Paper No. 256 Optimization of Naphtha Feedstock Blending for - - PowerPoint PPT Presentation

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Sep 01, 2023 457 likes •543 views

Paper No. 256 Optimization of Naphtha Feedstock Blending for Integrated Olefins-Aromatics Plant Production Scheduling Y. Ota, K. Namatame, H. Hamataka, K. Nakagawa and H. Abe Mitsubishi Chemical Corporation A. Cervantes, I. B. Tjoa and F.

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Optimization of Naphtha Feedstock Blending for Integrated Olefins-Aromatics Plant Production Scheduling

Y. Ota, K. Namatame, H. Hamataka, K. Nakagawa and H. Abe

Mitsubishi Chemical Corporation

A. Cervantes, I. B. Tjoa and F. Valli

MC Research & Innovation Center

Paper No. 256

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Hierarchy of Decisions in Business Activities

Optimization efforts for improving profitability

Future Operation Production Scheduling Production Planning Strategic Planning Time Now

Efforts Benefits

Here we focus on production scheduling

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Issues in Ethylene Plant Production Scheduling Issues in Ethylene Plant Production Scheduling

Model requirement:

Accurate (rigorous) daily production model
Mixed Integer Nonlinear model

Main Focus:

Find optimal feedstock allocation for daily production scheduling, for meeting production demands

Ethylene Plant Inventories Demands

Daily Interaction between sections

Vessel scheduling Blending How to allocate feedstock?

PPY ETA BBP GSL ETY

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Case Study: Real Production Scheduling for 2 Ethylene and 2 Benzene Plants

Byproducts Ethylene Byproducts Propylene Benzene

Light Naphtha Heavy Naphtha

Utilities

No.1 Ethylene No.1 Ethylene Plant Plant No.2 Ethylene No.2 Ethylene Plant Plant No.1 Benzene Plant No.1 Benzene Plant No.2 Benzene No.2 Benzene Plant Plant

Ethylene Propylene Gasoline Gasoline

Production target? Optimal composition? How to meet Production Objective? 4

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Modeling and Solution Approach

Modeling approach

Unit operation model building capability
Equation based modeling approach

» MINLP model

Modeling platform: GAMS modeling language
Model size

» Continuous variables ~ 60k » Binary variables ~ 1k

Solution approach

» Process knowledge based decomposition strategy » Use standard solvers:

– MILP: OSL – NLP: ConOpt

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Naphtha Properties

Composition of Charging Tanks

0.60 0.62 0.64 0.66 0.68 0.70 0.72 5 10 15 20 25 30

Periods SG MAN_T1 MAN_T2 OPT_T1 OPT_T2

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Naphtha Inventories

Fixed ETY & PPY Demands Max ETY & PPY Productions

20 40 60 80 Base Opt % of total Inventory Light Medium Heavy 20 40 60 80 Base Opt % of total Inventory Light Medium Heavy

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