Puigjaner L. (ed.) Syngas from Waste

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SPE

¼ e

: ð26Þ

When an alarm signal is ﬁred, identiﬁcation stage begins. The purpose of this

stage is to determine the variable, or set of variables, that appears to be the ones

with major deviations. Variable identiﬁcation is usually carried out by analysing

the so-called variable contributions to the out of control statistic. Variable con-

tributions to T

and SPE statistics are calculated as in Eq. 27.

j; k

¼ t

1

j; k

j; :ðÞ



1

; ð27Þ

where c

j; k

is the contribution of the jth variable in the kth observation, P

j; :ðÞ

is the

jth row in P and S is the covariance of T. Variable contribution to SPE-statistics is

calculated as in Eq. 28.

SPE

j; k

¼ e

j; k

: ð28Þ

A thorough analysis about variable contributions, its limits and interpretation

can be found on Westerhuis et al. [18]. Figure 8 presents the general structure for

stage II when a monitoring scheme as described above is implemented.

4.3.1 Application of PCA to IGCC Plant Data

As an application example, the previous PCA-monitoring strategy is used to set an

on-linemonitoring strategy for an IGCC plant. A set of 12 process variables is

chosen as the one having the most meaningful and important information related to

Fig. 8 General procedure for

application of PCA to

monitoring (Stage II)

318 A. D. Bojarski et al.

the plant operation. After a proper data pre-treatment and off-line analysis, a

set of 60 process realizations is taken as the reference population deﬁning

the NOC. The proﬁles for the T

and SPE statistics for these normal data are shown

in Figs. 9 and 10.

In addition, an extra data trend containing slight excursions from the normal

operational data is considered to picture the detection and identiﬁcation capabil-

ities of the whole strategy. The principal deviations included in this data are

because of an abnormal value in the coal feed. Figures 11 and 12 show the proﬁles

for T

and SPE, respectively. It is easy to notice that SPE shows more points out of

the control region than T

In this case, the process excursion is noticed just by one of the statistics and

therefore, only its contribution plot should be analyzed to ﬁnd the variable (or set

of variables) that shows the deviation. It is a common practice to set that a given

number of consecutive observations should fall outside of the control region before

an alarm signal is ﬁred. In this case, this number was set to consider ﬁve con-

secutive measurements. When contribution plots are analyzed, it is easy to note

0 10 20 30 40 50 60

Observation

Value

= 0.05

Fig. 9 T2 Chart for

observations contained in

NOC

0 10 20 30 40 50 60

Observation

SPE Values

= 0.05

Fig. 10 SPE Chart for

observations contained in

NOC

Industrial Data Collection 319

that variable 11 (relation O

/Coal) has the most important effect, closely followed

by variable 12 (Steam/Coal), see Fig. 13.

Clearly PCA helps in identifying the variables that might be responsible for

plant excursions and allows improving the knowledge of the process. However,

0 5 10 15 20 25 30 35 40 45 50 55

Observarion

Values

= 0.05

Fig. 11 T2 Chart for the

considered faulty data trend

0 5 10 15 20 25 30 35 40 45 50 55

Observation

SPE Values

α = 0.05

Fig. 12 SPE Chart for the

faulty data trend

1 2 3 4 5 6 7 8 9 10 11 12

0.2

0.4

0.6

0.8

1.2

1.4

Variable

j,27

SPE

Fig. 13 SPE variable

contribution for observation #

320 A. D. Bojarski et al.

it has to be emphasised that PCA-based monitorisation schemes depend on the

adequacy of the process representation by the selected principal components.

5 Conclusion

The chapter has discussed the possible control schemes present in IGCC plants,

together with the generalities related to the variables which are of usual concern. The

problem of data reconciliation using process simulation has been exempliﬁed using

different models and approaches for improving the reliability of measurements and

for the actual estimation of parameters. An overview of possible data-mining tech-

niques, mainly those related to PCA, has been provided. The problem of applying

PCA to process monitoring has been addressed and it was discussed in detail.

References

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322 A. D. Bojarski et al.

Index

Abrasion index, 17

Absorption, 127

Acquisition, 204

Adsorbent, 127

Adsorption, 95

Adsorption of sulphur compounds, 123

Advantages, 3

Advantages and disadvantages, 288

Air Separation Unit (ASU), 77

Ammonia, 93

Analysis of O&M, 289

Analytical hierarchy process

Areas of protection, 239, 271

Artiﬁcial Neural Networks (ANN), 180

Autothermal, 4

Bed length, 132

Bioaccumulation

Bioenergy sector, 60

Biological degradation, 18

Biomass, 2, 57

Biomass drying, 13

Biomass gasiﬁcation, 61

Biomass handling, 289

Biomass waste, 11

Bioreﬁneries, 90

Carbon Capture and Storage (CCS), 97

Carbon conversion, 286

Catalysts, 147

Catalysts for the WGS reaction, 163

Category mid-points, 239

Ceramic ﬁlter, 101

Ceramic ﬁlter system, 288

CGE, 189

Characterisation factor, 239

Chemical, 95

Chemical environmental fate

Chemical products, 93

Clean gas composition, 286

Cleaning process, 280

Cleaning systems, 92

Climate change, 12

Clogging, 131

and H

separation, 293

Capture and Storage, 146

emission reduction

per unit of energy, 282

selective membrane, 146

sequestration, 125

Cost of Energy (COE), 262

Co-gasiﬁcation, 289

Cogeneration, 3

Coke, 57

Cold, 92

Cold Gas Efﬁciency (CGE), 63

Cold streams, 205

Combined Cycle (CC), 279

Combined pinch analysis/mathematical

programming approach, 202

Combustion, 69

Combustion agent, 128

Composition, 124

Composition of the gas

Compressed air, 281

Concentration, 95

Conceptual design, 7, 171

Condensation cycle, 280

Contaminating gases

L. Puigjaner (ed.), Syngas from Waste, Green Energy and Technology,

DOI: 10.1007/978-0-85729-540-8, Ó Springer-Verlag London Limited 2011

323

C (cont.)

Conversion with water steam, 293

Convex hull, 217

Coordinated control, 304

Co-production, 90, 253

Co-production of electricity, 255

COS control, 129

COS hydrolysis reactor, 101

Cost Beneﬁt Analysis (CBA), 32

Cost of CO

captured, 265

Cradle to gate, 268

Cross-section of the grains, 133

Cyanides, 280

Data, 204

Data mining, 311

Data pretreatment, 316

Data reconciliation, 304

Data reconciliation problem, 305

Decision making, 228

Degree of Freedom (DOF), 176

Densiﬁcation, 2, 42

Design of the heat exchangers

Disjunctions, 215

Diversiﬁcation, 288

Dolomite, 124

Dose response, 238

Double pipe heat exchanger, 215

Dry, 92

Eco-indicator 99, 33

Efﬁciency increase, 277

Electricity production, 92

Electrolytes formation, 95

Elemental analysis, 3

Emerging Technologies, 121

Emission estimation, 236

Emission factor

Emissions reduction, 277

Endothermic, 4

End-point categories, 33

End-points

Energy density, 11, 18

Energy resources

Energy supply chains, 7

Energy valorisation, 13

Enhanced Oil Recovering techniques, 146

Entrained bed gasiﬁer, 59

Environmental impact, 11, 238, 268

Environmental interventions, 235

Environmental mechanism, 239

Environmental metrics, 235

Environmental models, 237

Environmental risk assessment, 238

Equation of State (EOS), 67

Equation oriented (EO), 176

Equilibrium constant (K

), 70, 104

Equivalence Ratio (ER), 79, 191

Exchanger types, 215

Exhaust gases, 280

Exothermic reactions, 4

Expansion, 281

Exposure assessment, 238

Feedstock Costs (FC), 233

Feedstock dust preparation, 75

Feedstocks

Filtration, 95

Fischer-Tropsch (FT) fuels, 94

Fixed bed gasiﬁer, 58

Fixed plate shell and tube heat exchanger, 215

Flexibility of IGCC technology, 296

Flue gas scrubbing system, 282

Fluidised bed gasiﬁer, 59

Fly ash, 280

Fly ash dedusting, 286

Fossil fuels, 12

Fuel cells, 5, 92, 141

Fuel density, 15

Fuel ﬂexibility, 286

Fugitive emissions, 236

Functional Unit, 230, 268

Gas cleaning, 7

Gas Turbine (GT), 77, 202, 280

Gasiﬁcation, 1, 56, 69, 145

Gasiﬁcation agent, 2

Gasiﬁcation island, 279, 280

Gasiﬁcation projects, 6

Gasiﬁer, 278

Gasiﬁer load, 290

Generating the synthetic gas

Geological storage, 291

Goal deﬁnition and scope

GRA, 17

Grain core, 122

Grain size distribution, 122, 131

Grain sizes, 132

Grand Composite Curve (GCC),

208

Green Supply Chain Management, 29

324 Index

Greenhouse gases (GHG), 97

Grindability, 14

Grindability index, 15

Grinding system, 290

GT cycle, 184

permeability, 153

production, 288

selective membrane, 146

Halogens, 280

Hardgrove Grindability Index (HGI), 19

Hazard assessment, 238

Heat, 93

Heat exchanger network, 201, 215

Heat integration, 202

Heat recovery, 201

Heat Recovery Steam Generator (HRSG), 280

Heating value, 2, 35

Heat-recovery approach temperature, 203

Height of the bed, 128

HEN, 203

Henry’s law, 95

Henry’s law constant, 95, 102

(T), 102

High Pressure (HP), 78

Higher Heating Value (HHV), 75

Highly integrated designs, 281

High-pressure steam (HP), 202

Hot streams, 205

Hotelling’s T

, 316

HRAT, 201

Hydrogen permeability, 154

Hydrogen production, 93, 183

IGCC power plants, 92

IGCC process superstructure, 182

IGCC processes optimisation, 288

IGCC technology, 277, 278

Immediate analysis, 3

IMPACT 2002+, 33

‘‘Important value’’ adsorbents, 121

Incidence matrix, 306

Industrial application, 277

Inertisation, 129, 280

Integer Infeasible Path Optimization, 221

Integrated Gasiﬁcation Combined Cycle

(IGCC), 12

Integrated Gasiﬁcation Combined Cycle power

plants (IGCC), 59

Integration, 175, 280

Integration between the ASU and CC, 281

Integration of water-steam systems, 281

Intermediate Pressure (IP), 78

Investment cost, 287

Key Performance Indicators (KPIs), 172, 230

Knowledge Discovery in Databases, 300

Kriging, 180

kWh

, 262

LCA, 268

LCA results interpretation, 246

LCIA, 269

LCIA (Life Cycle Impact Assessment), 269

Le Châtelier’s principle, 71

Life cycle, 170, 269

Life Cycle Assessment, 29, 268

Life Cycle Impact Assessment, 29, 37,

239, 269

Life Cycle Inventory, 29, 244

Life Cycle Inventory-LCI, 269

Life extension, 288

Lignocellulosic materials, 17

Limestone, 125

Linear Programming (LP), 172

Liquefaction system, 113

Liquid fuels, 94

Logical constraints, 216

Low value, 121

Lower Heating Value (LHV), 75

Low-pressure (LP), 202

Manganese oxides, 139

Mathematical model, 150

Mathematical programming, 202

MCDA techniques, 241

MDEA absorber, 101

Measurement vector, 305

Mechanical durability, 17

Mechanical methods, 95

Mechanical resistance, 122

Medium-pressure (MP)

Membrane reactor, 146

Membranes, 147

Metal adsorbents

Metal oxides, 135

Metallic membranes, 164

Meta-model, 179

Index 325

M (cont.)

Methanol, 112

Methanol and Dimethyl Ether (DME), 94

Methyldiethanolamine, 124

Mid-point impacts, 33

MINLP optimization, 172, 215

Mixed Integer Linear Problems (MILP), 172

Mixed Integer Non-Linear Problem

(MINLP), 203

Mixtures of sorbents, 125

Modelling, 7

Molar volume, 122

Moving bed, 58

Multi Attribute Utility Theory (MAUT), 242

Multi Attribute Value Theory (MAVT), 242

Multi-layer perceptron networks, 180

Multimedia compartment models, 237

Multiobjective Optimisation (MOO), 178

Multi-objective optimization, 7

Multiple objective programming, 240

Multiple objectives, 228

Multiple-Criteria Decision Analysis

(MCDA)., 229

Multi-production, 288

Multi-utility conﬁgurations, 214

Municipal sewage sludge, 12

‘‘Nadir’’ solutions, 273

Net efﬁciency, 280, 291

Net power, 267

Net Present Worth or Value (NPW, NPV), 231

Network synthesis, 201

Normalisation, 240

emissions, 283

Number of cycles, 136

Objective function, 216

Optimisation, 172

Optimised design, 287

Outer-Approximation/Equality-Relaxation, 221

Oxidation, 69

Oxidation reactions, 130

Oxidizing agent, 3

Oxo-alcohols, 93

Oxy-combustion, 98

Packing, 122

Pareto frontiers (PFs),, 241, 272

Pareto Fronts, 272

Pareto optimal solutions, 240, 273

Partial oxidation, 4

Particle size distribution, 13

Particle sizes

Pelletization, 11, 17

Pellets, 17

Persistence

Petcoke, 57, 278

Physical absorption, 95

Pinch analysis, 201

Plate and frame heat exchanger, 215

Plug ﬂow, 152

Polygeneration, 90

Polynomial models, 180

Porosity, 122

Post-combustion, 98

Power generation, 184, 201

Pre-combustion, 98

PRENFLO gasiﬁer, 74

Pressure ratio (PR), 185

Pressure Swing Adsorption, 146

Pressure Swing Adsorption, PSA system, 113

Pre-treatment, 11, 18

Process intensiﬁcation, 7

Process simulation, 176

Process simulation environments, 176

Process Systems Engineering, 7

Producer gas, 56

Proximate analyses, 16, 67

Proximate and ultimate analyses

Puertollano IGCC power plant, 279

Puriﬁcation processes, 121

Pyrolysis, 2, 68

Pyrrhotite, 126

Random errors, 305, 306

Random measurement errors, 305

Raw materials, 2

RDF fraction, 12

Recovery fraction, 105

Rectisol process, 112

Reference plant, 262

Reﬁnery residuals, 12

Regeneration, 130

Reliability, 288

Renewable energies, 12

Renewables

Residence time, 72

Residual heat, 280

Reusing, 135

Reversibility, 125

326 Index

Risk characterisation, 238

Risk in the environmental sense, 238

Saturated steam, 281

Scaling parameters, 233

Scenario analysis, 229

Selective membrane, 146

Selexol, 96

Sensible heat, 3

Sensitivity Analyses (SA), 11, 82

Sensitivity analysis, 191, 201

Sequential modular (SM)., 176

Shifting reaction, 296

Sievert’s law, 153

Simpliﬁed ﬂow diagram, 280

Simultaneous heat integration, 203

Size distribution, 122

Slag discharge system, 286

Slag/ash separation, 286

Sluicing systems, 286

Small organic compounds, 93

Solid sorbents, 122

Sorbent sintering, 123

Sour water stripper, 101

Split fraction, 105

Squared Prediction Error (SPE), 316

ST cycle, 186

State Task Network (STN), 34

Statistical process control, 311

Statistical process monitoring, 316

Steam reforming, 56

Steam turbine, 280

Sulphidisation/regeneration cycles, 139

Sulphur compounds, 121, 124, 280

Sulphur recovery, 283

Sulphur removal, 107

Sulphuration, 131

Superheating, 281

Superstructure, 7, 172, 215, 254

Supply Chain, 27

Supply Chain Management, 27

Surrogate model, 179

Sweeping gas, 150

Syngas, 1

, 277

Syngas applications, 288

Syngas composition, 59

Syngas ﬁnal application, 288

Syngas from coal, 56

Syngas from natural gas, 56

Syngas Puriﬁcation, 121

Syngas superstructure, 173

Synthesis, 201

Synthesis gas, 55, 280

Synthetic gas natural, 93

System boundaries, 268

Tars, 61

Technique for Order by Similarity to Ideal

Solution (TOPSIS), 243, 273

Techniques applicable for MOO, 240

Temperature Swing Adsorption, 146

The functional unit, 230

The life cycle inventories, 244

The minimisation of Gibb’s free energy, 66

Thermal efﬁciency, 5

Thermo-chemical principles, 1

TOP pellets, 19

Topological changes, 260

Torrefaction, 11, 18

Toxicity, 235

Troilite, 126

Tube HE with U-tubes, 218

Ultimate, 16, 67

Unreacted-core-shrinking model, 67

Utility, 201

Utopian solution, 273

Venturi, 124

Venturi scrubber

Volatiles combustion, 68

Volumetric heating value, 16

Waste gasiﬁcation, 2, 61

Waste nitrogen

Wastes, 57

Wastewater treatment, 283

Water Gas Shift, 146

Water Gas Shift (WGS) reactor, 93

Water Gas Shift Membrane Reactor, 145, 149

Water Heat Boiler (WHB), 75

Water leakages,

288

Water scrubbing, 95

Wet, 92

Zero emissions, 288

Index 327

Puigjaner L. (ed.) Syngas from Waste - Emerging Technologies

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