Dev. Chem. Eng. Mineral Process. 13(3/4), pp. 1-10, 2005. 1 Real-time Optimization of Acetaldehyde Production Process Shao Zhijiang*, Wang Jinlin and Qian Jixin Institute of Systems Engineering, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, P.R. China A real-time optimization (RTO) system of an acetaldehyde production process is presented in this paper. Data reconciliation with steady state detection and gross error detection, soft sensoring of coarse acetaldehyde concentration, formulation and implementation of yield optimization are discussed in detail. The effect of the RTO system described was validated in an industrial chemical plant, with an average 2.68% increase in yield promotion obtained, which confirms the effectiveness and practicability of the proposed technique. Introduction Invention of the Wacker process [1, 2], an industrial process for the commercial manufacture of acetaldehyde by directly oxidizing ethene, is a milestone in the petrochemical industry. It provides the basis for industrial-scale production of acetic acid, acetone, ethyl acetate, ketene, acetic anhydride, butyraldehyde and vinyl acetate. In the acetaldehyde production process, yield is one of the most important indexes and represents the plant equipment operating efficiency. Traditionally, this type of process was operated in an ad hoc manner, meaning a prevailing reliance on experience accumulated over several years. A systematic and rational approach was required in order to overcome different sources of environmental and process fluctuations, and adjust the set points of basic control loops accordingly. Therefore real-time optimization (RTO) techniques were developed, which were intended to significantly improve the operation of process in a timely manner, and maintain maximum productivity from the process. The need to monitor the process operations, to model the process, to predict flow rates and composions of product acetaldehyde, and to optimize yield and improve efficiency are all key problems for RTO. This paper addresses different topics for this purpose. Data reconciliation with steady state detection and gross error detection, model development and real-time updating, soft sensoring for key component and yield prediction, real-time optimization and its implementation are discussed below. * Author for correspondence ([email protected]).
Transcript
Dev. Chem. Eng. Mineral Process. 13(3/4), pp. 1-10, 2005.
1
Real-time Optimization of Acetaldehyde
Production Process
Shao Zhijiang*, Wang Jinlin and Qian Jixin
Institute of Systems Engineering, Department of Control Science and
Engineering, Zhejiang University, Hangzhou 310027, P.R. China
A real-time optimization (RTO) system of an acetaldehyde production process is presented in this paper. Data reconciliation with steady state detection and gross error detection, soft sensoring of coarse acetaldehyde concentration, formulation and implementation of yield optimization are discussed in detail. The effect of the RTO system described was validated in an industrial chemical plant, with an average 2.68% increase in yield promotion obtained, which confirms the effectiveness and practicability of the proposed technique.
Introduction Invention of the Wacker process [1, 2], an industrial process for the commercial manufacture of acetaldehyde by directly oxidizing ethene, is a milestone in the petrochemical industry. It provides the basis for industrial-scale production of acetic acid, acetone, ethyl acetate, ketene, acetic anhydride, butyraldehyde and vinyl acetate. In the acetaldehyde production process, yield is one of the most important indexes and represents the plant equipment operating efficiency.
Traditionally, this type of process was operated in an ad hoc manner, meaning a prevailing reliance on experience accumulated over several years. A systematic and rational approach was required in order to overcome different sources of environmental and process fluctuations, and adjust the set points of basic control loops accordingly. Therefore real-time optimization (RTO) techniques were developed, which were intended to significantly improve the operation of process in a timely manner, and maintain maximum productivity from the process.
The need to monitor the process operations, to model the process, to predict flow rates and composions of product acetaldehyde, and to optimize yield and improve efficiency are all key problems for RTO. This paper addresses different topics for this purpose. Data reconciliation with steady state detection and gross error detection, model development and real-time updating, soft sensoring for key component and yield prediction, real-time optimization and its implementation are discussed below.
The Wacker Process The Wacker process is one of a relatively small number of industrial reactions involving homogeneous catalysis. It involves the oxidation of ethylene (ethene) to acetaldehyde (ethanal) using Pd salt as catalyst and CuCl2 as a co-catalyst. The role of the CuCl2 is essentially to ensure that the reaction system remains homogeneous. The overall reaction can be simply written as:
42HC + 0.5 2O → 22 ,CuClPdCl CHOCH3 … (1)
This oxidation reaction can be divided into three steps [3]. First, bubbling ethylene and oxygen when treated by an acidified water solution of palladium and cupric chlorides to yield acetaldehyde; reaction is catalyzed by PdCl2-CuCl2. During the reaction, palladium forms a complex with ethylene and is reduced to Pd(0):
42HC + 2PdCl + OH2 →← CHOCH 3 + Pd + 2 HCl … (2)
Second, Pd is reoxidized by Cu(II) in the liquid phase:
2 22 2Pd CuCl PdCl CuCl+ → + … (3)
The process operates at 50-130oC and at pressures of 3-10 atm. Finally, regeneration of cupric chloride occurs in a separate oxidizer:
2 2 2
12 2 2
2CuCl O HCl CuCl H O+ + → + … (4)
The flowsheet of the Wacker process is shown in Figure 1. This method is technically simple, smooth in reaction, and high in selectivity. The favorable economics of the process are due to the abundance of ethylene. It is regarded as the most economic industrial process route, and has been widely used in many countries.
Rea
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Vent Gas
Acetaldehyde
Waste waterReaction unit Rectifying unit
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Waste waterReaction unit Rectifying unit
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Figure 1. The flow sheet of acetaldehyde production process.
Real-time Optimization of Acetaldehyde Production Process
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Data Reconciliation for Acetaldehyde Production Process Before the acetaldehyde production process is modeled and optimized, availability and quality of measurement should be examined and material balances should be confirmed. Data reconciliation [4] could be defined as a method which tends to give a maximum of credit to the measurement information in respect of sensor accuracy.
Data reconciliation was mostly investigated on a molecular basis, i.e. only stream flow rates and components were taken into consideration, with pure physical change assumed. When facing with reaction process, material balances based on atoms should be considered instead as neither species or numbers of atoms change in chemical reaction. This could be used as a general rule for data reconciliation with reaction processes. In fact, atomic carbon (C) was chosen as the primary atom and balanced for data reconciliation purposes in this work.
In the entire data reconciliation framework, steady state detection and gross error detection [5] are of great importance. Stable operation is generally assumed for modeling and optimization in RTO. Real-time data should be examined first to ensure that this assumption is not violated. Data collections which conflict with steady state detection criterion are eliminated. However, gross errors in measurement should be detected to improve data quality. Gross errors are those measurements with systematic deviation, either simply as sensor errors or they reflect some deterioration or leakage problems in the process.
For RTO of acetaldehyde process, real-time data and laboratory data were integrated and merged first, both in a steady state manner. Then data was reconciled and rectified. Here data reconciliation and gross error detection were combined and executed simultaneously. Calculations are outlined as follows:
1. Real-time measurement verified for steady state assumption; merged and synchronized with laboratory data.
2. Data reconciliation problem with atomic carbon balance formulated and solved using nonlinear programming.
3. Detect gross error existence on reconciled data. If detected, eliminate the corresponding measurement and return to step 2. If not, then go to step 4.
4. Obtain final reconciled and balanced data.
The flow chart used for data reconciliation of the acetaldehyde process is shown in Figure 2. Flow rate of vent gas is measured in units of volume, and should be transformed into mass flow rate before data reconciliation. Density of gas is needed for this transformation. Approximately, density changes with temperature and pressure and can be calculated by ideal gas equation of state:
gasρ ＝ P*100*30.44/8.314/(T+273.15) … (5)
where P is the pressure (bar); T is temperature (℃); , and average molecular weight of vent gas is 30.44. The calculated density has units of kg/m3. Flow rate of coarse acetaldehyde (liquid phase) is also measured by volume, and also needs compensation. Concentration was regressed with data exported from Aspen Plus Property, and the relationship with density and temperature is:
where T is temperature of coarse acetaldehyde (℃); C is concentration of coarse acetaldehyde. Typical data before and after data reconciliation are listed in Table 1.
The weights for data reconciliation are configured by precision of the corresponding instruments. The gross error critical value was set to 0.05. The atomic balance for carbon is not satisfied before data reconciliation (relative error 4.47%), while balance achieved after data is reconciled. Also from the data reconciliation procedure, we know that FC1220 includes some gross error. Plant operators confirmed this by calculation. The result was that FC1220 calibrated by the maintenance group (approx. 2t/h) was found as intrinsic zero-drift error! Overall, quality of measurement was improved and balanced data were provided as an important basis for modeling and optimization.
Soft Sensoring and Model Updating Yield is one of the most important indexes for the acetaldehyde production process. Yield was calculated for operations evaluation on a weekly (average) basis. It was required that yield was modeled, and used for prediction and optimization in real time . From observation and analysis of historical data, it was concluded that acetaldehyde loss in rectification units was relatively small, compared with that in the reaction unit.
FR-1211
FFC-1204
TR-1617
FI-1201
FCAL-1203
R-101
V-102
FC-1209
C-101
FCASL-1210
PR-1405 TR-1616
FC-1214
Recycled Gas
FC-1220
FCAL-1203-Feedstock Ethene FCASL-1210-Fresh Water to Absorber FC-1214-Recycle Water FC-1220- Condensed Water FI-1201-Flowrate of Vent Gas FR-1211- Flow Rate of Coarse Acetaldehyde PR-1405-Pressure of Vent Gas TR-1616-Temperature of Vent Gas FFC-1204-Oxygen TR-1617-Temperature of Coarse Acetaldehyde FC-1209-Desalted Water C101-ACC-Concentration of Acetaldehyde
Figure 2. The process flow chart of acetaldehyde reaction and absorption.
Real-time Optimization of Acetaldehyde Production Process
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Furthermore, operation of the rectification unit is quite stable. In order to simplify the calculation, we consequently took into consideration only the reaction unit for yield model. The model of acetaldehyde yield is:
Yield = Ethene Conversion Rate * Rate of Acetaldehyde Production = Flowrate of Acetaldehyde Produced / Flowrate of Feedstock Ethene *100%
where 1211F is the volume flow rate of coarse acetaldehyde (m3/h); 1203F is the
mass flow of ethane (t/h); accy is the concentration of acetaldehyde in stream 1211F ;
accρ is the density of 1211F (kg/m3), and is regressed as Equation (6). Molecular
weights of acetaldehyde and ethane are 44 and 28, respectively. Thus we have the yield model. However, lack of online measurement for
concentration of acetaldehyde limited the yield model working as a key index for process optimization. In fact, only three laboratory analyses were available in 24 hours each day. Therefore, the difficult task was to construct a coarse acetaldehyde model with on-line prediction capability. Again the steady state assumption was made. Since only intermediate measurements and off-line historical data were used, instead
Table1. Measured data and reconciled data.
Item Measured Data Reconciled Data Feedstock Ethene 6.1620 t/h 6.3182 t/h Oxygen 3.0858 t/h 3.1484 t/h Fresh Water to Absorber 34.110 t/h 34.266 t/h Recycled Water 16.910 t/h 17.066 t/h Desalted Water 17.570 t/h 17.726 t/h Condensed Water 8.1000 t/h 7.7873 t/h Flowrate of Vent Gas 86.070 m3/h 86.069 m3/h Flowrate of Coarse Acetaldehyde 75.40 m3/h 75.254 m3/h Pressure of Vent Gas 3.1990 bar 3.1922 bar Temperature of Vent Gas 17.620℃ 17.620℃ Temperature of Coarse Acetaldehyde 48.340℃ 48.345℃ Concentration of Acetaldehyde 12.350% 12.36 % Ethane component of feedback ethene 99.50% 99.34% Ethene component of vent gas 68.20% 68.20% Carbon dioxide component of vent gas 18.10% 18.10% Acetaldehyde in coarse acetaldehyde 12.35% 12.36% Acetyl hydrate in coarse acetaldehyde 1.410% 1.470% Total input mass 77.838 t/h 78.526 t/h Total output mass 78.979 t/h 78.526 t/h Mole number of input carbon atom 437.16 kmol 447.743 kmol Mole number of output carbon atom 447.90 kmol 447.743 kmol
Shao Zhijiang, Wang Jinlin and Qian Jixin
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of “hard” and expensive on-line chromatogram instruments, the model was called a “soft-sensoring” model. The model runs every 15-30 minutes, fast enough to reflect changes of process operation. It collects real-time data and completes calculations, behaving like a real measurement device. Also it will be self-calibrated each time laboratory analyses arrives.
Selection of Intermediate Variables The intermediate variables used for model development could be classified into three categories:
? recycle gas system (flowrate, concentration of ethane, etc.); ? catalysts (concentration of Pb, sum of Cu, etc.); ? reaction unit (feed flowrate of ethene, temperature and pressure, etc.).
Variables with relatively minor effect on model output were eliminated; variables in the final model are summarized in Table 2.
The model of coarse acetaldehyde was built by regressing steady state data. Linear and different kinds of nonlinear models were compared in terms of regression error, revivification error and validity coefficient. The Least Square Method was used as a standard solver for regression. It was concluded that introduction of nonlinearity does not necessarily help improve the model. For simplicity and credibility, the linear model was finally adopted for coarse acetaldehyde prediction.
Soft Sensoring Implementation and Model Updating The soft sensoring model must be routinely updated in order to reflect the current status of process operations. The model used online is of incremental mode, in which differences of intermediate variable are used for prediction:
1 1 1 11 1 2 2 14 14* * ... *k k k ky a x a x a x+ + + +∆ = ∆ + ∆ + + ∆
Table 2. Variable list of soft sensoring model.
Tags Description Symbol Availability FC-1206 The flow of recycle gas x1 Real time AI-1106 The content of ethene in recycle gas x2 Real time AI-1102 The content of oxygen in recycle gas x3 Real time FC-1210 The flow of fresh water x4 Real time V102-SumCu The concentration of sum Cu x5 Every 3 h V102-Pd The concentration of Pb x6 Every 3 h FI-1203 The flow of ethene x7 Real time V102-CuRatio The ratio of Cu to Cu+ x8 Every 3 h V-102 PH The value of V102-PH x9 Every 3 h TI-1607 The bottom temperature of reactor x10 Real time PC-1416 The pressure of reactor x11 Real time FI-1201 The flow of given out gas x12 Real time FC-1209 The flow of doff brine x13 Real time FC-1214 The flow of recycled wastewater x14 Real time
Real-time Optimization of Acetaldehyde Production Process
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1 1 1( )k k k k ky y y f x y+ + += + ∆ = + ∆ … (8)
where yk and yk+1 are the last and next predicted value of coarse acetaldehyde concentration, respectively; 1kx +∆ is the difference between current value and last value of each intermediate variable.
When laboratory analyses of coarse acetaldehyde concentration arrives, the model is simply updated as follows:
k ky y= % … (9)
where ky% is the laboratory data. In order to verify the effectiveness of the model, relative error of model output can
be defined as: 1 1 1( ) /k k ky y yλ + + += − % % … (10)
1ky + is the predicted value of soft sensoring model (it is not validated); 1ky +% is the laboratory data to be updated. The validation of model output is shown in Figure 3.
The model has now been in use for on-line prediction of coarse acetaldehyde concentration for more than 10 months. Statistical analysis shows that relative error of predicted data over the last four months has maximum of 8.7546%, average of 1.7827%, and standard deviation of 1.5647. It can be concluded that the model is effective and practical, and is ready for optimization application.
Aug Sep Oct Nov Dec10
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start_time=02-Aug-2003end_time=14-Nov-2003
Comparison of Soft Sensor Output and Lab Data
C101-ACC: Soft Sensor OutputC101-ACC: Lab Data
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Max Error=8.7546Avg Error=1.7827Std Error=1.5647
Figure 3. Comparison of model prediction and laboratory data.
Shao Zhijiang, Wang Jinlin and Qian Jixin
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Real Time Optimization and Its Implementation Formulation of Yield Optimization Problem
Optimization of the acetaldehyde yield is formulated by merging yield model (Equation 7) and coarse acetaldehyde concentration model (Equation 6). The overall optimization problem is as follows.
xi = rt(xi)， i = 1,2,3,7,9,10 //calculate the objective function
Constraints condition:
xj_min ≤ xj ≤ xj_max j = 4,5,6,8,11,12,13,14
where yf is predicted value of F1211 which was modeled in a similar way to coarse acetaldehyde concentration yacc; rt(xi ) is real time value of each xi; xj_max and xj_min are upper and lower bound limits of xj which are imposed for operational safety considerations.
Note that x1, x2, x3, x7 , x9, x10 and T1617 are not manipulated variables, although they appear in the final model. They are regarded as disturbances when implementing the optimization.
Implementation of RTO Figure 4 illustrates the framework of RTO implementation, in which the techniques discussed above are integrated. The system was developed and implemented on DELTA-V system. The resultant optimization problem is of a nonlinear nature, and a nonlinear programming (NLP) solver is adopted. After manipulated variables are optimized, they are filtered before sending to the DCS as setpoint values. Here a first-order filter was used to smooth the output change. Effect of RTO RTO system for yield optimization was implemented in an industrial chemical plant in mid-2003. This is an acetaldehyde production plant with a design capacity of 5 tons ethane per hour. It is currently operating at 20% above design. The effect of RTO was validated over two-weeks formal testing, and the results are shown in Figure 5. After the RTO system was enabled, the yield of acetaldehyde showed improvement.
Statistical analysis shows that the average value of yield was 89.32% before optimization and 92.00% after, which means an increase of 2.68%. It was estimated that the feedstock ethene was 6.2 ton per hour. Thus ethene consumed each year was 52,080 tons/year (i.e. 6.2 x 24 x 350 tons). If the price of acetaldehyde is assumed approximately as 4,350 yuan/ton, then RTO would make a profit for acetaldehyde production of 9,541,000 yuan per year (i.e. 52,080 x 2.68% x 44/28 x 4,350 yuan).
Real-time Optimization of Acetaldehyde Production Process
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Figure 4. The framework of Real Time Optimization implementation.
Optimization Model
Soft Sensor Model
Real Time Data from DCS Lab Data
NLP Solver
Output Filter
DCS
Output
Data Reconciliation Merge
Steady State Detection
Set Points
Update
Optimization
Figure 4. The framework of real time optimization implementation.
Comparision of Yield Before and After OptimizedYieldThe average value before optimizedThe average value after optimized
Figure 5. Yield before and after optimization.
Shao Zhijiang, Wang Jinlin and Qian Jixin
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Conclusions In this paper, real-time optimization (RTO) of an acetaldehyde production process is discussed. Data reconciliation with steady state detection and gross error detection, soft sensoring of coarse acetaldehyde concentration, formulation and implementation of yield optimization are researched in detail. The effect of proposed RTO scheme was validated in an industrial chemical plant operating at 20% in excess of design capacity, which implies the effectiveness and practicability of the technique.
Acknowledgements This research was supported by the 973 Program of China (No.2002CB312200), and partially supported by the National High Technology Research and Development Program of China (No.2002AA412110).
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membrane reactor. Chem. Eng. Commun., 88, 31-47. 2. Nowinska, K., and Dudko, D. 1997. Transition metal salts of heteropoly acids as palladium
co-catalysts for Wacker oxidation of ethene, Applied Catalysis A – General, 159, 75-87. 3. Reilly, C.R., and Lerou, J.J. 1998. Supported liquid phase catalysis in selected oxidation, Catalysis
Today, 41, 433-441. 4. Leung, G., and Pang, K.H. 1990. A data reconciliation strategy: From on-line implementation to
off-line application, AIChE Spring National Meeting, Orlando, Florida, USA. 5. Iordache C., Mah, R., and Tamhane, A. 1985. Performance studies of measurement test for detection of
gross errors in process data. AIChE J., 31, 1187-1201.
Received: 13 November 2003; Accepted after revision: 31 August 2004.
