ing. jesús velásquez bermúdez, eng. d. chief scientist ... · arcilla. transporte →...
TRANSCRIPT
ADVANCED ANALYTICS
SMART GRIDS & INDUSTRIAL ENERGY EFFICIENCY
Ing. Jesús Velásquez Bermúdez, Eng. D.
Chief Scientist, DecisionWare Europe S.L.
THE FRAGILITY OF OUR WORLD
ADVANCED ANALYTICS
SMART GRIDS
CASE: OIL PRODUCTION
ENERGY EFFICIENCY & INDUSTRIAL PROCESS
CASE: HEAVY INDUSTRIES (CEMENT)
MANAGEMENT OF ENERGY IN INDUSTRIAL SYSTEMS
CASES: OIL REFINING
OIL TRANSPORT
TOPICS
ADVANCED ANALYTICS
SMART GRIDS & INDUSTRIAL ENERGY EFFICIENCY
THE FRAGILITY OF OUR WORLD
La noción del compromiso que cada generación tiene con sus sucesores está en el corazón delconcepto de desarrollo sostenible, el cual fue plasmado por la World Commission onEnvironment and Development (Brundtland Comisión, WCED 1987) en su informe “OurCommon Future” que afirma que el desarrollo sostenible:
“satisface las necesidades de la presente generación sin comprometer lasposibilidades de las futuras generaciones para satisfacer sus propiasnecesidades”.
Esto solo se consigue con el uso racional (científico) de los recursos naturales, renovables yno-renovables, cuya gestión óptima y eficaz es responsabilidad de toda la humanidad.
ADVANCED ANALYTICS
SMART GRIDS & EFICIENCIA ENERGÉTICA INDUSTRIAL
ADVANCED ANALYTICS
El ser humano tiene una capacidad de procesamiento numérico limitada
que no se ha incrementado significativamente en las últimas centurias.
Lo que ha incrementado la humanidad es su capacidad de análisis. La
capacidad de cálculo de los computadores y del software se ha
incrementado en miles de millones de veces en los últimos veinte años.
La unión de mentes analíticas con máquinas, dotadas de gran capacidad
de cálculo, es lo que determina el rápido cambio tecnológico que
experimenta la humanidad.
DATA SCIENTIST: THE BEST JOB OF 2015
http://www.businessinsider.com/best-jobs-of-2015-2015-4#ixzz3Y63WdGSK
ADVANCED ANALYTICS
SMART GRIDS & EFICIENCIA ENERGÉTICA INDUSTRIAL
SMART GRIDS
Source: Toward a Smarter Grid ABB´s Vision of the Power System of the Future
Source: Toward a Smarter Grid ABB´s Vision of the Power System of the Future
Source: Toward a Smarter Grid ABB´s Vision of the Power System of the Future
OFF LINE OPTIMIZATION
(PLANNING)
1. RE-DESIGN THE ENERGY SUPPLY SYSTEM
2. OPTIMIZE THE INDUSTRIAL PROCESS IN THE PLANTSUSING COMPLEX MATHEMATICAL MODELS
3. INTEGRATE THE PLANNING AND SCHEDULING OF THEINDUSTRIAL PROCESS (MATTER TRANSFORMATION)WITH THE INDUSTRIAL SERVICES (ENERGYTRANSFORMATION) INCLUDING THE TRANSACTIONS INTHE ENERGY MARKETS (ET-RM) USING OPTIMIZATIONMODELS.
ON LINE OPTIMIZATION
(OPERATING)
1. USE AN OPTIMIZATION MODEL FOR PRE-DISPATCH THE ENERGY SYSTEM , 24 HOURS IN ADVANCE
2. USE MODELS ORIENTED TO OPTIMUM CONTROL IN REAL TIME (DISPATCH OF ENERGY SYSTEM)
3. SHORT TIME ET&RM
ENERGY EFFICIENCY ADVANCED ANALYTICS STRATEGY
ADVANCED ANALYTICS
SMART GRIDS & INDUSTRIAL ENERGY EFFICIENCY
REDESIGN OF POWER SUPPLY SYSTEMS
CASE: OIL PRODUCTION
The first step in achieving energy efficiency in industrial
plants is related to the redesign of systems of power supply,
since in many cases the design of such systems does not
include the concepts and technologies associated with the
new paradigm: distributed generation and storage of energy,
foundation of the "smart grids".
ELECTROGENE
PRODUCTION FACILITIESBAR / BUS
OILWELL EXTRACTOR
OFFICES
THERMOELECTRICAL
WATER/GAS WELL INJECTOR
ELECTRIC SYSTEM – OIL FIELD
2005
ELECTROGENE
PRODUCTION FACILITIESBAR / BUS
OILWELL EXTRACTOR
OFFICES
THERMOELECTRICAL
INTERCONNECTIONNATIONAL SYSTEM
WATER/GAS WELL INJECTOR
ELECTRIC SYSTEM – OIL FIELD
2005
ELECTROGENE
PRODUCTION FACILITIESBAR / BUS
OILWELL EXTRACTOR
OFFICES
THERMOELECTRICAL
INTERCONNECTIONNATIONAL SYSTEM
WATER/GAS WELL INJECTOR
RENEWABLESOURCES
ENERGYSTORAGE
ELECTRIC SYSTEM – OIL FIELD
2014
ADVANCED ANALYTICS
SMART GRIDS & EFICIENCIA ENERGÉTICA INDUSTRIAL
ENERGY EFFICIENCY & INDUSTRIAL PROCESS
All industrial processes have variable speed.
Power consumption as a function of the speed of the process is
not linear, it is exponential.
The production facilities do not have defined a fixed capacity,
it is established in accordance with the speed of the process and
the time running at that speed.
CONSUMO DE ENERGÍA EN UN MEDIO DE TRANSPORTEBARCO
CONSUMO DE ENERGÍA EN UN MEDIO DE TRANSPORTETREN DE ALTA VELOCIDAD
XXXXXCONSUMO DE ENERGÍA EN UN MEDIO DE TRANSPORTEOLEODUCTO
ADVANCED ANALYTICS
SMART GRIDS & INDUSTRIAL ENERGY EFFICIENCY
REAL TIME OPTIMIZATION
Real Time Optimization maintains the operating process is its "optimal"set-points based on the periodic re-optimization, taking into account thedifference between the environmental conditions with the forecastedconditions. It is composed of the following models :
MPI: Identification of the model equations and the parameters thatdefine the system dynamics.
STE: State estimation and reconciliation model of system oriented tothe re-estimation of the system parameters (state estimation, datareconciliation and gross and random error detection)
OCO: Optimal control model using real-time variables.
REAL TIME OPTIMIZATION
STRATEGIC PLANNING
INVERSIÓN A LARGO PLAZO
LI: LÓGICA DE INVERSIÓN
OM: OPERACIONES MENSUALES
SALES & OPERATION PLANING
MÉDIUM TERM
METAS OPERACIONALES A MEDIANO PLAZO
OM: OPERACIONES MENSUALES
SALES & OPERATION PLANING
SHORT TERM
METAS DE OPERACIONALES A CORTO PLAZO
OM: OPERACIONES MENSUALES
OS: OPERACIONES SEMANALES
OPERATIONS SCHEDULING
ORDENES DE OPERACION
OS: OPERACIONES SEMANALES
OD: OPERACIONES DIARIAS
REAL-TIME OPTIMIZATION
CONTROL DE PROCESOS
OI: OPERACIONES INSTANTANEAS
COORDINACION DE HERRAMIENTAS EN LA PLANIFICACION INTEGRADA
OS OM
Horizonte Planificación
OD OS
Condición de Frontera
OM
LI
OM
OI
OPCHAIN-APS
OPCHAIN-APS
OPCHAIN-RTO
PLANNINGTACTICAL SHORT TERM
PROGRAMMINGOPERATIONS
REAL-TIMEOPTIMIZATION
MARKETDATA
ONLINE PLANT DATA
PLANTTARGETS
FUELS AND RAW
MATERIALPROPERTIES
MATHEMATICAL MODELS+
OPTIMIZATION TECHNOLOGIES
OPTIMAL FUEL MIX AND OPTIMAL SET POINTS
TECHNO-ECONOMICAL OPTIMIZATION OF THE INDUSTRIAL PROCESS
RTO is a well-known mathematical approachto keep the process running on its set-pointsor targets "optimally" based on periodic re-optimization, as a means to take into accountchanging process and environmentalconditions (uncertainty).
Results:
Stable processes ("smoother operation"), Better control of process variables, Optimal production levels, Reduction in energy consumption (kW/ton), "Exact" control of emissions, Increased profits, etc.
HourlyFrequency
PRODUCTION and PROCESS
PROCESS CONTROL SYSTEM
Reconciliation Optimization
Regression of Parameters(“Model Update”)
REAL TIME OPTIMIZATION
UNCERTAINTY
STATE ROUTE ESTIMATED KALMAN FILTER
x(t-1/t-1)x(t/t-1)
StateSystem“X(t)”
- Fifferencesr(t)=z(t)-Z/t/t-1)
Forecast to Posteriori
SystemStatusForecast to
x(t/t)
DYNAMIC MODELPRODUCTIVE PROCESStX(t) = Ft[X(t),u(t)]
t=t+1
Controlu(t)
DYNAMIC MODELMEASUREMENT SYSTEM
Z(t) = Ht[X(t)]
PRODUCTION SYSTEM
SCADA
Measurementobserved
z(t)
ForecastMeasurement
Z(t/t-1)
DYNAMIC MODELCORRECTING
x(t/t) = x(t/t-1) + M(t)r(t)
Controlu(t)
Forecast to PrioriState System
x(t/t-1)
ID
SETTINGSPRIORI MODEL
Priori parametersProcess model
b(0)
HISTORIC SERIES
ESTIMATESTATE
SETTINGSMODEL
ESTIMATESTATE
VARIABLESPHYSICAL
SCADA
Observation
Observation
Parámetrosa Posteriori
b(t)
VariablesFísicas
a Posteriorix(t)
DUAL STATE ESTIMATE
ModelPriori parameters
b(t/t-1)
Physical variablesx(t/t-1)
SERIES HISTORY
Observation
Modelsettings
a posteriorib(t/t)
Operationoptimal
u*(t)
OPCHAIN-RTO-MPEID
PARAMETERS MODEL
OPCHAIN-RTO-KALMANDUAL STATE ESTIMATION
PHYSICAL VARIABLESMODEL PARAMETERS
OPCHAIN-RTO-GDDPOPTIMIZACIÓN
OPTIMAL CONTROLPROCESS
PRODUCTION SYSTEMSCADA
OPCHAIN-RTO
ADVANCED ANALYTICS
SMART GRIDS & INDUSTRIAL ENERGY EFFICIENCY
CASE: CEMENT PRODUCTION
To achieve energy efficiency, it is necessary to optimize industrial
processes guiding them to points of operation of minimum energy
consumption, considering indicators of profitability of the
operation, and control of contaminant emissions, which are more
demanding every day.
The via is the design and implementation of mathematical models
that represent, with detail, such processes and thus allow the
optimization of them.
CADENA DE PRODUCCIÓN EN LA INDUSTRIA PESADAINDUSTRIA DEL CEMENTO
EXTRACCIÓN DE MATERIAS PRIMAS
1. Mineral rico en calcio o en materiales calcáreos: Piedra Caliza; Tiza; Etc.
2. Mineral rico en sílice o arcillosos: Arcilla.
Transporte →Trituradoras
REDUCCIÓN DE TAMAÑO
1 metro → < 80 mm
MATERIALES TRITURADOS
↓ANALIZADOR EN
LÍNEA (Composición química)
Apilador → diferentespilas de materiales ypara reducir lavariabilidad en lacomposición químicade las materiasprimas.
TRANSPORTADOR DE CINTA
↓ALIMENTADOR
↓Proporciones
adecuadas (Tipo de clínker a
producir)↓
Trituración a finura deseada en molinos
↓Polvo → silo
↓Reducción de Variaciones
(mezcla con aire)
CLINKERIZACIÓN
Mezcla en bruto homogenizada ↓
pre-calentador(calcinación parcial)
↓Horno rotatorio
↓Clínker
↓Enfriador
↓Clínker (~ 100ºC) → Silo
Clinker
MOLIENDA CEMENTO FINO
Silo de clínker↓
Alimentador (proporción con yeso y materiales adicionales)
↓Molienda final
↓Silos de cemento
DISTRIBUCIÓN CEMENTO
Cemento (silos)
↓Empaquetado Bolsas
Carga a granel.↓
Distribución↓
• Vía terrestre;• Vía marítima.
CADENA DE PRODUCCIÓN EN LA INDUSTRIA PESADAINDUSTRIA DEL CEMENTO
THERMAL ENERGYGAS EMISSIONS
ELECTRICENERGY
ELECTRICENERGY
ENERGY EFFICIENCY OPTIMIZATION
Fuente: Optimización de la Energía en la Industria del Cemento. Revista AAB.
ELECTRIC ENERGY
ELECTRIC ENERGY
THERMAL ENERGY
ENERGY EFFICIENCY OPTIMIZATION
Fuente: Optimización de la Energía en la Industria del Cemento. Revista AAB.
CONTAMINANTEMISSIONS
ENERGY EFFICIENCY OPTIMIZATION
Fuente: Optimización de la Energía en la Industria del Cemento. Revista AAB.
BLENDINGRAW MATERIALS + CORRECTORS
BLENDINGCLINKER + ADDITIONALS
BLENDINGCOMBUSTIBLES
Homogeneizadora(hm)
MMHt,hm
Silo Cemento(si)
ICXt,si,ce
CentrosConsumo
MolinoMaterias Primas
(mo)
MMOt,mo
TrituradoraMaterias Primas
(tr)
MMTt,tr
Molino Clinker(mo)
MMRt,mt
Silo Clinker(si)
ISXt,si
Horno(ho)
ComprasCorrectores
(mp)
InventarioCaliza – Correctores
(mp)
IMPt,mp
Mina Caliza(mp)
CMPt,mp
IOMPt,mp
PMPt,mp
MMPt,mp
FMHOt,ho × MMPt,mp
MHQt,ho,cq
MMMt
InventarioCombustible
(ff)
IMPt,mp
MHHt,hm,ho
MFFt,ho,ff
Emisiones
EMIt,ho,cq
MCQt,ho,cq
SiloAdición
IADt
SeparadorDinámico
(mo)
SiloYesoIYEt
MYSt,mo,ce
MADt,mo,ce
MXSt,mo,si,ce
MCKt,ho
MHMt,ho,mo,,ce
MHSt,ho,si
MRRt,mo,ce
MXDt,mo,de,ce
MRSt,si,mo,ce
MSDt,si,de,ce
MACt,mo,ce
RAW MILLMODEL
Blending Raw MaterialsCrushingGrinding
Homogenization
KILN MODELBlending Combustibles, Combustion, Emissions
CEMENT MILL MODEL
Blending ClinkerGrindingStoring
CEMENT PROCESS MODEL
MODELING OF THE PRODUCTION PROCESS OF THE CEMENT
Homogeneizadora(hm)
MMHt,hm
Silo Cemento(si)
ICXt,si,ce
CentrosConsumo
MolinoMaterias Primas
(mo)
MMOt,mo
TrituradoraMaterias Primas
(tr)
MMTt,tr
Molino Clinker(mo)
MMRt,mt
Silo Clinker(si)
ISXt,si
Horno(ho)
ComprasCorrectores
(mp)
InventarioCaliza – Correctores
(mp)
IMPt,mp
Mina Caliza(mp)
CMPt,mp
IOMPt,mp
PMPt,mp
MMPt,mp
FMHOt,ho × MMPt,mp
MHQt,ho,cq
MMMt
InventarioCombustible
(ff)
IMPt,mp
MHHt,hm,ho
MFFt,ho,ff
Emisiones
GCHt,ho,cq
MCQt,ho,cq
SiloAdición
IADt
Separador Dinámico
(mo)
SiloYesoIYEt
MYSt,mo,ce
MADt,mo,ce
MXSt,mo,si,ce
MCKt,ho
MHMt,ho,mo,,ce
MHSt,ho,si
MRRt,mo,ce
MXDt,mo,de,ce
MRSt,si,mo,ce
MSDt,si,de,ce
MACt,mo,ce
MODELING OF THE PRODUCTION PROCESS OF THE CEMENT
Homogeneizadora(hm)
MMHt,hm
Silo Cemento(si)
ICXt,si,ce
CentrosConsumo
MolinoMaterias Primas
(mo)
MMOt,mo
TrituradoraMaterias Primas
(tr)
MMTt,tr
Molino Clinker(mo)
MMRt,mt
Silo Clinker(si)
ISXt,si
Horno(ho)
ComprasCorrectores
(mp)
InventarioCaliza – Correctores
(mp)
IMPt,mp
Mina Caliza(mp)
CMPt,mp
IOMPt,mp
PMPt,mp
MMPt,mp
FMHOt,ho × MMPt,mp
MHQt,ho,cq
MMMt
InventarioCombustible
(ff)
IMPt,mp
MHHt,hm,ho
MFFt,ho,ff
Emisiones
EMIt,ho,cq
MCQt,ho,cq
SiloAdición
IADt
SeparadorDinámico
(mo)
SiloYesoIYEt
MYSt,mo,ce
MADt,mo,ce
MXSt,mo,si,ce
MCKt,ho
MHMt,ho,mo,,ce
MHSt,ho,si
MRRt,mo,ce
MXDt,mo,de,ce
MRSt,si,mo,ce
MSDt,si,de,ce
MACt,mo,ce
RAW MILLMODEL
Blending Raw MaterialsCrushingGrinding
Homogenization
KILN MODELBlending Combustibles, Combustion, Emissions
CEMENT MILL MODEL
Blending ClinkerGrindingStoring
MODELING OF THE PRODUCTION PROCESS OF THE CEMENT
Homogeneizadora(hm)
MMHt,hm
Silo Cemento(si)
ICXt,si,ce
CentrosConsumo
MolinoMaterias Primas
(mo)
MMOt,mo
TrituradoraMaterias Primas
(tr)
MMTt,tr
Molino Clinker(mo)
MMRt,mt
Silo Clinker(si)
ISXt,si
Horno(ho)
ComprasCorrectores
(mp)
InventarioCaliza – Correctores
(mp)
IMPt,mp
Mina Caliza(mp)
CMPt,mp
IOMPt,mp
PMPt,mp
MMPt,mp
FMHOt,ho × MMPt,mp
MHQt,ho,cq
MMMt
InventarioCombustible
(ff)
IMPt,mp
MHHt,hm,ho
MFFt,ho,ff
Emisiones
GCHt,ho,cq
MCQt,ho,cq
SiloAdición
IADt
Separador Dinámico
(mo)
SiloYesoIYEt
MYSt,mo,ce
MADt,mo,ce
MXSt,mo,si,ce
MCKt,ho
MHMt,ho,mo,,ce
MHSt,ho,si
MRRt,mo,ce
MXDt,mo,de,ce
MRSt,si,mo,ce
MSDt,si,de,ce
MACt,mo,ce
MODELING OF THE PRODUCTION PROCESS OF THE CEMENT
Homogeneizadora(hm)
MMHt,hm
Silo Cemento(si)
ICXt,si,ce
CentrosConsumo
MolinoMaterias Primas
(mo)
MMOt,mo
TrituradoraMaterias Primas
(tr)
MMTt,tr
Molino Clinker(mo)
MMRt,mt
Silo Clinker(si)
ISXt,si
Horno(ho)
ComprasCorrectores
(mp)
InventarioCaliza – Correctores
(mp)
IMPt,mp
Mina Caliza(mp)
CMPt,mp
IOMPt,mp
PMPt,mp
MMPt,mp
FMHOt,ho × MMPt,mp
MHQt,ho,cq
MMMt
InventarioCombustible
(ff)
IMPt,mp
MHHt,hm,ho
MFFt,ho,ff
Emisiones
GCHt,ho,cq
MCQt,ho,cq
SiloAdición
IADt
Separador Dinámico
(mo)
SiloYesoIYEt
MYSt,mo,ce
MADt,mo,ce
MXSt,mo,si,ce
MCKt,ho
MHMt,ho,mo,,ce
MHSt,ho,si
MRRt,mo,ce
MXDt,mo,de,ce
MRSt,si,mo,ce
MSDt,si,de,ce
MACt,mo,ce
MODELING OF THE PRODUCTION PROCESS OF THE CEMENT
BLENDINGRAW MATERIALS + CORRECTORS
Homogeneizadora(hm)
MMHt,hm
Silo Cemento(si)
ICXt,si,ce
CentrosConsumo
MolinoMaterias Primas
(mo)
MMOt,mo
TrituradoraMaterias Primas
(tr)
MMTt,tr
Molino Clinker(mo)
MMRt,mt
Silo Clinker(si)
ISXt,si
Horno(ho)
ComprasCorrectores
(mp)
InventarioCaliza – Correctores
(mp)
IMPt,mp
Mina Caliza(mp)
CMPt,mp
IOMPt,mp
PMPt,mp
MMPt,mp
FMHOt,ho × MMPt,mp
MHQt,ho,cq
MMMt
InventarioCombustible
(ff)
IMPt,mp
MHHt,hm,ho
MFFt,ho,ff
Emisiones
GCHt,ho,cq
MCQt,ho,cq
SiloAdición
IADt
Separador Dinámico
(mo)
SiloYesoIYEt
MYSt,mo,ce
MADt,mo,ce
MXSt,mo,si,ce
MCKt,ho
MHMt,ho,mo,,ce
MHSt,ho,si
MRRt,mo,ce
MXDt,mo,de,ce
MRSt,si,mo,ce
MSDt,si,de,ce
MACt,mo,ce
MODELING OF THE PRODUCTION PROCESS OF CEMENT
DETAILED MODEL OF THE KILN
DETAILED MODEL OF CLINKERIZATION
Homogeneizadora(hm)
MMHt,hm
Silo Cemento(si)
ICXt,si,ce
CentrosConsumo
MolinoMaterias Primas
(mo)
MMOt,mo
TrituradoraMaterias Primas
(tr)
MMTt,tr
Molino Clinker(mo)
MMRt,mt
Silo Clinker(si)
ISXt,si
Horno(ho)
ComprasCorrectores
(mp)
InventarioCaliza – Correctores
(mp)
IMPt,mp
Mina Caliza(mp)
CMPt,mp
IOMPt,mp
PMPt,mp
MMPt,mp
FMHOt,ho × MMPt,mp
MHQt,ho,cq
MMMt
InventarioCombustible
(ff)
IMPt,mp
MHHt,hm,ho
MFFt,ho,ff
Emisiones
GCHt,ho,cq
MCQt,ho,cq
SiloAdición
IADt
Separador Dinámico
(mo)
SiloYesoIYEt
MYSt,mo,ce
MADt,mo,ce
MXSt,mo,si,ce
MCKt,ho
MHMt,ho,mo,,ce
MHSt,ho,si
MRRt,mo,ce
MXDt,mo,de,ce
MRSt,si,mo,ce
MSDt,si,de,ce
MACt,mo,ce
MODELING OF THE PRODUCTION PROCESS OF CEMENT
CONECTIVIDAD DE VARIABLES PROCESO: MOLIENDA DEL CLINKER
Silo Cemento(si)
ICXt,si,ce
Molino Clinker(mo)
Silo Clinker(si)
ISXt,si
Horno(ho)
MCQt,ho,cq
Separador Dinámico(mo)
Silo Yeso
IYEt
MYSt,mo,ce MADt,mo,ce
MXSt,mo,si,ce
MCKt,ho
MHMt,ho,mo,,ce
MHSt,ho,si
MRRt,mo,ce
MXDt,mo,de,ce
MRSt,si,mo,ce
MACt,mo,ce
MACt,mo,ce
CentrosConsumo
Silo Adicionales
IADt
MCEt,mo,ce = MACt,mo,ce + MRRt,mo,ce
MPSt,mo,ce
MPMt,mo,ce
MMLt,mo,ce
CCQt,mo,ce,cq
MCQt,ho,cq
FQMCcc=YES,cq FQMCcc=ADI,cq
CCQt,mo,ce,cq
CCQt,mo,ce,cq
CCQt,mo,ce,cq
Masa Producto (ton)Tamaño de Partículas (ton)Composición Química (ton)
MPRt,mo,ce
MSDt,si,de,ce
MPHRLPRaw Materials
+ KilnLinear Model
MMPHORRaw Materials
+ KilnNon-Linear Model
MPHRM1Raw Materials
+ Kiln + Cement MillLinear Model
(Material Balance)
MPHRM2Raw Materials
+ Kiln + Cement MillNon-Linear Model
(Power Consumption)
Solución Factible Lineal
Solución FactibleLineal
Solución Óptima NO- Lineal
CEMENT PLANT PRODUCTION MODEL
Solución Óptima NO- Lineal Parcial
ADVANCED ANALYTICS
SMART GRIDS & INDUSTRIAL ENERGY EFFICIENCY
INTEGRATE ENERGY MANAGEMENT
IN INDUSTRIAL SYSTEMS
Optimal management of power of industrial systems implies a
holistic position oriented to integrate the production planning of
goods with energy services that are consumed in the production
plant; and finally integrate it with possibilities of self-production
and negotiation of this energy in the energy markets.
PLANT OFINDUSTRIAL PROCESSES
PLANT OFINDUSTRIAL SERVICES
MATTERTRANFORMATION
ENERGYTRANSFORMATION
DESTILACIÓNATMOSFÉRICA
NAFHTA LIVIANA
NAFHTA INTERMEDIA
NAFHTA PESADA
JET-FUEL
KEROSENE
ACPM
GASES CONTAMINANTES
GASES ATMOSFERICOS
CRUDOSCMA-CMB
CRACKINGORTHOFLOW
DESTILACIONVACIO
CRUDOREDUCIDO
MODELING OF A PROCESS UNIT
DESTILACIÓNATMOSFÉRICA
NAFHTA LIVIANA
NAFHTA INTERMEDIA
NAFHTA PESADA
JET-FUEL
KEROSENE
ACPM
GASES CONTAMINANTES
GASES ATMOSFERICOS
CRUDOSCMA-CMB
CRACKINGORTHOFLOW
DESTILACIONVACIO
CRUDOREDUCIDO
SERVICESUNIT
MODELING OF A PROCESS UNIT+ A SERVICES UNIT
INDUSTRIAL SERVICES PLANT
Electricity Market
Productswith
Aggregate Value
Raw Materialinput
Fuel Oil
Water
Gas
Electricity
Boiler 1
Boiler 1
Unid
Turbine
Turbine
COLECTOR
COLECTOR
COLECTOR
…
PROCESS PLANT
Process 1
Process 2
Process 3
Process 4
Steam typesElectricity
Types of WaterGas
Electricity
PLANT SERVICESAUTO-GENERATOR
ENERGY MARKET
PROCESS PLANT
Sell/Buy EnergySupport System INTERCONNECTION
NETWORKSELECTRICITY
ContractsTrading
ADVANCED ANALYTICS
SMART GRIDS & INDUSTRIAL ENERGY EFFICIENCY
CASE: OIL TRANSPORT VIA PIPES
In all industrial systems, the economic efficiency go through the
energy efficiency, and vice versa; however, when exist differential
tariff for basic energetics exist two different criteria to approach
to the optimality: the economic and the environmental.
Then, the mathematical models are the foundation to provide the
information needed to balance these two objectives when they
enter in contradiction.
OLEODUCTO VASCONIA CAUCASIASISTEMA OCENSA
VASCONIA recibe los crudos de Ocensa y los provenientes de loscampos ubicados en el Alto Magdalena. Desde esta estación, el crudodestinado a consumo interno del país es impulsado hacia la refineríade Barrancabermeja.
CAUCASIA encargada de darle más presión a los 20.000barriles de crudo que se reciben por hora desde laestación de Vasconia y que se envían al terminalmarítimo de Coveñas.
Terminal Marítimo de Coveñas, ubicado en el límite entre Sucre yCórdoba, el crudo es almacenado en tanques y posteriormentetransportado por la línea submarina hasta la monoboya para elcargue de los buquetanques.
Fuente: https://www.ocensa.com.co/actividades/Pages/Nuestro-Sistema.aspx
OLEODUCTO VASCONIA CAUCASIASISTEMA OCENSA
Fuente: https://www.ocensa.com.co/actividades/Pages/Nuestro-Sistema.aspx
VASCONIA *
CAUCASIA**
** *
OPERATION OF THE PUMPING STATIONSOPERATION PATTERNS
SERIAL
PARALLEL
OPERATION OF THE PUMPING STATIONSOPERATION PATTERNS
REAL COLOMBIAN CASE
0
20000
40000
60000
80000
100000
120000
140000
0 5 10 15 20
HP
HORA
Consumo Energía Acumulada, MC vs M$
M$-Costo MC-Consumo
LEAST-COST POLICY GENERATES OVERCONSUMPTION OF ENERGY OF THE ORDER OF
19.43%
OLEODUCTO XXXXXMINIMUM COST (M$) vs. MINIMUM ENERGY (MC)
-4000
1000
6000
11000
16000
21000
26000
31000
36000
0 5 10 15 20
00
0$
HORA
Costos Acumulado Operación, MC vs M$
M$-Costo MC-Costo
MINIMUM CONSUMPTION POLICY GENERATES OVER EXPENDITURE OF THE ORDER OF THE
16.21%
OLEODUCTO XXXXXMINIMUM COST (M$) vs. MINIMUM ENERGY (MC)
0
500
1000
1500
2000
2500
0 5 10 15 20
00
0$
HORA
Costos horario Operación, MC vs M$
TOTAL-M$ TOTAL-MC
OLEODUCTO XXXXXMINIMUM COST (M$) vs. MINIMUM ENERGY (MC)
STOPMINIMUM FLOW
STOPMAXIMUM TARIFF
DO ANALYTICS LLC es una compañía, spin-off de DECISIONWARE International Corp.,
dedicada a la producción y al mercadeo de la tecnología de optimización
OPTEX MATHEMATICAL MODELING SYSTEM
DECISIONWARE International Corp. es una empresa dedicada a la producción de
modelos matemáticos de optimización en diferentes sectores, utilizando múltiples
tecnologías de optimización y las metodologías de optimización más avanzadas.
EXPERIENCIA PRODUCTOS Y SERVICIOS
ACERCA DE:
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www.decisionware.net
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