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北京化工大学博士学位论文用纸

分布的流程工业数据模糊离散化方法

，

建立了实时流程数据的模糊-粗糙信息状态

空间模型和基于信息粒度矩阵算法的模糊诊断规则的提取

；

基于以上研究提岀了

集成

MSNLPCA-

粗糙集多粒度挖掘的过程监测与诊断模型

。

针对流程工业数据的强耦合

、

高相关性的特征

，

研究了两种粒度数据挖掘方

法

，

即动态模糊聚类-分级算法和动态核聚类算法及其在流程报警管理和操作优化

中的应用

。

利用主元分析来确定模糊聚类的个数和基于相关指数法的类内变量等

级排序

，

并应用到乙烯裂解炉报警系统的报警优化管理中

，

有效降低流程的报警

量

，

提高操作效率

，

避免处理报警的盲目性

；

利用动态核聚类算法应用到乙烯裂

解炉的最优操作模式识别和裂解原料选择中来提髙裂解炉的操作性能

，

取得了较

好的应用效果

。

集成多种粒度数据挖掘方法

，

研究和实现了基丁数据挖掘技术的乙烯裂解炉

在线监测与操作优化系统

（

中石化科学技术研究开发项目

，

NO.E03007

）

现已投

入了工业的实际应用

，

取得了较好的经济效益

，

验证了粒度数据挖掘技术在解决

流程工业的建模

、

监控

、

诊断与操作优化等实际应用中的有效性

。

关键词

：

数据挖掘

，

信息粒度矩阵

，

粒度计算

，

MS-NLPCA,

动态模糊聚类-分

级算法

，

流程诊断与优化

北京化工大学博士学位论文用纸

STUDIES

GRANULARITY

DATA

MINING

AND

ITS

APPLIGATION

PROCESS

INDUSTRY

ABSTRACT

With

the

large-scale,

complication

and

modernization

process

industry

such

petroleum

and

chemical

engineering,

large

number

data

about

material,

product,

equipment,

process,

eration

and

on,

are

generated

from

manufacture

and

research

them.

Extracting

deeply

knowledge,

optimal

operation

condition

and

manageable

patxeTn

from

large

data

production

and

management,

namely,

process

industrial

data

mining,

one

the

most

important

technologies

realize

online

monitoring,

fault

diagnosis,

safety

estimation,

product

management,

marketing

analysis

and

prediction

and

process

industry.

addition,

can

provide

effective

decision

support

for

industrial

safety

operation

and

efficient

manufacture.

The

first

important

task

process

industrial

data

mining

select

and

build

effective

and

suitable

data

mining

algorithms

process

industrial

data.

The

granularity

data

mining

can

research

system

from

different

level

versions,

mine

process

operating

model

and

relative

variables

differeni

granularity

space

according

practical

applications.

Moreover,

can

discover

the

relationships

and

rules

among

process

variables

and

find

the

local

global

optimization

among

different

granularity

space

solve

the

process

diagnosis

and

operating

optimization

effectively.

The

cracking

furnace

system

the

key

equipment

ethylene

manufacturing

process,

which

has

lot

typical

characteristic

general

continues

petrochemical

process.

this

paper

the

process

industrial

granularity

data

mining

mainly

based

the

ethylene

cracking

furnace

system.

According

the

high

dimensions

and

uncertainty

process

industrial

data,

the

fuzzy

set

and

rough

set

granularity

data

mining

are

studied

for

process

data.

overcome

the

roughness

that

can

not

completely

discern

knowledge

granularity,

the

nature

relationship

between

information

granularity

principle

and

roughness

Ill

北京化工大学博士学位论文用纸

knowledge

studied,

and

the

algorithms

granularity

computing

and

optimal

attribute

reduct

based

granularity

entropy

are

proposed.

pursue

fast

and

efficient

granularity

data

mining

algorithm

for

process

industry,

rough

data

mining

model

based

information

granularity

matrix

algorithm

proposed

according

fuzzy

information

granularity

matrix

principle.

And

the

basis

information

granularity

matrix

algorithm,

the

information

compression

granularity

matrix

algorithm

and

incremental

rule

acquisition

are

proposed.

The

proposed

rough

data

mining

model

and

data

mining

algorithm

are

understood

easily

and

operating

conveniently,

can

decrease

the

store

space

and

improve

the

efficiency

process

industrial

data

mining.

According

process

data

with

noise,

multi-frequency

and

dynamics

characteristic,

the

paper

makes

several

researches

follows:

Adopting

wavelet

transformation

based

data

moving

window

extract

feature

and

filter

noise,

and

then

studies

granular!

data

mining

nonlinear

principal

component

analysis

(NLPCA)

based

inp

training

neural

network

(ITNN),

moreover

improves

the

learning

algorithm

ITK

Using

the

mulli-granularity

space

analysis

wavelet

transformation,

adapt

mulli-scale

nonlinear

PCA

(MSNLPCA)

granularity

data

mining

method

proposec

extract

feature

and

abnormal

states

monitoring

for

process

industrial

time-serial

d:,

、

Using

the

proposed

information

granularity

matrix

algorithm

acquire

fuzzy

diagnos

；

rules,

fuzzy

discretization

method

continued

data

studied

normal

distribution

process

data

and

fuzzy-rough

information

state

space

model

about

real-time

proces

data

built.

Based

above

researches

multi-granularity

process

monitoring

an.

diagnosis

mode!

integrating

MSNLPCA-Rough

set

proposed.

According

the

strong

coupling

and

high

interrelation

process

data,

granularity

data

mining

methods

are

studied,

namely,

dynamical

fuzz\

clustering-ranking

and

kernel

clustering

algorithm.

Using

PCA

decide

the

number

fuzzy

clustering

and

ranking

the

variables

each

cluster

based

interrelation

index,

fuzzy

clustering-ranking

algorithm

used

process

alarms

optimal

management

improve

operating

efficiency

and

avoid

blindness

dealing

with

alarms.

Dynamic

kernel

clustering

algorithm

used

recognize

optimal

operating

pattern

and

select

the

better

cracking

crude

oil

improve

the

operating

ability

ethylene

cracking

furnace

剩余107页未读，继续阅读

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2022-05-25

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流程工业粒度数据挖掘技术研究与应用.pdf

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