Development of augmented virtual reality-based operator
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1 Korean J. Chem. Eng., 38(2), 1-12 (2021) DOI: 10.1007/s11814-021-0804-6 INVITED REVIEW PAPER pISSN: 0256-1115 eISSN: 1975-7220 INVITED REVIEW PAPER † To whom correspondence should be addressed. E-mail: [email protected], [email protected]Copyright by The Korean Institute of Chemical Engineers. Development of augmented virtual reality-based operator training system for accident prevention in a refinery Changjun Ko * , Hodong Lee * , Youngsub Lim ** , *** ,† , and Won Bo Lee * ,† *School of Chemical and Biological Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Korea **Department of Naval Architecture and Ocean Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Korea ***Research Institute of Marine Systems Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea (Received 30 December 2020 • Revised 29 March 2021 • Accepted 11 April 2021) AbstractA new operator training system that trains both control room and field operators by coupling dynamic processes and accident simulations, thereby preventing potential hazards in a chemical plant, is proposed. The two types of operators were trained in different training environments — a conventional distributed control system inter- face for the control room operators and an augmented virtual reality-based system for the field operators. To provide quantitative process changes and accident information driven by the actions of the trainees in real time, two types of simulation, dynamic processes and accidents, were implemented. The former was accomplished through a real-time dynamic process simulation using Aspen HYSYS; the latter was achieved by replacing the high-accuracy accident simu- lation model based on computational fluid dynamics with a variational autoencoder with deep convolutional layers and a deep neural network surrogate model. The resulting two types of outcomes were transferred across each training environment in a platform called the process and accident interactive simulation engine using object linking and embedding technology. In the last step, an augmented virtual reality-based platform was attached to the process and accident interactive simulation engine, making communication between the control room and field operators possible in the proposed operator training system platform. Keywords: Potential Hazards, Operator Training System, Dynamic Process Simulation, Computational Fluid Dynam- ics, Variational Autoencoder with Deep Convolutional Neural Layers, Augmented Virtual Reality INTRODUCTION Although the advancement of the chemical industry has brought significant benefits to society, the increased scale of chemical pro- cesses has also given rise to potential risks. Today, as processes become more complex, the hazard of an accident and damage is growing as well. For instance, the explosion accident on March 23, 2005 at the British Petroleum refinery in Texas caused 195 casual- ties. According to the investigation report, inappropriate training for the operations personnel in advance was considered as the main reason for the accident [1]. In other studies, the main reason for accidents in plants has been identified as the incorrect operation of process units by operators owing to a lack of previous training [2], accompanied by lack of relevant knowledge of the process [3], resulting in a large number of man-made disasters. For quite some time, many people in the chemical industry have been trying to solve these problems. They mostly focused on reduc- ing accident damage by properly educating operators on how to respond in emergency situations [4,5]. This idea has resulted in an increase in the size of the operator training system (OTS) manufac- turing market [6], and accordingly, remarkable developments have been made in the related field. Early OTSs mainly focused mainly on dynamic process simulations [7-10] such as UniSim® by Hon- eywell and Aspen® by AspenTech. On these platforms, training sce- narios usually start with upsets of process variables such as tem- perature, pressure, and flow rate due to adverse events in one or more process unit. When these scenarios evolve, trainees are advised to take appropriate actions to bring those upsets back to normal states. These OTSs are used to educate control room operators (CROPs) and contribute greatly to improving the coping ability of operators in emergency situations. However, these types of pro- cess-simulation-based OTSs have limitations in educating field oper- ators (FOPs) because accidents such as leaks, fires, and explosions are excluded in training scenarios. Because agile cooperation between CROPs and FOPs is crucial in real accident situations [11], efforts have been made in recent years to combine the results of accident simulation with dynamic process simulation, and furthermore with virtual reality (VR). Cha et al. developed a training program in VR that computed the fire effect using computational fluid dynamics (CFD) [12]. Schneider Electric tried to map the visual effect of fire onto VR using the EYESIM® platform, wherein process simulation results are mapped and displayed in real time in VR [13]. These methodologies try to solve the integration problem of the existing dynamic simulation and accident simulation, but the results have not been effective. Therefore, to connect process simulation with accident simulation,
Development of augmented virtual reality-based operator
20-01435.fmINVITED REVIEW PAPER
Development of augmented virtual reality-based operator training
system for accident prevention in a refinery
Changjun Ko*, Hodong Lee*, Youngsub Lim** , ***
,†, and Won Bo Lee*,†
*School of Chemical and Biological Engineering, Seoul National
University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Korea **Department
of Naval Architecture and Ocean Engineering, Seoul National
University,
Gwanak-ro 1, Gwanak-gu, Seoul 08826, Korea ***Research Institute of
Marine Systems Engineering, Seoul National University,
1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea (Received 30 December
2020 • Revised 29 March 2021 • Accepted 11 April 2021)
AbstractA new operator training system that trains both control
room and field operators by coupling dynamic processes and accident
simulations, thereby preventing potential hazards in a chemical
plant, is proposed. The two types of operators were trained in
different training environments — a conventional distributed
control system inter- face for the control room operators and an
augmented virtual reality-based system for the field operators. To
provide quantitative process changes and accident information
driven by the actions of the trainees in real time, two types of
simulation, dynamic processes and accidents, were implemented. The
former was accomplished through a real-time dynamic process
simulation using Aspen HYSYS; the latter was achieved by replacing
the high-accuracy accident simu- lation model based on
computational fluid dynamics with a variational autoencoder with
deep convolutional layers and a deep neural network surrogate
model. The resulting two types of outcomes were transferred across
each training environment in a platform called the process and
accident interactive simulation engine using object linking and
embedding technology. In the last step, an augmented virtual
reality-based platform was attached to the process and accident
interactive simulation engine, making communication between the
control room and field operators possible in the proposed operator
training system platform. Keywords: Potential Hazards, Operator
Training System, Dynamic Process Simulation, Computational Fluid
Dynam-
ics, Variational Autoencoder with Deep Convolutional Neural Layers,
Augmented Virtual Reality
INTRODUCTION
Although the advancement of the chemical industry has brought
significant benefits to society, the increased scale of chemical
pro- cesses has also given rise to potential risks. Today, as
processes become more complex, the hazard of an accident and damage
is growing as well. For instance, the explosion accident on March
23, 2005 at the British Petroleum refinery in Texas caused 195
casual- ties. According to the investigation report, inappropriate
training for the operations personnel in advance was considered as
the main reason for the accident [1]. In other studies, the main
reason for accidents in plants has been identified as the incorrect
operation of process units by operators owing to a lack of previous
training [2], accompanied by lack of relevant knowledge of the
process [3], resulting in a large number of man-made
disasters.
For quite some time, many people in the chemical industry have been
trying to solve these problems. They mostly focused on reduc- ing
accident damage by properly educating operators on how to respond
in emergency situations [4,5]. This idea has resulted in an
increase in the size of the operator training system (OTS) manufac-
turing market [6], and accordingly, remarkable developments
have
been made in the related field. Early OTSs mainly focused mainly on
dynamic process simulations [7-10] such as UniSim® by Hon- eywell
and Aspen® by AspenTech. On these platforms, training sce- narios
usually start with upsets of process variables such as tem-
perature, pressure, and flow rate due to adverse events in one or
more process unit. When these scenarios evolve, trainees are
advised to take appropriate actions to bring those upsets back to
normal states. These OTSs are used to educate control room
operators (CROPs) and contribute greatly to improving the coping
ability of operators in emergency situations. However, these types
of pro- cess-simulation-based OTSs have limitations in educating
field oper- ators (FOPs) because accidents such as leaks, fires,
and explosions are excluded in training scenarios.
Because agile cooperation between CROPs and FOPs is crucial in real
accident situations [11], efforts have been made in recent years to
combine the results of accident simulation with dynamic process
simulation, and furthermore with virtual reality (VR). Cha et al.
developed a training program in VR that computed the fire effect
using computational fluid dynamics (CFD) [12]. Schneider Electric
tried to map the visual effect of fire onto VR using the EYESIM®
platform, wherein process simulation results are mapped and
displayed in real time in VR [13]. These methodologies try to solve
the integration problem of the existing dynamic simulation and
accident simulation, but the results have not been effective.
Therefore, to connect process simulation with accident
simulation,
2 C. Ko et al.
May, 2019
Manca et al. introduced augmented virtual reality (AVR) as a solu-
tion wherein additional process information such as pressure, tem-
perature, and flow rate is imbedded to the VR by integrating the
process simulator of UniSim® with the self-developed accident
simulator of AXIM [14]. In addition, they used the objective link-
ing and embedding (OLE) technique to effectively connect the
results of the two modules. As a result, they succeeded in creating
a pio- neering AVR-based training platform by mapping the
two-dimen- sional pool fire accident simulation results to VR.
However, this method has a limitation in that it is based on simple
parametric two-dimensional accident results, making it difficult to
yield quite accurate three-dimensional accident information that is
to be sup- plied to trainees.
When estimating accident results, CFD, which can consider var- ious
terrain conditions and fluid characteristics, has been the most
widely used simulation tool in recent years. There are various mod-
els such as dispersion, fire, and explosion in CFD, and many types
of commercial CFD tools are currently being produced and used by
various organizations [15-17]. Nevertheless, CFD models have a
critical drawback: the computation time. Most CFD tools take more
than an hour to compute the results [18]. However, accident result
data provided to the VR should be calculated very quickly, because
the accident consequence is changed by the action of the trainee
almost in real time. For example, the size of the gas cloud
generated by a leakage accident changes in real time depending on
how quickly the training center shuts off the block valve near the
leakage accident. For this reason, CFD could not be applied to AVR-
based OTS, which requires rapid calculation. To solve this prob-
lem, studies on surrogate models that can simply express complex
structures of a certain model have been conducted. Palmer et al.
used Gaussian process regression (GPR) meta models to optimize an
ammonia synthesis plant [19]. Chen et al. expanded the domain of
meta-models to time-space-dependent output variables in con-
nection with GPR [20]. Widodo et al. used support vector machine in
machine condition monitoring and fault diagnosis, and Fauvel et al.
adopted a spatial-spectral kernel-based approach for the clas-
sification of remote-sensing images [21,22].
However, the above-mentioned studies built only relatively sim- ple
models with only a small number of input and output variables, the
results of which were not sufficient to generate information to be
provided to trainees moving in a large virtual environment. In
addition, support vector machine algorithm and kernel-based learn-
ing are not suitable for nonlinear and complex data sets.
Therefore, a model that can address large data and numerous input
and out- put variables is essential to applying big data-based
machine learn- ing techniques to chemical processes [23]. According
to various studies that deal with meta-modeling [24],
dimensionality reduc- tion is an essential factor because the huge
size and complexity of the data makes analysis difficult.
Autoencoders (AEs) are one of the possible techniques since they
can provide nonlinear transfor- mation conducted by the nonlinear
activation functions and mul- tiple layers. In addition, when
convolutional neural networks (CNNs) are combined with AEs, a
significant improvement on model per- formance can be shown
[25,26]. Furthermore, a new methodology of variational AEs (VAEs)
that introduces the latent space concept generated by features of
the training set has been developed as a
powerful model [27]. In particular, Na et al. combined a VAE with
deep CNNs and demonstrated excellent improvement in perfor- mance
[28].
After performing dimensionality reduction, a regression process
that can express the correlation between input and output is nec-
essary. Recently, deep neural networks (DNNs) with multiple hid-
den layers have shown great performance improvement with the
resolution of vanishing and exploding gradients, overfitting, and
low learning rates [29]. Ioffe et al. proposed batch normalization
(BN) to further address the vanishing and exploding gradient
problem [30]. Finally, numerous optimization algorithms such as
momentum optimization, Nesterov Accelerated Gradient, AdaGrad,
RMSProp, and Adam Optimization have been proposed to effectively
esti- mate the weight parameters of neural networks, to be highly
likely to obtain high-performance surrogate models [31,32].
Herein, we develop an AVR-based OTS for the heavy oil desul-
furization (HOD) process. To provide trainees process and accident
information that is coupled with each other, process simulation and
accident simulation are carried out using the commercial software
Aspen HYSYS and FLACS, respectively. Thereafter, a surrogate model
with respect to jet fire in FLACS is constructed by random sampling
of processed data for real-time application of CFD-based surrogate
model to the AVR-based target OTS platform. To build the proposed
surrogate model, the input is defined as wind speed, wind
direction, release duration, and time step and the outputs are
defined as total heat flux at each grid in three-dimensional space.
First, output is compressed using a VAE with deep convolutional
layers (VAEDC) and secondly, the relationship between the input and
the output is estimated using a DNN. The performance of the newly
constructed model is compared with that of other commonly used
models to evaluate the performance improvement. Finally, the
proposed model, which can calculate output from any input in a
sub-second, is demonstrated to show that a tool to bridge acci-
dent results not only with dynamic process results but also with VR
is successfully manufactured.
STRUCTURE OF OTS PLATFORM
1. Structure of Proposed OTS with AVR As described in the previous
section, to train both CROPs and
FOPs simultaneously, the coupling between process and accident
simulations is indispensable. The structure of the proposed OTS is
displayed in Fig. 1. There are two main platforms in the structure:
a distributed control system (DCS) platform and an AVR platform.
The DCS platform, called the process and accident interactive simu-
lation engine (PAISE), serves as providing process information to
CROPs; therefore, the real-time trend of the process variables can
be seen in the DCS platform while the AVR platform serves to
providing accident information to FOPs.
In this study, an abnormal pressure rise that brings about a gas
release and subsequent jet fire is demonstrated as the representa-
tive training process. When the training scenario begins, the
process variables such as temperature, pressure, and flow rate are
calculated according to predetermined conditions by Aspen HYSYS.
These values are transferred to the DCS platform to calculate the
release rate using a discharge equation, which is the input of the
accident
Development of augmented virtual reality-based operator training
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Korean J. Chem. Eng.
model.
(1)
where Cdisc, A, , , and P denote the discharge coefficient, hole
area, specific heat ratio, fluid density, and pressure,
respectively. With the calculated release rate, a CFD-based jet
fire accident is simu- lated using FLACS V10.7r1. The heat flux (Q)
data are obtained as simulation output at each grid of
three-dimensional space.
However, since the CFD-based accident model takes a long time to
calculate the results, therefore, data cannot be provided in real
time with this method. The solution to this obstacle is to build a
surrogate model that can replace the CFD-based jet fire accident
model with the same input and output, of which the details are
provided in the later section. After the CFD-based model is
replaced by the surrogate model, the output values of heat flux are
trans- ferred to the AVR platform. In the AVR platform, additional
equip- ment such as haptic, navigation, optical, and acoustic
devices are attached to escalate the vivid training atmosphere and
provide im-
proved training effects to trainees. 2. Target Process and Outline
of Training Scheme
The HOD process has a critical role in the petrochemical pro- cess.
It aims to remove residue sulfur in atmospheric residue oil using a
reaction with hydrogen, as expressed below:
(2)
(3)
The reaction occurs with five reactors in series, as indicated in
Fig. 2(a). These reactors are operated under harsh conditions of
approximately 180 barg and 360 oC, of which the detailed informa-
tion is listed in Table 1. These extreme operating conditions lead
to a high likelihood of accidents, such as release and fire,
resulting in massive damage. For these high potential risks, it is
critical to edu- cate operators to take appropriate measures in the
case of accidents, thereby minimizing the physical and economic
damage to the plant. The most probable abnormal situation by the
hazard and operability report from the licensing company is process
upsets derived from equipment failure that controls the pressure,
such as
Release rate CdiscA P 2 -- 1
1 1 ---------
Fig. 1. Structure of proposed AVR-based OTS.
4 C. Ko et al.
May, 2019
compressor failure. When initiated, pressurized hydrogen can bring
about a release of hydrogen and light hydrocarbon gases through
pipeline flanges near the reactor, which is operated under the
extreme conditions mentioned above, and subsequently cause jet
fire. This accident could propagate throughout the plant and result
in a mas- sive calamity. Therefore, a sequence of process upsets
and acci- dents are modeled for the training scenario. As described
in the previous section, process upsets are modeled by Aspen
HYSYS
according to predefined conditions to provide the trainees with
almost the same process variable trends as in case of a real situa-
tion. The physical and thermodynamic conditions are computed by the
modified Peng-Robinson state model equation [33]. When the upset
scenario is initiated, a simple pressure-flow equation is used
additionally to compute the changed conditions of the pro- cess
variables.
(4)
where K and P denote the conductance of a certain pressure node,
such as a valve or heat exchanger, and the change in pres- sure
across the node, respectively. As errors are accumulated, the
process variables attain predefined release conditions at certain
points, and the release rate is calculated by Eq. (1). The obtained
release rate is used as an input for the jet fire accident model.
3. Accident Data Preparation
The jet fire scenario is modeled by FLACS to provide the suffi-
cient samples to generate a surrogate model. The size of the entire
simulation domain is 50 m, 50 m, and 30 m in the x, y, and z
Flow K P
Fig. 2. Layout and geometry of HOD process. (a) Process flow
diagram, (b) plot plan, (c) 3D view of CAD image in FLACS.
Table 1. Steady-state operating conditions of the reactors
Unit
description Pressure (barg)
Flow (104 kg/h)
Reactor 1 192.4 388.1 2.381 Reactor 2 191.6 385.4 2.403 Reactor 3
186.4 382.7 2.435 Reactor 4 181.1 381.8 2.473 Reactor 5 175.6 380.2
2.494
Development of augmented virtual reality-based operator training
system for accident prevention in a refinery 5
Korean J. Chem. Eng.
directions, respectively, with a uniform grid of 1.6 m. The
location of the release point is marked in Fig. 2(a), 2(b), and
2(c). The sim- ulation inputs are selected to create discrete
outputs. The other sim- ulation conditions are shown in Table
2.
The entire process of training is indicated in Fig. 3. First, the
three input variables of wind speed, wind direction, and release
dura- tion are randomly sampled using the Latin hypercube method
[34]. FLACS input files CS (scenario) and CL (leak) are generated
using these variables, and 785 cases in total are simulated to
obtain data points for training. The resulting data are acquired in
the form of binary files and transformed to American Standard Code
for Information Interchange (ASCII) files for post-processing. From
the ASCII files, the Q values (28×28×180), of which the dimen-
sions match the number of x, y coordinates, and time steps, respec-
tively, are extracted. Because the height that has the greatest
impact on a person is near 2 m, the Q values of z coordinates at
1.6 m are
used and other Q values of z coordinates are abandoned. In the last
step, the data are shifted and rescaled to the range [0,1] using
the following Eq. (5):
(5)
where Qmax and Qmin denote the maximum and minimum Q values among
the original samples.
TRAINING SCHEME
1. Definition of Training Scenario As described in Section 2.1.,
the dynamic process simulation
and accident data (Q) from the VAEDC-DNN surrogate model must be
coupled in the AVR-based OTS for appropriate commu- nication
between the CROPs and FOPs. The representative train- ing scenario
in Fig. 4 indicates the sequence of the training and the
configuration of how the process and accident models func- tion
interactively. The training procedure is accelerated for quick
evaluation, which gives trainees relatively short 180 s for actions
to be taken.
I. When the training scenario begins, process upsets are initiated
by the quench gas compressor failure, and the results of the
process variable changes are obtained by a dynamic process
simulation.
II. All controllers (FC1-5, TC1, and PC1) are set to the manual
mode and the CROPs are directed to take the first action, which is
to manipulate the actuators (valves) manually to prevent the upsets
from propagating further. However, even if they take the proper
actions, errors continue to increase according to the predefined
train-
Qscaled Q Qmin
Table 2. Accident scenario conditions Variable Value
Ambient temperature 25 oC Ambient pressure 1 bar Wind direction 0-2
Wind speed 0.5-5 m/s Discharge rate 14.4 kg/s Release duration
60-180 s Pasquill class None Total simulation time 180 s
Fig. 3. Flow chart of data processing.
Fig. 4. Training scenario by communication of CROPs and FOPs.
6 C. Ko et al.
May, 2019
ing scenario. III. When the errors attain the predefined threshold,
a gas release
and subsequent jet fire occurs. The CROPs are directed to notify
the FOPs of the accident site and initiate the shut-down procedure
of the actuators (valves) in DCS. Further dynamic simulations are
conducted accordingly.
IV. Upon receiving the order from the CROPs, the FOPs ap- proach
the accident site and close the block valve.
V. If the FOPs report to the CROPs that the action has been taken,
the training ends.
For each training, when a jet fire occurs, the accident effect of Q
is calculated in real time by the pre-trained surrogate model with
two randomly selected input variables and one fixed input variable:
wind speed (0.5-5 m/s), wind direction (0-2 rad), and release
duration (180 s). When the FOPs approach the accident site in AVR,
the information, e.g., x, y, and t, which denotes the Carte- sian
coordinates of the FOPs and time step in the virtual environ- ment,
is transferred to PAISE using OLE technology as in Fig. 1 [35].
With the x, y, and t information from the AVR and Q from the
surrogate model, the lethality of the FOPs can be calculated with
the simplified following radiation equations with the assump- tion
that because the trainee is sufficiently close to the radiation
source (jet fire), the effect of convection is negligible
[36].
(6)
where Lrad denotes the radiation dose, and t0 is the initial time
step when the jet fire first occurs [36].
(7)
(8)
Pr denotes the radiation probit and Pdeath the lethality of the
trainee in the AVR. The calculated result can be transferred to the
AVR using OLE technology in real time. When the FOPs close the
block valve in AVR, the decreasing effect of the jet fire is
recalcu- lated by the surrogate model with the inputs of the
original wind speed, wind direction, and new release duration as
follows:
(9)
where taction represents the time step when the FOPs close the
block valve in AVR. The lethality of the FOPs in AVR is updated
with the newly calculated accident result. If the release duration
is greater than 180 s, which is the predefined maximum allowable
time to act, the training scenario ends in failure. Otherwise, the
information of the jet fire accident is provided to the FOPs in
real time throughout the entire training procedure, making
appropri- ate communication with CROPs possible. 2. Dynamic Process
Simulation Modeling
The process model is constructed based on the information dis-
played in Fig. 2(a) by Aspen HYSYS. The goal is to simulate the
change in process variables over time as the training scenario
evolves. Accordingly, based on the steady state model, modeling of
the dynamic process simulation is implemented. Sizing informa-
tion, which is an essential element of dynamic process
simulation,
is obtained from the process flow diagram (PFD) and piping and
instrumentation diagram (P&ID) from the licensing company. In
addition, proportional-integral (PI) controllers are used for con-
trol logic realization based on the PI Eq. (10):
(10)
where OP(t) denotes the controller output value and E(t) error
between the controller setpoint and process variable. Kc and i are
tuning parameters that represent the controller gain and integral
time, respectively. The tuning parameters for removing the
disturbance of the process are set to the general values, as
indicated in Table 3. 3. Accident Simulation Modeling
Inputs and outputs are to be defined prior to the training proce-
dure. As described in Section 2.4, the input to the original CFD
model is wind speed (m/s), wind direction (rad), and release dura-
tion (s). In addition, one more input of time step (s) is added to
the input to take into account of times series, resulting in a
four- dimensional input space denoted as vR1×4. The output of the
surrogate model is two-dimensional contour images of Qscaled
denoted as xR28×28 as described in Section 2.3. The goal of the
surrogate model is to map the input space v to the output space x
using data points by FLACS calculation. To accomplish high per-
formance, the model is constructed of two parts: a VAEDC and a DNN.
The function of the VAEDC is to compress and reconstruct the data
and the DNN is to generate a regression model with the compressed
data. The computing process of a neuron in each layer can be
expressed as follows [37]:
(11)
where Y, b, y, and w denote the output of the neuron, bias term,
input of neuron, and connection weight, respectively. Of the total
800 data points, 720 samples are used as the training set (vtrain,
xtrain), and 80 samples are used as the test set (vtest, xtest) to
assess the trained result. Since the number of data set is small,
ten-fold cross validation method is implemented. Namely, the
samples are ran- domly split into ten distinct subsets called
folds, then the training model picks a different test fold for
evaluation every time and training on the other nine folds. Fig.
5(a) and 5(b) displays the structure of VAEDC and DNN respectively.
The detailed informa- tion of the proposed VAEDC-DNN model was
introduced in the previous research [38].
The performance of the proposed model is compared with that of
other models based on AEs and neural networks (NNs): NN with a
simple AE (AE-NN), NN and DNN with deep convolu- tional AE
(DCAE-NN, DCAE-DNN), DNN with a simple VAE (VAE-DNN), and DNN with
VAEDC (VAEDC-DNN). The detailed information of each model is
specified in Table 4.
Lrad Q x, y, t
Area of unit grid ----------------------------------------
Pdeath 1 2 -- 1 erf Pr 5
2 -------------
Yi, j, k bk yi', j', k'wu, v, k', k
Table 3. Tuning parameters of PI controllers System Kc i
Flow 0.1 0.2 Pressure 2 2 Temperature 1 20
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Korean J. Chem. Eng.
RESULTS AND DISCUSSION
1. Dynamic Process Modeling Results Fig. 6 displays the results of
the dynamic process simulation at
the location of block valve 1 (B/V 1). The simulation was acceler-
ated eight times faster than the actual situation for rapid
training. When training was initiated, physical and thermodynamic
calcu- lations were conducted according to the predefined scenario
in
Fig. 5. Structure of VAEDC-DNN surrogate model. (a) Structure of
VAEDC encoding part, (b) structure of DNN.
Table 4. Different surrogate models for comparison of VAEDC-DNN
model performance AE-NN DAE-NN DAE-DNN DCAE-NN DCAE-DNN VAE-DNN
VAEDC-DNN
Dimensionality reduction part description
5 Fully connected hidden layers, 2 convolutional layers
with max pooling
variational layer
layers with max pooling
Loss function Binary cross entropy Variational lower bound
Regression part description NN NN DNN NN DNN DNN DNN
Loss function Mean squared error
8 C. Ko et al.
May, 2019
Fig. 4. Fig. 6(a) shows the time-line of the training scenario. The
pressure and temperature increased abnormally in the initial stage
of the training, whereas the flow rate remained at approximately
the same value. Near the time step of 15 min, the CROPs set the
controller mode to manual and closed the valve manually (1st
action). However, since the error kept increasing as the predefined
sce- nario, pressure and temperature continued to rise. At the time
step of 50 min, the pressure reached the predefined release
threshold and the leak began. The release rate was calculated by
the dis- charge model of Eq. (1) in Section 2.1, the parameters of
which were updated by the calculated values of the dynamic process
sim- ulation in real time. Once the leak began, the values of the
pro- cess variables, e.g., pressure, temperature, and flow rate,
began to decrease as part of the stream was released through the
leak. At the time step of 60 min, the CROPs notified the FOPs of
the acci- dent site and initiated the shut-down procedure of the
actuators (valves) in the DCS according to the training sequence in
Section 3.1. Consequently, the values of the process variables
continued to decrease, and if the FOPs reacted appropriately, the
training ended. In this manner, the dynamic process simulation
provided the CROPs and FOPs with a guideline on how to address
emergencies in a real plant and served as a means of close
communication between CROPs and FOPs in the emergency situation. 2.
Accident Modeling Results
In Table 5 and Fig. 7, the mean and standard deviation of the
mean squared errors obtained from ten folds of each modelare shown.
The mean squared error in each fold was obtained between
and xtest for test data sets of 80 samples (xtest, vtest). The
VAEDC- DNN model showed the highest performance (the lowest mean
and standard deviation) of 0.00272 and 0.00235. The mean value of
the worst case (AE-NN) was as low as 0.0124, but the standard
deviation was as large as 0.0113, which means it is difficult to
say that the training results were effectively generalized. The
lowest mean and standard deviation signify that complex features
were efficiently extracted by efficient mapping from the variable
space to the latent space and the relationship between input and
output is well inferred. It can be observed that when DNN was used,
the
xgen test
Fig. 6. Trend of process variables at B/V 1 during training. (a)
Time-line of the events, (b) pressure, (c) temperature, (d) flow
rate.
Table 5. Comparison of mean and standard deviation of different
models Model Mean Standard deviation
AE-NN 0.01240 0.01130 DAE-NN 0.00878 0.00864 DAE-DNN 0.00522
0.00345 DCAE-NN 0.00709 0.00715 DCAE-DNN 0.00328 0.00292 VAE-DNN
0.00298 0.00285 VAEDC-DNN 0.00272 0.00235
Development of augmented virtual reality-based operator training
system for accident prevention in a refinery 9
Korean J. Chem. Eng.
performance of the models improved over the models with NN.
However, the performance improved even when NN was used in the
order of VAE, DCAE, DAE, and AE. This phenomenon indi- cates the
great performance of dimensionality reduction, which directly
denotes that the way in which nonlinear images are com- pressed
serves to not only reduce the storage space but also to extract the
features in suitable forms. The best performance was con- firmed
when VAE was combined with deep convolutional layers.
To identify the performance of the different surrogate model
intuitively, the reconstructed images are presented in Fig. 8. In
each column, randomly selected 2D contour images for
AE-NN,xgen
test
Fig. 7. Comparison of mean and standard deviation of different
models.
Fig. 8. Comparison of generated Q images by different models with
original images by FLACS.
DAE-DNN, DCAE-DNN, and VAEDC-DNN simulated from FLACS (CFD) are
displayed. Owing to the characteristics of the nonlinear complexity
of the images, the result differs in shape for each model, which
indicates that it is difficult to reconstruct the overall
macroscopic shape of the images using poor-performance
dimensionality reduction models. This can be especially observed in
the third and fourth columns in Fig. 8. In the surrogate models
with dimensionality reduction, AE and VAE, excellent performance
improvement can be identified. However, other models claim issues
in predicting images. The AE-NN model is a right example of this.
The approximate trends are reasonably similar; however, in the case
of the fifth column, the prediction is significantly inaccurate.
Reviewing the overall trend from Fig. 7 and Fig. 8, the introduc-
tion of BN contributed to advancement the surrogate model per-
formance. The two best models, DCAE-DNN and VAEDC-DNN, showed best
prediction performance compared with the other models, which shows
the importance of convolutional layers. Be- tween these models, the
VAEDC-DNN model demonstrated supe- rior performance, which can be
seen in the first column where Qscaled images were reconstructed
with fine gradients of boundar- ies. The VAEDC-DNN model could
detect features that were undetectable in the AEDC-DNN model. 3.
AVR-based OTS Platform
Two types of information--process information from Aspen HYSYS and
accident information from the VAEDC-DNN surro- gate model--was
combined to construct a platform to train both the CROPs and FOPs.
As mentioned in Section 2.1, the training PAISE platform was
introduced; the interface is displayed in Fig. 9(a). As can be
identified in Fig. 1, PAISE serves not only to bridge the process
model, accident model, and AVR, but also provides
10 C. Ko et al.
May, 2019
process information to the CROPs. The interface of PAISE is simi-
lar to that of other conventional DCS-based OTSs. It indicates the
trend of the process variables when the training scenario is initi-
ated; the status of controllers and actuators (valves) can also be
identified. To educate the FOPs, a VR interface was also developed,
as displayed in Fig. 9(b). In the virtual environment, the trainees
could walk around the plant and observe changes in the process
equipment, e.g., pressure and temperature gauges, and operate
valves.
When emergency situations occur, proper communication and
cooperation of the CROPs and FOPs are essential. Therefore, a
comprehensive AVR-based training platform was constructed, as
indicated in Fig. 9(c); on the right is the PAISE interface for
pro- viding process information to the CROPs; on the left is the
AVR with a combination of VR and other wearable gears such as
acoustic, optical, and haptic devices for providing accident
information to the FOPs. During training, as the training scenario
evolves, the two types of operators interact with each other to
prevent process upsets and accidents from propagating to a greater
disaster.
Fig. 10 displays the training processes of a FOP with the demon-
stration version of the developed AVR-based OTS. During the
training, the trainee experiences an augmented environment with the
aid of an optical device as indicated in Fig. 10(a), and control-
ler device as indicated in Fig. 10(b). The mapping of the accident
result, i.e., lethality to the trainee, is in progress with OLE
technol- ogy. When completed, the high degree-of-freedom for the
train-
Fig. 9. AVR-based training environment. (a) DCS interface (PAISE),
(b) VR interface, (c) training platform combined with DCS
interface, VR interface, and other wearable devices.
Fig. 10. Training examples (FOPs). (a) Jet fire image realization,
(b) adjustment of haptic device during training.
Development of augmented virtual reality-based operator training
system for accident prevention in a refinery 11
Korean J. Chem. Eng.
ees’ action and remarkably accurate process and accident infor-
mation-based OTS are established.
CONCLUSION
In this study, a new OTS to train both CROPs and FOPs by coupling
the dynamic process and accident models was intro- duced. The
dynamic process model was constructed using Aspen HYSYS to provide
the trainee with correct process variable infor- mation during the
entire training. The physical and thermody- namic conditions were
computed using the modified Peng-Robinson state model equation, and
the pressure-flow equation was used for the calculation of the
dynamic behavior. Consequently, the trend of the process variables
was obtained quantitatively, then used as an input for the accident
modeling. The accident model was con- structed using the VAEDC-DNN
surrogate model for real-time application of accident results in an
AVR-based OTS. A VAEDC was introduced as a means of dimensionality
reduction. The pro- posed model consists of two parts: VAEDC,
dimensionality reduc- tion of input images to the latent space, and
DNN, regression of four-dimensional input space of wind speed, wind
direction, release duration, and time steps. Using the constructed
VAEDC-DNN model, the two-dimensional contour images were compressed
to the latent space; the variable space was also mapped to the
latent space. Other surrogate models were also constructed to
compare the performance with using mean squared error. The VAEDC-
DNN model demonstrated the best performance with a minimum mean
squared error of 0.00278. It was proven that the VAEDC- DNN
surrogate model excelled at extracting features from com- plex
images. The high performance of the VAEDC-DNN surro- gate model was
then applied to the AVR-based OTS, where the target process was the
HOD process, which is subject to frequent accidents because of the
harsh operating conditions. Finally, an integrated platform with
communication between the CROPs and FOPs, conducted with the aid of
PAISE and an AVR platform, was constructed.
According to the proposed method, a versatile OTS can train both
CROPs and FOPs simultaneously. First, it has a high degree-
of-freedom in terms of trainees’ actions. The change in the pro-
cess variables from the actions of the CROPs in DCS is calculated
by process simulation in real time; the change in accident results,
e.g., Q, from the actions of the FOPs, is also calculated in real
time with the high-performance VAEDC-DNN model. Second, a bet- ter
training effect over conventional OTSs can be realized, espe-
cially for the FOPs because the training scenario is initiated with
random inputs of wind speed and wind direction, thereby provid- ing
diverse accident results. Lastly, simulation-based process infor-
mation of the surrogate model-derived accident information ensures
high accuracy for the trainees, consequently helping reduce the
damage of potential catastrophic accidents in advance.
ACKNOWLEDGEMENT
This research was supported by a grant (19IFIP-B087592-06) from the
Smart Civil Infrastructure Research Program funded by the Ministry
of Land, Infrastructure and Transport (MOLIT) of the
Korean government and the Korea Agency for Infrastructure Tech-
nology Advancement (KAIA), a grant (NRF-2019R1A2C1085081) from the
National Research Foundation of Korea (NRF) funded by the Korean
government (MSIT), and Yullin Technologies Co., Ltd.
NOMENCLATURE
Cdisc : discharge coefficient A : cross-sectional area of orifice
[m2] : specific heat ratio : density [kg/m3] Q : total heat flux
[W/m2] xi : length of cell in i-direction t : simulation time step
Kc : controller gain i : integral time z : latent space Nz : number
of latent variables v : variable space Nv : the number of variables
Ntrain : the number of training datasets
Abbreviations OTS : operator training system DCS : distributed
control system CROP : control room operator FOP : field operator VR
: virtual reality AVR : augmented virtual reality CFD :
computational fluid dynamics AE : Autoencoder VAE : variational
autoencoder CNN : convolutional neural network DNN : deep neural
network VAEDC : variational autoencoder with deep convolutional
layers HOD : heavy oil desulfurization PAISE : process and accident
interactive simulation engine
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en afgedrukt. De gemaakte PDF-documenten kunnen worden geopend met
Acrobat en Adobe Reader 5.0 en hoger.) /NOR
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/PTB
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/SUO
<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>
/SVE
<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>
/ENU (Use these settings to create Adobe PDF documents for journal
articles and eBooks for online presentation. Created PDF documents
can be opened with Acrobat and Adobe Reader 5.0 and later.) /KOR
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>> >> setdistillerparams << /HWResolution [2400
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