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Integrated Process Control, Fault Diagnostics, Process Optimization and Production Planning - Industrial IT
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Copyright!!:> IFAC On-Line Fault Detection and Supervision in the Chemical Process Industries, Jejudo Island, Korea, 200 I
~
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Integrated process control, fault diagnostics, process optimization and production planning - Industrial IT Erik Dahlquist 1,2 ,Tomas Lindberg 1,2, Christer Karlsson I , Malardalen University, Galia Weidl Bigaran 3 Austin Davey 3
l
,2,
Carlo
1Malardalen Univ,72123 Vasteras,Sweden, ,2ABB,72 I67 Vasteras,Sweden, 3 Visy Pulp and Paper,Tumut,Australia ABSTRACT In the presentation a total system is presented, making use of data reconciliation, different types of diagnostics with respect to sensors, control loops and processes. These are used as inputs to a root cause analysis system, optimization and advanced control, using among others MPC, model predictive control. The system is being implemented at Visy Pulp and Paper mill in Tumut, Australia. Copyright © 2001 IFAC Keywords: Control,system,Optimization,Processes,Simulators,Diagnostics 1 INTRODUCTION Information System
Several techniques have been developed to automate process industries, with the final goal to put them into "Auto pilot mode". In a joint development effort between ABB and Visy Pulp and Paper a big step towards this is now being implemented in a real mill. The mill is situated in Tumut, Australia. It is a green field mill with a Kraft fiberline (low solids continuous digester), recovery and power plant, waste line (recycle paper) and a paper machine. The mill will most probably be expanded further later on. Also a number of Universities and Institutes are involved in the R&D and development work, primarily Imperial College in London, Sydney University in Australia and Malardalen University in Vasteras, Sweden.
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2 SYSTEM OVERVIEW The control system consists of field instruments, process controllers with all low-level controls, like PID controls, interlocking etc, but also basic diagnostics. The diagnostics at the base level is e.g. recursive variance analysis of all sensors and PID controls and set points determining if loops are oscillating. Also vibration analysis of all major drives is done to determine e.g. bad bearings etc.
Figure 1. System Overview
The analysis is first of all making use of data reconciliation. For parts of the mill we also use pressure -flow networks calculations, together with the energy and mass balances.
Signals from the DeS are stored in a history database. From this "snap shots" are taken, and analyzed in a separate PC
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The deviation between calculated values and DeS values for the same variables are compared. The variance of the deviation is calculated in a recursive way together with the absolute value. When the absolute value passes a certain limit, a bias is calculated. Also dynamic data reconciliation is performed with the time series.
3 MATHEMATICAL MODELLING For each equipment models are done in gPROMS and VisualStudio environment ( VisC++, VisFortran). Primarily ABB and Malardalen University have developed the mathematical models. The pressure -flow network calculations are done using gPROMS, developed by Imperial College. This environment has the advantage, that we can build one model that is used for steady state and dynamic simulation, as well as both steady state and dynamic optimization. More elaborate calculations are done including energy balances, tiber/particle separations, chemical reactions etc, to get the complete energy and mass balances, both steady state and dynamically.
For the pressure -flow calculations, input is the valve openings, pump speed and known data as pump curves, valve characteristics and geometry of the system. Tuning of the system is done by parameter estimation towards the actual process, with the pressure, flow and temperature meters. The pressures -flow calculations together with the energy balance are used together with mathematical models for the specific pulp and paper equipment, to give a simulation including also reactions, separations, evaporation! condensation etc. These calculations are used also for simulation purposes to run scenarios for off-line and on-line applications. Mass and energy balances are also used for overall optimization, giving set points to the different parts of the mill. Also local optimization is done for the most important parts of the mill, together with advanced controls.
For the modeling, the Navier -Stokes and energy equations are the starting point. From these equations simplitications are done, and characteristics for specific equipment are added. Like for a screen, clogging is calculated as a function of rotary speed, whole/slot size, concentration etc. Separation of fibers in the screen is then calculated for different fiber sizes. In the example below, comparisons are done between model predictions of fiber separation and real measurements for the fiber separation in a screen. In some models we also combine computational fluid dynamics and process simulation, also described in Bezzo et al (2000). Our models with combinations have been done primarily on recovery boilers, bark boilers (CFB), lime kilns and digesters.
The configuration is done in the newly developed ASPECT system, developed by ABB. This is an object-oriented environment, where all different lcind of information is configured for an object, like a valve, a screen, a digester etc. The information is geometric data, product data, links to supplier data base, history of the operations like how many times a valve has opened/closed, connected controls, simulation model, valve characteristics etc. It also includes what type of diagnostics to perform on the equipment, as well as action rules to perform if quick response is needed, to perform root cause analysis or control actions, and to stabilize the process.
Table I: For a 1.4 mm hole screen, assuming 0.7 mm long and 0.025 mm wide fibers the following results can be seen with respect to separation efficiency for different fiOOr sizes, calculated with the model resp. measured using "Fiber master";
By loolcing at one process display, all information can be retrieved for a single object. It is also possible to configure all aspects through one entrance. If you move into a valve, loolcing for geometric dimensions, it will also be possible to proceed directly to another valve, to find the dimensioning data for this, without having to go backwards first. From this new valve, you can proceed looking also at e.g. how many times the valves has been opening/closing, or when it was installed etc. The overall system is presented in Dahlquist et al (1999; 2000).
Qrejl attept SepantioD effic:ieDcy Qfeed 11m2.! ~.5 0.5-1 2-5 mm fiben ExpCalc EIp Calc Exp Calc 0.4 99 74.8 77.1 80.580.6 92.0 91.8 0.7 16.5 89.3 89.0 92.3 93.0 97.6 100
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4 DATA RECONCILIATION AND DIAGNOSTICS
Table 2: For a 0.4 mm slot screen, the separation efficiency measured resp. calculated with the model are seen for different flow rates and reject to accept ratios: Experimental
To check the validity of the process and sensor data, data reconciliation and sequential processing is done (cooperation with Sydney Univ.). This includes checking mass and energy balances. Complementary to this also e.g. drives performance is measured by vibration analysis and specific equipment are analyzed by combining data from different measurements. As an example we can measure the digester, where a combination of kappa number, dissolved lignin, total solids and free alkali of fibers and liquors are used together with pressure, flow and temperature measurements, to diagnose the performance of the digestion of wood chips. These information are used to find out the status of the processes, as well as all major equipment, including sensors, valves, pumps, reactors, evaporators and boilers, separators like filters, screens and paper machines etc. Process knowledge on in this case pulp and paper processes is included in the evaluation of what the root cause of a fault is, together with more general knowledge for behavior of pumps, valves etc.
Calculated Sep efllcieucy
Accept
100
lOO
50
100
lOO
IIml.s
50
Q]ej! Q_feed 0.4 0.19 0.09
0.61 0.51
0.63 0.51
0.33 0.17 0.18 0.12
0.35 0.25 0.18 0.15
As can be seen, the predictions are quite good, except in the extreme points. It shall be noticed that very little measurements have been done in mills with respect to separation for different fiber sizes. Instead it is common to just measure the overall mass balance, without knowing the fiber size (or particle size) distribution of either what is going in or out! This makes it complicated to discuss the accuracy of mathematical models, as the models are compared to a very undefined "reality"! A good mathematical model gives the possibility to really investigate how the process responds to different actions, but it has to be understood, that it is normally very difficult to get "absolute answers" from the process, as also the "absolute values" depend on measurement conditions.
The data reconciliation is principally the method developed by Romagnoli and Sanchez (2000). As a primer to implementation at the green field mill at Visy, the method has been implemented and tested at a Power Plant ( ENA Power, Enkoping, Sweden) on-line.
In the models operational characteristics for practically any kind of operational mode are included. In most cases, there are one or more model versions done, for all equipment. One model is a simple balance with some additional functions, one is a sophisticated model, and sometimes there is also something in-between. The reason for this is that when the focus is on calculation speed, it is better to have a rough model executed very fast, when we want to study the performance of the whole plant. For specific parts of the plant, it is more interesting to run a detailed model. As it is not the full plant, execution speed can be good enough also with the detailed model. Sometime in the future, when computer power is significantly better than today, we can have the detailed equipment models also for complete plants.
A process model is built to structure the dependencies between the variables. A flow matrix is created to model the direction of flow through the model. Variable classification is used to find redundancy in a set of equations. This set of equations can then be isolated and computed, errors can be detected and redundant variables can be corrected. Statistical methods (Chi-test) are used to find systematic errors. The set of equations with redundant variables is examined in sequence to find the most likely variable with an error. The search algorithm works from a hypothesis when it is searching for an error in a variable. The hypothesis can be that there is only one error at every search to correct. A model is made of the economizer in the Enkoping power plant in Enkoping, Sweden. The flue gas channel begins after the boiler and then passes the superheaters, the economizer, air preheater, flue gas condenser, flue gas fan, and
It shall be noticed that the execution speed is real
time or significantly faster than real time, to make it possible to make predictions for the future etc.
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finally the chimney that leads the flue gas out in the atmosphere. In this model the economizer, air preheater, flue gas condenser and flue gas fan is included. The economizer is modeled with complete mass and energy balances. The other components have mass balance and mass concentration balance, but lack energy equations.
preheater there are some leakage of outside air to the flue gas due to the pressure differences. Assume outside air has the composition: X 0, = 0.21 and X N 2 = 0.79. The water in the
flue gases is condensed. The water evaporation energy is used to preheat district heat water. After the flue gas condenser the mass ,.-----, concentration of carbon dioxide and oxygen are measured. Assume that the flue gases are saturated with water at the temperature present after the flue gas condenser.
DistrHeat Water 9
The flow sheet shows the main mass flows and their directions. The set up of the sensors used in the model equations is also included in the flow sheet. Heat transfer is not shown in this flow --, sheet.
..!:::::----'
,.10
4.1 Set up of sensors around the economiser FIO and related equipment Condensate '---------' The sensors are connected to a data control system and can be logged to a data file. Time delay between sensors is not taken into Figure 2. Flue gas cooling at ENA Power Plant consideration because of the steady state condition. Location is referring to the flow sheet To determine the composition of the flue gas, either carbon dioxide or nitrogen must be in figure 2, above. In mass concentrations the first number is the location of the sensor and the measured. Carbon dioxide is measured after the last number is the type of gas in the flue gas. flue gas condenser and can be transferred to the Variables in bold Italics are computed variables. economizer through the flue gas condenser and The gas components are: X2I,X8I= 02;X22 = the air preheater. This is done by setting up H20 ;X23,X83= C02; X24,X84 = N2 equations for the mass concentration of the gases, from the flue gas condenser to the economizer. The flue gas mass flow is measured 4.2 Equations to model the economiser indirectly by measuring the number of revolutions of the flue gas fan. The number of 4.2.1 Energy balance equations revolutions is proportional to the flue gas mass flow through the flue gas fan. The process is Pure energyflow from flue gas to feed water assumed to be in a steady state during the data 2 3 t +t4 t' +t ) reconciliation. There are four components in the U A . ----(1) flue gas from combustion of biomass; oxygen, eco eco ( 2 2 nitrogen, water and carbon dioxide. In the air For short heat exchangers the average there are two components: oxygen and nitrogen. temperature can be used with acceptable Time delay between sensor readings at different accuracy. places in the process is not considered due to steady state. Leakage in mass flows except in the F/uegas air preheater is not considered. 2-4 (2 4) The economizer is a heat exchanger and uses hot m• fg8 • C pJg • t - t - U eco Aeco' flue gas to preheat feed water. Mass concentration of oxygen and water are measured (2) in the flue gas before the gas enters the economizer. The model assumes no leakage of Feed water air or feed water. The air preheater is also a heat exchanger. It transfers heat from the hot flue gases to air before the air enters the combustion. In the air
(" ;,. _" ;,3 )=0
50
,;,1fw (
'Cl-3 p.fw
.(t -t 1
3
)+UeaJ AeaJ .
1 3 2 t +t4 t +t )_ - - - - - - - -0
2
(3)
2
LeaJcage airfrom outside air to the flue gases • 5
mJeakage
Mass balance for flue gas
4.2.2
8 . 8 • (X 02
m fg
Economizer ·4 m/oral
• 2
= m/oral =
·4 m lOraI
+
·5 mleakllge
(5)
,;,8 +,;,10
23
=(
fg
Flue gas condenser • 4
• 5
= m toral + mleakllge -
• 10 mcondensate
leakage.
X
83
(13)
84
(14)
Mass concentration ofnitrogen before the economizer
X
4
24
,;,8 +,;,10
=(
fg
_,;,5:1)
conde1l.'Jate ·8 fg
leakage.
X
(7)
j:\
The four equations above are results of manipulations of the mass balance and mass concentrations in the economizer, air preheater and flue gas condenser. The accuracy and reliability of the water and oxygen mass concentration sensors have not yet been investigated. Subtraction of mass concentrations can be hazardous if the concentrations are either small or near equal. Other equations can be needed to compute air leakage and condensate flow if the above equations are unreliable according to the subtraction of two mass concentrations.
Functions for specific heat coefficients
4.2.4
Flue gas 4
4
j:)
j:1
= LXj 'Cp,i = LX j , f(tj)j
(8)
Water
c p •w = f(t j
)
(9)
where i is the gas component and j is the location 4.2.5
_,;,5:1)
condensate ·8 m fg
m
~ £.J X Jl.. -l=O
Cp,fg
(12)
(6)
General mass concentration equation for flue gas
4.2.3
X2) . 10 X202 02 - m condesate'
Mass concentration ofcarbon dioxide before the economizer
X ·8 m/oral
-
5 _X2 ) (X 02 O2
(4)
Air preheater ·6 m lOral
=
4.3 Setting up the equation system as bilinear balances The variables need to be manipulated to get the to the goal to minimize the errors in the variables. The method described was developed by Crowe. (1986). First the streams are divided into three categories see table 1 below.
Detenninable variables expressed as functions of measurements
Total flue gas massflow through the flue gas fan
';'}g = f(rpm fgf )
(10)
The proportional function between revolutions per minute and flue gas flow can be extracted from the tests done by the manufacturer of the fan. In this case a linear function is computed in Excel to fit the data pairs. Note that the constant is negative ( -0.05) and precautions must be taken to avoid a number of revolutions below 1, which would give a negative flue gas flow.
4.4 Streams in the economiser model The streams are gathered in vectors. The vectors will be manipulated according to the error-invariable-method and after that they can be used in a minimization problem. Measured and computed mass flows (Category 1)
Condensate flow in the flue gas condenser
51
• 2 . 2 mtolill,fg ·X~
I' J ch
=
. 2 m total ./g •
X2
. 2 mtotal./g·
X
• 2 mtotal./g·
X2
Define the following vectors:
H 20 2 CO2
(15)
N2
. I
mtolill,fw
Measured mass flows
~[~:]
fu
(16) (23)
Unmeasured mass flows
fv =0
(17) E is the error-in-variable component.
Unknown pure energyflows (Category 3) (18)
v= qeco
Substitute the above vectors into the former system and we get:
Unmeasured total massflows (Category 2.)
V=O
(19)
4.5 Bilinear form of the equations The component mass! energy balances are written:
BI
fell +B2·V·d +B3'v=O
.
[Du
E4
•
[;,
El
E2
o'l
Bs E8 E3
El .
[fM] fell = e
(21) II'
(
T UI-I
T UI-I
T
T
lV;;n\E flll
after manipulation the above equations can be put together to:
B2
BI ]
0 - [E 4
If the weighting matrices are known the minimization problem can be stated as:
fell +E2 • V·d +E3 'v+ fM + E s . fv = 0
BI
(24)
(20)
The normalization equations are written: E]·
O»l.[~]=
0"
flllEflll +E f ""
TUI-I)
f""E f "" +Ee
T
e Ee
S.t.
fM fell
[Du
0"
o»lH] = (25)
V'E d =0
=>
0 B I ] [fM] - [ E E[ . fell 4
fv v
=e
From here it is later possible to use QR decomposition and reconcile and estimate the variables. The model equations are being verified with a data set from the process at steady state. A program is written to handle the data reconciliation. This is being implemented into the OCSIIMS (Info System) at the power plant during 2 Q, 2001.
= 0 (22)
v • Put in knowledge from the vectors above into the column vector.
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Aside of sensor check, also fouling of heat exchangers is determined as well as the actual feed flow and moisture content of the fuel. This is of importance for the payment of the biomass fuel. This as the price is in $ per kg organic with a certain HHV (Higher heating value), and thus has to be compensated for ash, moisture and uncertain measurements of the weight of the fuel entering the plant on a conveyor.
rules are mainly generic, which yields an incremental and local algorithm. The search propagates along static connections, giving a very fast execution. Both methods have been implemented in 02 (Gensym) environment, the latter also in C-eode and Common Lisp. A third method is Case Based Reasoning. This method presented by e.g. Funck ( 1995), is looking at fault patterns. It is very fast if you have integer values ( I or 0 ), where you search for the pattern of a large number of alarms and/or faults. Each combination of fault messages corresponds to one type of fault. This method has been used by e.g. telephone companies, to diagnose problems in switchboards. Jensen (1996) and Heckerman (1996) have been implementing learning Bayesian networks, to make it possible to adapt the root cause analysis with new faults, and the patterns of the measurements associated with the faults. This has been implemented in the Microsoft software diagnostic systems, as well as in HPs and others. In Asea Brown Boveri (1991) some of these systems are compared. Bakhtazad ( 2000 ) have proposed combination of PCA analysis as a prestep followed by multidimensional wavelet domain hidden Markov trees to detect and classify abnormal process situations. This has a potential complementary function to some of the other methods mentioned. In the ABB Aspect System, the following is implemented as one aspect. After data reconciliation, loop diagnostics, vibration analysis, fault development, alarms, etc., a fuzzyneuro preprocessor is grouping the information, before sending it to an adaptive Baeyesian network. Here detection of upcoming process, sensors and equipment problems is done. Also manual or automatic inputs from operators or maintenance people are included.
Also diagnostics with respect to loop and sensor performance using different indices is done. A poorly tuned PlO loop can be detected, and retuned. Different indices can be used to detect poorly performing loops. There are a number of different indices implemented for both loops and sensors, to detect problems. At Visy Tumut mill, data reconciliation will be implemented all over the plant. This includes more than 2000 signals from sensors and drives, and 400 control loops. 5 ROOT CAUSE ANALYSIS Several methods have been used for alarm filtering, fault detection and isolation and root cause analysis. Leung and Romagnoli (1999) have used a method with Bayesian belief networks with a recursive depth-first search from each alarm node to calculate probability. The system is used together with a rule -based system to detect and temporarily delete cyclic loops on-line. This gives a fault diagnostics from a number of alarms. Before presenting the probability of different faults, a "delay creation" algorithm is used to look for time delays, to filter out secondary alarms in relation to primary alarms. Larsson (\994) and Lind (1991) have taken another approach using Multilevel Flow Models (MFM), for alarm analysis, fault diagnosis and data reconciliation. The equipment and components are configured in a network, with flow in-between the components. Comparison is done between "actual state" and "normal state". When the state is not normal, there is a fault indicated. This is analyzed using a recursive depth-first search. Alarm filtering is done by a rule-based system. Selecting a goal for the diagnosis starts the search. This can be for the whole model, or part of it The search propagates downwards from the goal, via achieve relations, into connected network of flow functions, each having a rule or a diagnostic question. An appropriate alarm state is set. Functions that are working are skipped, while the ones having a fault are further analyzed. The
When a probable fault is diagnosed, a message is sent to both the operator station and the maintenance system. The maintenance system, in this case from IFS, acknowledge the fault message, with respect to what is the probable fault, and send a request to the service staff to fix. This message is sent to first one service man, who verifies if he can fix it or not, in the requested time frame. If he can not, the system sends the request to the next service man, etc. When the serviceman come to site, he knows from the fault message what is the most probable root cause. By going into the aspect system he can find out data about the equipment to fix.
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These data are updated by the supplier, and found by the direct link to the suppliers web site. Also the actual data of the original equipment is stored. From this information purchase and ordering of spares is done in an easy way.
scenarios are run using a gPROMS model for specific parts of the plant, to verify the dynamic pass. If a limit like overflow in a vessel is reached, back off is done, so that constraints are fulfilled.
If there a re several possible faults, that can give a certain problem picture, the service man also has other possible faults listed. If there is still a fault after checking the most probable fault, he goes to the next etc.
If something suddenly happens, actions are made using advanced control packages for the different parts of the plant, to stabilize operations, until a new pass has been calculated for optimal operation. This is the third level for the optimization. The system described above is being implemented in the Visy Tumut mill in several steps. In the first all principle functions are implemented, but not for all equipment, but a limited number, to test robustness. This is done from mid 200 1. After this, additional parts of the plant are included successively the next few years. In Dahlquist et al. (1999) we describe the soft sensor concept for paper quality prediction implemented at ASSI Dynas ,Kramfors, Sweden. Here a PLS model has been used. Also PCA models have been used on the same data sets. A comparison between model predictions and lab measurements during 4 month is shown in the figure below.
Statistics on fault predictions and fit to real faults is stored, and used in a statistical way, to increase the prediction reliability. This statistics is also stored under each type of object, in the ASPECT system. Valves of the same type are carrying the same information, but also statistics for history of a specific valve etc is stored. In the root cause analysis also process diagnostics is included, like channeling in the digester, screws not filling, screens clogged etc. Quality aspects, like paper strength or pulp kappa, is determined using special sensors ( kappa, NIR,fiber size distribution, paper strength measuring from robot made paper sheet etc), together with "normal" measurements like pressure, flow,temperaure, consistency etc in the process, as well as operational variables like wire speed of the paper machine.
Time SnW PIoC
6 ADVANCED CONTROL AND OPTIMlZAnON
-A "·8
After diagnostics has been performed, also sensor signals of poor sensors are replaced by calculated ( adjusted ) measurements. These are used in the controls, both conventional and advanced Multivariable control of the type MPC (e.g. ASS's 3d-MPC, which can handle up to 32 simultaneous inputs, and 16 outputs). As an example of results using MPC instead of normal PlO ,NOx was reduced by 50 % in a bark boiler at a Swedish pulp mill. Simultaneously the power output was optimized. The 3d-MPC is using a model, and an optimization algorithm, coordinating the different variables to each other in an optimal way.
Figure 3. Predicted values of tear index (dotted line) compared to lab measurements of final paper (solid line) during a four-month period In Pettersson (1998) a gray box model is used for predictions of the stability in a three layered paper box sheet The total concept is named" Industrial IT.., and implementations are being done also in other chemical industries, as well as for oil and gas processes, power plants and metal and mineral
The reconciled data are also used together with overall production goals to do optimization. This is done in three steps. First an overall plan is made for a month ahead. From this schedule, the actual plant status is taken into account for producing set points to the different parts of the plant, for the next hours. In the next level
54
industries etc. In Hess ( 2000 ) optimization of a gasoline blender using gPROMS in the Industrial IT environment is described. 7
simulation, Computers and Engineering 24, p 653-658.
Crowe C.M.. (1986) "Reconciliation of process flow rates by matrix projection". Part Ill: The nonlinear case. AIChE J. 32, pages 616-623.
CONCLUSIONS
The future in process control will involve all steps from sensor measurements, DeS control on several levels, advanced control using multivariable controls, soft sensor measurements and special sensors for quality control, data reconciliation, signal and loop diagnostics, root cause analysis and optimization on several levels, both steady state and dynamic. Integration will be towards both maintenance and business systems. Mathematical models will be used for many purposes, and on-line simulations to test different scenarios, will be used.
Dahlquist E., H. Ekwall, J Lindberg, S Sundstrom, T. Liljenberg, S. Backa (1999): Online characterization of pulp - Stock preparation department, SPCI in Stockholm June 1999. Dahlquist E., H Ekwall, (1999):Dynamic Process Simulators for Multipurpose use in Pulp and Paper Industry, INTERKAMA-ISA TECH Conference in Dusseldorf 18-20 October 1999 Dahlquist E., R. Horton, A. Davey, C-F Lindberg (2000): Smart Enterprise™ - Online simulation for optimization, diagnostics and advanced model based control for Pulp & Paper industry, AIChE in Atlanta, March 2000.
Notations:
m
Cp
=
mass flow
(kg/s)
=
specific heat (kJ/kg,K)
temperature (OC) 2 A = area (m ) U =overall heat transfet coefficient (W/m2,K) = mass fraction (kg/kg) X Subscripts : fg = flue gas fgc =flue gas condenser = flue gas fan fgf fw = feed water = water w Superscripts: I .. 11 = Stream numbers 1-3 = Property valued as average between location I and 3 t
Chemical
=
Funck P. Univ.
(1995). PhD thesis, Edinburgh
Heckerman D. (1996): A tutorial on learning with Bayesian networks. Microsoft Research tech report ,MSR-TR-95-06 Hess T. (2000): Process optimization with dynamic modeling offers big benefits, I&CS,August, p 43-48. Jensen F. (1996), An introduction Bayesian Networks, VCL Press.
to
Larsson J-E. (1994): Diagnostic reasoning strategies for means-end models.Automatica, 30(5)
8 REFERENCES:
Leung D. ,J. Romagnoli (1999) :Intelligent Alarm Filtering- A dynamic Approach, Computers and Chemical Engineering Supplement, p605-608
Asea Brown Boveri,Satt Control and Opt of Automatic Control, Lunds Univ (1991): Knowledge-Based Real Time Control Systems- IT4 project, Studentlitteratur.
Lind M. (1991): On the modeling of diagnostic tasks. Proceedings of the third European conference on cognitive science approaches to process control, Cardiff, Wales
Bakhtazad A. , A. Palazoglu, J. A. Romagnoli (2000): Detection and classification of abnormal process sitauations using multidimensional wavelet domain hidden Markov trees,Computers and Chemical Engineering 24 ,p 769-775
(1998): On Model Based Pettersson J. Estimation of Quality Variables for Paper Manufacturing. Tech LicThesis , KTH
Bezzo F. ,S. Macchietto, C.C. Pantelides (2000): A general framework for integration of computational fluid dynamics and process
Romagnoli J., M. C. Sanchez (2000) : Data Processing and Reconsiliation for Chemical Process Operations, Academic Press.
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Copyright!!:> IFAC On-Line Fault Detection and Supervision in the Chemical Process Industries, Jejudo Island, Korea, 200 I
~
Publications www.elsevier.com/locate/ifac
Integrated process control, fault diagnostics, process optimization and production planning - Industrial IT Erik Dahlquist 1,2 ,Tomas Lindberg 1,2, Christer Karlsson I , Malardalen University, Galia Weidl Bigaran 3 Austin Davey 3
l
,2,
Carlo
1Malardalen Univ,72123 Vasteras,Sweden, ,2ABB,72 I67 Vasteras,Sweden, 3 Visy Pulp and Paper,Tumut,Australia ABSTRACT In the presentation a total system is presented, making use of data reconciliation, different types of diagnostics with respect to sensors, control loops and processes. These are used as inputs to a root cause analysis system, optimization and advanced control, using among others MPC, model predictive control. The system is being implemented at Visy Pulp and Paper mill in Tumut, Australia. Copyright © 2001 IFAC Keywords: Control,system,Optimization,Processes,Simulators,Diagnostics 1 INTRODUCTION Information System
Several techniques have been developed to automate process industries, with the final goal to put them into "Auto pilot mode". In a joint development effort between ABB and Visy Pulp and Paper a big step towards this is now being implemented in a real mill. The mill is situated in Tumut, Australia. It is a green field mill with a Kraft fiberline (low solids continuous digester), recovery and power plant, waste line (recycle paper) and a paper machine. The mill will most probably be expanded further later on. Also a number of Universities and Institutes are involved in the R&D and development work, primarily Imperial College in London, Sydney University in Australia and Malardalen University in Vasteras, Sweden.
Customer Order
Internet
2 SYSTEM OVERVIEW The control system consists of field instruments, process controllers with all low-level controls, like PID controls, interlocking etc, but also basic diagnostics. The diagnostics at the base level is e.g. recursive variance analysis of all sensors and PID controls and set points determining if loops are oscillating. Also vibration analysis of all major drives is done to determine e.g. bad bearings etc.
Figure 1. System Overview
The analysis is first of all making use of data reconciliation. For parts of the mill we also use pressure -flow networks calculations, together with the energy and mass balances.
Signals from the DeS are stored in a history database. From this "snap shots" are taken, and analyzed in a separate PC
47
The deviation between calculated values and DeS values for the same variables are compared. The variance of the deviation is calculated in a recursive way together with the absolute value. When the absolute value passes a certain limit, a bias is calculated. Also dynamic data reconciliation is performed with the time series.
3 MATHEMATICAL MODELLING For each equipment models are done in gPROMS and VisualStudio environment ( VisC++, VisFortran). Primarily ABB and Malardalen University have developed the mathematical models. The pressure -flow network calculations are done using gPROMS, developed by Imperial College. This environment has the advantage, that we can build one model that is used for steady state and dynamic simulation, as well as both steady state and dynamic optimization. More elaborate calculations are done including energy balances, tiber/particle separations, chemical reactions etc, to get the complete energy and mass balances, both steady state and dynamically.
For the pressure -flow calculations, input is the valve openings, pump speed and known data as pump curves, valve characteristics and geometry of the system. Tuning of the system is done by parameter estimation towards the actual process, with the pressure, flow and temperature meters. The pressures -flow calculations together with the energy balance are used together with mathematical models for the specific pulp and paper equipment, to give a simulation including also reactions, separations, evaporation! condensation etc. These calculations are used also for simulation purposes to run scenarios for off-line and on-line applications. Mass and energy balances are also used for overall optimization, giving set points to the different parts of the mill. Also local optimization is done for the most important parts of the mill, together with advanced controls.
For the modeling, the Navier -Stokes and energy equations are the starting point. From these equations simplitications are done, and characteristics for specific equipment are added. Like for a screen, clogging is calculated as a function of rotary speed, whole/slot size, concentration etc. Separation of fibers in the screen is then calculated for different fiber sizes. In the example below, comparisons are done between model predictions of fiber separation and real measurements for the fiber separation in a screen. In some models we also combine computational fluid dynamics and process simulation, also described in Bezzo et al (2000). Our models with combinations have been done primarily on recovery boilers, bark boilers (CFB), lime kilns and digesters.
The configuration is done in the newly developed ASPECT system, developed by ABB. This is an object-oriented environment, where all different lcind of information is configured for an object, like a valve, a screen, a digester etc. The information is geometric data, product data, links to supplier data base, history of the operations like how many times a valve has opened/closed, connected controls, simulation model, valve characteristics etc. It also includes what type of diagnostics to perform on the equipment, as well as action rules to perform if quick response is needed, to perform root cause analysis or control actions, and to stabilize the process.
Table I: For a 1.4 mm hole screen, assuming 0.7 mm long and 0.025 mm wide fibers the following results can be seen with respect to separation efficiency for different fiOOr sizes, calculated with the model resp. measured using "Fiber master";
By loolcing at one process display, all information can be retrieved for a single object. It is also possible to configure all aspects through one entrance. If you move into a valve, loolcing for geometric dimensions, it will also be possible to proceed directly to another valve, to find the dimensioning data for this, without having to go backwards first. From this new valve, you can proceed looking also at e.g. how many times the valves has been opening/closing, or when it was installed etc. The overall system is presented in Dahlquist et al (1999; 2000).
Qrejl attept SepantioD effic:ieDcy Qfeed 11m2.! ~.5 0.5-1 2-5 mm fiben ExpCalc EIp Calc Exp Calc 0.4 99 74.8 77.1 80.580.6 92.0 91.8 0.7 16.5 89.3 89.0 92.3 93.0 97.6 100
48
4 DATA RECONCILIATION AND DIAGNOSTICS
Table 2: For a 0.4 mm slot screen, the separation efficiency measured resp. calculated with the model are seen for different flow rates and reject to accept ratios: Experimental
To check the validity of the process and sensor data, data reconciliation and sequential processing is done (cooperation with Sydney Univ.). This includes checking mass and energy balances. Complementary to this also e.g. drives performance is measured by vibration analysis and specific equipment are analyzed by combining data from different measurements. As an example we can measure the digester, where a combination of kappa number, dissolved lignin, total solids and free alkali of fibers and liquors are used together with pressure, flow and temperature measurements, to diagnose the performance of the digestion of wood chips. These information are used to find out the status of the processes, as well as all major equipment, including sensors, valves, pumps, reactors, evaporators and boilers, separators like filters, screens and paper machines etc. Process knowledge on in this case pulp and paper processes is included in the evaluation of what the root cause of a fault is, together with more general knowledge for behavior of pumps, valves etc.
Calculated Sep efllcieucy
Accept
100
lOO
50
100
lOO
IIml.s
50
Q]ej! Q_feed 0.4 0.19 0.09
0.61 0.51
0.63 0.51
0.33 0.17 0.18 0.12
0.35 0.25 0.18 0.15
As can be seen, the predictions are quite good, except in the extreme points. It shall be noticed that very little measurements have been done in mills with respect to separation for different fiber sizes. Instead it is common to just measure the overall mass balance, without knowing the fiber size (or particle size) distribution of either what is going in or out! This makes it complicated to discuss the accuracy of mathematical models, as the models are compared to a very undefined "reality"! A good mathematical model gives the possibility to really investigate how the process responds to different actions, but it has to be understood, that it is normally very difficult to get "absolute answers" from the process, as also the "absolute values" depend on measurement conditions.
The data reconciliation is principally the method developed by Romagnoli and Sanchez (2000). As a primer to implementation at the green field mill at Visy, the method has been implemented and tested at a Power Plant ( ENA Power, Enkoping, Sweden) on-line.
In the models operational characteristics for practically any kind of operational mode are included. In most cases, there are one or more model versions done, for all equipment. One model is a simple balance with some additional functions, one is a sophisticated model, and sometimes there is also something in-between. The reason for this is that when the focus is on calculation speed, it is better to have a rough model executed very fast, when we want to study the performance of the whole plant. For specific parts of the plant, it is more interesting to run a detailed model. As it is not the full plant, execution speed can be good enough also with the detailed model. Sometime in the future, when computer power is significantly better than today, we can have the detailed equipment models also for complete plants.
A process model is built to structure the dependencies between the variables. A flow matrix is created to model the direction of flow through the model. Variable classification is used to find redundancy in a set of equations. This set of equations can then be isolated and computed, errors can be detected and redundant variables can be corrected. Statistical methods (Chi-test) are used to find systematic errors. The set of equations with redundant variables is examined in sequence to find the most likely variable with an error. The search algorithm works from a hypothesis when it is searching for an error in a variable. The hypothesis can be that there is only one error at every search to correct. A model is made of the economizer in the Enkoping power plant in Enkoping, Sweden. The flue gas channel begins after the boiler and then passes the superheaters, the economizer, air preheater, flue gas condenser, flue gas fan, and
It shall be noticed that the execution speed is real
time or significantly faster than real time, to make it possible to make predictions for the future etc.
49
finally the chimney that leads the flue gas out in the atmosphere. In this model the economizer, air preheater, flue gas condenser and flue gas fan is included. The economizer is modeled with complete mass and energy balances. The other components have mass balance and mass concentration balance, but lack energy equations.
preheater there are some leakage of outside air to the flue gas due to the pressure differences. Assume outside air has the composition: X 0, = 0.21 and X N 2 = 0.79. The water in the
flue gases is condensed. The water evaporation energy is used to preheat district heat water. After the flue gas condenser the mass ,.-----, concentration of carbon dioxide and oxygen are measured. Assume that the flue gases are saturated with water at the temperature present after the flue gas condenser.
DistrHeat Water 9
The flow sheet shows the main mass flows and their directions. The set up of the sensors used in the model equations is also included in the flow sheet. Heat transfer is not shown in this flow --, sheet.
..!:::::----'
,.10
4.1 Set up of sensors around the economiser FIO and related equipment Condensate '---------' The sensors are connected to a data control system and can be logged to a data file. Time delay between sensors is not taken into Figure 2. Flue gas cooling at ENA Power Plant consideration because of the steady state condition. Location is referring to the flow sheet To determine the composition of the flue gas, either carbon dioxide or nitrogen must be in figure 2, above. In mass concentrations the first number is the location of the sensor and the measured. Carbon dioxide is measured after the last number is the type of gas in the flue gas. flue gas condenser and can be transferred to the Variables in bold Italics are computed variables. economizer through the flue gas condenser and The gas components are: X2I,X8I= 02;X22 = the air preheater. This is done by setting up H20 ;X23,X83= C02; X24,X84 = N2 equations for the mass concentration of the gases, from the flue gas condenser to the economizer. The flue gas mass flow is measured 4.2 Equations to model the economiser indirectly by measuring the number of revolutions of the flue gas fan. The number of 4.2.1 Energy balance equations revolutions is proportional to the flue gas mass flow through the flue gas fan. The process is Pure energyflow from flue gas to feed water assumed to be in a steady state during the data 2 3 t +t4 t' +t ) reconciliation. There are four components in the U A . ----(1) flue gas from combustion of biomass; oxygen, eco eco ( 2 2 nitrogen, water and carbon dioxide. In the air For short heat exchangers the average there are two components: oxygen and nitrogen. temperature can be used with acceptable Time delay between sensor readings at different accuracy. places in the process is not considered due to steady state. Leakage in mass flows except in the F/uegas air preheater is not considered. 2-4 (2 4) The economizer is a heat exchanger and uses hot m• fg8 • C pJg • t - t - U eco Aeco' flue gas to preheat feed water. Mass concentration of oxygen and water are measured (2) in the flue gas before the gas enters the economizer. The model assumes no leakage of Feed water air or feed water. The air preheater is also a heat exchanger. It transfers heat from the hot flue gases to air before the air enters the combustion. In the air
(" ;,. _" ;,3 )=0
50
,;,1fw (
'Cl-3 p.fw
.(t -t 1
3
)+UeaJ AeaJ .
1 3 2 t +t4 t +t )_ - - - - - - - -0
2
(3)
2
LeaJcage airfrom outside air to the flue gases • 5
mJeakage
Mass balance for flue gas
4.2.2
8 . 8 • (X 02
m fg
Economizer ·4 m/oral
• 2
= m/oral =
·4 m lOraI
+
·5 mleakllge
(5)
,;,8 +,;,10
23
=(
fg
Flue gas condenser • 4
• 5
= m toral + mleakllge -
• 10 mcondensate
leakage.
X
83
(13)
84
(14)
Mass concentration ofnitrogen before the economizer
X
4
24
,;,8 +,;,10
=(
fg
_,;,5:1)
conde1l.'Jate ·8 fg
leakage.
X
(7)
j:\
The four equations above are results of manipulations of the mass balance and mass concentrations in the economizer, air preheater and flue gas condenser. The accuracy and reliability of the water and oxygen mass concentration sensors have not yet been investigated. Subtraction of mass concentrations can be hazardous if the concentrations are either small or near equal. Other equations can be needed to compute air leakage and condensate flow if the above equations are unreliable according to the subtraction of two mass concentrations.
Functions for specific heat coefficients
4.2.4
Flue gas 4
4
j:)
j:1
= LXj 'Cp,i = LX j , f(tj)j
(8)
Water
c p •w = f(t j
)
(9)
where i is the gas component and j is the location 4.2.5
_,;,5:1)
condensate ·8 m fg
m
~ £.J X Jl.. -l=O
Cp,fg
(12)
(6)
General mass concentration equation for flue gas
4.2.3
X2) . 10 X202 02 - m condesate'
Mass concentration ofcarbon dioxide before the economizer
X ·8 m/oral
-
5 _X2 ) (X 02 O2
(4)
Air preheater ·6 m lOral
=
4.3 Setting up the equation system as bilinear balances The variables need to be manipulated to get the to the goal to minimize the errors in the variables. The method described was developed by Crowe. (1986). First the streams are divided into three categories see table 1 below.
Detenninable variables expressed as functions of measurements
Total flue gas massflow through the flue gas fan
';'}g = f(rpm fgf )
(10)
The proportional function between revolutions per minute and flue gas flow can be extracted from the tests done by the manufacturer of the fan. In this case a linear function is computed in Excel to fit the data pairs. Note that the constant is negative ( -0.05) and precautions must be taken to avoid a number of revolutions below 1, which would give a negative flue gas flow.
4.4 Streams in the economiser model The streams are gathered in vectors. The vectors will be manipulated according to the error-invariable-method and after that they can be used in a minimization problem. Measured and computed mass flows (Category 1)
Condensate flow in the flue gas condenser
51
• 2 . 2 mtolill,fg ·X~
I' J ch
=
. 2 m total ./g •
X2
. 2 mtotal./g·
X
• 2 mtotal./g·
X2
Define the following vectors:
H 20 2 CO2
(15)
N2
. I
mtolill,fw
Measured mass flows
~[~:]
fu
(16) (23)
Unmeasured mass flows
fv =0
(17) E is the error-in-variable component.
Unknown pure energyflows (Category 3) (18)
v= qeco
Substitute the above vectors into the former system and we get:
Unmeasured total massflows (Category 2.)
V=O
(19)
4.5 Bilinear form of the equations The component mass! energy balances are written:
BI
fell +B2·V·d +B3'v=O
.
[Du
E4
•
[;,
El
E2
o'l
Bs E8 E3
El .
[fM] fell = e
(21) II'
(
T UI-I
T UI-I
T
T
lV;;n\E flll
after manipulation the above equations can be put together to:
B2
BI ]
0 - [E 4
If the weighting matrices are known the minimization problem can be stated as:
fell +E2 • V·d +E3 'v+ fM + E s . fv = 0
BI
(24)
(20)
The normalization equations are written: E]·
O»l.[~]=
0"
flllEflll +E f ""
TUI-I)
f""E f "" +Ee
T
e Ee
S.t.
fM fell
[Du
0"
o»lH] = (25)
V'E d =0
=>
0 B I ] [fM] - [ E E[ . fell 4
fv v
=e
From here it is later possible to use QR decomposition and reconcile and estimate the variables. The model equations are being verified with a data set from the process at steady state. A program is written to handle the data reconciliation. This is being implemented into the OCSIIMS (Info System) at the power plant during 2 Q, 2001.
= 0 (22)
v • Put in knowledge from the vectors above into the column vector.
52
Aside of sensor check, also fouling of heat exchangers is determined as well as the actual feed flow and moisture content of the fuel. This is of importance for the payment of the biomass fuel. This as the price is in $ per kg organic with a certain HHV (Higher heating value), and thus has to be compensated for ash, moisture and uncertain measurements of the weight of the fuel entering the plant on a conveyor.
rules are mainly generic, which yields an incremental and local algorithm. The search propagates along static connections, giving a very fast execution. Both methods have been implemented in 02 (Gensym) environment, the latter also in C-eode and Common Lisp. A third method is Case Based Reasoning. This method presented by e.g. Funck ( 1995), is looking at fault patterns. It is very fast if you have integer values ( I or 0 ), where you search for the pattern of a large number of alarms and/or faults. Each combination of fault messages corresponds to one type of fault. This method has been used by e.g. telephone companies, to diagnose problems in switchboards. Jensen (1996) and Heckerman (1996) have been implementing learning Bayesian networks, to make it possible to adapt the root cause analysis with new faults, and the patterns of the measurements associated with the faults. This has been implemented in the Microsoft software diagnostic systems, as well as in HPs and others. In Asea Brown Boveri (1991) some of these systems are compared. Bakhtazad ( 2000 ) have proposed combination of PCA analysis as a prestep followed by multidimensional wavelet domain hidden Markov trees to detect and classify abnormal process situations. This has a potential complementary function to some of the other methods mentioned. In the ABB Aspect System, the following is implemented as one aspect. After data reconciliation, loop diagnostics, vibration analysis, fault development, alarms, etc., a fuzzyneuro preprocessor is grouping the information, before sending it to an adaptive Baeyesian network. Here detection of upcoming process, sensors and equipment problems is done. Also manual or automatic inputs from operators or maintenance people are included.
Also diagnostics with respect to loop and sensor performance using different indices is done. A poorly tuned PlO loop can be detected, and retuned. Different indices can be used to detect poorly performing loops. There are a number of different indices implemented for both loops and sensors, to detect problems. At Visy Tumut mill, data reconciliation will be implemented all over the plant. This includes more than 2000 signals from sensors and drives, and 400 control loops. 5 ROOT CAUSE ANALYSIS Several methods have been used for alarm filtering, fault detection and isolation and root cause analysis. Leung and Romagnoli (1999) have used a method with Bayesian belief networks with a recursive depth-first search from each alarm node to calculate probability. The system is used together with a rule -based system to detect and temporarily delete cyclic loops on-line. This gives a fault diagnostics from a number of alarms. Before presenting the probability of different faults, a "delay creation" algorithm is used to look for time delays, to filter out secondary alarms in relation to primary alarms. Larsson (\994) and Lind (1991) have taken another approach using Multilevel Flow Models (MFM), for alarm analysis, fault diagnosis and data reconciliation. The equipment and components are configured in a network, with flow in-between the components. Comparison is done between "actual state" and "normal state". When the state is not normal, there is a fault indicated. This is analyzed using a recursive depth-first search. Alarm filtering is done by a rule-based system. Selecting a goal for the diagnosis starts the search. This can be for the whole model, or part of it The search propagates downwards from the goal, via achieve relations, into connected network of flow functions, each having a rule or a diagnostic question. An appropriate alarm state is set. Functions that are working are skipped, while the ones having a fault are further analyzed. The
When a probable fault is diagnosed, a message is sent to both the operator station and the maintenance system. The maintenance system, in this case from IFS, acknowledge the fault message, with respect to what is the probable fault, and send a request to the service staff to fix. This message is sent to first one service man, who verifies if he can fix it or not, in the requested time frame. If he can not, the system sends the request to the next service man, etc. When the serviceman come to site, he knows from the fault message what is the most probable root cause. By going into the aspect system he can find out data about the equipment to fix.
53
These data are updated by the supplier, and found by the direct link to the suppliers web site. Also the actual data of the original equipment is stored. From this information purchase and ordering of spares is done in an easy way.
scenarios are run using a gPROMS model for specific parts of the plant, to verify the dynamic pass. If a limit like overflow in a vessel is reached, back off is done, so that constraints are fulfilled.
If there a re several possible faults, that can give a certain problem picture, the service man also has other possible faults listed. If there is still a fault after checking the most probable fault, he goes to the next etc.
If something suddenly happens, actions are made using advanced control packages for the different parts of the plant, to stabilize operations, until a new pass has been calculated for optimal operation. This is the third level for the optimization. The system described above is being implemented in the Visy Tumut mill in several steps. In the first all principle functions are implemented, but not for all equipment, but a limited number, to test robustness. This is done from mid 200 1. After this, additional parts of the plant are included successively the next few years. In Dahlquist et al. (1999) we describe the soft sensor concept for paper quality prediction implemented at ASSI Dynas ,Kramfors, Sweden. Here a PLS model has been used. Also PCA models have been used on the same data sets. A comparison between model predictions and lab measurements during 4 month is shown in the figure below.
Statistics on fault predictions and fit to real faults is stored, and used in a statistical way, to increase the prediction reliability. This statistics is also stored under each type of object, in the ASPECT system. Valves of the same type are carrying the same information, but also statistics for history of a specific valve etc is stored. In the root cause analysis also process diagnostics is included, like channeling in the digester, screws not filling, screens clogged etc. Quality aspects, like paper strength or pulp kappa, is determined using special sensors ( kappa, NIR,fiber size distribution, paper strength measuring from robot made paper sheet etc), together with "normal" measurements like pressure, flow,temperaure, consistency etc in the process, as well as operational variables like wire speed of the paper machine.
Time SnW PIoC
6 ADVANCED CONTROL AND OPTIMlZAnON
-A "·8
After diagnostics has been performed, also sensor signals of poor sensors are replaced by calculated ( adjusted ) measurements. These are used in the controls, both conventional and advanced Multivariable control of the type MPC (e.g. ASS's 3d-MPC, which can handle up to 32 simultaneous inputs, and 16 outputs). As an example of results using MPC instead of normal PlO ,NOx was reduced by 50 % in a bark boiler at a Swedish pulp mill. Simultaneously the power output was optimized. The 3d-MPC is using a model, and an optimization algorithm, coordinating the different variables to each other in an optimal way.
Figure 3. Predicted values of tear index (dotted line) compared to lab measurements of final paper (solid line) during a four-month period In Pettersson (1998) a gray box model is used for predictions of the stability in a three layered paper box sheet The total concept is named" Industrial IT.., and implementations are being done also in other chemical industries, as well as for oil and gas processes, power plants and metal and mineral
The reconciled data are also used together with overall production goals to do optimization. This is done in three steps. First an overall plan is made for a month ahead. From this schedule, the actual plant status is taken into account for producing set points to the different parts of the plant, for the next hours. In the next level
54
industries etc. In Hess ( 2000 ) optimization of a gasoline blender using gPROMS in the Industrial IT environment is described. 7
simulation, Computers and Engineering 24, p 653-658.
Crowe C.M.. (1986) "Reconciliation of process flow rates by matrix projection". Part Ill: The nonlinear case. AIChE J. 32, pages 616-623.
CONCLUSIONS
The future in process control will involve all steps from sensor measurements, DeS control on several levels, advanced control using multivariable controls, soft sensor measurements and special sensors for quality control, data reconciliation, signal and loop diagnostics, root cause analysis and optimization on several levels, both steady state and dynamic. Integration will be towards both maintenance and business systems. Mathematical models will be used for many purposes, and on-line simulations to test different scenarios, will be used.
Dahlquist E., H. Ekwall, J Lindberg, S Sundstrom, T. Liljenberg, S. Backa (1999): Online characterization of pulp - Stock preparation department, SPCI in Stockholm June 1999. Dahlquist E., H Ekwall, (1999):Dynamic Process Simulators for Multipurpose use in Pulp and Paper Industry, INTERKAMA-ISA TECH Conference in Dusseldorf 18-20 October 1999 Dahlquist E., R. Horton, A. Davey, C-F Lindberg (2000): Smart Enterprise™ - Online simulation for optimization, diagnostics and advanced model based control for Pulp & Paper industry, AIChE in Atlanta, March 2000.
Notations:
m
Cp
=
mass flow
(kg/s)
=
specific heat (kJ/kg,K)
temperature (OC) 2 A = area (m ) U =overall heat transfet coefficient (W/m2,K) = mass fraction (kg/kg) X Subscripts : fg = flue gas fgc =flue gas condenser = flue gas fan fgf fw = feed water = water w Superscripts: I .. 11 = Stream numbers 1-3 = Property valued as average between location I and 3 t
Chemical
=
Funck P. Univ.
(1995). PhD thesis, Edinburgh
Heckerman D. (1996): A tutorial on learning with Bayesian networks. Microsoft Research tech report ,MSR-TR-95-06 Hess T. (2000): Process optimization with dynamic modeling offers big benefits, I&CS,August, p 43-48. Jensen F. (1996), An introduction Bayesian Networks, VCL Press.
to
Larsson J-E. (1994): Diagnostic reasoning strategies for means-end models.Automatica, 30(5)
8 REFERENCES:
Leung D. ,J. Romagnoli (1999) :Intelligent Alarm Filtering- A dynamic Approach, Computers and Chemical Engineering Supplement, p605-608
Asea Brown Boveri,Satt Control and Opt of Automatic Control, Lunds Univ (1991): Knowledge-Based Real Time Control Systems- IT4 project, Studentlitteratur.
Lind M. (1991): On the modeling of diagnostic tasks. Proceedings of the third European conference on cognitive science approaches to process control, Cardiff, Wales
Bakhtazad A. , A. Palazoglu, J. A. Romagnoli (2000): Detection and classification of abnormal process sitauations using multidimensional wavelet domain hidden Markov trees,Computers and Chemical Engineering 24 ,p 769-775
(1998): On Model Based Pettersson J. Estimation of Quality Variables for Paper Manufacturing. Tech LicThesis , KTH
Bezzo F. ,S. Macchietto, C.C. Pantelides (2000): A general framework for integration of computational fluid dynamics and process
Romagnoli J., M. C. Sanchez (2000) : Data Processing and Reconsiliation for Chemical Process Operations, Academic Press.
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