C1,0.0,100.0,C1[7],BEGIN,END
,,,,C2,0.0,100.0,C2[7],BEGIN,END
,,,,C3,0.0,100.0,C3[7],BEGIN,END
,,,,C4,0.0,100.0,C4[7],BEGIN,END
,,,,C5,0.0,100.0,C5[7],BEGIN,END
,,,,C6,0.0,100.0,C6[7],BEGIN,END
&sUnit,&sOperation,&sPort,&sState,&sComponent,@rComponent_Lower,@rComponent_Upper,@rComponent_Target,@rBegin_Time,@rEnd_Time
&sUnit,&sOperation,&sPort,&sState,&sProperty,@rProperty_Lower,@rProperty_Upper,@rProperty_Target,@rBegin_Time,@rEnd_Time
Cleaners,Recleaners,8,CleanerTails,P1,0.0,100.0,P1[8],BEGIN,END
,,,,P2,0.0,100.0,P2[8],BEGIN,END
Cleaners,Recleaners,9,,P1,0.0,100.0,P1[9],BEGIN,END
,,,,P2,0.0,100.0,P2[9],BEGIN,END
Conditioner,,4,RougherFeed,P1,0.0,100.0,P1[4],BEGIN,END
,,,,P2,0.0,100.0,P2[4],BEGIN,END
Feed,,1,,P1,0.0,100.0,P1[1],BEGIN,END
,,,,P2,0.0,100.0,P2[1],BEGIN,END
PyriteCircuit,,2,,P1,0.0,100.0,P1[2],BEGIN,END
,,,,P2,0.0,100.0,P2[2],BEGIN,END
PyriteCircuit,,3,PyriteTails,P1,0.0,100.0,P1[3],BEGIN,END
,,,,P2,0.0,100.0,P2[3],BEGIN,END
Roughers,Scavengers,5,ScavengerConc,P1,0.0,100.0,P1[5],BEGIN,END
,,,,P2,0.0,100.0,P2[5],BEGIN,END
Roughers,Scavengers,6,RougherConc,P1,0.0,100.0,P1[6],BEGIN,END
,,,,P2,0.0,100.0,P2[6],BEGIN,END
Roughers,Scavengers,7,,P1,0.0,100.0,P1[7],BEGIN,END
,,,,P2,0.0,100.0,P2[7],BEGIN,END
&sUnit,&sOperation,&sPort,&sState,&sProperty,@rProperty_Lower,@rProperty_Upper,@rProperty_Target,@rBegin_Time,@rEnd_Time

Más contenido relacionado

La actualidad más candente

Advanced Parameter Estimation (APE) for Motor Gasoline Blending (MGB) Indust...
Advanced Parameter Estimation (APE) for Motor Gasoline Blending (MGB)  Indust...Advanced Parameter Estimation (APE) for Motor Gasoline Blending (MGB)  Indust...
Advanced Parameter Estimation (APE) for Motor Gasoline Blending (MGB) Indust...Alkis Vazacopoulos
 
Time Series Estimation of Gas Furnace Data in IMPL and CPLEX Industrial Model...
Time Series Estimation of Gas Furnace Data in IMPL and CPLEX Industrial Model...Time Series Estimation of Gas Furnace Data in IMPL and CPLEX Industrial Model...
Time Series Estimation of Gas Furnace Data in IMPL and CPLEX Industrial Model...Alkis Vazacopoulos
 
Quick Development and Deployment of Industrial Applications using Excel/VBA, ...
Quick Development and Deployment of Industrial Applications using Excel/VBA, ...Quick Development and Deployment of Industrial Applications using Excel/VBA, ...
Quick Development and Deployment of Industrial Applications using Excel/VBA, ...Alkis Vazacopoulos
 
Advanced Modeling of Industrial Optimization Problems
Advanced Modeling of Industrial Optimization ProblemsAdvanced Modeling of Industrial Optimization Problems
Advanced Modeling of Industrial Optimization ProblemsAlkis Vazacopoulos
 
Modeling of assembly line balancing for optimized number of stations and time
Modeling of assembly line balancing for optimized number of stations and timeModeling of assembly line balancing for optimized number of stations and time
Modeling of assembly line balancing for optimized number of stations and timeIAEME Publication
 
Surrogate modeling for industrial design
Surrogate modeling for industrial designSurrogate modeling for industrial design
Surrogate modeling for industrial designShinwoo Jang
 
Masters Thesis Defense Talk
Masters Thesis Defense TalkMasters Thesis Defense Talk
Masters Thesis Defense TalkRavi Gummadi
 
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...inventionjournals
 
Oracle I/O Supply and Demand
Oracle I/O Supply and DemandOracle I/O Supply and Demand
Oracle I/O Supply and DemandBob Sneed
 
The Database Environment Chapter 8
The Database Environment Chapter 8The Database Environment Chapter 8
The Database Environment Chapter 8Jeanie Arnoco
 
Design and Implementation of Automated Visualization for Input/Output for Pro...
Design and Implementation of Automated Visualization for Input/Output for Pro...Design and Implementation of Automated Visualization for Input/Output for Pro...
Design and Implementation of Automated Visualization for Input/Output for Pro...ijseajournal
 
Advanced Process Monitoring for Startups, Shutdowns & Switchovers Industrial ...
Advanced Process Monitoring for Startups, Shutdowns & Switchovers Industrial ...Advanced Process Monitoring for Startups, Shutdowns & Switchovers Industrial ...
Advanced Process Monitoring for Startups, Shutdowns & Switchovers Industrial ...Alkis Vazacopoulos
 
Raminder kaur presentation_two
Raminder kaur presentation_twoRaminder kaur presentation_two
Raminder kaur presentation_tworamikaurraminder
 
2011/2012 CAST report on Application Software Quality (CRASH)
2011/2012 CAST report on Application Software Quality (CRASH)2011/2012 CAST report on Application Software Quality (CRASH)
2011/2012 CAST report on Application Software Quality (CRASH)CAST
 
Hybrid Dynamic Simulation (HDS) Industrial Modeling Framework (HDS-IMF)
Hybrid Dynamic Simulation (HDS)  Industrial Modeling Framework (HDS-IMF)Hybrid Dynamic Simulation (HDS)  Industrial Modeling Framework (HDS-IMF)
Hybrid Dynamic Simulation (HDS) Industrial Modeling Framework (HDS-IMF)Alkis Vazacopoulos
 
Modeling distribution networks with neplan
Modeling distribution networks with neplanModeling distribution networks with neplan
Modeling distribution networks with neplanYusuf A. KHALIL
 
Different simulation softwares in power system
Different simulation softwares in power systemDifferent simulation softwares in power system
Different simulation softwares in power systemJitendra Bhadoriya
 
STRUCTURAL VALIDATION OF SOFTWARE PRODUCT LINE VARIANTS: A GRAPH TRANSFORMATI...
STRUCTURAL VALIDATION OF SOFTWARE PRODUCT LINE VARIANTS: A GRAPH TRANSFORMATI...STRUCTURAL VALIDATION OF SOFTWARE PRODUCT LINE VARIANTS: A GRAPH TRANSFORMATI...
STRUCTURAL VALIDATION OF SOFTWARE PRODUCT LINE VARIANTS: A GRAPH TRANSFORMATI...IJSEA
 

La actualidad más candente (20)

Advanced Parameter Estimation (APE) for Motor Gasoline Blending (MGB) Indust...
Advanced Parameter Estimation (APE) for Motor Gasoline Blending (MGB)  Indust...Advanced Parameter Estimation (APE) for Motor Gasoline Blending (MGB)  Indust...
Advanced Parameter Estimation (APE) for Motor Gasoline Blending (MGB) Indust...
 
Time Series Estimation of Gas Furnace Data in IMPL and CPLEX Industrial Model...
Time Series Estimation of Gas Furnace Data in IMPL and CPLEX Industrial Model...Time Series Estimation of Gas Furnace Data in IMPL and CPLEX Industrial Model...
Time Series Estimation of Gas Furnace Data in IMPL and CPLEX Industrial Model...
 
Quick Development and Deployment of Industrial Applications using Excel/VBA, ...
Quick Development and Deployment of Industrial Applications using Excel/VBA, ...Quick Development and Deployment of Industrial Applications using Excel/VBA, ...
Quick Development and Deployment of Industrial Applications using Excel/VBA, ...
 
Advanced Modeling of Industrial Optimization Problems
Advanced Modeling of Industrial Optimization ProblemsAdvanced Modeling of Industrial Optimization Problems
Advanced Modeling of Industrial Optimization Problems
 
Modeling of assembly line balancing for optimized number of stations and time
Modeling of assembly line balancing for optimized number of stations and timeModeling of assembly line balancing for optimized number of stations and time
Modeling of assembly line balancing for optimized number of stations and time
 
Surrogate modeling for industrial design
Surrogate modeling for industrial designSurrogate modeling for industrial design
Surrogate modeling for industrial design
 
Masters Thesis Defense Talk
Masters Thesis Defense TalkMasters Thesis Defense Talk
Masters Thesis Defense Talk
 
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...
Solving Assembly Line Balancing Problem Using A Hybrid Genetic Algorithm With...
 
Oracle I/O Supply and Demand
Oracle I/O Supply and DemandOracle I/O Supply and Demand
Oracle I/O Supply and Demand
 
The Database Environment Chapter 8
The Database Environment Chapter 8The Database Environment Chapter 8
The Database Environment Chapter 8
 
Design and Implementation of Automated Visualization for Input/Output for Pro...
Design and Implementation of Automated Visualization for Input/Output for Pro...Design and Implementation of Automated Visualization for Input/Output for Pro...
Design and Implementation of Automated Visualization for Input/Output for Pro...
 
Advanced Process Monitoring for Startups, Shutdowns & Switchovers Industrial ...
Advanced Process Monitoring for Startups, Shutdowns & Switchovers Industrial ...Advanced Process Monitoring for Startups, Shutdowns & Switchovers Industrial ...
Advanced Process Monitoring for Startups, Shutdowns & Switchovers Industrial ...
 
Raminder kaur presentation_two
Raminder kaur presentation_twoRaminder kaur presentation_two
Raminder kaur presentation_two
 
2011/2012 CAST report on Application Software Quality (CRASH)
2011/2012 CAST report on Application Software Quality (CRASH)2011/2012 CAST report on Application Software Quality (CRASH)
2011/2012 CAST report on Application Software Quality (CRASH)
 
8
88
8
 
Hybrid Dynamic Simulation (HDS) Industrial Modeling Framework (HDS-IMF)
Hybrid Dynamic Simulation (HDS)  Industrial Modeling Framework (HDS-IMF)Hybrid Dynamic Simulation (HDS)  Industrial Modeling Framework (HDS-IMF)
Hybrid Dynamic Simulation (HDS) Industrial Modeling Framework (HDS-IMF)
 
Modeling distribution networks with neplan
Modeling distribution networks with neplanModeling distribution networks with neplan
Modeling distribution networks with neplan
 
Different simulation softwares in power system
Different simulation softwares in power systemDifferent simulation softwares in power system
Different simulation softwares in power system
 
STRUCTURAL VALIDATION OF SOFTWARE PRODUCT LINE VARIANTS: A GRAPH TRANSFORMATI...
STRUCTURAL VALIDATION OF SOFTWARE PRODUCT LINE VARIANTS: A GRAPH TRANSFORMATI...STRUCTURAL VALIDATION OF SOFTWARE PRODUCT LINE VARIANTS: A GRAPH TRANSFORMATI...
STRUCTURAL VALIDATION OF SOFTWARE PRODUCT LINE VARIANTS: A GRAPH TRANSFORMATI...
 
2453
24532453
2453
 

Destacado

Destacado (20)

In search of change agency black cmg
In search of change agency black cmgIn search of change agency black cmg
In search of change agency black cmg
 
Faq onlinestudents fa10
Faq onlinestudents fa10Faq onlinestudents fa10
Faq onlinestudents fa10
 
Pac1 Lev Manovich
Pac1 Lev ManovichPac1 Lev Manovich
Pac1 Lev Manovich
 
Birds cl1
Birds cl1Birds cl1
Birds cl1
 
James e cook jr!
James e cook jr!James e cook jr!
James e cook jr!
 
אזרחות ומנהיגות יום פתוח
אזרחות ומנהיגות  יום פתוחאזרחות ומנהיגות  יום פתוח
אזרחות ומנהיגות יום פתוח
 
Alex - Von der idee zur premiere
Alex  - Von der idee zur premiereAlex  - Von der idee zur premiere
Alex - Von der idee zur premiere
 
Kredyty, finanse, doradztwo w Wroclaw
Kredyty, finanse, doradztwo w WroclawKredyty, finanse, doradztwo w Wroclaw
Kredyty, finanse, doradztwo w Wroclaw
 
Price list until 31 march 2012 with order form
Price list until 31 march 2012 with order formPrice list until 31 march 2012 with order form
Price list until 31 march 2012 with order form
 
Rebeca salas y hilary jimenes
Rebeca salas y hilary jimenesRebeca salas y hilary jimenes
Rebeca salas y hilary jimenes
 
Eng 204
Eng 204Eng 204
Eng 204
 
Agrumar visitounos powerpont
Agrumar  visitounos powerpontAgrumar  visitounos powerpont
Agrumar visitounos powerpont
 
Ahmet yeşilpınar 20100104
Ahmet yeşilpınar 20100104Ahmet yeşilpınar 20100104
Ahmet yeşilpınar 20100104
 
Impl reference manual_for_logic_logistics
Impl reference manual_for_logic_logisticsImpl reference manual_for_logic_logistics
Impl reference manual_for_logic_logistics
 
ДНЗ № 189 ( круглый стол)
ДНЗ № 189 ( круглый стол)ДНЗ № 189 ( круглый стол)
ДНЗ № 189 ( круглый стол)
 
Cloud computing
Cloud computingCloud computing
Cloud computing
 
Goal setting - dr. s. swapna kumar
Goal  setting - dr. s. swapna kumarGoal  setting - dr. s. swapna kumar
Goal setting - dr. s. swapna kumar
 
Trial
TrialTrial
Trial
 
Mercalli il mondo negli occhi
Mercalli il mondo negli occhiMercalli il mondo negli occhi
Mercalli il mondo negli occhi
 
[PyConTW 2013] doctest
[PyConTW 2013] doctest[PyConTW 2013] doctest
[PyConTW 2013] doctest
 

Similar a Advanced Production Accounting of a Flotation Plant

Advanced property tracking Industrial Modeling Framework
Advanced property tracking Industrial Modeling FrameworkAdvanced property tracking Industrial Modeling Framework
Advanced property tracking Industrial Modeling FrameworkAlkis Vazacopoulos
 
Maritime Industrial Modeling Framework - IMPRESS
Maritime Industrial Modeling Framework - IMPRESSMaritime Industrial Modeling Framework - IMPRESS
Maritime Industrial Modeling Framework - IMPRESSAlkis Vazacopoulos
 
Partial Differential Equations (PDE’s) Industrial Modeling Framework (PDE-IMF)
Partial Differential Equations (PDE’s)  Industrial Modeling Framework (PDE-IMF)Partial Differential Equations (PDE’s)  Industrial Modeling Framework (PDE-IMF)
Partial Differential Equations (PDE’s) Industrial Modeling Framework (PDE-IMF)Alkis Vazacopoulos
 
Impl reference manual_for_quantities
Impl reference manual_for_quantitiesImpl reference manual_for_quantities
Impl reference manual_for_quantitiesAlkis Vazacopoulos
 
Finite Impulse Response Estimation of Gas Furnace Data in IMPL Industrial Mod...
Finite Impulse Response Estimation of Gas Furnace Data in IMPL Industrial Mod...Finite Impulse Response Estimation of Gas Furnace Data in IMPL Industrial Mod...
Finite Impulse Response Estimation of Gas Furnace Data in IMPL Industrial Mod...Alkis Vazacopoulos
 
Industrial Modeling Service (IMS-IMPL)
Industrial Modeling Service (IMS-IMPL)Industrial Modeling Service (IMS-IMPL)
Industrial Modeling Service (IMS-IMPL)Alkis Vazacopoulos
 
SUPPLY CHAIN OPTIMIZATIONL: JET FUEL SUPPLY CHAIN
SUPPLY CHAIN OPTIMIZATIONL: JET FUEL SUPPLY CHAINSUPPLY CHAIN OPTIMIZATIONL: JET FUEL SUPPLY CHAIN
SUPPLY CHAIN OPTIMIZATIONL: JET FUEL SUPPLY CHAINAlkis Vazacopoulos
 
Pipeline optimization Industrial Modeling Framework
Pipeline optimization Industrial Modeling FrameworkPipeline optimization Industrial Modeling Framework
Pipeline optimization Industrial Modeling FrameworkAlkis Vazacopoulos
 
Limited Budget but Effective End to End MLOps Practices (Machine Learning Mod...
Limited Budget but Effective End to End MLOps Practices (Machine Learning Mod...Limited Budget but Effective End to End MLOps Practices (Machine Learning Mod...
Limited Budget but Effective End to End MLOps Practices (Machine Learning Mod...IRJET Journal
 
Advanced Production Control Using Julia & IMPL
Advanced Production Control Using Julia & IMPLAdvanced Production Control Using Julia & IMPL
Advanced Production Control Using Julia & IMPLAlkis Vazacopoulos
 
Rejunevating software reengineering processes
Rejunevating software reengineering processesRejunevating software reengineering processes
Rejunevating software reengineering processesmanishthaper
 
Partitioning and Positioning (to Solve MINLP Problems) Industrial Modeling Fr...
Partitioning and Positioning (to Solve MINLP Problems) Industrial Modeling Fr...Partitioning and Positioning (to Solve MINLP Problems) Industrial Modeling Fr...
Partitioning and Positioning (to Solve MINLP Problems) Industrial Modeling Fr...Alkis Vazacopoulos
 
MANAGING AND ANALYSING SOFTWARE PRODUCT LINE REQUIREMENTS
MANAGING AND ANALYSING SOFTWARE PRODUCT LINE REQUIREMENTSMANAGING AND ANALYSING SOFTWARE PRODUCT LINE REQUIREMENTS
MANAGING AND ANALYSING SOFTWARE PRODUCT LINE REQUIREMENTSijseajournal
 
Developing the next generation of Real Time Optimization Technologies (Blend ...
Developing the next generation of Real Time Optimization Technologies (Blend ...Developing the next generation of Real Time Optimization Technologies (Blend ...
Developing the next generation of Real Time Optimization Technologies (Blend ...Alkis Vazacopoulos
 
Traffic Simulator
Traffic SimulatorTraffic Simulator
Traffic Simulatorgystell
 
Server-Solvers-Interacter-Interfacer-Modeler-Presolver Libraries and Executab...
Server-Solvers-Interacter-Interfacer-Modeler-Presolver Libraries and Executab...Server-Solvers-Interacter-Interfacer-Modeler-Presolver Libraries and Executab...
Server-Solvers-Interacter-Interfacer-Modeler-Presolver Libraries and Executab...Alkis Vazacopoulos
 

Similar a Advanced Production Accounting of a Flotation Plant (20)

Advanced property tracking Industrial Modeling Framework
Advanced property tracking Industrial Modeling FrameworkAdvanced property tracking Industrial Modeling Framework
Advanced property tracking Industrial Modeling Framework
 
Maritime Industrial Modeling Framework - IMPRESS
Maritime Industrial Modeling Framework - IMPRESSMaritime Industrial Modeling Framework - IMPRESS
Maritime Industrial Modeling Framework - IMPRESS
 
Partial Differential Equations (PDE’s) Industrial Modeling Framework (PDE-IMF)
Partial Differential Equations (PDE’s)  Industrial Modeling Framework (PDE-IMF)Partial Differential Equations (PDE’s)  Industrial Modeling Framework (PDE-IMF)
Partial Differential Equations (PDE’s) Industrial Modeling Framework (PDE-IMF)
 
Impl reference manual_for_quantities
Impl reference manual_for_quantitiesImpl reference manual_for_quantities
Impl reference manual_for_quantities
 
oracle-complex-event-processing-066421
oracle-complex-event-processing-066421oracle-complex-event-processing-066421
oracle-complex-event-processing-066421
 
Finite Impulse Response Estimation of Gas Furnace Data in IMPL Industrial Mod...
Finite Impulse Response Estimation of Gas Furnace Data in IMPL Industrial Mod...Finite Impulse Response Estimation of Gas Furnace Data in IMPL Industrial Mod...
Finite Impulse Response Estimation of Gas Furnace Data in IMPL Industrial Mod...
 
Industrial Modeling Service (IMS-IMPL)
Industrial Modeling Service (IMS-IMPL)Industrial Modeling Service (IMS-IMPL)
Industrial Modeling Service (IMS-IMPL)
 
SUPPLY CHAIN OPTIMIZATIONL: JET FUEL SUPPLY CHAIN
SUPPLY CHAIN OPTIMIZATIONL: JET FUEL SUPPLY CHAINSUPPLY CHAIN OPTIMIZATIONL: JET FUEL SUPPLY CHAIN
SUPPLY CHAIN OPTIMIZATIONL: JET FUEL SUPPLY CHAIN
 
Pipeline optimization Industrial Modeling Framework
Pipeline optimization Industrial Modeling FrameworkPipeline optimization Industrial Modeling Framework
Pipeline optimization Industrial Modeling Framework
 
Limited Budget but Effective End to End MLOps Practices (Machine Learning Mod...
Limited Budget but Effective End to End MLOps Practices (Machine Learning Mod...Limited Budget but Effective End to End MLOps Practices (Machine Learning Mod...
Limited Budget but Effective End to End MLOps Practices (Machine Learning Mod...
 
Jet fuelsupplychaindesign
Jet fuelsupplychaindesignJet fuelsupplychaindesign
Jet fuelsupplychaindesign
 
Advanced Production Control Using Julia & IMPL
Advanced Production Control Using Julia & IMPLAdvanced Production Control Using Julia & IMPL
Advanced Production Control Using Julia & IMPL
 
Rejunevating software reengineering processes
Rejunevating software reengineering processesRejunevating software reengineering processes
Rejunevating software reengineering processes
 
Partitioning and Positioning (to Solve MINLP Problems) Industrial Modeling Fr...
Partitioning and Positioning (to Solve MINLP Problems) Industrial Modeling Fr...Partitioning and Positioning (to Solve MINLP Problems) Industrial Modeling Fr...
Partitioning and Positioning (to Solve MINLP Problems) Industrial Modeling Fr...
 
RakeshDhanani
RakeshDhananiRakeshDhanani
RakeshDhanani
 
MANAGING AND ANALYSING SOFTWARE PRODUCT LINE REQUIREMENTS
MANAGING AND ANALYSING SOFTWARE PRODUCT LINE REQUIREMENTSMANAGING AND ANALYSING SOFTWARE PRODUCT LINE REQUIREMENTS
MANAGING AND ANALYSING SOFTWARE PRODUCT LINE REQUIREMENTS
 
Developing the next generation of Real Time Optimization Technologies (Blend ...
Developing the next generation of Real Time Optimization Technologies (Blend ...Developing the next generation of Real Time Optimization Technologies (Blend ...
Developing the next generation of Real Time Optimization Technologies (Blend ...
 
Traffic Simulator
Traffic SimulatorTraffic Simulator
Traffic Simulator
 
Server-Solvers-Interacter-Interfacer-Modeler-Presolver Libraries and Executab...
Server-Solvers-Interacter-Interfacer-Modeler-Presolver Libraries and Executab...Server-Solvers-Interacter-Interfacer-Modeler-Presolver Libraries and Executab...
Server-Solvers-Interacter-Interfacer-Modeler-Presolver Libraries and Executab...
 
Lear unified env_paper-1
Lear unified env_paper-1Lear unified env_paper-1
Lear unified env_paper-1
 

Más de Alkis Vazacopoulos

Automatic Fine-tuning Xpress-MP to Solve MIP
Automatic Fine-tuning Xpress-MP to Solve MIPAutomatic Fine-tuning Xpress-MP to Solve MIP
Automatic Fine-tuning Xpress-MP to Solve MIPAlkis Vazacopoulos
 
Amazing results with ODH|CPLEX
Amazing results with ODH|CPLEXAmazing results with ODH|CPLEX
Amazing results with ODH|CPLEXAlkis Vazacopoulos
 
Bia project poster fantasy football
Bia project poster  fantasy football Bia project poster  fantasy football
Bia project poster fantasy football Alkis Vazacopoulos
 
NFL Game schedule optimization
NFL Game schedule optimization NFL Game schedule optimization
NFL Game schedule optimization Alkis Vazacopoulos
 
2017 Business Intelligence & Analytics Corporate Event Stevens Institute of T...
2017 Business Intelligence & Analytics Corporate Event Stevens Institute of T...2017 Business Intelligence & Analytics Corporate Event Stevens Institute of T...
2017 Business Intelligence & Analytics Corporate Event Stevens Institute of T...Alkis Vazacopoulos
 
Very largeoptimizationparallel
Very largeoptimizationparallelVery largeoptimizationparallel
Very largeoptimizationparallelAlkis Vazacopoulos
 
Optimization Direct: Introduction and recent case studies
Optimization Direct: Introduction and recent case studiesOptimization Direct: Introduction and recent case studies
Optimization Direct: Introduction and recent case studiesAlkis Vazacopoulos
 
Informs 2016 Solving Planning and Scheduling Problems with CPLEX
Informs 2016 Solving Planning and Scheduling Problems with CPLEX Informs 2016 Solving Planning and Scheduling Problems with CPLEX
Informs 2016 Solving Planning and Scheduling Problems with CPLEX Alkis Vazacopoulos
 
Missing-Value Handling in Dynamic Model Estimation using IMPL
Missing-Value Handling in Dynamic Model Estimation using IMPL Missing-Value Handling in Dynamic Model Estimation using IMPL
Missing-Value Handling in Dynamic Model Estimation using IMPL Alkis Vazacopoulos
 
Dither Signal Design Problem (DSDP) for Closed-Loop Estimation Industrial Mod...
Dither Signal Design Problem (DSDP) for Closed-Loop Estimation Industrial Mod...Dither Signal Design Problem (DSDP) for Closed-Loop Estimation Industrial Mod...
Dither Signal Design Problem (DSDP) for Closed-Loop Estimation Industrial Mod...Alkis Vazacopoulos
 
Distillation Curve Optimization Using Monotonic Interpolation
Distillation Curve Optimization Using Monotonic InterpolationDistillation Curve Optimization Using Monotonic Interpolation
Distillation Curve Optimization Using Monotonic InterpolationAlkis Vazacopoulos
 
Multi-Utility Scheduling Optimization (MUSO) Industrial Modeling Framework (M...
Multi-Utility Scheduling Optimization (MUSO) Industrial Modeling Framework (M...Multi-Utility Scheduling Optimization (MUSO) Industrial Modeling Framework (M...
Multi-Utility Scheduling Optimization (MUSO) Industrial Modeling Framework (M...Alkis Vazacopoulos
 
Phenomenological Decomposition Heuristics for Process Design Synthesis of Oil...
Phenomenological Decomposition Heuristics for Process Design Synthesis of Oil...Phenomenological Decomposition Heuristics for Process Design Synthesis of Oil...
Phenomenological Decomposition Heuristics for Process Design Synthesis of Oil...Alkis Vazacopoulos
 

Más de Alkis Vazacopoulos (20)

Automatic Fine-tuning Xpress-MP to Solve MIP
Automatic Fine-tuning Xpress-MP to Solve MIPAutomatic Fine-tuning Xpress-MP to Solve MIP
Automatic Fine-tuning Xpress-MP to Solve MIP
 
Data mining 2004
Data mining 2004Data mining 2004
Data mining 2004
 
Amazing results with ODH|CPLEX
Amazing results with ODH|CPLEXAmazing results with ODH|CPLEX
Amazing results with ODH|CPLEX
 
Bia project poster fantasy football
Bia project poster  fantasy football Bia project poster  fantasy football
Bia project poster fantasy football
 
NFL Game schedule optimization
NFL Game schedule optimization NFL Game schedule optimization
NFL Game schedule optimization
 
2017 Business Intelligence & Analytics Corporate Event Stevens Institute of T...
2017 Business Intelligence & Analytics Corporate Event Stevens Institute of T...2017 Business Intelligence & Analytics Corporate Event Stevens Institute of T...
2017 Business Intelligence & Analytics Corporate Event Stevens Institute of T...
 
Posters 2017
Posters 2017Posters 2017
Posters 2017
 
Very largeoptimizationparallel
Very largeoptimizationparallelVery largeoptimizationparallel
Very largeoptimizationparallel
 
Retail Pricing Optimization
Retail Pricing Optimization Retail Pricing Optimization
Retail Pricing Optimization
 
Optimization Direct: Introduction and recent case studies
Optimization Direct: Introduction and recent case studiesOptimization Direct: Introduction and recent case studies
Optimization Direct: Introduction and recent case studies
 
Informs 2016 Solving Planning and Scheduling Problems with CPLEX
Informs 2016 Solving Planning and Scheduling Problems with CPLEX Informs 2016 Solving Planning and Scheduling Problems with CPLEX
Informs 2016 Solving Planning and Scheduling Problems with CPLEX
 
ODHeuristics
ODHeuristicsODHeuristics
ODHeuristics
 
Missing-Value Handling in Dynamic Model Estimation using IMPL
Missing-Value Handling in Dynamic Model Estimation using IMPL Missing-Value Handling in Dynamic Model Estimation using IMPL
Missing-Value Handling in Dynamic Model Estimation using IMPL
 
Dither Signal Design Problem (DSDP) for Closed-Loop Estimation Industrial Mod...
Dither Signal Design Problem (DSDP) for Closed-Loop Estimation Industrial Mod...Dither Signal Design Problem (DSDP) for Closed-Loop Estimation Industrial Mod...
Dither Signal Design Problem (DSDP) for Closed-Loop Estimation Industrial Mod...
 
Xmr im
Xmr imXmr im
Xmr im
 
Distillation Curve Optimization Using Monotonic Interpolation
Distillation Curve Optimization Using Monotonic InterpolationDistillation Curve Optimization Using Monotonic Interpolation
Distillation Curve Optimization Using Monotonic Interpolation
 
Multi-Utility Scheduling Optimization (MUSO) Industrial Modeling Framework (M...
Multi-Utility Scheduling Optimization (MUSO) Industrial Modeling Framework (M...Multi-Utility Scheduling Optimization (MUSO) Industrial Modeling Framework (M...
Multi-Utility Scheduling Optimization (MUSO) Industrial Modeling Framework (M...
 
IMPL Data Analysis
IMPL Data AnalysisIMPL Data Analysis
IMPL Data Analysis
 
Benefits of using IMPL
Benefits of using IMPLBenefits of using IMPL
Benefits of using IMPL
 
Phenomenological Decomposition Heuristics for Process Design Synthesis of Oil...
Phenomenological Decomposition Heuristics for Process Design Synthesis of Oil...Phenomenological Decomposition Heuristics for Process Design Synthesis of Oil...
Phenomenological Decomposition Heuristics for Process Design Synthesis of Oil...
 

Último

CCS336-Cloud-Services-Management-Lecture-Notes-1.pptx
CCS336-Cloud-Services-Management-Lecture-Notes-1.pptxCCS336-Cloud-Services-Management-Lecture-Notes-1.pptx
CCS336-Cloud-Services-Management-Lecture-Notes-1.pptxdhiyaneswaranv1
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Guido X Jansen
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...PrithaVashisht1
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityAggregage
 
5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best PracticesDataArchiva
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerPavel Šabatka
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?sonikadigital1
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationGiorgio Carbone
 
Optimal Decision Making - Cost Reduction in Logistics
Optimal Decision Making - Cost Reduction in LogisticsOptimal Decision Making - Cost Reduction in Logistics
Optimal Decision Making - Cost Reduction in LogisticsThinkInnovation
 
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxDwiAyuSitiHartinah
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxVenkatasubramani13
 
Rock Songs common codes and conventions.pptx
Rock Songs common codes and conventions.pptxRock Songs common codes and conventions.pptx
Rock Songs common codes and conventions.pptxFinatron037
 
ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructuresonikadigital1
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introductionsanjaymuralee1
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionajayrajaganeshkayala
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Vladislav Solodkiy
 

Último (16)

CCS336-Cloud-Services-Management-Lecture-Notes-1.pptx
CCS336-Cloud-Services-Management-Lecture-Notes-1.pptxCCS336-Cloud-Services-Management-Lecture-Notes-1.pptx
CCS336-Cloud-Services-Management-Lecture-Notes-1.pptx
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
 
5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayer
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - Presentation
 
Optimal Decision Making - Cost Reduction in Logistics
Optimal Decision Making - Cost Reduction in LogisticsOptimal Decision Making - Cost Reduction in Logistics
Optimal Decision Making - Cost Reduction in Logistics
 
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptx
 
Rock Songs common codes and conventions.pptx
Rock Songs common codes and conventions.pptxRock Songs common codes and conventions.pptx
Rock Songs common codes and conventions.pptx
 
ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructure
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introduction
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual intervention
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023
 

Advanced Production Accounting of a Flotation Plant

  • 1. Advanced Production Accounting of a Flotation Plant Industrial Modeling Framework (APA-FP-IMF) i n d u s t r IAL g o r i t h m s LLC. (IAL) www.industrialgorithms.com August 2014 Introduction to Advanced Production Accounting, UOPSS and QLQP Presented in this short document is a description of what we call "Advanced" Production Accounting (APA) applied to a small Tin-Iron Flotation Plant found in Woollacott and Stange (1987) where their “smoothing” algorithm used can be partially found in Hodouin (2010). APA is the term given to the technique of vetting, screening or cleaning the past production data using statistical data reconciliation and regression (DRR) when continuous-processes are assumed to be at steady-state (Kelly and Hedengren, 2013) i.e., there is no significant material accumulation. For this case, the model and data define a simultaneous quantity and quality bilinear DRR problem (Kelly, 2004b). Figure 1a shows the Flotation Plant using names for the nodes and simple number indices for its streams where Figure 1b depicts the same problem configured in our unit-operation-port- state superstructure (UOPSS) (Kelly, 2004a, 2005; Zyngier and Kelly, 2012). Figure 1a. Flotation Plant Flowsheet (Woollacott and Stange, 1987).
  • 2. Figure 1b. Flotation Plant UOPSS Flowsheet. The diamond shapes or objects are the sources and sinks known as perimeters, the rectangle shapes with the cross-hairs are continuous-process units and as mentioned these units should have a steady-state detection algorithm (SSD) installed to determine if the units are steady or stationary. The circle shapes with no cross-hairs are in-ports which can accept one or more inlet flows and are considered to be simple or uncontrolled mixers. The cross-haired circles are out-ports which can allow one or more outlet flows and are considered to be simple or uncontrolled splitters. The lines, arcs or edges in between the various shapes are known as internal and external streams and represent in this context the flows or transfers of materials from one shape to another. This example and its flow and assay data are taken directly from Woollacott and Stange (1987) as mentioned but is mapped to our UOPSS modeling framework which includes only one time-period typically defined for one business or calendar day. For this problem the configuration is as follows. There are nine (9) mass flows, six (6) components in mass percent (representing the size distributions of >44.3, >30.2, >22.3, >15.8, >12.4 and <12.4 micro-meters) and two (2) properties (tin and iron) in mass percent. The components and properties are called assays in metallurgical accounting applications. Each continuous-process unit-operation is configured as a blackbox subtype with a “%” character suffixed (or prefixed) indicating that an overall quantity balance is applied (see Appendix A) and each quality also has a “%” character suffixed (or prefixed) indicating that an overall quality balance is to applied (see Appendix B). More details and discussion on these types of bilinear data reconciliation problems can be found in Kelly (2004b). Industrial Modeling Framework (IMF), IMPL and SSIIMPLE To implement the mathematical formulation of this and other systems, IAL offers a unique approach and is incorporated into our Industrial Modeling Programming Language we call IMPL. IMPL 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, C#, VBA, Java (SWIG), Python (CTYPES) and/or Julia (CCALL) 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, KNITRO and WORHP as well as our own implementation of SLP called SLPQPE
  • 3. (Successive Linear & Quadratic Programming Engine) which is a very competitive alternative to the other nonlinear solvers and embeds all available LP and QP solvers. In addition and specific to DRR problems, we also have a special solver called SECQPE standing for Sequential Equality-Constrained QP Engine which computes the least-squares solution and a post-solver called SORVE standing for Supplemental Observability, Redundancy and Variability Estimator to estimate the usual DRR statistics. SECQPE also includes a Levenberg-Marquardt regularization method for nonlinear data regression problems and can be presolved using SLPQPE i.e., SLPQPE warm-starts SECQPE. SORVE is run after the SECQPE solver and also computes the well-known "maximum-power" gross-error statistics (measurement and nodal/constraint tests) to help locate outliers, defects and/or faults i.e., mal- functions in the measurement system and mis-specifications in the logging system. The underlying system architecture of IMPL is called SSIIMPLE (we hope literally) which is short for Server, Solvers, Interfacer (IML), Interacter (IPL), Modeler, Presolver Libraries and Executable. The Server, Solvers, Presolver and Executable are primarily model or problem- independent whereas the Interfacer, Interacter 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, IMPL's standard Interfacer, Interacter 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 IMPL 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 *.ILP 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, NOT, EQ, NE, LE, LT, GE, GT and CIP, LIP, SIP and KIP (constant, linear and monotonic spline interpolations) as well as user-written extrinsic functions (XFCN). It is also possible to import another type of foreign file called the *.INL file where both linear and nonlinear constraints can be added easily using new or existing IMPL variables. 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 Gnome Dia and Matplotlib modules respectively but other prototypes embedded within Microsoft Excel/VBA for example can be created in a straightforward manner.
  • 4. However, the primary purpose of the IMF's is to provide a timely, cost-effective, manageable and maintainable deployment of IMPL to formulate and optimize complex industrial manufacturing systems in either off-line or on-line environments. Using IMPL 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 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 IMPL 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. Flotation Plant "Advanced" Production Accounting Synopsis At this point we explore further the purpose of "advanced" production accounting in terms of its diagnostic capability of aiding in the detection, identification and elimination of "bad" production data where "bad" really implies inconsistent data. The major advantage of DRR is its ability to use redundant data which is sometimes referred to as over-determined or over-specified problems. The redundancy primarily occurs because of the inclusion of a model i.e., equations or equality constraints relating all of the quantity and quality variables together including the laws of conservation of matter (i.e., a mass, volume or mole basis). It interesting to quote a recent trade magazine article on “metal accounting risks” (Lothian, 2012) regarding assessing the accuracy of metallurgical balances: “attempting to achieve this without a statistical data reconciliation engine is impossible”. From our Table 1, taken directly from Woollacott and Stange (1987)’s Table I, all 9 stream assays are measured where we use their Error Model 1 which applies a 10% standard error uncertainty to all components and properties (i.e., the standard-deviation equals 0.10 times its actual, raw or measured value). In addition, shown in their Table II are the measured flowrates with estimates of their uncertainty. Table 1. Flotation Plant’s Measurements (Woollacott and Stange, 1987).
  • 5. After modeling and solving this bilinear DRR problem in twenty-five (25) iterations using IMPL’s SECQPE, we arrive at a solution with an objective function of 8.92 which has a Hotelling statistic of 21.66 at 95% confidence and 36 – 2 = 34 degrees-of-freedom indicating that no gross-errors are present. There are thirty-six (36) equations (i.e., 4 nodes times (1 quantity + 8 qualities)), seventy-nine (79) measured variables of which all are declared to be redundant and the two (2) unmeasured quantity or flow variables are declared to be observable for a total of eighty-one (81) variables. Unfortunately Woollacott and Stange (1987) do not report the objective function value of their “smoothed” algorithm, which is somewhat similar to other well-known data reconciliation algorithms except that it uses a hierarchical solution strategy briefly described in Hodouin (2010). However, they present the solutions for several Error Models (except Error Model 1 which is the one we use) in their Table VII and our Table 2. Fortunately, our reconciled results found in our Table 3 using Error Model 1 are relatively close to their smoothed results using Error Models 2, 3, 4 and 5 which confirms that our model and data are consistent.
  • 6. Table 2. Flotation Plant’s “Smoothed” Flow Results from Woollacott and Stange (1987). Table 3. Flotation Plant’s Reconciled Flow Results from IMPL-SECQPE. 1. Feed 6.47 2. PyriteConcentrate 0.61 3. PyriteTailings 5.86 4. RougherFeed 11.48 5. ScavengerConcentrate 1.70 6. RougherConcentrate 4.03 7. FinalTailings 5.74 8. CleanerTailings 3.91 9. FinalConcentrate 0.12 In summary, we have highlighted the advanced production accounting of a small Flotation Plant with both mass quantity and quality data and no statistically detectable gross-errors are present. References Woollacott, L.C., Stange, W., “Guidelines for the derivation of reliable material balances from plant data”, Journal of South Africa Institute of Mining and Metallurgy, 87, 207-217, (1987). Kelly, J.D., "Production modeling for multimodal operations", Chemical Engineering Progress, February, 44, (2004a). Kelly, J.D., "Formulating large-scale quantity-quality bilinear data reconciliation problems", Computers & Chemical Engineering, 357-362, (2004b). 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).
  • 7. Hodouin, “Process observers and data reconciliation using mass and energy balance equations”, Chapter 2 in Advanced Control and Supervision of Mineral Processing Plants, editors Sbarbaro, D., del Villar, R., Springer, (2010). Zyngier, D., Kelly, J.D., "UOPSS: a new paradigm for modeling production planning and scheduling systems", ESCAPE 22, June, (2012). Lothian, K., “Is your business safe from metal accounting risks”, Global Mining, www.energydigital.com, November, (2012). Kelly, J.D., Hedengren, J.D., "A steady-state detection (SDD) algorithm to detect non-stationary drifts in processes", Journal of Process Control, 23, 326, (2013). Appendix A – APA-FP-IMF.UPS (UOPSS) File i M P l (c) Copyright and Property of i n d u s t r I A L g o r i t h m s LLC. checksum,80 !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ! Unit-Operation-Port-State-Superstructure (UOPSS) *.UPS File. ! (This file is automatically generated from the Python program IALConstructer.py) !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! &sUnit,&sOperation,@sType,@sSubtype,@sUse Cleaners,Recleaners,processc,blackbox%, Conditioner,,processc,blackbox%, Feed,,perimeter,, FinalConc,,perimeter,, FinalTails,,perimeter,, PyriteCircuit,,processc,blackbox%, PyriteConc,,perimeter,, Roughers,Scavengers,processc,blackbox%, &sUnit,&sOperation,@sType,@sSubtype,@sUse ! Number of UO shapes = 8 &sAlias,&sUnit,&sOperation ALLPARTS,Cleaners,Recleaners ALLPARTS,Conditioner, ALLPARTS,Feed, ALLPARTS,FinalConc, ALLPARTS,FinalTails, ALLPARTS,PyriteCircuit, ALLPARTS,PyriteConc, ALLPARTS,Roughers,Scavengers &sAlias,&sUnit,&sOperation &sUnit,&sOperation,&sPort,&sState,@sType,@sSubtype Cleaners,Recleaners,6,,in, Cleaners,Recleaners,8,CleanerTails,out, Cleaners,Recleaners,9,,out, Conditioner,,3,,in, Conditioner,,4,RougherFeed,out, Conditioner,,5,,in, Conditioner,,8,,in, Feed,,1,,out, FinalConc,,9,,in, FinalTails,,7,,in, PyriteCircuit,,1,,in, PyriteCircuit,,2,,out, PyriteCircuit,,3,PyriteTails,out, PyriteConc,,2,,in, Roughers,Scavengers,4,,in, Roughers,Scavengers,5,ScavengerConc,out, Roughers,Scavengers,6,RougherConc,out, Roughers,Scavengers,7,,out, &sUnit,&sOperation,&sPort,&sState,@sType,@sSubtype ! Number of UOPS shapes = 18 &sAlias,&sUnit,&sOperation,&sPort,&sState ALLINPORTS,Cleaners,Recleaners,6, ALLINPORTS,Conditioner,,3, ALLINPORTS,Conditioner,,5, ALLINPORTS,Conditioner,,8, ALLINPORTS,FinalConc,,9, ALLINPORTS,FinalTails,,7, ALLINPORTS,PyriteCircuit,,1, ALLINPORTS,PyriteConc,,2, ALLINPORTS,Roughers,Scavengers,4,
  • 8. ALLOUTPORTS,Cleaners,Recleaners,8,CleanerTails ALLOUTPORTS,Cleaners,Recleaners,9, ALLOUTPORTS,Conditioner,,4,RougherFeed ALLOUTPORTS,Feed,,1, ALLOUTPORTS,PyriteCircuit,,2, ALLOUTPORTS,PyriteCircuit,,3,PyriteTails ALLOUTPORTS,Roughers,Scavengers,5,ScavengerConc ALLOUTPORTS,Roughers,Scavengers,6,RougherConc ALLOUTPORTS,Roughers,Scavengers,7, &sAlias,&sUnit,&sOperation,&sPort,&sState &sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState Cleaners,Recleaners,8,CleanerTails,Conditioner,,8, Cleaners,Recleaners,9,,FinalConc,,9, Conditioner,,4,RougherFeed,Roughers,Scavengers,4, Feed,,1,,PyriteCircuit,,1, PyriteCircuit,,2,,PyriteConc,,2, PyriteCircuit,,3,PyriteTails,Conditioner,,3, Roughers,Scavengers,5,ScavengerConc,Conditioner,,5, Roughers,Scavengers,6,RougherConc,Cleaners,Recleaners,6, Roughers,Scavengers,7,,FinalTails,,7, &sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState ! Number of UOPSPSUO shapes = 9 &sAlias,&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState ALLPATHS,Roughers,Scavengers,6,RougherConc,Cleaners,Recleaners,6, ALLPATHS,PyriteCircuit,,3,PyriteTails,Conditioner,,3, ALLPATHS,Roughers,Scavengers,5,ScavengerConc,Conditioner,,5, ALLPATHS,Cleaners,Recleaners,8,CleanerTails,Conditioner,,8, ALLPATHS,Cleaners,Recleaners,9,,FinalConc,,9, ALLPATHS,Roughers,Scavengers,7,,FinalTails,,7, ALLPATHS,Feed,,1,,PyriteCircuit,,1, ALLPATHS,PyriteCircuit,,2,,PyriteConc,,2, ALLPATHS,Conditioner,,4,RougherFeed,Roughers,Scavengers,4, &sAlias,&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState Appendix B – APA-FP-IMF.IML File i M P l (c) Copyright and Property of i n d u s t r I A L g o r i t h m s LLC. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ! Calculation Data (Parameters) !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! &sCalc,@sValue !Miscellaneous Constants. NNON,-99999 LARGEBOUND,1E+3 !Horizon and Period Times. START,-1.0 BEGIN,0.0 END,1.0 PERIOD,1.0 ! Measured Quality Tolerance Fraction (Error Model 1) TOL,0.10 !Measured Stream Flows. F1_V,NNON F2_V,0.61 F3_V,6.94 F4_V,NNON F5_V,1.7 F6_V,4.14 F7_V,5.75 F8_V,3.42 F9_V,0.11 F1_L,0.0 F1_U,LARGEBOUND F1_T,F1_V F1_W,0.0 F2_L,0.0 F2_U,LARGEBOUND F2_T,F2_V F2_W,NE(F2_V;NNON)* 1.0/(0.02)^2 F3_L,0.0 F3_U,LARGEBOUND F3_T,F3_V F3_W,NE(F3_V;NNON)* 1.0/(2.12)^2 F4_L,0.0
  • 9. F4_U,LARGEBOUND F4_T,F4_V F4_W,0.0 F5_L,0.0 F5_U,LARGEBOUND F5_T,F5_V F5_W,NE(F5_V;NNON)* 1.0/(0.10)^2 F6_L,0.0 F6_U,LARGEBOUND F6_T,F6_V F6_W,NE(F6_V;NNON)* 1.0/(2.0)^2 F7_L,0.0 F7_U,LARGEBOUND F7_T,F7_V F7_W,NE(F7_V;NNON)* 1.0/(0.18)^2 F8_L,0.0 F8_U,LARGEBOUND F8_T,F8_V F8_W,NE(F8_V;NNON)* 1.0/(2.14)^2 F9_L,0.0 F9_U,LARGEBOUND F9_T,F9_V F9_W,NE(F9_V;NNON)* 1.0/(0.03)^2 &sCalc,@sValue !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ! Constant Data (Parameters) !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! &sData,@sValue ! Measured Stream Component Assays in %mass (>44.3, >30.2, >22.3, >15.8, >12.4, <12.4 microns). C1,2.6 ,5.2 ,2.4 ,1.9 ,1.7 ,1.2 ,1.9 ,1.1 ,2.3 C2,15.9 ,15.4 ,17 ,11.3 ,6.8 ,6.3 ,17.1 ,6.8 ,4.5 C3,29.6 ,26 ,29.7 ,21 ,16.3 ,15.9 ,30.8 ,16.2 ,14.8 C4,22.3 ,22 ,21.2 ,21.3 ,20.9 ,22.4 ,22.4 ,21.5 ,27.4 C5,7.5 ,7.6 ,6.9 ,8.4 ,9 ,9.9 ,7.1 ,9.6 ,12.7 C6,22.1 ,23.8 ,22.8 ,36.1 ,45.3 ,44.4
  • 10. ,20.7 ,44.8 ,38.3 ! Measured Stream Property Assays in %mass (Tin, Iron). P1,0.795 ,1.095 ,0.78 ,2.455 ,3.18 ,5.26 ,0.26 ,4.70 ,25.35 P2,5.5 ,11.87 ,4.61 ,6.79 ,9.69 ,10.78 ,3.88 ,10.58 ,19.66 &sData,@sValue !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ! Chronological Data (Periods) !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! @rPastTHD,@rFutureTHD,@rTPD START,END,PERIOD @rPastTHD,@rFutureTHD,@rTPD !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ! Construction Data (Pointers) !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Include-@sFile_Name APA-FP-IMF.ups Include-@sFile_Name !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ! Capacity Data (Prototypes) !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! &sUnit,&sOperation,@rRate_Lower,@rRate_Upper ALLPARTS,0.0,LARGEBOUND &sUnit,&sOperation,@rRate_Lower,@rRate_Upper &sUnit,&sOperation,&sPort,&sState,@rTeeRate_Lower,@rTeeRate_Upper ALLINPORTS,0.0,LARGEBOUND ALLOUTPORTS,0.0,LARGEBOUND &sUnit,&sOperation,&sPort,&sState,@rTeeRate_Lower,@rTeeRate_Upper &sUnit,&sOperation,&sPort,&sState,@rTotalRate_Lower,@rTotalRate_Upper ALLINPORTS,0.0,LARGEBOUND ALLOUTPORTS,0.0,LARGEBOUND &sUnit,&sOperation,&sPort,&sState,@rTotalRate_Lower,@rTotalRate_Upper &sUnit,&sOperation,&sPort,&sState,@rYield_Lower,@rYield_Upper,@rYield_Fixed ALLINPORTS,0.0,LARGEBOUND ALLOUTPORTS,0.0,LARGEBOUND &sUnit,&sOperation,&sPort,&sState,@rYield_Lower,@rYield_Upper,@rYield_Fixed !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ! Constituent Data (Properties) !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! &sComponent C1 C2 C3 C4 C5 C6 &sComponent &sTemplate,&sComponent,@rComponent_Lower,@rComponent_Upper,@rComponent_Target CT,C1,0.0,100.0,100.0 ,C2,0.0,100.0,100.0 ,C3,0.0,100.0,100.0 ,C4,0.0,100.0,100.0 ,C5,0.0,100.0,100.0 ,C6,0.0,100.0,100.0 &sTemplate,&sComponent,@rComponent_Lower,@rComponent_Upper,@rComponent_Target &sUnit,&sOperation,&sPort,&sState,&sComponent,@rComponent_Lower,@rComponent_Upper,@rComponent_Target ALLINPORTS,CT% ALLOUTPORTS,CT% &sUnit,&sOperation,&sPort,&sState,&sComponent,@rComponent_Lower,@rComponent_Upper,@rComponent_Target &sProperty
  • 11. P1 P2 &sProperty &sTemplate,&sProperty,@rProperty_Lower,@rProperty_Upper,@rProperty_Target PT,P1,0.0,100.0,100.0 ,P2,0.0,100.0,100.0 &sTemplate,&sProperty,@rProperty_Lower,@rProperty_Upper,@rProperty_Target &sUnit,&sOperation,&sPort,&sState,&sProperty,@rProperty_Lower,@rProperty_Upper,@rProperty_Target ALLINPORTS,PT% ALLOUTPORTS,PT% &sUnit,&sOperation,&sPort,&sState,&sProperty,@rProperty_Lower,@rProperty_Upper,@rProperty_Target !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ! Cost Data (Pricing) !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! &sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState, @rFlowPro_Weight,@rFlowPer1_Weight,@rFlowPer2_Weight,@rFlowPen_Weight Cleaners,Recleaners,8,CleanerTails,Conditioner,,8,,,,F8_W, Cleaners,Recleaners,9,,FinalConc,,9,,,,F9_W, Conditioner,,4,RougherFeed,Roughers,Scavengers,4,,,,F4_W, Feed,,1,,PyriteCircuit,,1,,,,F1_W, PyriteCircuit,,2,,PyriteConc,,2,,,,F2_W, PyriteCircuit,,3,PyriteTails,Conditioner,,3,,,,F3_W, Roughers,Scavengers,5,ScavengerConc,Conditioner,,5,,,,F5_W, Roughers,Scavengers,6,RougherConc,Cleaners,Recleaners,6,,,,F6_W, Roughers,Scavengers,7,,FinalTails,,7,,,,F7_W, &sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState, @rFlowPro_Weight,@rFlowPer1_Weight,@rFlowPer2_Weight,@rFlowPen_Weight &sUnit,&sOperation,&sPort,&sState,&sComponent, @rComponentPro_Weight,@rComponentPer1_Weight,@rComponentPer2_Weight,@rComponentPen_Weight Cleaners,Recleaners,8,CleanerTails,C1,,,1.0/(C1[8]*TOL)^2.0, ,,,,C2,,,1.0/(C2[8]*TOL)^2.0, ,,,,C3,,,1.0/(C3[8]*TOL)^2.0, ,,,,C4,,,1.0/(C4[8]*TOL)^2.0, ,,,,C5,,,1.0/(C5[8]*TOL)^2.0, ,,,,C6,,,1.0/(C6[8]*TOL)^2.0, Cleaners,Recleaners,9,,C1,,,1.0/(C1[9]*TOL)^2.0, ,,,,C2,,,1.0/(C2[9]*TOL)^2.0, ,,,,C3,,,1.0/(C3[9]*TOL)^2.0, ,,,,C4,,,1.0/(C4[9]*TOL)^2.0, ,,,,C5,,,1.0/(C5[9]*TOL)^2.0, ,,,,C6,,,1.0/(C6[9]*TOL)^2.0, Conditioner,,4,RougherFeed,C1,,,1.0/(C1[4]*TOL)^2.0, ,,,,C2,,,1.0/(C2[4]*TOL)^2.0, ,,,,C3,,,1.0/(C3[4]*TOL)^2.0, ,,,,C4,,,1.0/(C4[4]*TOL)^2.0, ,,,,C5,,,1.0/(C5[4]*TOL)^2.0, ,,,,C6,,,1.0/(C6[4]*TOL)^2.0, Feed,,1,,C1,,,1.0/(C1[1]*TOL)^2.0, ,,,,C2,,,1.0/(C2[1]*TOL)^2.0, ,,,,C3,,,1.0/(C3[1]*TOL)^2.0, ,,,,C4,,,1.0/(C4[1]*TOL)^2.0, ,,,,C5,,,1.0/(C5[1]*TOL)^2.0, ,,,,C6,,,1.0/(C6[1]*TOL)^2.0, PyriteCircuit,,2,,C1,,,1.0/(C1[2]*TOL)^2.0, ,,,,C2,,,1.0/(C2[2]*TOL)^2.0, ,,,,C3,,,1.0/(C3[2]*TOL)^2.0, ,,,,C4,,,1.0/(C4[2]*TOL)^2.0, ,,,,C5,,,1.0/(C5[2]*TOL)^2.0, ,,,,C6,,,1.0/(C6[2]*TOL)^2.0, PyriteCircuit,,3,PyriteTails,C1,,,1.0/(C1[3]*TOL)^2.0, ,,,,C2,,,1.0/(C2[3]*TOL)^2.0, ,,,,C3,,,1.0/(C3[3]*TOL)^2.0, ,,,,C4,,,1.0/(C4[3]*TOL)^2.0, ,,,,C5,,,1.0/(C5[3]*TOL)^2.0, ,,,,C6,,,1.0/(C6[3]*TOL)^2.0, Roughers,Scavengers,5,ScavengerConc,C1,,,1.0/(C1[5]*TOL)^2.0, ,,,,C2,,,1.0/(C2[5]*TOL)^2.0, ,,,,C3,,,1.0/(C3[5]*TOL)^2.0, ,,,,C4,,,1.0/(C4[5]*TOL)^2.0, ,,,,C5,,,1.0/(C5[5]*TOL)^2.0, ,,,,C6,,,1.0/(C6[5]*TOL)^2.0, Roughers,Scavengers,6,RougherConc,C1,,,1.0/(C1[6]*TOL)^2.0, ,,,,C2,,,1.0/(C2[6]*TOL)^2.0, ,,,,C3,,,1.0/(C3[6]*TOL)^2.0, ,,,,C4,,,1.0/(C4[6]*TOL)^2.0, ,,,,C5,,,1.0/(C5[6]*TOL)^2.0, ,,,,C6,,,1.0/(C6[6]*TOL)^2.0, Roughers,Scavengers,7,,C1,,,1.0/(C1[7]*TOL)^2.0, ,,,,C2,,,1.0/(C2[7]*TOL)^2.0, ,,,,C3,,,1.0/(C3[7]*TOL)^2.0, ,,,,C4,,,1.0/(C4[7]*TOL)^2.0, ,,,,C5,,,1.0/(C5[7]*TOL)^2.0, ,,,,C6,,,1.0/(C6[7]*TOL)^2.0, &sUnit,&sOperation,&sPort,&sState,&sComponent, @rComponentPro_Weight,@rComponentPer1_Weight,@rComponentPer2_Weight,@rComponentPen_Weight &sUnit,&sOperation,&sPort,&sState,&sProperty, @rPropertyPro_Weight,@rPropertyPer1_Weight,@rPropertyPer2_Weight,@rPropertyPen_Weight Cleaners,Recleaners,8,CleanerTails,P1,,,1.0/(P1[8]*TOL)^2.0, ,,,,P2,,,1.0/(P2[8]*TOL)^2.0,
  • 12. Cleaners,Recleaners,9,,P1,,,1.0/(P1[9]*TOL)^2.0, ,,,,P2,,,1.0/(P2[9]*TOL)^2.0, Conditioner,,4,RougherFeed,P1,,,1.0/(P1[4]*TOL)^2.0, ,,,,P2,,,1.0/(P2[4]*TOL)^2.0, Feed,,1,,P1,,,1.0/(P1[1]*TOL)^2.0, ,,,,P2,,,1.0/(P2[1]*TOL)^2.0, PyriteCircuit,,2,,P1,,,1.0/(P1[2]*TOL)^2.0, ,,,,P2,,,1.0/(P2[2]*TOL)^2.0, PyriteCircuit,,3,PyriteTails,P1,,,1.0/(P1[3]*TOL)^2.0, ,,,,P2,,,1.0/(P2[3]*TOL)^2.0, Roughers,Scavengers,5,ScavengerConc,P1,,,1.0/(P1[5]*TOL)^2.0, ,,,,P2,,,1.0/(P2[5]*TOL)^2.0, Roughers,Scavengers,6,RougherConc,P1,,,1.0/(P1[6]*TOL)^2.0, ,,,,P2,,,1.0/(P2[6]*TOL)^2.0, Roughers,Scavengers,7,,P1,,,1.0/(P1[7]*TOL)^2.0, ,,,,P2,,,1.0/(P2[7]*TOL)^2.0, &sUnit,&sOperation,&sPort,&sState,&sProperty, @rPropertyPro_Weight,@rPropertyPer1_Weight,@rPropertyPer2_Weight,@rPropertyPen_Weight !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ! Content Data (Past, Present Provisos) !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ! Command Data (Future Provisos) !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! &sUnit,&sOperation,@rSetup_Lower,@rSetup_Upper,@rBegin_Time,@rEnd_Time ALLPARTS,1,1,BEGIN,END &sUnit,&sOperation,@rSetup_Lower,@rSetup_Upper,@rBegin_Time,@rEnd_Time &sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState,@rSetup_Lower,@rSetup_Upper,@rBegin_Time,@rEnd_Time ALLPATHS,1,1,BEGIN,END &sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState,@rSetup_Lower,@rSetup_Upper,@rBegin_Time,@rEnd_Time &sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState, @rRate_Lower,@rRate_Upper,@rRate_Target,@rBegin_Time,@rEnd_Time Cleaners,Recleaners,8,CleanerTails,Conditioner,,8,,F8_L,F8_U,F8_T,BEGIN,END Cleaners,Recleaners,9,,FinalConc,,9,,F9_L,F9_U,F9_T,BEGIN,END Conditioner,,4,RougherFeed,Roughers,Scavengers,4,,F4_L,F4_U,F4_T,BEGIN,END Feed,,1,,PyriteCircuit,,1,,F1_L,F1_U,F1_T,BEGIN,END PyriteCircuit,,2,,PyriteConc,,2,,F2_L,F2_U,F2_T,BEGIN,END PyriteCircuit,,3,PyriteTails,Conditioner,,3,,F3_L,F3_U,F3_T,BEGIN,END Roughers,Scavengers,5,ScavengerConc,Conditioner,,5,,F5_L,F5_U,F5_T,BEGIN,END Roughers,Scavengers,6,RougherConc,Cleaners,Recleaners,6,,F6_L,F6_U,F6_T,BEGIN,END Roughers,Scavengers,7,,FinalTails,,7,,F7_L,F7_U,F7_T,BEGIN,END &sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState, @rRate_Lower,@rRate_Upper,@rRate_Target,@rBegin_Time,@rEnd_Time &sUnit,&sOperation,&sPort,&sState,&sComponent,@rComponent_Lower,@rComponent_Upper,@rComponent_Target,@rBegin_Time,@rEnd_Time Cleaners,Recleaners,8,CleanerTails,C1,0.0,100.0,C1[8],BEGIN,END ,,,,C2,0.0,100.0,C2[8],BEGIN,END ,,,,C3,0.0,100.0,C3[8],BEGIN,END ,,,,C4,0.0,100.0,C4[8],BEGIN,END ,,,,C5,0.0,100.0,C5[8],BEGIN,END ,,,,C6,0.0,100.0,C6[8],BEGIN,END Cleaners,Recleaners,9,,C1,0.0,100.0,C1[9],BEGIN,END ,,,,C2,0.0,100.0,C2[9],BEGIN,END ,,,,C3,0.0,100.0,C3[9],BEGIN,END ,,,,C4,0.0,100.0,C4[9],BEGIN,END ,,,,C5,0.0,100.0,C5[9],BEGIN,END ,,,,C6,0.0,100.0,C6[9],BEGIN,END Conditioner,,4,RougherFeed,C1,0.0,100.0,C1[4],BEGIN,END ,,,,C2,0.0,100.0,C2[4],BEGIN,END ,,,,C3,0.0,100.0,C3[4],BEGIN,END ,,,,C4,0.0,100.0,C4[4],BEGIN,END ,,,,C5,0.0,100.0,C5[4],BEGIN,END ,,,,C6,0.0,100.0,C6[4],BEGIN,END Feed,,1,,C1,0.0,100.0,C1[1],BEGIN,END ,,,,C2,0.0,100.0,C2[1],BEGIN,END ,,,,C3,0.0,100.0,C3[1],BEGIN,END ,,,,C4,0.0,100.0,C4[1],BEGIN,END ,,,,C5,0.0,100.0,C5[1],BEGIN,END ,,,,C6,0.0,100.0,C6[1],BEGIN,END PyriteCircuit,,2,,C1,0.0,100.0,C1[2],BEGIN,END ,,,,C2,0.0,100.0,C2[2],BEGIN,END ,,,,C3,0.0,100.0,C3[2],BEGIN,END ,,,,C4,0.0,100.0,C4[2],BEGIN,END ,,,,C5,0.0,100.0,C5[2],BEGIN,END ,,,,C6,0.0,100.0,C6[2],BEGIN,END PyriteCircuit,,3,PyriteTails,C1,0.0,100.0,C1[3],BEGIN,END ,,,,C2,0.0,100.0,C2[3],BEGIN,END ,,,,C3,0.0,100.0,C3[3],BEGIN,END ,,,,C4,0.0,100.0,C4[3],BEGIN,END ,,,,C5,0.0,100.0,C5[3],BEGIN,END ,,,,C6,0.0,100.0,C6[3],BEGIN,END Roughers,Scavengers,5,ScavengerConc,C1,0.0,100.0,C1[5],BEGIN,END ,,,,C2,0.0,100.0,C2[5],BEGIN,END ,,,,C3,0.0,100.0,C3[5],BEGIN,END ,,,,C4,0.0,100.0,C4[5],BEGIN,END ,,,,C5,0.0,100.0,C5[5],BEGIN,END ,,,,C6,0.0,100.0,C6[5],BEGIN,END Roughers,Scavengers,6,RougherConc,C1,0.0,100.0,C1[6],BEGIN,END ,,,,C2,0.0,100.0,C2[6],BEGIN,END ,,,,C3,0.0,100.0,C3[6],BEGIN,END
  • 13. ,,,,C4,0.0,100.0,C4[6],BEGIN,END ,,,,C5,0.0,100.0,C5[6],BEGIN,END ,,,,C6,0.0,100.0,C6[6],BEGIN,END Roughers,Scavengers,7,,C1,0.0,100.0,C1[7],BEGIN,END ,,,,C2,0.0,100.0,C2[7],BEGIN,END ,,,,C3,0.0,100.0,C3[7],BEGIN,END ,,,,C4,0.0,100.0,C4[7],BEGIN,END ,,,,C5,0.0,100.0,C5[7],BEGIN,END ,,,,C6,0.0,100.0,C6[7],BEGIN,END &sUnit,&sOperation,&sPort,&sState,&sComponent,@rComponent_Lower,@rComponent_Upper,@rComponent_Target,@rBegin_Time,@rEnd_Time &sUnit,&sOperation,&sPort,&sState,&sProperty,@rProperty_Lower,@rProperty_Upper,@rProperty_Target,@rBegin_Time,@rEnd_Time Cleaners,Recleaners,8,CleanerTails,P1,0.0,100.0,P1[8],BEGIN,END ,,,,P2,0.0,100.0,P2[8],BEGIN,END Cleaners,Recleaners,9,,P1,0.0,100.0,P1[9],BEGIN,END ,,,,P2,0.0,100.0,P2[9],BEGIN,END Conditioner,,4,RougherFeed,P1,0.0,100.0,P1[4],BEGIN,END ,,,,P2,0.0,100.0,P2[4],BEGIN,END Feed,,1,,P1,0.0,100.0,P1[1],BEGIN,END ,,,,P2,0.0,100.0,P2[1],BEGIN,END PyriteCircuit,,2,,P1,0.0,100.0,P1[2],BEGIN,END ,,,,P2,0.0,100.0,P2[2],BEGIN,END PyriteCircuit,,3,PyriteTails,P1,0.0,100.0,P1[3],BEGIN,END ,,,,P2,0.0,100.0,P2[3],BEGIN,END Roughers,Scavengers,5,ScavengerConc,P1,0.0,100.0,P1[5],BEGIN,END ,,,,P2,0.0,100.0,P2[5],BEGIN,END Roughers,Scavengers,6,RougherConc,P1,0.0,100.0,P1[6],BEGIN,END ,,,,P2,0.0,100.0,P2[6],BEGIN,END Roughers,Scavengers,7,,P1,0.0,100.0,P1[7],BEGIN,END ,,,,P2,0.0,100.0,P2[7],BEGIN,END &sUnit,&sOperation,&sPort,&sState,&sProperty,@rProperty_Lower,@rProperty_Upper,@rProperty_Target,@rBegin_Time,@rEnd_Time