Ing. Jesús Velásquez Bermúdez, Eng. D. Chief Scientist ... · Arcilla. Transporte →...

ADVANCED ANALYTICS

SMART GRIDS & INDUSTRIAL ENERGY EFFICIENCY

Ing. Jesús Velásquez Bermúdez, Eng. D.

Chief Scientist, DecisionWare Europe S.L.

[email protected]

mailto:[email protected]

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


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

http://doanalytics.net/Documents/OPTEX-R&D.pdf

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.

http://doanalytics.net/Documents/OPTEX-R&D.pdf

DATA SCIENTIST: THE BEST JOB OF 2015

http://www.businessinsider.com/best-jobs-of-2015-2015-4#ixzz3Y63WdGSK

http://www.businessinsider.com/best-jobs-of-2015-2015-4#ixzz3Y63WdGSK

ADVANCED ANALYTICS


SMART GRIDS

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


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


OILWELL EXTRACTOR

OFFICES

THERMOELECTRICAL

INTERCONNECTIONNATIONAL SYSTEM



2005

ELECTROGENE


OILWELL EXTRACTOR

OFFICES

THERMOELECTRICAL

INTERCONNECTIONNATIONAL SYSTEM


RENEWABLESOURCES

ENERGYSTORAGE


2014

ADVANCED ANALYTICS


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

http://www.google.com.co/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwiv8qiT9pzQAhWEQiYKHS7EAXAQjRwIBw&url=http://www.abc.es/fotos/20120402/conduccion-seguira-trazado-similar-1502613649546.html&psig=AFQjCNFWfIBsFuClNTLSzvz_gIYpxFMM1A&ust=1478817236785441

ADVANCED ANALYTICS


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.


STRATEGIC PLANNING

INVERSIÓN A LARGO PLAZO

LI: LÓGICA DE INVERSIÓN

OM: OPERACIONES MENSUALES

SALES & OPERATION PLANING

MÉDIUM TERM

METAS OPERACIONALES A MEDIANO PLAZO


SALES & OPERATION PLANING

SHORT TERM

METAS DE OPERACIONALES A CORTO PLAZO


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”)


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


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



CONTAMINANTEMISSIONS



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


(mo)

MMOt,mo


(tr)

MMTt,tr

Molino Clinker(mo)

MMRt,mt

Silo Clinker(si)

ISXt,si

Horno(ho)

ComprasCorrectores

(mp)


(mp)

IMPt,mp

Mina Caliza(mp)

CMPt,mp

IOMPt,mp

PMPt,mp

MMPt,mp

FMHOt,ho × MMPt,mp

MHQt,ho,cq

MMMt


(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


Homogeneizadora(hm)

MMHt,hm

Silo Cemento(si)

ICXt,si,ce

CentrosConsumo


(mo)

MMOt,mo


(tr)

MMTt,tr

Molino Clinker(mo)

MMRt,mt

Silo Clinker(si)

ISXt,si

Horno(ho)

ComprasCorrectores

(mp)


(mp)

IMPt,mp

Mina Caliza(mp)

CMPt,mp

IOMPt,mp

PMPt,mp

MMPt,mp

FMHOt,ho × MMPt,mp

MHQt,ho,cq

MMMt


(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


Homogeneizadora(hm)

MMHt,hm

Silo Cemento(si)

ICXt,si,ce

CentrosConsumo


(mo)

MMOt,mo


(tr)

MMTt,tr

Molino Clinker(mo)

MMRt,mt

Silo Clinker(si)

ISXt,si

Horno(ho)

ComprasCorrectores

(mp)


(mp)

IMPt,mp

Mina Caliza(mp)

CMPt,mp

IOMPt,mp

PMPt,mp

MMPt,mp

FMHOt,ho × MMPt,mp

MHQt,ho,cq

MMMt


(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


BLENDINGRAW MATERIALS + CORRECTORS

Homogeneizadora(hm)

MMHt,hm

Silo Cemento(si)

ICXt,si,ce

CentrosConsumo


(mo)

MMOt,mo


(tr)

MMTt,tr

Molino Clinker(mo)

MMRt,mt

Silo Clinker(si)

ISXt,si

Horno(ho)

ComprasCorrectores

(mp)


(mp)

IMPt,mp

Mina Caliza(mp)

CMPt,mp

IOMPt,mp

PMPt,mp

MMPt,mp

FMHOt,ho × MMPt,mp

MHQt,ho,cq

MMMt


(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


(mo)

MMOt,mo


(tr)

MMTt,tr

Molino Clinker(mo)

MMRt,mt

Silo Clinker(si)

ISXt,si

Horno(ho)

ComprasCorrectores

(mp)


(mp)

IMPt,mp

Mina Caliza(mp)

CMPt,mp

IOMPt,mp

PMPt,mp

MMPt,mp

FMHOt,ho × MMPt,mp

MHQt,ho,cq

MMMt


(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


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


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%


0

500

1000

1500

2000

2500

0 5 10 15 20

00

0$

HORA

Costos horario Operación, MC vs M$

TOTAL-M$ TOTAL-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

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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:

http://www.doanalytics.net/Documents/OPTEX-Presentation.pdf

http://www.decisionware.net/documents/DW-Experience.pdf

http://www.decisionware.net/documents/OPCHAIN-ESO-Energy-Models&Services.pdf

Your Smarter DecisionA Knowledge Company Supporting

www.decisionware.net

Bogotá D.C., Lima, Madrid, México D.F.

Ing. Jesús Velásquez Bermúdez, Eng. D. Chief Scientist ... · Arcilla. Transporte →...

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