Two Case Studies of plant data processing for Refinery ...PredMaint_AspenTech.pdf · Aspen HYSYS...

Two Case Studies of plant data processing for Refinery Optimization: Reactor Modeling and Prescriptive MaintenanceBologna, 7 July 2017

Lorenzo Masoni, AspenTech srl

© 2017 Aspen Technology, Inc. All rights reserved.22 © 2017 Aspen Technology, Inc. All rights reserved.

Contents

• Introduction

• Case 1: Aspen HYSYS Reactor modelling for Refinery Margin

improvement

• Case 2: Reducing Equipment Downtime with Prescriptive Maintenance

by Aspen Mtell

3 © 2017 Aspen Technology, Inc. All rights reserved.

Aspen HYSYS Petroleum Refining for Margin Improvement

© 2017 Aspen Technology, Inc. All rights reserved.4

HYSYS Petroleum Refining

Rigorous columns with the HYSYS forward/backward solver makes it easy to

build distillation units for the refining model.

Accurate prediction of heavy crude properties

Hundreds of assays ready for

use.


Gas Processing

Atm

osp

he

ric

Dis

tilla

tio

n

Va

cu

um

Dis

tilla

tio

n

Pro

du

ct

Ble

nd

ing

Kerosene HT

Catalytic Reformer

Fluid Catalytic Cracking (FCC)

Diesel HT

Isomerization

Delayed Coker

Visbreaker

Hydrocracker

Gas Oil HT

Resid HT

Alkylation

Naphtha HT

Di-Olefin HDTR

Selective HDS

Aspen HYSYS Petroleum RefiningOne Complete Solution for Refinery Modeling


HYSYS Reactor Model development

• HYSYS Petroleum Refining Reactors are typically calibrated

and then used in process modeling activities and/or to update

the planning models

• Calibration requires a set of operating, feed, and product data

covering the operating window to be simulated in HYSYS

Petroleum Refining

• The calibration and validation process can be completed by a

process engineer, specialist, or AT Services

Build Simulation Model

Calibrate Using Plant Data

Validate Predictions

Deploy Model

Process Improvements & Performance

Monitoring

Planning Model Update


Rigorous Model of Cat Cracker in HYSYS Petroleum Refining

Rigorous HYSYS Model for Process Unit eg. FCC


Plant Data for Reactor Calibration

• Mass balance of the unit: feed and product flowrates. Test run mass balance errors

should all be within around +/- 2 %wt

• Feed properties (e.g. Density, Distillation, Sulphur)

• Operating data (e.g. Reactor Temperatures, Pressures)

• Product properties (e.g. Density, Distillation, Sulphur, PONA)


21 Lump Kinetic Pathways

LEGEND

C Light Ends

G Gasoline

Pl Light Paraffins

Ph Heavy Paraffins

Nl Light Naphthenes

Nh Heavy Naphthenes

Asl Light Aromatic with Sulfur

Ash Heavy Aromatics with Sulfur

Ar1l Light 1-ring Aromatics

Ar2l Light 2-ring Aromatics

Ar1h Heavy 1-ring Aromatics



Rp Resid Paraffins

Rn Resid Naphthenes

Ras Resid Aromatics with Sulfur

Ra1 Resid 1-ring Aromatics



Kcoke Kinetic Coke

Mcoke Metals Coke

21 Kinetic Lumps 40 Reactions


Engineering models for Operations


Refinery models: Process Engineering and What-if Studies

Aspen HYSYS Refinery models can be used for supporting the operations:

– Conduct studies for predicting the model responses when changing Feed and Reactor

Operative conditions.

Examples on FCC unit:

– variation of mix of different feeds;

– identification of the Gasoline Overcracking;

– new catalyst studies

– Use the Optimizer Tool to maximize the profitability of the unit on daily basis, by taking into

account Feeds & Products prices and operative constraints of the unit

– Troubleshooting of the Reactor and the Fractionation section


Process Engineering Case Studies

Analyse Rigorous Model, test results,

e.g., FCC Overcracking response

Case Studies can be performed in HYSYS and also through Aspen Simulation Workbook / Excel interface


Engineering models for Planning support

© 2017 Aspen Technology, Inc. All rights reserved.

BUSINESS CHALLENGE & OBJECTIVE

CASE STUDY: Hyundai Oilbank

Use of FCC Simulation to Fine Tune Operation and PIMS Model

Update using Aspen HYSYS Petroleum Refining

• High offset between planned and actual FCC

unit yields due to not considering the effect of

change in feed quality.

• Lost profit margin due to non-optimal operation

of FCC unit.

Ref: Presentation from Hyundai Oilbank titled “Use of FCC Simulation to Fine Tune Operation and PIMS Model Update using Aspen HYSYS Petroleum Refining” By Eun Gyeong Kwon at Aspen Technology User Conference OPTIMIZE-17 hosted at Houston Texas (April-25 & 26, 2017)

https://www.google.co.kr/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&ved=0CAcQjRxqFQoTCMeD_tK518gCFSSIpgodqn4GPQ&url=https://namu.wiki/w/%ED%98%84%EB%8C%80%EC%98%A4%EC%9D%BC%EB%B1%85%ED%81%AC&psig=AFQjCNEUrS0yQLTRr9osy_A6FQihhNPoSQ&ust=1445650360624278



SOLUTION OVERVIEW

• Built a rigorous kinetic model of the FCC

reactor unit in Aspen HYSYS

– to generate LP vectors for accurate planning

models

– Discover opportunities to boost profit margins from

the FCC unit by process optimization

• FCC simulation model predictions proved to be

very close to the actual operations. (overall

average offset : 0.38% )





Input Plant Data to

FCC Model

Calibrate the FCC

Reactor

Validate the

Reactor model

with Fractionation

Confirm the

Reactor Model for

LP Stream

Structure

Run the Case

Studies

Case Study Output

to PIMS Supported

Excel Utility

To LP Model




RESULTS & BENEFITS

• Accuracy of FCC unit planning is improved to

98%.

• Identified optimization opportunities that

increased the capacity of the FCC unit

resulting in an increased profit of $36

Million/Year







Aspen Mtell

Creating A World That Doesn’t Breakdown


R

ASSET OPERATIONS

ASSET

DESIGN

ASSET MAINTENANCE

AspenTech Asset Optimization Strategy

ASSET LIFECYCLE

Anticipate asset lifecycle

Optimized with operations

Profitable across business cycles

APM


R

ASSET OPERATIONS

ASSET

DESIGN

Aspen APM Approach

Anticipate asset lifecycle

Profitable across business cyclesPrevent Process Disruptions

(Operational Analytics)

ASSET MAINTENANCE

Optimized with operationsASSET

LIFECYCLEAvoid Unplanned Downtime

(Maintenance Analytics)

Improve Asset Availability(Reliability, Availability & Maintainability)

Mtell

Fidelis

Avoid Unplanned Downtime(Maintenance Analytics)


Prescriptive Maintenance

Symptom Analysis

Diagnosis

Consideration of Treatments

Prescription for Action


Live Autonomous Agents Finding Failures

Hidden Failure Agents(passive on demand)

Identify past failures Failure Agents - (Live 24/7)

Trained to ask what is the exact pattern that lead to failure?

Then live 7/24 they examine incoming data for recurrences

Send alerts, works orders, prescriptive action, & full digital work-scope.

Anomaly Agents - (Live 24/7)

Reports any excursion – could be failure or new/unknown “normal” – needs a little human help

Automatically request inspection – enter work order directly into EAM

Check box 1) failure then automate training of a new Failure Agent – more accurate warns earlier

Check box 2) New normal – system automatically adds new patter to normal

Learn ◦ ◦ ◦ Adapt


Agents need sensor input … how many

Seal Pressure

Upper bearing

Temps: 1 & 2

Lower Bearing

Temps: 1 & 2

Discharge Pressure

VFD Speed

Motor Winding Temps:

1 PHA 2 PHA

1 PHB 2 PHB

1 PHC 2 PHC

Top & Bottom Bearings:

Axial Vibration

Tangent Vibration


6 Simple Steps

3. Create & deploy agents

1. Import history 6. Rapidly apply to other assets

5. Learn & retrain on changes

4. Automatic work orders

2. Pattern Recognition


Many Agents per Asset

Failure Agent 001Knows precise signature of patterns

leading to bearing failure

Anomaly AgentKnows all learned patterns

matching all normal operating states

Failure Agent 002Knows precise signature of patterns

leading to seals failure

Failure Agent 003+Many other agents assigned to detect

exact failure patterns


Agents Learn Normal / Learn FailureSe

nso

r ti

me-

seri

es

Failure from EAMEarliest signature by Mtell

Precise time-to-failureInspection work orders

Failure

Condition

Normal


Pattern match

Failure Signature

Agent Signatures

Using precise pattern recognition

Pattern match

Normal

No pattern match

Abnormal


One Example

GREEN – “trained” normal baseline RED – “prediction interval”

Training data ►

Waveform for probabilistic fit ►

ML alarm events►

2011 2012 2013 2014 2015


One Example

Training data ►


ML alarm events►

2011 2012 2014

30-days

notice

2015

Simulated Real-time Data

Predicted

failure

Training data ►


ML alarm events►

Alert fires here

2013

1 2 3


Smart Machine Results

Maintenance costs

decrease dramatically

Machines

last longer

Net output

increases dramatically

Machines stop

breaking down


Current Industries

Upstream Chemicals

Transportation Water / Wastewater Pharma

Mining

Refining

Pulp & Paper


Summary

• Aspen HYSYS Reactors for Planning and Operations

Support

• FCC model technology and calibration using plant data-

sets

• Prescriptive Maintenance with Aspen Mtell using

machine learning technology


Q&A

Lorenzo Masoni

[email protected]

mailto:[email protected]

Thank YouLorenzo Masoni, AspenTech Professional Serviceswww.aspentech.com

Two Case Studies of plant data processing for Refinery ...PredMaint_AspenTech.pdf · Aspen HYSYS...

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