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Process Control and Optimization in Energy Utilization and Environmental Protection.

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Dev. Chem. Eng. Mineral Process., 8(3/4),pp.333-350,2000.
Process Control and Optimization in Energy
Utilization and Environmental Protection
M. 0. Tad6
School of Chemical Engineering, Curtin University of Technology,
GPO Box U1981,PERTH, Western Australia 6845, AUSTRAUA
An evolving goal in power plants for many decades is to have the process control
system predict and optimize the set-point changes continuously in real time and to
automatically regulate flows, temperatures, and pressures. This goal has now
become more significant and more involved because a fourth dimension, the
environment, has been added to the three traditional dimensions (namely, safet>l,
eficiency and cost) of real time power plant performance. In this paper, the
emerging role of process control and optimization is reviewed within the context of
energy utilimtwn and environmental protection.
Case studies that have been
reported in this area are discussed. Some limitations of the current technology are
The Clean Air Act Amendments of 1990 (CAA) ensured that mechanical systems got
the “air time” during the design and engineering phases of compliance, process
control is now becoming one of the dominant themes as retrofitted plants begin
operating in the post-CAA world. This is mainly because the need to meet the
multiple goals of safety, high efficiency, low operating cost, and low emissions
involves a complex optimization process that goes beyond the traditional functions of
combustion control. Advanced process control has become not only desirable but is
now necessary to optimize all the competing demands placed on today’s power plants.
First and foremost the number of variables that the combustion control system
must manipulate has grown significantly, so also is the inter-relationship among these
Secondly, optimizing for emissions is now being watched through
continuous emission monitors (CEM) and is governed by stiff penalties but also with
incentive allowance. Thirdly, the cost of highly reliable computer capability has
significantly reduced, making it practical to apply sophisticated control algorithms,
maintain large amounts of data, and run calculations and analyses on personal
computers or workstations.
According to Makansi (1994): “controllingNO, emission has emerged as the first
post-CAA arena in which advanced process control has become desirable, perhaps
even necessary. Many plants that installed low NO, burners, for example, have
discovered that mechanical retrofit is not suflcient
- at
least if heat rate, safety,
ability to cycle, ash quality, electrostatic precipitator (ESP) operation, and other
factors are not to be sacrificed.” Previously, compliance with NO, limits meant a
one-time annual test, today CEM requires continuous reporting. The implication is
that the unit has to be tuned all the time. The problem is not as significant for SOz
emissions since this pollutant is being controlled by a separate downstream process.
Applying advanced process control to combustion goes well beyond utility CAA
retrofits. Recent coal-fired independent power projects have found that applying
advanced combustion (or process) control saves money by reducing the size of
downstream equipment, making ash salable, reducing reagent consumption, etc.
Also, the extremely low NO, emissions produced by today’s gas turbines are in large
part due to the advanced process control systems employed by those machines.
The purpose of this paper is to review the role of process control and optimization
in energy utilization and environmental protection. The necessity of these areas will
be discussed, together with their capabilities for improved advanced control of
combustion systems to satisfy mandatory environmentalregulations. Case studies for
control system retrofit guidelines will be given together with examples of energy
savings that can be obtained by implementing specific advanced control and
optimization procedures.
Control & Optimization in Energy Utilization and Environmental Protection
The Role of Process Control and Automation
Process control and automation provide the following benefits for a power plant:
1. Faster plant start-up and shut-down by automating control sequences.
2. Higher availability by detecting and pinpointing root causes of impending
3. Higher thermal efficiency by moving variable set points closer to operating
Improved profile of emissions by precisely controlling the combustion process.
5. Lower operating costs by reducing staff requirements.
Advanced control systems are not only for new projects. Older power plants can also
benefit from lowered maintenance costs when they replace outdated pneumatic or
electronic-analog devices. In power plant refurbishment projects, microprocessor
controls were first used in limited areas. Today, state-of-the-art control systems are
finding acceptance in the control rooms for the worldwide power generation industry.
A typical structure of the power plant control system is shown in Figure 1 where
the programmable logic controller (PLC) modules were connected to sensors
measuring the process parameters. In a specific application (Kontopoulos et al.,
1997), the PLC received 28 analogue measurements of which 14 corresponded to
temperature measurements, 6 to pressures, 4 to flow rates, 2 to oxygen and carbon
monoxide sensors, and 1 to the rotation speed of a large blower motor. The data
acquisition module communicated with an industrial PC (based on an Intel 80486/66
MHz processor) over a Data Highway Plus industrial Local Area Network (LAN).
The LAN interface module received process instrumentation signals from the PLC.
The control software module includes a real-time database, and the expert system
module implements the control strategy with a facility for developing the rule base
expert system. The real time dynamic model of the process predicted important
process parameters and assisted the expert module in building qualified conclusions
for rule based system. It is usual to employ multivariable control with constraints on
inputs and/or outputs, in a robust algorithm that is independent of platform but can
reside in a distributed control system (DCS). Such systems have the capability to
reject poor data, and update process parameters when data quality is improved.
M.O. Tade
Figure 1. Structure of the control system.
Multivariable control among other things requires proper management of time
constants and therefore differs in its need to access data from other functions available
in the DCS. Thus model-based predictive control (Ansari and Tad6 1998) is popular,
and an example is steam temperature control (Makansi, 1995). Feedback alone is not
suitable for rapid start-up and shut-down. It is also not sufficient for controlling
Control & Optimization in Energy Utilization and Environmental Protection
incremental damage that accumulates in thick-walled components as a result of
temperature fluctuations. Therefore, sophisticated process control loops today include
a model that predicts a value for a parameter, then adjusts the manipulated variable,
instead of waiting to respond after deviations have occurred. In the case of NO,
control, the system must regulate fuel and air flows to individual burners based on
changes in 02 concentration in the furnace, inferred changes in combustibles
concentration, fluctuations in furnace vertical temperature profiles, and flame
frequency spectra. There is also the need to monitor and regulate the capability of air
and fuel flows to the individual burners or burner levels. This is discussed below.
Management of alarms is another issue for process automation. Alarms must be
prioritized and managed. Some alarms are of negligible importance and some are
Alarm management is now being built into the process control and
automation system. Operators are being trained not to react impulsively to alarms and
to allow the automation system to do what it is capable of.
Another emerging area of
process control is new ways to visualize data and information and prioritise their
display for the operators.
It should be noted that the vast capabilities of an
automation system could be underutilized if plant personnel are uncomfortable with
it. Therefore, operator training and education is important to reap the benefits of
advanced process control.
The Role of Process Optimization
The idea of optimizing operations is becoming a fundamental objective at power
plants. The transition from an industry focused on capacity expansion to one focused
on economic operation has made the need for process optimization imperative. As
new objectives are forced on power stations, these days with a real-time component,
use of computer-based process optimization becomes inevitable. Many information
technology (IT) professionals regard real-time optimization as the next frontier beyond automation and control (Makansi, 1995).
The analytical and calculation techniques in the core of process optimization are
also being applied to many parts of the power plant enterprise, such as predictive
monitoring and maintenance, general performance monitoring, emissions monitoring,
33 7
M.O. Tad6
optimizing emissions among units, and across the spectrum of fuel and non-fuel
stations (Makansi, 1998). This is an important step, since most of the developmental
work has been limited to meeting NO, emissions without undue sacrifice to heat rate
and other fossil-fired unit performance parameters. In many ways, process and
economic optimization are different from design optimization. Most power plants use
the performance and maintenance model loosely based on, or derived from, the
original design parameters of the plant. The new process optimization techniques
model the equipment based on its present, or real-time, condition and store that
information for future use.
An important trend is to apply numerical or statistical optimization methods in
conjunction with,or instead of, methods based on first principles. In a simplified
form, the idea is to analyze large quantities of data and determine statistical
relationships and patterns among them instead of simply relying on theoretical or
fundamental chemical, mechanical, and thermodynamic equations that describe the
process in an idealized way
- with
empirical correction curves for non-idealized
behaviour. The key difference from the design optimization is that numerical
optimization techniques build knowledge of the process to be optimized using the
real-time or archival data collected and stored by the computer control system. In
general, the models start with the number of inputs and outputs and the constraints
placed on each. In environmental terms, these constraints may be emissions levels; in
economic terms, fuel price; in quality terms, the purity of a product; or in reliability
terms, equivalent operating hours for a gas turbine.
Typical numerical optimization techniques are known in this field as fuzzy logic,
neural networks, knowledge-based expert systems, sequential process optimization,
constrained model predictive control, multi-dimensional constrained optimization,
etc. A detailed introduction to these methods can be found in Edgar and Himmelblau
(1988). A brief description of some of these methods can be found in Makansi
(1995), where Figure 2 is given for sequential optimization. An introduction to
practical neuro-adaptive process control can be found in Mills, Zomaya and Tad6
( 1995).
Control & Optimization in Energy Utilization and Environmental Protection
Describe unit
Dejine Roal
Respond to
Operator advisory
Link to DCS
parameter settings
Measure results
Enter data
Figure 2. Sequential optimimtion scheme.
Diwekar et al. (1997) discussed optimal design of advanced power systems under
uncertainty. The necessity to include uncertainty in optimizing power systems is due
to the fact that performance data are typically scant, accurate predictive models do not
exist, and many technical as well as economic parameters are not well established. In
that work, results were presented to illustrate the use of the proposed method for the
environmental control design of an advanced energy system for electric power
A multi-objective optimal unit sizing method was described by
Yokoyama et al. (1994) for hybrid types of power generation systems. A hierarchical
Optimal unit sizing by n o n l i n e a r p r o g e g
Initial values of device
capacities and electric
contract demand
Operation planning
by sirmlation
total cost
Penalty method
capacities and electric
contnact demand
Evaluation of annual
energy consunption
objective function
Device capacities and
electric contract demand
Evaluation of device
performance characteristics
Weather conditions and
electricity demand
Determination of
operational strategies
criterion satisfied?
capacities and electric
contract demnd
Figure 3. Hierarchical optimization method for unit sizing and operational planning.
Control & Optimization in Energy Utilization and Environmental Protection
optimization technique shown in Figure 3 was proposed for unit sizing and
operational planning. Groscurth and Kiimmel (1990) demonstrated that the removal
of C02 from exhaust gases of power plants and co-generation units in combination
with energy optimization strategies may reduce the emissions of this infrared-active
gas by more than 70%. The strategy involved the use of energy saving technology
together with vector-optimizationand COz removal techniques.
Relevant Software Packages
According to Makansi (1994), organizations such as Electric Power Research Institute
(EPRI) have recognized that software can help plant operators to balance heat rate
with emissions. EPRI is combining software modules developed for fuel quality, heat
rate, SO2 and NO, emissions, solid waste, and CEMs into one package called
Advisory Plant and Environmental Control System (APECS). The purpose of the
package is to automate computation of control system set-points needed to achieve
environmental dispatch or optimize unit load and emissions constraints at the lowest
cost. Having taken the costs for ash sales or disposal, fuel, auxiliary power, emissions
credits, etc.. into account.
Constrained sequential optimization (CSO) is also another package that has been
demonstrated to reduce NO, emissions by 27% beyond what low-NO, burners
(LNBs)can accomplish (Makansi, 1994). CSO combines parametric testing with
operator experience based on boiler tuning. This is significant, since normal
parametric testing does not reflect the multiple interactions of changes in one
parameter. Also operator-based experience does not adequately optimize the process
because control settings are constantly moving. Therefore, in CSO applications,
control settings are adjusted simultaneously, performance effects are observed,
models are created reflecting the influence of all input parameters on one or more
performance indicators, and new, more appropriate settings are advised. Each
subsequent run is compared to previous runs based on statistical analyses. Results
from a recent application at the Tennessee Valley Authority’s New Johnsonville
station (Makansi, 1996) are shown in Table 1. The results show NO, emissions from
all six units were reduced by 5 to 16% to bring them all under the 0.45 lb/million Btu
Table 1: Full-load optimization' at Tennessee Valley Authority's New Johnsonville plant
CO, ppm
coz, %
NOx,Ib/million Btu
Efficiency, 5%
Unit 1
Unit 2
340.00 288.00
Minimum-load optimization
NOx, ppm
431.00 223.00 207.00 207.00
N G . lb/millionBtu
125-MWnon-reheat tangentially fired boilers, vintage 1950
Unit 3
280.00 262.00
Unit 4
Unit 5
307.00 280.00
Unit 6
343.00 286.00
Control & Optimization in Energy Utilization and Environmental Protection
threshold for compliance under the CAA Title IV. Note that these results were
accomplished while reducing the loss-on-ignition (LOI) values by 20 to 55% for all
units and improving boiler efficiencies. Furthermore to achieve these gains at New
Johnsonville, it took an average of one week to optimize each unit. Units at this plant
are all 125-MW tangentially fired boilers installed during the early 1950s. A few
other applications can be found in Makansi (1998).
The various packages available can be distinguished using the following criteria:
1. Integration of the package with existing plant control and supervisory system,
database, etc.
2. Open or closed loop capability.
3. Computing platform.
4. Model development capability.
5. Retraining capability.
6. Handling of plant subsystems.
7. Local versus global optimization.
8. Continuing vendor support.
On the control side (Makansi, 1998), Honeywell Inc. Industrial Automation &
Control, Phoenix, Arizona announced its expanded business optimization suite of
applications software in June 1998. It has been claimed that the software goes beyond
process optimization. Elsag Bailey announced an order from Edmonton (Alberta,
Canada) Power’s Clover Bar Station to supply a Symphony enterprise management
and control system that among other things, interfaces with an expert system from
Gemsyn Corp., Cambridge, Mass.,for monitoring, diagnostics, optimization and
scheduling. ABB Power Plant Controls, Windsor, Conn., announced a new version of
Optimax to improve plant operation and maintenance. Aspen Technology has also
announced its intentions to penetrate the power generation industry, their NeuCop
package for coal-fired boiler optimization and control has been applied to reducing
NO, emissions, improving heat rate, and minimizing unburned carbon in ash.
Improved instrumentation can also lead to more economical decisions by both the
control system and the operators.
This is driving the development of highly
responsive, accurate and more reliable sensors for boiler temperature measurement
and other related characteristics.
Boiler Controls and Optimization
Details of boiler control and optimization can be found in Liptak and Venczel(l985).
The main points to remember include:
1. The systems often interact, e.g. air flow affects steam temperature, feedwater flow
pressure, and fuel flow affects drum level and furnace draft.
Designs with the minimum of these interactionsare the most desirable.
2. For flexibility and rangeability, linear flow signals are necessary. Control valves
and piston operators need linearizing positioners.
3. Fuels should be totalized on an air-required basis.
4. Tie-back arrangements, which simplify the task of getting quickly on automatic
control, are very important in complex systems.
5 . The flows of fuel and air should be controlled such that the flow rates reaching the
burner always represent a safe combination.
Examples of specific control features include:
1. Safety interlocks
2. Boiler-pressure controls
3. Measurable fuel controls
4. Waste or auxiliary fuel controls
5. Air flow measurement and control
6. Furnace draft control
7. Air-fuel ratio control
8. Steam temperature control
9. Steam pressure optimization
10. Pump speed control
The major goals of optimization of boilers include:
1. Minimize excess air and flue gas temperature
2. Measure efficiency
Control & Optimization in Energy Utilization and Environmental Protection
Use the most efficient boilers
Know when to perform maintenance
3. Minimize steam pressure
Turbines thereby open up turbine governors
Reduce feed pump discharge pressures
Reduce heat loss through pipe walls
4. Minimize blowdown
5. Provide accountability
Monitor losses
Recover condensate heat
Recent trends in the control of air-to-fuel ratio are briefly discussed below.
Trends in the Control of Air-to-Fuel Ratio
Improved control of combustion mixture ratio is amongst the most economical
methods for improving efficiency and minimizing pollution from combustion plants.
The link between operating air-to-fuel ratio (AFR), exhaust temperature and
efficiency is well documented. However, there are few available methods for the
monitoring and control of AFR. This number reduces significantly if feedback
control is required based on the AFR at the point of use. The fundamental problem is
the lack of good sensors.
Young et al. (1997) described a peak seeker system involving a sensor that tracks
more closely the mixture ratio deviations, and responds more quickly to changes and
is capable of being targeted at an individual burner. The system is based on the
observation that radiation emitted by all well-mixed flames exhibits a peak on or close
to the point of optimum combustion. This is the case for luminous and non-luminous
flames. Two test furnaces (an 8 k W oil fired furnace and a 27 kW gas furnace) were
commissioned in the Sheffield University laboratories to study the proposed
technique in detail. A dynamic response analysis of the system was done using
perturbation signal analysis. The performance of a furnace control system based on
the peak seeker was explored on the test furnaces using the PC based system. It was
found to work well in the oil furnace with steady and stable response over many tests.
The performance was less satisfactory for the gas furnace. This was in part due to
reduced signavnoiseratio and the location of the control point.
The development of adaptive deadband positioner for improved control of aidgas
ratio was discussed by Grant et al. (1997). The paper outlined a method which
ensures a minimum of flow ratio error whilst preventing undesirable limit cycling
within a computer controlled A.C. valve drive system. The proposed approach may
be realized as a one-shot auto-tuned variable deadband positioner or as a semi-
continuous on-line adaptive deadband positioner scheme. A key feature of the
scheme is the use of an identified nonlinear model in which the model parameter
values are found to be virtually constant over the valve operating range. The
nonlinear model structure is the nonlinear form of the autoregressive moving average
(NARMA) structure. A bilinear multiplicative term was also included to ensure a
satisfactory characterization of the valve. The approach requires that only a modest
estimation routine be employed. This is a significant advantage to achieve realistic
improvements on practical industrial plants.
Seiver (1998) implemented an advanced process control scheme at a resin
manufacturing complex with the goal of minimizing flare fuel gas usage while
maintaining sufficient energy to be in environmental regulatory compliance. Prior to
this implementation, the flare system was controlled manually by plant operators with
no consideration for minimum fuel gas usage. Since its implementation, advanced
process control has saved the plant thousands of dollars in fuel gas costs and also
reduced unnecessary fuel gas emissions (Seiver, 1998). Hazard analysis techniques
were used in developing the control scheme, and an overview of the control method,
the economic evaluation, and the hazard analysis techniques are presented.
Case Studies for Control System Retrofit Guidelines
In 1992, EPRI commissioned a project to examine the influence of new digital control
technology in power plants. This resulted in a comprehensive three-volume report
discussing the key phases of control system retrofits, technical assessment of a digital
control system, and utility-authored case studies focusing on individual retrofit
approaches. The following is a brief extract from the results of the study.
Control & Optimization in Energy Utilization and Environmental Protection
Volume 1 of the guidelines provides a method for identifying, evaluating,
justifying, and implementinga control system retrofit project. This volume will assist
utility engineers in performing a cost-benefit analysis and in establishing criteria for
different upgrade options by using sensitivity analysis. Potential benefits and cost
savings include: (a) net heat rate improvements; (b) increased availability; (c) reduced
startup; (d) enhanced load change response; (e) greater availability of spare parts; and
(f)reduced maintenance labour cost.
Volume 2 contains technical information about software and hardware that can be
used for control system retrofits. This volume describes computerized options for
evaluating performance enhancements and estimating the cost of control system
upgrades and retrofits.
Volume 3 documents utility response to the three-phase retrofit approach. It also
discusses post-retrofit evaluation concerns such as assuring that original project goals
are met, operator acceptance is high, and load maneuverability has increased.
Overall, the case studies show that a new control system can better monitor plant
parameters such as steam pressure, steam temperature, and excess air. In one
particular case, a retrofit resulted in a net heat-rate improvement of 137 BtuflrWh,
with low-load heat-rate improvements as high as 613 BtuflrWh.
Case Studies of Potential Energy Savings
“To optimize the energy utilization in processes (and in pam’cular in the chemical
industry) it is useful to identifi (when possible) process configurations where the
driving forces are uniform and the entropy generation approaches zero as the size of
the plant increases. Such ideal configurations can be used as references and, to
obtain practical plant j b w diagrams, they have to be modijied according to the
peculiar process and the plant, the economical factors, and the safety and
environmental requirements (Bruui et al., 1998).”
The search for the ideal
configuration is particularly easy, and useful, for the separation processes. Such
configurations are strictly related to the useful work for membrane separations, others
to heat supplies and rejections in distillation, evaporation, crystallization, etc. The
authors proposed practical application of these principles for process design using
M.O. Tad6
fundamentals of thermodynamics. It was shown that the procedure reduced the steam
consumption of the Vinyl chloride monomer (VCM) process from 1.65 kgkg of VCM
to 0.8 kgkg of VCM,against an estimated theoretical value of 0.6.
Lee (1997) discussed strategies for the optimization of energy use in the Korean
steel industry. A 4-stage plan was used between 1980 and 1996 involving the
following objectives:
1. l*'stage (1980-1984): less reliance on petroleum
2. 2"dstage (1985-1987):reduction of energy input
3. 3d stage (1988-1991): maximize waste heat recovery
4. 4" stage (1992-1996): maximize energy efficiency
Five strategic issues were recommended for consideration to improve energy
saving technologies in the steel industry, namely:
1. Expansion of the existing energy saving technology
Coke dry quenching, waste heat recovery of sinter cooler, waste heat recovery
for converter gas, pre-heat for scrap, etc.
2. Expansion of pulverized coal injection, use of clean energy
3. Innovative technological development; COREX, near net shape casting
4. Cooperative efforts with the local community
Energy supply to the local community for the purpose of cooling and heating
energy by using waste heat recovery energy from steel works
Maximize usage of wastes such as plastics, and waste oil
Maximize usage of by-products such as slag
5. Tighten technical cooperation or transfer from developed countries to developing
It was noted that one of the future tasks in the steel industry is the shift towards
environmentally Wendly management to ensure competitive advantage over other
substitute materials. Accordingly, both the reduction of input (including energy and
raw material) and the expansion of recycling and reuse would be the most efficient
way to solve energy savings and environmental concerns.
Control C? Optimization in Energy Utilization and Environmental Protection
Performance monitoring is an essential component of today’s IT network system,
therefore when power plants are fully automated all performance decisions are made
by the l’T facility. Performance goals are also changing in the power plant industry.
Performance now means the entire range of constraints faced by a power plant,
namely, environmental and regulatory, safety, and economic. This realization has
prompted the developers of performance monitoring software packages to broaden
their offerings.
Process optimization and control lie where the twin goals of
economic performance and environmental compliance meet.
From humble
beginnings in environmental management, developers of powerful process
optimization and advanced control software packages are now seeking full integration
with the distributed control system and real time cost minimization of the entire plant.
All the above allude to a broader role for process control and optimization in the
power plant industry to ensure optimum use of energy and compliance with
environmental regulations.
Recommendations for fundamental research for process optimization and control
with applicationsin the energylpower industry include the following:
1. Improved process modeling and characterization of specific energylpower plant
process, e.g. boiler, furnace, etc.
2. Development of inferential variables to quantify unmeasurable data, for example,
continuous on-line analyzer for coal is not available nor is there a means to
measure coal flow to individual burners.
3. Development of more accurate and reliable sensors for process variables.
4. Development of maintenance management and diagnostic tool kit, for example,
corrosive environments will benefit from continuous on-line corrosion monitors.
5 . Development of guidelines and procedure for implementation of advanced process
control on specific energylpowerplant process.
Ansari. R.M. and T&, M.O. 1998. Nonlinear Model-Based Multivariable Control of a Debutanizer. J.
Process Control, 8(4), 279-286.
Bruzzi, V., Colaianni, M. and Zanderighi, L. 1998. Energy Savings in Chemical Plants: A Vinyl Chloride
Case History. Energy Convers. Mgm, 39(16-18). 1853-1862.
M.O. Tadi
Edgar,T.M. and Himmelblau, D.M. 1988.Optunizationof Chemical Processes. McGraw-Hill, New Yo&.
EPRI. 1992.Control System Retrofit Guidelines, Pmjects 2710-16,pinal Rcport.
Groscuih, H.M. and KUmmel. R. 1990.Thumceconomics and C& Emissions.Energy, l5(2), 73-80.
Grant, M.P.. Bumham, K.J. and Locken F.P. 1997.Development of a Model-Based Deadband Positioner
for Improved Air/Gas Ratio Control. Measurement & Control,30.104-106.
Kontopoulos. A., Krallis. K., Koukourakis,E.. Dmaxas, N.,Kostis. N.,Broussaud, A. and Guyot, 0. 1997.
A Hybrid,Knowledge-Based System as a Process Control 'Tool' for Improved Energy Efficiency in
Alumina CalciningFurnaces.Applied Thcrmal Engimering, 17 (8-10).935-945.
Lee, J.E. 1997. Energy Optimization in the Steel Jndustry: A Company Casc. Scandi~vianJ. of Met.
(Suppl.), 26,3945.
Liptak, B.G. and Vcnczel, K. 1985.Instrument EngineasHandbook. Chilton: Section 8.2.
Makansi, J. 1998.Plants Gain Confidence in Optimization Software. Power, Septemba/october.59-64.
Malransi, J. 1995.Information Technology for Power Plant Management. Power, June. 41-72.
Makansi, J. 1994.Rocess Control Plays Quiet But Huge Role in CAA compliance. Power, January. 57-60.
Mills,P.M., ZOmaya, A.Y.and Tadt. M.O. 1995.Neuro-Adaptive procesS Control - A Practical Approach,
John Wiley & Sons M.,
Sciva. D.S.1998. Integmting Hazard Analysis into the Implementation of Advanced Process Control.
Process safrty Progress, 17(2), 104-106.
Yokoyama, R., Ito, K. and Yuasa, Y. 1994. Multi-objective Optimal Unit Sizing of Hybrid Power
Generation System Utilizing Photovoltaic and Wind Energy. J. Solar Energy f i g . , 116,167-173.
Young, K.J., Vara-MUAoz. M.C. and Swithdank, J. 1997. Research and Development of a
SpectroscopicallyBascd Mixture Ratio Sensor. Mwuremnr & Control. 30,111-114.
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