The IML file is our user readable import or input file to the IMPL modeling and solving platform. IMPL is an acronym for Industrial Modeling and Programming Language provided by Industrial Algorithms LLC. The IML file allows the user to configure the necessary data to model and solve large-scale and complex industrial optimization problems (IOP's) such as planning, scheduling, control and data reconciliation and regression in either off or on-line environments.
Please see our IML “(Basic) Reference Manual for Quantities” for a complete introduction on the basics of IML. This manual describes the configuration data necessary to model and solve IOP’s with quality variables and constraints i.e., densities, components, properties, conditions and coefficients.
The symbol "&" denotes an address, index, pointer or key, the "@" denotes an attribute, property, characteristic or value and the prefix "s" stands for string of which there are two other prefixes "r" and "i" for reals (double precision) and integers respectively. String addresses and attributes are case sensitive and do not require any quotes where essentially any character is allowed including spaces except for ",". Each address string field may have no more than 64 characters for it to be considered as unique and each attribute string field may have no more than 512 characters.
If find how to create
E-R Diagrams ? Here is a Doc making things for you simple and easy .
Entity Relationship Diagrams (ERDs) illustrate the logical structure of databases.
I find many students find uneasiness in creating ER-Diagrams conceptually , but they understand how to create tables and columns for a Software project . Here is an approach creating to first create Tables and from tables ..ER Diagrams
If find how to create
E-R Diagrams ? Here is a Doc making things for you simple and easy .
Entity Relationship Diagrams (ERDs) illustrate the logical structure of databases.
I find many students find uneasiness in creating ER-Diagrams conceptually , but they understand how to create tables and columns for a Software project . Here is an approach creating to first create Tables and from tables ..ER Diagrams
A Case Elaboration Methodology for a Semantic Web Service Discovery System Ba...IJERA Editor
The Case Based Reasoning is a paradigm of intelligent reasoning which consists on reusing results of previously solved problems (Source Cases) to solve new problems (Target Cases). It has been formalized as a five-step process consisting of: "Elaboration", "Retrieve", "Reuse", "Revise" and "Retain". In this paper we focus on the first phase of the CBR cycle with all of the required modeling to formalize a Case in our CBR-based system for semantic Web service discovery (CBR4WSD). This phase consists in formalizing the problem description and its structuring before launching the “Retrieve” phase and select the most appropriate Source Cases from the Case Base. We identify a set of basic descriptors to formalize Cases handled in our CBR4WSD system. In this conduct and in accordance with CBR policies, we put forward our Case representation model.
Solve Production Allocation and Reconciliation Problems using the same NetworkAlkis Vazacopoulos
Production allocation is a business accounting practice used throughout the processing world to proportionately and quantitatively assign measurement error and production expenditures or overheads to internal and external business owners. Reconciliation is a scientific function to vet production data of gross errors or non-random variation if it occurs and to find more precise estimates of the measured values. The consequence of our proposed technique is to allow these two functions the capability to use the same production network or flow-path. Only one model is required to be maintained eliminating the possibility that potentially costly mis-allocation will occur due to business and engineering model-mismatch. Mis-allocation due to measurement errors can still be problematic as we illustrate in an example, but should be reduced over time because of the reconciliation measurement diagnostics.
A Case Elaboration Methodology for a Semantic Web Service Discovery System Ba...IJERA Editor
The Case Based Reasoning is a paradigm of intelligent reasoning which consists on reusing results of previously solved problems (Source Cases) to solve new problems (Target Cases). It has been formalized as a five-step process consisting of: "Elaboration", "Retrieve", "Reuse", "Revise" and "Retain". In this paper we focus on the first phase of the CBR cycle with all of the required modeling to formalize a Case in our CBR-based system for semantic Web service discovery (CBR4WSD). This phase consists in formalizing the problem description and its structuring before launching the “Retrieve” phase and select the most appropriate Source Cases from the Case Base. We identify a set of basic descriptors to formalize Cases handled in our CBR4WSD system. In this conduct and in accordance with CBR policies, we put forward our Case representation model.
Solve Production Allocation and Reconciliation Problems using the same NetworkAlkis Vazacopoulos
Production allocation is a business accounting practice used throughout the processing world to proportionately and quantitatively assign measurement error and production expenditures or overheads to internal and external business owners. Reconciliation is a scientific function to vet production data of gross errors or non-random variation if it occurs and to find more precise estimates of the measured values. The consequence of our proposed technique is to allow these two functions the capability to use the same production network or flow-path. Only one model is required to be maintained eliminating the possibility that potentially costly mis-allocation will occur due to business and engineering model-mismatch. Mis-allocation due to measurement errors can still be problematic as we illustrate in an example, but should be reduced over time because of the reconciliation measurement diagnostics.
Reimagine your enterprise: Make Human Centered Design the Heart of Your Digit...Kenneth Kwan
Companies in every industry are trying to find new sources of value
through digital technology. But most of their efforts have not translated
into enough market impact and growth. They need something bolder
and more disruptive, but still very simple. They need reimagination.
Reimagination means putting the user at the center of everything
your company does — strategy, product development, operations,
marketing, sales, and customer service. It means using the full power
of digital media and technology to build empathy with that user, and
weaving that relationship into the fabric of your company. This practice
is known as “human centered design” (HCD): the reshaping of an entire
enterprise and its capabilities system around the customer or user
experience.
HCD represents a new way of life for business. It evokes many of the
attributes of a startup — creativity, speed, bias for action, flexibility
with risk, and radical collaboration. To achieve this entrepreneurial
vigor in your company, you may have to consciously break down long
established internal barriers. You must embrace five basic principles:
Embed human centered design in everything you do, build brand value
holistically, design for three years out (but build for today), stand up
new structures and teams, and nurture your existing digital culture.
Generalized capital investment planning of oil-refineries using CPLEX-MILP an...Alkis Vazacopoulos
Performing capital investment planning (CIP) is traditionally done using linear (LP) or nonlinear (NLP) models whereby a gamut of scenarios are generated and manually searched to make expand and/or install decisions. Though mixed-integer nonlinear (MINLP) solvers have made significant advancements, they are often slow for industrial expenditure optimizations. We propose a more tractable approach using mixed-integer linear (MILP) model and input-output (Leontief) models whereby the nonlinearities are approximated to linearized operations, activities, or modes in large-scaled flowsheet problems. To model the different types of CIP's known as revamping, retrofitting, and repairing, we unify the modeling by combining planning balances with the scheduling concepts of sequence-dependent changeovers to represent the construction, commission, and correction stages explicitly. Similar applications can be applied to process design synthesis, asset allocation and utilization, and turnaround and inspection scheduling. Two motivating examples illustrate the modeling, and a retrofit example and an oil-refinery investment planning are highlighted.
The IML file is our user readable import or input file to the IMPL modeling and solving platform. IMPL is an acronym for Industrial Modeling and Programming Language provided by Industrial Algorithms LLC. The IML file allows the user to configure the necessary data to model and solve large-scale and complex industrial optimization problems (IOP's) such as planning, scheduling, control and data reconciliation and regression in either off or on-line environments.
The data configurable in the IML file are broken-down into several categories or classes where these data categories are used as further sections in this basic reference manual. This reference manual is specific only to the quantity dimension of what we refer to as the Quantity-Logic-Quality Phenomena (QLQP). The QLQP provides a useful phenomenological break-down of the problem complexity where the quantity dimension details quantities such as flows, rates, holdups and yields where the quantities can be related to any stock or signal including time. The other two dimensions are not the focus of this documentation but for completeness of the description, logic data have setups, startups, switchovers-to-itself, shutdowns and switchover-to-others (sequence-dependent transitions) and quality data have densities, components, properties and conditions. In addition to the QLQP , we also have what we call the Unit-Operation-Port-State Superstructure (UOPSS). This provides the flowsheet or topology of the IOP in terms of the various shapes, constructs or objects necessary to configure it. The UOPSS is more than a single network given that it is comprised of two networks we call the "physical" network and the "procedural" network. The physical network involves the units and ports (equipment, structural) and the procedural network involves the operations and states (activities, functional). The combination or cross-product of the two derives the "projectional" superstructure and it is these superstructure constructs or UOPSS keys that we apply, attach or associate specific QLQP attributes where projections are also known as hypothetical, logical or virtual constructs. Ultimately, when we augment the superstructure with the time or temporal dimension as well as including multiple sites or echelons i.e., sub-superstructures, we essentially are configuring what is known as a "hyperstructure".
The IML file is our user readable import or input file to the IMPL modeling and solving platform. IMPL is an acronym for Industrial Modeling and Programming Language provided by Industrial Algorithms LLC. The IML file allows the user to configure the necessary data to model and solve large-scale and complex industrial optimization problems (IOP's) such as planning, scheduling, control and data reconciliation and regression in either off or on-line environments.
Please see our IML “(Basic) Reference Manual for Quantities” for a complete introduction on the basics of IML. This manual describes the configuration data necessary to model and solve IOP’s with logic and logistics (quantity and logic) variables and constraints i.e., setups, startups, shutdowns, switchovers, sequence-dependent switchovers, etc.
The symbol "&" denotes an address, index, pointer or key, the "@" denotes an attribute, property, characteristic or value and the prefix "s" stands for string of which there are two other prefixes "r" and "i" for reals (double precision) and integers respectively. String addresses and attributes are case sensitive and do not require any quotes where essentially any character is allowed including spaces except for ",". Each address string field may have no more than 64 characters for it to be considered as unique and each attribute string field may have no more than 512 characters.
Presented in this short document is a description of what is called Advanced Process Monitoring (APM) as described by Hedengren (2013). APM is the term given to the technique of estimating unmeasured but observable variables or "states" using statistical data reconciliation and regression (DRR) in an off-line or real-time environment and is also referred to as Moving Horizon Estimation (MHE) (Robertson et. al., 1996). Essentially, the model and data define a simultaneous nonlinear and dynamic DRR problem where the model is either engineering-based (first-principles, fundamental, mechanistic, causal, rigorous) or empirical-based (correlation, statistical data-based, observational, regressed) or some combination of both (hybrid).
Advanced Production Accounting of an Olefins Plant Industrial Modeling Framew...Alkis Vazacopoulos
Presented in this short document is a description of what we call "Advanced" Production Accounting (APA) applied to a small Olefins Plant found in Sanchez and Romagnoli (1996). 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 mass or volume linear DRR problem. Figure 1a shows the Olefins Plant using simple number indices for both the nodes and streams where Figure 1b depicts the same problem configured in our unit-operation-port-state superstructure (UOPSS) (Kelly, 2004, 2005; Zyngier and Kelly, 2012).
Advanced Parameter Estimation (APE) for Motor Gasoline Blending (MGB) Indust...Alkis Vazacopoulos
Presented in this short document is a description of how to model and solve advanced parameter estimation (APE) problems in IMPL. APE is the term given to the application of estimating, fitting or calibrating parameters in models involving a network, topology, superstructure or flowsheet. When estimating parameters with multiple linear regression (MLR), ordinary least squares (OLS), ridge regression (RR), principal component regression (PCR) and partial least squares (PLS) there is no explicit model but simply an X-block and Y-block of data. Hence, these methods are referred to as “non-parametric” or “data-based” methods as opposed to the “parametric” or “model-based” method used here. To solve these types of problems we use what is commonly referred to as “error-in-variables” (EIV) regression which is conveniently implemented as nonlinear data reconciliation and regression (NDRR) using the technology found in Kelly (1998a; 1998b; 1999) and Kelly and Zyngier (2008a). The primary benefit of using EIV (NDRR) over the other regression methods is that we can easily handle the inclusion of conservation laws and constitutive relations, explicitly, a must for any industrial estimation problem (IEP).
Server-Solvers-Interacter-Interfacer-Modeler-Presolver Libraries and Executab...Alkis Vazacopoulos
The term SSIIMPLE is used to describe IMPL’s system architecture which stands for Server-Solvers-Interacter-Interfacer-Modeler-Presolver Libraries and Executable. IMPL is an acronym for Industrial Modeling and Programming Language provided by Industrial Algorithms LLC. SSIIMPLE is designed to be portable to both Windows and Linux operating systems on 32 and 64-bit platforms and to have the smallest footprint as possible in order to allow what we call “poor man’s parallelism” (PMP). This essentially means running as many IMPL problem instances as there are CPU’s or threads where each IMPL problem instance would essentially use the same model data but with different solver settings, solvers, initial-values, column orderings, etc. However, it is also possible to modify either or both of static and dynamic model data as well as the solver settings within a given problem instance thread.
Presented in this short document is a description of what we call "Advanced" Property Tracking or Tracing (APT). APT is the term given to the technique of predicting, simulating, calculating or estimating the properties (i.e., densities, compositions, conditions, qualities, etc.) in a network or superstructure with significant inventory using statistical data reconciliation and regression (DRR)
Finite Impulse Response Estimation of Gas Furnace Data in IMPL Industrial Mod...Alkis Vazacopoulos
Presented in this short document is a description of how to estimate deterministic and stochastic non-parametric finite impulse response (FIR) models in IMPL applied to industrial gas furnace data identical to that found in TSE-GFD-IMF using parametric transfer-functions. The methodology of time-series analysis or system identification involves essentially three (3) stages (Box and Jenkins, 1976): (1) model structure identification, (2) model parameter estimation and (3) model checking and diagnostics. We do not address (1) which requires stationarity and seasonality assessment/adjustment, auto-, cross- and partial-correlation, etc. to establish the parametric transfer function polynomial degrees especially when we are using non-parametric FIR estimation. Instead we focus only on the parameter estimation and diagnostics. These types of parameter estimation problems involve dynamic and nonlinear relationships shown below and we solve these using IMPL’s Sequential Equality-Constrained QP Engine (SECQPE) and Supplemental Observability, Redundancy and Variability Estimator (SORVE). Other types of non-parametric identification known as Subspace Identification (Qin, 2006) and can used to estimate state-space models.
Chapter 01 Introduction to Java by Tushar B KuteTushar B Kute
The lecture was condcuted by Tushar B Kute at YCMOU, Nashik through VLC orgnanized by MSBTE. The contents can be found in book "Core Java Programming - A Practical Approach' by Laxmi Publications.
We tested ODH|CPLEX 4.24 on Miplib Open-v7 Models, a public collection of 286 models to which and optimal solution has not been proven. 257 of these are known to have a feasible solution.
ODH|CPLEX proved optimality on 6 models and found better solutions in 2 hours, to 40% of the models with 12 threads and 35% with 8 threads. ODH|CPLEX matched on 21% of the models.
EX Optimization Studio* solves large-scale optimization problems and enables better business decisions and resulting financial benefits in areas such as supply chain management, operations, healthcare, retail, transportation, logistics and asset management. It has been applied in sectors as diverse as manufacturing, processing, distribution, retailing, transport, finance and investment. CPLEX Optimization Studio is an analytical decision support toolkit for rapid development and deployment of optimization models using mathematical and constraint programming. It combines an integrated development environment (IDE) with the powerful Optimization Programming Language (OPL) and high-performance ILOG CPLEX optimizer solvers. CPLEX Optimization Studio enables clients to: Optimize business decisions with high-performance optimization engines. Develop and deploy optimization models quickly by using flexible interfaces and prebuilt deployment scenarios. Create real-world applications that can significantly improve business outcomes. Optimization Direct has partnered with and entered into a technology licensing and distribution agreement with IBM. By combining the founders' industry and software experience and IBM’s CPLEX Optimization Studio product with the arsenal of Optimization modeling and solving tools from IBM provides customers the most powerful capabilities in the industry.
Missing-Value Handling in Dynamic Model Estimation using IMPL Alkis Vazacopoulos
Presented in this short document is a description of how IMPL handles missing-values or missing-data when estimating dynamic models which inherently involve time-lagged or time-shifted input and output variables. Missing-values in a data set imply that for some reason the data is not available most likely due to a mal-functioning instrument or even lack of proper accounting. Missing-data handling is relatively well-studied especially for time-series or dynamic data given that it is not as easy as removing, ignoring or deleting bad sections of data when static or steady-state models are calibrated (Honaker and King, 2010; Smits and Baggelaar, 2010; Fisher and Waclawski, 2015). Unfortunately, all of their methods involve what is known as “imputation” i.e., replacing or substituting missing-data with some reasonably assumed value which is at the very least is a biased estimate. When regression techniques such as PLS and PCR are used (Nelson et. al., 2006) then missing-data can be handled without imputation by computing the input-output covariance matrices excluding the contribution from the missing-values given the temporal and structural redundancy in the system. However, it is shown in Dayal (1996) that using PLS and other types of regression techniques such as Canonical Correlation Regression (CCR) and Reduced Rank Regression (RRR) to fit non-parsimonious and non-parametric finite impulse/step response models (FIR/FSR), that this is not as reliable as fitting lower-ordered transfer functions especially considering the robust stability of the resulting model predictive controller if that is its intended use.
Our Industrial Modeling Service (IMS) involves several important (but rarely implemented) methods to significantly improve and advance your existing models and data. Since it is well-known that good decision-making requires good models and data, IMS is ideally suited to support this continuous-improvement endeavour. IMS is specifically designed to either co-exist with your existing design, planning, scheduling, etc. applications or these same models and data can be used seamlessly into our Industrial Modeling and Programming Language (IMPL) to create new value-added applications. The following techniques form the basis of our IMS offering.
This short note describes a relatively simple methodology, procedure or approach to increase the performance of already installed industrial models used for optimization, control, simulation and/or monitoring purposes. The method is called Excess or X-Model Regression (XMR) where the concept of “excess modeling” or an X-model is taken from the field of thermodynamics to describe the departure or residual behaviour of real (non-ideal) gases and liquids from their ideal state (Kyle, 1999; Poling et. al., 2001; Smith et. al., 2001). It has also been applied to model the non-ideal or nonlinear behaviour of blending motor gasoline octanes with its synergistic and antagonistic interactional effects (Muller, 1992).
The fundamental idea of XMR is to calibrate, train, fit or estimate, using actual data and multiple linear regression (MLR) or ordinary least squares (OLS), the deviations of the measured responses from the existing model responses. The existing model may be a glass, grey or black-box model (known or unknown, linear or nonlinear, implicit/open or explicit/closed) depending on the use of the model. That is, for optimization and control the model structure and parameters are available given that derivative information is required although for simulation and monitoring, the model may only be observed through the dependent output variables given the necessary independent input variables.
Presented in this short document is a description of how to model and solve multi-utility scheduling optimization (MUSO) problems in IMPL. Multi-utility systems (co/tri-generation) are typically found in petroleum refineries and petrochemical plants (multi-commodity systems) especially when fuel-gas (i.e., off-gases of methane and ethane) is a co- or by-product of the production from which multi-pressure heating-, motive- and process-steam are generated on-site. Other utilities include hydrogen, electricity, water, cooling media, air, nitrogen, chemicals, etc. where a multi-utility system is shown in Figure 1 with an intermediate or integrated utility (both produced and consumed) such as fuel-gas, steam or electricity. Itemized benefit areas just for better management of an integrated steam network can be found in Pelham (2013) where his sample multi-pressure steam utility flowsheet is found in Figure 2.
Presented in this short document is a description of modeling and solving partial differential equations (PDE’s) in both the temporal and spatial dimensions using IMPL. The sample PDE problem is taken from Cutlip and Shacham (1999 and 2014) and models the process of unsteady-state heat transfer or conduction in a one dimensional (1D) slab with one face insulated and constant thermal conductivity as discussed by Geankoplis (1993).
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Runway Orientation Based on the Wind Rose Diagram.pptx
Impl reference manual_for_qualities
1.
i
M
P
l
Industrial
Modeling
Language
(IML)
"(Advanced)
Reference
Manual
for
Qualities"
i
n
d
u
s
t
r
I
A
L
g
o
r
i
t
h
m
s
LLC.
www.industrialgorithms.com
Version
1.0
April
2014
IAL-‐IMPL-‐IML-‐RMQQ-‐1-‐0.docx
Copyright
and
Property
of
Industrial
Algorithms
LLC.
2. Introduction
The
IML
file
is
our
user
readable
import
or
input
file
to
the
IMPL
modeling
and
solving
platform.
IMPL
is
an
acronym
for
Industrial
Modeling
and
Programming
Language
provided
by
Industrial
Algorithms
LLC.
The
IML
file
allows
the
user
to
configure
the
necessary
data
to
model
and
solve
large-‐scale
and
complex
industrial
optimization
problems
(IOP's)
such
as
planning,
scheduling,
control
and
data
reconciliation
and
regression
in
either
off
or
on-‐line
environments.
Please
see
our
IML
“(Basic)
Reference
Manual
for
Quantities”
for
a
complete
introduction
on
the
basics
of
IML.
This
manual
describes
the
configuration
data
necessary
to
model
and
solve
IOP’s
with
quality
variables
and
constraints
i.e.,
densities,
components,
properties,
conditions
and
coefficients.
The
symbol
"&"
denotes
an
address,
index,
pointer
or
key,
the
"@"
denotes
an
attribute,
property,
characteristic
or
value
and
the
prefix
"s"
stands
for
string
of
which
there
are
two
other
prefixes
"r"
and
"i"
for
reals
(double
precision)
and
integers
respectively.
String
addresses
and
attributes
are
case
sensitive
and
do
not
require
any
quotes
where
essentially
any
character
is
allowed
including
spaces
except
for
",".
Each
address
string
field
may
have
no
more
than
64
characters
for
it
to
be
considered
as
unique
and
each
attribute
string
field
may
have
no
more
than
512
characters.
Constituent
Data
IMPL
allows
for
the
configuration
of
several
global
sets
to
create
user-‐defined
intensive
quality
variables
assigned,
associated
or
attached
to
any
unit-‐operation-‐port-‐state
where
conditions
and
coefficients
can
only
be
assigned
to
unit-‐operations
of
subtype
blackbox.
Factors
do
not
propagate
across
the
flowsheet
or
superstructure
like
the
other
intensive
qualities
enumerated
below
and
are
essentially
constant.
&sFactor
FACTOR
&sFactor
3. Densities
allow
any
mass
to
volume,
volume
to
mole,
energy
to
mass,
etc.
type
of
mass,
mole,
volume,
energy,
etc.
basis
conversions.
&sDensity
DENSITY
&sDensity
Components
are
similar
to
pure-‐components,
pseudo-‐components,
hypotheticals,
used
in
process
engineering
simulators.
&sComponent
COMPONENT
&sComponent
Properties
are
any
non-‐density
and
non-‐component
such
as
research
and
motor
octane,
sulfur,
melting
point,
etc.
&sProperty
PROPERTY
&sProperty
Conditions
are
essentially
non-‐densities,
non-‐components
and
non-‐properties
such
as
temperature,
pressure,
severity,
conversion,
etc.
that
can
be
used
to
model
the
ad
hoc
behavior
of
blackbox
unit-‐
operation
subtypes.
&sCondition
CONDITION
&sCondition
Coefficients
are
similar
to
conditions
and
may
either
be
of
the
“static”
or
“dynamic”
type
where
static
coefficients
have
no
implied
temporal
dimension
and
represent
parameters
that
can
be
fitted
or
estimated
to
past/present
data
in
data
reconciliation
and
regression
problems
for
example.
Dynamic
coefficients
may
be
used
to
allow
function
calls
to
third-‐party
DLL’s
or
SO’s
to
compute
physical
properties
such
as
enthalpy,
entropy
or
equilibrium
values
and
these
quality
variables
are
indexed
by
time-‐periods
as
their
type
suggests.
4. The
attributes
after
type
are
only
valid
for
dynamic
coefficients
where
the
path,
library
and
function
names
determine
how
to
locate
and
call
the
third-‐party
function.
The
number
of
conditions
states
the
number
of
condition
arguments
to
the
third-‐party
function,
the
perturb
size
is
the
size
of
the
perturbation
to
compute
first-‐order
derivatives
(10-‐6
)
with
respect
to
the
conditions
and
the
list
of
condition
names
separated
by
commas
are
the
condition
argument
names
also
known
in
the
global
condition
set.
&sCoefficient,@sType,@sPath_Name,@sLibrary_Name,@sFunction_Name,
@iNumber_Conditions,@rPerturb_Size,@sCondition_Names
COEFFICIENT,TYPE,PATH,LIBRARY,FUNCTION,NCONDITIONS,PERTURBSIZE,CONDITIONS
&sCoefficient,@sType,@sPath_Name,@sLibrary_Name,@sFunction_Name,
@iNumber_Conditions,@rPerturb_Size,@sCondition_Names
Chains
are
reactions
found
inside
unit-‐operations
of
type
process
and
of
subtype
reactor.
Chains
are
used
to
configure
stoichiometry-‐data
i.e.,
reaction
coefficients
per
chain
or
reaction.
&sChain
CHAIN
&sChain
Cuts
are
sub-‐
or
meta-‐components
found
inside
unit-‐operations
of
type
process
and
of
subtype
fractionator.
Cuts
are
used
to
configure
assay-‐data
in
terms
of
how
a
component
is
distributed
or
distilled
over
for
example
its
temperature
boiling-‐point
range
where
each
cut
has
a
starting
or
initial
boiling-‐point
and
an
ending
or
final
boiling-‐point.
&sCut,@rInitialPoint_Value,@rFinalPoint_Value
CUT,IVALUE,FVALUE
&sCut,@rInitialPoint_Value,@rFinalPoint_Value
Component-‐density’s
and
property-‐density’s
are
used
to
model
heterogeneous
components
and
properties
in
the
sense
that
a
mass-‐based
quality
such
as
sulfur
can
be
calculated
or
predicted
using
a
volume-‐based
quantity
or
flow.
&sComponent,@sDensity
COMPONENT,DENSITY
&sComponent,@sDensity
&sProperty,@sDensity
PROPERTY,DENSITY
&sProperty,@sDensity
5.
Property-‐property’s
and
condition-‐condition’s
are
ranking,
volatility
or
ordering
inequality
constraints
to
ensure
that
the
first
quality
variable
result
is
greater
than
the
second
quality
variable
result.
Ranking
constraints
are
useful
when
solving
with
linear
and
spline
interpolations
in
order
to
maintain
the
monotonicity
of
the
x-‐axis
or
abscissa.
&sProperty,@sProperty
PROPERTY,PROPERTY2
&sProperty,@sProperty
&sCondition,@sCondition
CONDITION,CONDITION2
&sCondition,@sCondition
Property-‐transforms
are
nonlinear
expressions
or
formulas
that
can
be
applied
to
a
single
property
to
transform
it
before
and
after
the
solving
to
some
other
number
and
is
essentially
useful
for
blending
and
mixing
unit-‐operations.
An
example
of
a
property-‐transform
or
blending-‐index
is
converting
SG
to
API
i.e,
API=141.5/SG-131.5.
PropertyTransform-&sProperty,@sType,@rValue,@sValue
PROPERTY,TYPE,RVALUE,SVALUE
PropertyTransform-&sProperty,@sType,@rValue,@sValue
Properties-‐property
are
nonlinear
expressions
or
formulas
that
can
be
used
to
model
derived
or
secondary
properties
and
are
useful
to
model
one
dependent
property
as
a
function
of
any
other
independent
or
dependent
property
i.e.,
ROAD=(RON+MON)/2.
PropertiesProperty-&sProperty,@sType,@rValue,@sValue
PROPERTY,TYPE,RVALUE,SVALUE
PropertiesProperty-&sProperty,@sType,@rValue,@sValue
Condition
Data
(For
Unit-‐Operation
Blackboxes
Only)
For
unit-‐operations
of
type
process
and
subtype
blackbox
we
can
assign,
associate
or
attach
condition
variables
from
the
global
set
of
conditions
and
global
set
of
coefficients.
Then,
these
unit-‐operation-‐
conditions
can
be
used
in
nonlinear
expressions
or
formula
to
model
any
nonlinear
relationship
that
may
be
required
to
accurately
and
precisely
represent
its
behavior
over
time.
6.
In
most
situations,
condition
variables
are
dependent
on
upstream
and/or
downstream
unit-‐operation
and/or
unit-‐operation-‐port-‐state
quantity
and
quality
variables
and
these
can
be
configured
using
the
following
linear
and
simple
connection
,
transfer
or
linking
types
of
equations.
UOHoldupUOCondition-&sUnit,&sOperation,&sUnit,&sOperation,&sCondition
UNIT,OPERATION,UNIT2,OPERATION2,CONDITION
UOHoldupUOCondition-&sUnit,&sOperation,&sUnit,&sOperation,&sCondition
UOPSFlowUOCondition-&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sCondition
UNIT,OPERATION,PORT,STATE,UNIT2,OPERATION2,CONDITION
UOPSFlowUOCondition-&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sCondition
UOPSYieldUOCondition-&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sCondition
UNIT,OPERATION,PORT,STATE,UNIT2,OPERATION2,CONDITION
UOPSYieldUOCondition-&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sCondition
UOPSDensityUOCondition-&sUnit,&sOperation,&sPort,&sState,&sDensity,
&sUnit,&sOperation,&sCondition
UNIT,OPERATION,PORT,STATE,DENSITY,UNIT2,OPERATION2,CONDITION
UOPSDensityUOCondition-&sUnit,&sOperation,&sPort,&sState,&sDensity,
&sUnit,&sOperation,&sCondition
UOPSComponentUOCondition-&sUnit,&sOperation,&sPort,&sState,&sDensity,
&sUnit,&sOperation,&sCondition
UNIT,OPERATION,PORT,STATE,COMPONENT,UNIT2,OPERATION2,CONDITION
UOPSComponentUOCondition-&sUnit,&sOperation,&sPort,&sState,&sDensity,
&sUnit,&sOperation,&sCondition
UOPSPropertyUOCondition-&sUnit,&sOperation,&sPort,&sState,&sDensity,
&sUnit,&sOperation,&sCondition
UNIT,OPERATION,PORT,STATE,PROPERTY,UNIT2,OPERATION2,CONDITION
UOPSPropertyUOCondition-&sUnit,&sOperation,&sPort,&sState,&sDensity,
&sUnit,&sOperation,&sCondition
After
any
dependent
conditions
have
been
configured
on
the
unit-‐operation
blackbox,
then
nonlinear
formulas
of
how
to
relate
a
condition
expression
to
another
condition
on
the
same
unit-‐operation
as
well
as
relating
to
other
quantity
and
quality
variables
on
the
unit-‐operation-‐port-‐states
can
also
be
configured
as
follows.
ConditionsUOCondition-&sUnit,&sOperation,&sCondition,@sType,@rValue,@sValue
UNIT,OPERATION,CONDITION,TYPE,RVALUE,SVALUE
ConditionsUOCondition-&sUnit,&sOperation,&sCondition,@sType,@rValue,@sValue
ConditionsUOPSFlow-&sUnit,&sOperation,&sPort,&sState,@sType,@rValue,@sValue
7. UNIT,OPERATION,PORT,STATE,TYPE,RVALUE,SVALUE
ConditionsUOPSFlow-&sUnit,&sOperation,&sPort,&sState,@sType,@rValue,@sValue
ConditionsUOPSRate-&sUnit,&sOperation,&sPort,&sState,@sType,@rValue,@sValue
UNIT,OPERATION,PORT,STATE,TYPE,RVALUE,SVALUE
ConditionsUOPSRate-&sUnit,&sOperation,&sPort,&sState,@sType,@rValue,@sValue
ConditionsUOPSYield-&sUnit,&sOperation,&sPort,&sState,@sType,@rValue,@sValue
UNIT,OPERATION,PORT,STATE,TYPE,RVALUE,SVALUE
ConditionsUOPSYield-&sUnit,&sOperation,&sPort,&sState,@sType,@rValue,@sValue
ConditionsUOPSDensity-&sUnit,&sOperation,&sPort,&sState,&sDensity,
@sType,@rValue,@sValue
UNIT,OPERATION,PORT,STATE,DENSITY,TYPE,RVALUE,SVALUE
ConditionsUOPSDensity-&sUnit,&sOperation,&sPort,&sState,&sDensity,
@sType,@rValue,@sValue
ConditionsUOPSComponent-&sUnit,&sOperation,&sPort,&sState,&sComponent,
@sType,@rValue,@sValue
UNIT,OPERATION,PORT,STATE,COMPONENT,TYPE,RVALUE,SVALUE
ConditionsUOPSComponent-&sUnit,&sOperation,&sPort,&sState,&sComponent,
@sType,@rValue,@sValue
ConditionsUOPSProperty-&sUnit,&sOperation,&sPort,&sState,&sProperty,
@sType,@rValue,@sValue
UNIT,OPERATION,PORT,STATE,PROPERTY,TYPE,RVALUE,SVALUE
ConditionsUOPSProperty-&sUnit,&sOperation,&sPort,&sState,&sProperty,
@sType,@rValue,@sValue
Constituent
Capacity
Data
IMPL
allows
Constituent
Capacity
Data
to
be
configured
or
specified
to
each
unit-‐operation-‐port-‐state
in
the
superstructure.
If
a
quality
in
a
global
quality
set
is
not
assigned,
associated
or
attached
to
a
particular
unit-‐operation-‐port-‐state
internal
stream
then
the
quality
variable
will
not
be
created
or
generated
in
the
model.
A
quality
variable
must
have
a
lower
and
upper
(hard)
bound
but
it
may
or
may
not
have
a
target
(soft)
bound.
If
its
target
is
left
blank
or
it
is
specified
as
RNNON
then
a
target
is
ignored.
If
the
target
field
is
populated
but
its
corresponding
performance-‐weight
is
zero
(0)
then
the
target
will
be
used
as
an
initial-‐
value,
starting-‐point
or
default-‐result.
&sUnit,&sOperation,&sPort,&sState,&sFactor,@rFactor_Value
UNIT,OPERATION,PORT,STATE,FACTOR,F
VALUE
&sUnit,&sOperation,&sPort,&sState,&sFactor,@rFactor_Value
9. For
each
chain
and
for
each
unit-‐operation,
configure
its
lower
and
upper
extent
of
reaction
or
rate.
A
chain
or
reaction
can
be
likened
to
a
sub
batch
or
charge-‐size.
&sChain,&sUnit,&sOperation,@rRate_Lower,@rRate_Upper
CHAIN,
UNIT,OPERATION,LRATE,URATE
&sChain,&sUnit,&sOperation,@rRate_Lower,@rRate_Upper
The
component-‐cut-‐yields
(assay-‐data)
are
valid
for
unit-‐operations
of
type
process
and
subtype
fractionator
and
specify
for
each
component
and
for
each
cut
its
yield
value.
&sComponent,&sCut,@rYield_Value
COMPONENT,
CUT,YVALUE
&sComponent,&sCut,@rYield_Value
Component-‐cut-‐densities,
components
and
properties
provide
the
necessary
assay-‐data
to
calculate
or
predict
for
each
component
the
quality
of
each
cut
i.e.,
how
each
quality
is
distributed
or
profiled
over
the
temperature
boiling-‐point
range
of
the
component
discretized
by
the
cuts.
&sComponent,&sCut,&sDensity,@rDensity_Value
COMPONENT,
CUT,DENSITY,DVALUE
&sComponent,&sCut,&sDensity,@rDensity_Value
&sComponent,&sCut,&sComponent,@rComponent_Value
COMPONENT,
CUT,COMPONENT,CVALUE
&sComponent,&sCut,&sComponent,@rComponent_Value
&sComponent,&sCut,&sProperty,@rProperty_Value
COMPONENT,
CUT,PROPERTY,PVALUE
&sComponent,&sCut,&sProperty,@rProperty_Value
For
each
unit-‐operation-‐port-‐state
and
each
cut
,
these
values
provide
the
lower
and
upper
yield
bounds.
These
values
essentially
stipulate
how
each
cut
on
a
unit-‐operation-‐port-‐state
is
distributed
where
the
values
should
lie
between
zero
(0)
and
one
(1).
&sUnit,&sOperation,&sPort,&sState,&sCut,@rYield_Lower,@rYield_Upper
UNIT,OPERATION,PORT,STATE,
CUT,
LYIELD,UYIELD
&sUnit,&sOperation,&sPort,&sState,&sCut,@rYield_Lower,@rYield_Upper
Constituent
Cost
Data
10. The
Cost
Data
for
qualities
is
straightforward
where
again
we
have
a
profit-‐weight,
performance1-‐
weight
(1-‐norm
deviations
from
target),
performance2-‐weight
(2-‐norm)
and
penalty-‐weight
for
each
unit-‐operation-‐port-‐state-‐density,
component
and
property
as
well
as
unit-‐operation-‐condition
and
coefficient
sets
of
objective
function
weights.
&sUnit,&sOperation,&sPort,&sState,&sDensity,@rDensityPro_Weight,
@rDensityPer1_Weight,@rDensityPer2_Weight,@rDensityPen_Weight
UNIT,OPERATION,PORT,STATE,DENSITY,WDPRO,WDPER1,WDPER2,WDPEN
&sUnit,&sOperation,&sPort,&sState,&sDensity,@rDensityPro_Weight,
@rDensityPer1_Weight,@rDensityPer2_Weight,@rDensityPen_Weight
&sUnit,&sOperation,&sPort,&sState,&sComponent,@rComponentPro_Weight,
@rComponentPer1_Weight,@rComponentPer2_Weight,@rComponentPen_Weight
UNIT,OPERATION,PORT,STATE,COMPONENT,WCPRO,WCPER1,WCPER2,WCPEN
&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
UNIT,OPERATION,PORT,STATE,PROPERTY,WPPRO,WPPER1,WPPER2,WPPEN
&sUnit,&sOperation,&sPort,&sState,&sProperty,@rPropertyPro_Weight,
@rPropertyPer1_Weight,@rPropertyPer2_Weight,@rPropertyPen_Weight
&sUnit,&sOperation,&sCondition,@rConditionPro_Weight,
@rConditionPer1_Weight,@rConditionPer2_Weight,@rConditionPen_Weight
UNIT,OPERATION,CONDITION,WCPRO,WCPER1,WCPER2,WCPEN
&sUnit,&sOperation,&sCondition,@rConditionPro_Weight,
@rConditionPer1_Weight,@rConditionPer2_Weight,@rConditionPen_Weight
&sUnit,&sOperation,&sCoefficient,@rCoefficientPro_Weight,
@rCoefficientPer1_Weight,@rCoefficientPer2_Weight,@rCoefficientPen_Weight
UNIT,OPERATION,COEFFICIENT,WCPRO,WCPER1,WCPER2,WCPEN
&sUnit,&sOperation,&sCoefficient,@rCoefficientPro_Weight,
@rCoefficientPer1_Weight,@rCoefficientPer2_Weight,@rCoefficientPen_Weight
Constituent
Content
(Current)
Data
The
Constituent
Content
or
Current
Data
configures
the
opening
qualities
of
density,
component
and
property
for
the
physical
units
of
type
pool
in
the
past/present
time-‐horizon.
For
projectional
unit-‐
operations
of
type
process
and
subtype
blackbox
we
also
can
configure
their
opening
conditions.
&sUnit,&sDensity,@rDensity_Value,@rStart_Time
UNIT,DENSITY,DVALUE,START
&sUnit,&sDensity,@rDensity_Value,@rStart_Time
&sUnit,&sComponent,@rComponent_Value,@rStart_Time
11. UNIT,COMPONENT,CVALUE,START
&sUnit,&sComponent,@rComponent_Value,@rStart_Time
&sUnit,&sProperty,@rProperty_Value,@rStart_Time
UNIT,PROPERTY,PVALUE,START
&sUnit,&sProperty,@rProperty_Value,@rStart_Time
&sUnit,&sOperation,&sCondition,@rCondition_Value,@rStart_Time
UNIT,OPERATION,CONDITION,CVALUE,START
&sUnit,&sOperation,&sCondition,@rCondition_Value,@rStart_Time
Constituent
Command
(Control)
Data
The
Constituent
Command
or
Control
Data
configures
the
order,
transaction
or
proviso
details
of
how
the
lower,
upper
(hard)
and
target
(soft)
bounds
can
vary
over
time
for
unit-‐operation-‐port-‐state-‐
densities,
components
and
properties
and
unit-‐operation-‐conditions.
&sUnit,&sOperation,&sPort,&sState,&sDensity,
@rDensity_Lower,@rDensity_Upper,@rDensity_Target,@rBegin_Time,@rEnd_Time
UNIT,OPERATION,PORT,STATE,DENSITY
,DLOWER,DUPPER,DTARGET,BEGIN,END
&sUnit,&sOperation,&sPort,&sState,&sDensity,
@rDensity_Lower,@rDensity_Upper,@rDensity_Target,@rBegin_Time,@rEnd_Time
&sUnit,&sOperation,&sPort,&sState,&sComponent,
@rComponent_Lower,@rComponent_Upper,@rComponent_Target,@rBegin_Time,@rEnd_Time
UNIT,OPERATION,PORT,STATE,COMPONENT
,CLOWER,CUPPER,CTARGET,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
UNIT,OPERATION,PORT,STATE,PROPERTY,PLOWER,PUPPER,PTARGET,BEGIN,END
&sUnit,&sOperation,&sPort,&sState,&sProperty,
@rProperty_Lower,@rProperty_Upper,@rProperty_Target,@rBegin_Time,@rEnd_Time
&sUnit,&sOperation,&sCondition,
@rCondition_Lower,@rCondition_Upper,@rCondition_Target,@rBegin_Time,@rEnd_Time
UNIT,OPERATION,CONDITION,CLOWER,CUPPER,CTARGET,BEGIN,END
&sUnit,&sOperation,&sCondition,
@rCondition_Lower,@rCondition_Upper,@rCondition_Target,@rBegin_Time,@rEnd_Time
Configuration
Demo
(Pooling
Optimization
Problem)
12. The
Configuration
Demo
provided
below
is
a
small
pooling
optimization
problem
with
one
(1)
pool,
three
(3)
component
materials
(A,
B
and
C),
two
(2)
product
materials
(P1
and
P2),
one
(1)
property
sulfur
(S)
and
one
(1)
time-‐period
as
shown
in
Figure
1.0.
This
is
the
well-‐known
Haverly
pooling
problem
and
has
been
studied
extensively
in
the
chemical
engineering
literature
on
global
optimization
because
it
exhibits
three
(3)
local
optimum
of
$0,
$100
and
$400.
Figure
1.0
Flowsheet
of
Pooling
Optimization
Problem.
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
START,-1.0
BEGIN,0.0
END,1.0
PERIOD,1.0
&sCalc,@sValue
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
! Chronological Data (Periods)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!