Status of Thesis - UTPedia

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Status of Thesis

Title of thesis An Integrated Approach for Scheduling and Real-time Optimization in a Refrigerated Gas Plant

I NOORYUSMIZA YUSOFF (Matric No.: Gl030258) hereby allow my thesis to be placed at the Information Resource Center (IRC) of Universiti Teknologi PETRONAS (UTP) with the following conditions:

1. The thesis becomes the property ofUTP.

2. The IRC ofUTP may make copies of the thesis for academic purposes only.

3. This thesis is classified as:

0

Non-confidential

D

Confidential. Please fill in the following:

If this thesis is confidential, please state the reason(s):

Date:

The contents of the thesis will remain confidential for {no. of years}

Remarks on disclosure:

Nooryusmiza Yusoff

Date: 2-8·

O'f·

^.2-0ofJ

DR.M.RAMASAMY

Associate Profesaor ' Chemtc.al Engineering rie • Untversiti Teknologi PET/~":;"'

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UNIVERSITI TEKNOLOGI PETRONAS Approval by Supervisors

The undersigned certify that they have read, and recommend to the Postgraduate Studies Program for acceptance, a thesis entitle "An Integrated Approach for Scheduling and Real-time Optimization in a Refrigerated Gas Plant" by Nooryusmiza Yusoff for the fulfillment of the requirements for the degree of Doctor of Philosophy (PhD) in Chemical Engineering.

AP Dr. Marappagounder Ramasamy Department of Chemical Engineering Universiti Teknologi PETRONAS AP Dr. Suzana Yusup

Department of Chemical Engineering Universiti Teknologi PETRONAS

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UNIVERSITI TEKNOLOGI PETRONAS

An Integrated Approach

for Schednling and Real-time Optimization in a Refrigerated Gas Plant

By

Nooryusmiza Yusoff

A THESIS

SUBMITTED TO THE POSTGRADUATE STUDIES PROGRAM AS A REQUIREMENT FOR THE

DEGREE OF DOCTOR OF PHILOSOPHY IN CHEMICAL ENGINEERING

BANDARSEruiSKANDAR PERAK

SEPTEMBER 2009

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Declaration of Originality

I hereby declare that the thesis is based on my original work except for quotations and citations, which have been duly acknowledged. I also declare that, to the best of my knowledge, it has not been previously or concurrently submitted for any other degree at UTP or other institutions.

Nooryusmiza Yusoff Date:

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Acknowledgements

Reminiscing the early days of my journey, a master plan for my PhD study was only realized after several weeks of discussion with Prof. Dr. VR Radhakrishnan. He was my supervisor for almost a year. Unfortunately, our journey together was cut short when he passed away in late 2006. As if it was fated, I found a replacement in the form of AP Dr.

M. Ramasamy who was incidentally recruited earlier by the late Prof. Radha. Jointly, Dr.

Ramasamy and I continued the thesis work as charted a priori. In the middle of my journey, I came across an unfamiliar but a useful subject for my research work. The subject is called Taguchi method for design of experiment. With valuable inputs from AP Dr. Suzana Yusup, I managed to identify significance of optimization variables for applications in both model predictive control (MPC) and real-time optimization (RTO) studies.

To strengthen my practical knowledge, I endured four-month industrial training at Kerteh, Terengganu. There, I was attached at the Technical and Engineering Services Department helmed by Abdul Ghani. The training was wonderful as I gained first-hand experience in plant operation. On top of that, I had the opportunity to meet new acquaintance in Gas Processing Plant (GPP) of PETRONAS Gas Berhad. I treasured camaraderie with Wan Nasser, Manaf and Aishah who were instrumental in executing advanced processed control (APC) project; Adam and Shahril who were experts in GPP process simulation; Zainuddin who was in charged of energy and loss management system (ELMS) project; Semail who took me to Yokogawa's Vigilant Plant Rollout at Kuantan, Pahang; Azhar and others who showed me around the Main Control Room; Aidi

'

and Airi, old buddies from my undergraduate years in the U.S., who helped me gathering plant information; and last but not least, Charles, Zafran, Hafeez, Ikram, Joon-leong and Fauzi, who were my former students at Department of Chemical Engineering, UTP. In addition, I cherished advice from Prakash, Afifi and Lim on APC implementation issues during a two-day sharing trip to PETRONAS Penapisan Melaka.

I also wish to thank UTP management for granting me an opportunity to pursue PhD here. They could have sent me overseas just like they did to some of my colleagues.

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However, I might have to pay the 'price' later by starting research afresh when I rejoin the faculty. On another note, it would have been difficult to complete this thesis work without encouragement and understanding of Dr. Shuhaimi Mahadzir who heads the Department of Chemical Engineering, UTP. I highly appreciate his empathy in supporting my six-month study extension.

Finally, I am grateful for moral support of my mother Aminah and mother-in-law Pura. Their daily prayers provide me strength and courage. Now that I seem to have accomplished my short-term goal, I would like to share words of wisdom of Imam Ahmad al-Hambali (780-855 A.D.) who contemplated that:

"A knowledgeable person who is aware of his knowledge and practices it, follow him.

A knowledgeable person who does not practice his knowledge, remind him.

A person who realizes that he lacks of knowledge but relentlessly seeking for it, guide him.

Someone who is ignorant but pretending to be a knowledgeable person, curse be upon him. "

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Abstrak

Loji penyejukan gas (RGP) menghadapi cabaran-cabaran operasi melalui tiga cara iaitu:

1) di aliran masuk, gas mentah berbilang aliran dicampur-aduk hingga menyebabkan naik-turun dalam kadar aliran dan kandungan gas; 2) di dalam RGP, penutupan tidak berjadual sering terjadi akibat kepincangan peralatan; 3) di hiliran, kualiti produk yang ketat dikuatkuasakan oleh pelanggan-pelanggannya. Dari sudut perniagaan, RGP menandatangani pelbagai perjanjian dengan pengeluar-pengeluar gas mentah. Harga gas mentah berubah bergantung kepada kualiti gas dan tempoh kontrak. Harga-harga gas asli cecair yakni etana, propana, butana dan hasil pemeluwapan diapung kepada nilai-nilai pasaran. Sebaliknya, harga gas asli ditentukan oleh kerajaan.

Cabaran-cabaran ini memaksa RGP untuk meningkatkan kecekapan dan seterusnya mempertahankan keuntungan. Satu bidang yang dikenalpasti dalam peningkatan kecekapan ialah semasa perancangan pengendalian. Perancangan sebegini mengemukakan masalah penjadualanjangka pendek dan selanjar di mana sasaran-sasaran dilaksanakan secara langsung oleh alat-alat kawalan regulatori. Walaupun amalan ini diterimapakai sekarang, faedah ekonomi boleh dipertingkatkan dengan kekerapan penilaian semula sasaran-sasaran loji melalui pengoptimuman masa-nyata (RTO). Oleh kerana penjadualan diusahakan pada skala masa yang lebih panjang (hari-minggu) berbanding dengan RTO Gam-hari) dan kawalan (saat-minit), integrasi ketiga-tiga lapisan automasi ini adalah sukar.

Tesis ini mencadangkan satu rangkakerja yang menyepadukan penjadualan dan RTO untuk RGP. Pada lapisan atas, satu model dinamik RGP dikemukakan kepada tiga jenis masalah penjadualan yakni aliran masuk, beban, dan mod. Penjadualan aliran masuk merujuk kepada pencampuran pecahan-pecahan tertentu gas mentah yang reridah dan tinggi dengan kandungan hidrokarbon pada kadar loji biasa iaitu 280 tan/jam.

Penjadualan beban merujuk kepada mempelbagaikan kadar aliran gas mentah rendah kandungan hidrokarbon sebanyak ±30 tan/jam. Penjadualan mod merujuk kepada mengubah mod pengendalian loji daripada gas asli kepada gas asli cecair, dan seb~liknya.

Sasaran-sasaran daripada lapisan penjadualan dinamik dihulurkan kepada' lapisan RTO berkeadaan mantap. Ketidakseragaman antara model dan loji dikurangkan dengan cara menggantikan nilai-nilai pembolehubah utama antara model berdinamik dah model

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berkeadaan mantap. Trajektori-trajektori optimum diperolehi menggunakan algoritma pemprograman kuadratik berjujukan dengan kekangan. Trajektori-trajektori ini dilaksanakan secara berasingan oleh skim kawalan ramalan bermodel (MPC) dan alat-alat kawalan berkadar-kamiran (PI) untuk perbandingan. Lapan kajian kes bagi setiap masalah penjadualan dipersembah untuk menunjukkan kemujaraban teknik yang dicadangkan.

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Abstract

A refrigerated gas plant (RGP) faces operational challenges on three fronts namely: 1) at inlet, multiple feed gas streams are mixed causing fluctuation in flow and composition;

2) within RGP, unscheduled shutdowns due to equipment malfunction often occur; 3) at outlet, strict product specifications are enforced by its customers. In business aspect, RGP enters into diverse agreements with producers. Prices of feed gas vary depending upon quality of gas and tenure of contracts. Prices of liquids namely ethane, propane, butane and condensates are floated to market values. In contrast, price of sales gas is tightly regulated by government.

These challenges forces RGP to improve its efficiency in order to sustain profitability.

An identified area of improvement is during operational planning. This type of planning poses a short-term and continuous scheduling problem in which preconfigured setpoints are directly implemented by regulatory controllers. While this practice is currently accepted, economic benefits can be further realized by frequent reevaluation of plant states through real-time optimization (RTO). Since scheduling is performed at a much larger time-scale (days-weeks) as compared with RTO (hours-days) and control (seconds- minutes), integration of these three automation layers is difficult.

This thesis proposes an integrated framework of scheduling and RTO of the RGP. At top layer, a dynamic model of RGP is subjected to three types of scheduling problems namely input, load, and mode. Input scheduling refers to mixing of certain fractions of lean and rich feed gas streams at normal plant load of 280 ton/h. Load scheduling refers to varying flow rate of lean feed gas stream by ±30 ton/h. Mode scheduling refers to change of plant operating mode from sales gas to natural gas liquids, and vice-versa.

Setpoints from dynamic scheduling layer are passed to steady-state RTO layer. Modeling mismatch is minimized by rigorously exchanging values of key variables between dynamic and steady-state models. Optimal trajectories of setpoints are obtained using sequential quadratic programming algorithm with constraints. These trajectories are disjointedly implemented by model predictive control scheme and proportional-integral controllers for comparison. Eight case studies for each scheduling problem ar~ performed to illustrate efficacy of the proposed approach.

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TABLE OF CONTENTS

Status of Thesis

Approval by Supervisors Title

Declaration of Originality Dedication

Acknowledgment

Abstrak (in Bahasa Malaysia) Abstract (in English)

Table of Contents List of Tables List of Figures List of Symbols

CHAPTER 1: INTRODUCTION 1.1 Overview

1.2 Motivation

1.3 Issues with Integrated Framework of Scheduling and RTO 1.4 Thesis Objectives and Outline

CHAPTER 2: LITERATURE REVIEW 2.1 Plant Automation

2.2 2.1.1 2.1.2 2.1.3

Integration at Higher Decision-making Levels Integration at Lower Decision-making Levels

Integration between Top and Middle Decision-making Levels Concluding Remarks

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ii

111 lV

v

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V111 X

Xl

XV XV111 XX111

1 2 4 6

13 15 18 22 24

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CHAPTER 3: RGP MODELING AND CONTROL 3.1

3.2

Introduction

Process Description 3.3 Modeling

3.3.1 Steady-state Modeling 3.3.2 Dynamic Modeling 3.4 RGP Control Philosophies

3.4.1 Plant load control

3.4.2 Demethanizer overhead pressure control 3.4.3 Sales Gas Quality Control

3.4.4 Plant Temperature Control 3.5 Model Predictive Control

3.5 .1 System Identification 3.5.1.1 Step Test 3.5.1.2 PRBS Test 3.5.2 MPC Design

3.5.2.1 MPC Formulation 3.5.2.2

3.5.2.3

Design and Tuning Parameters Set Point Tracking

3.6 Concluding Remarks

CHAPTER 4: REAL-TIME OPTIMIZATION 4.1 Introduction

4.2 RTO Problem Formulation 4.3 Parametric Design

4.3.1 Taguchi Method

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26 28 29 29 35 46 46 48 49 51 53 56 56 61

67 67

68 69 74

75 75 81 82

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4.3.2 Analyses ofTaguchi Results 4.3.2.1 Effect ofNoise Factors 4.3.2.2

4.3.2.3

Average Profit Analysis

Signal-to-Noise Ratio (SNR) Analysis 4.3.3

4.3.4

Validation

Summary of Parametric Design 4.4 RTO Case Study

4.4.1 RTO Results and Discussion 4.4.1.1 Economics

4.4.1.2 Process 4.5 Concluding Remarks

CHAPTER 5: INTEGRATED APPROACH FOR SCHEDULING AND RTO 5.1 Introduction

5.2 Integration of Scheduling and RTO 5.3 Mode Scheduling

5.3.1 Scheduling from NGLs to SG Mode (Case A) 5.3.1.1 Process

5.3.1.2 Economics

5.3.2 Scheduling from SG to NGLs Mode (Case B) 5.3.2.1 Process

5.3.2.2 Economics 5.4 Load Scheduling

5.4.1 Load scheduling from 280 to 250 ton/h (Case C) 5.4.1.1 Process

5.4.1.2 Economics

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87 89 89 93 98 105 108 110 110 114 118

120 121 124 124 124 129 130 130 134 135 136 136 137

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5.4.2 Load scheduling from 280 to 310 tonlh (Case D) 5.4.2.1 Process

5.4.2.2 Economics

138 138 138

5.5 Input Scheduling 139

5.6

5.5.1 Input scheduling of feed gas streams A and B (Case E) 140

5.5.1.1 Process 140

5.5.1.2 Economics 141

5.5.2 Input scheduling of feed gas streams A and C (Case F) 141

5.5.2.1 Process 141

5.5.2.2 Economics Concluding Remarks

142 143 CHAPTER 6: CONTRIBUTIONS AND FUTURE RESEARCH A VENUES

6.1 6.2

Contributions

Future Research A venues REFERENCES

APPENDICES

A Peng-Robinson Equation of State (EOS) B

c

Additional Results from Chapter 5 List of Publications and Presentations

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144 152 155

166 168 186

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LIST OF TABLES

Table 3.1 Compositions of feed gas streams 29

Table 3.2 Steady-state specifications and normal operating conditions 31 (NOC) around major equipment

Table 3.3 Sizing of demethanizer C-1 01 column and reboiler E-1 04 36

Table 3.4 Sizing of absorber C-1 02 column 37

Table 3.5 Sizing of refrigeration cooler E-1 02 and air cooler E-1 06 38 Table 3.6 Dynamic specifications of coldboxes E-1 01, E-1 03 and E-1 05 39

Table 3.7 Suggested tuning parameter settings 44

Table 3.8 Control and tuning parameters 47

Table 3.9a Control and tuning parameters 49

Table 3.9b Surge control parameters 49

Table 3.12 Open-loop responses with u1 move 59

Table 3.13 Open-loop responses with u2 move 59

Table 3.14 FOPTD model parameters 61

Table 3.15 Design parameters of PRBS inputs 63

Table 3.16 Process gains Kp ofFOPTD, ARX and state-space (SS) models. 65

Table 3.17 MPC design and tuning parameters 68

Table 3.18 Nominal input and output values 69

Table 3.19 Ranges of actual input duty values 69

Table 3.20 Integral of Squared Errors (ISEs) [(°Ci·min] for different set point 70 changes

Table 3.21 Average input duties (kW/min) for different set point changes 70

Table 4.1 Economic data 77

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Table 4.2 Values and bounds of constraint variables 78 Table 4.3 Compositions of feed gas streams used for parametric design 80 Table 4.4 Description of factors and levels for RGP 83 Table 4.5 Taguchi internal arrays showing levels of controllable factors 84 Table 4.6 Taguchi external arrays showing levels of noise factors 84 Table 4.7 Results ofTaguchi crossed-orthogonal-array experiments 88

Table 4.8 Analysis of means for average profit 90

Table 4.9 Analysis of variance for average profit 91 Table 4.10 Results of signal-to-noise ratio (SNR) analysis 94

Table 4.11 Analysis of means (ANOM) for SNR 96

Table 4.12 Analysis of variance (ANOVA) for SNR 96

Table 4.13 Maximum differences of averages of controllable factors for Cases 99 1 to 9

Table 4.14 RGP profit values from experiments (HYSYS) and Taguchi 102 method (ANOM) at optimal conditions

Table 4.15 Bounds and description of optimization variables 110 Table 4.16 Values (RM/min) of economic parameters for base and RTO case 111

studies

Table 4.17 Values of optimization variables for base and RTO case studies 114 Table 4.18 Values of constraint variables for base and RTO case studies 115 Table 4.19 Values of selected plant model outputs for base and RTO case 116

studies

Table 5.1 Values of target variables for base and RTO cases in sales gas 125 mode

Table 5.2 Average values (RM/min) of economic parameters over 510 min 129 simulation time

Table 5.3 Values of target variables for base and RTO cases in natural gas 131 liquids mode

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