Real-time risk assessment using time series

Prévia do material em texto

Real-time risk assessment using time series: A
case study of a knockout drum
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Abstract. Industries are increasingly connected with the rise of the In-
dustrial Internet of Things. Gathering and preserving data about equip-
ment operation and production processes are now essential, enabling
real-time analysis and decision-making to enhance efficiency. At the same
time, the development of digital twins makes it possible to evaluate these
optimizations in a virtual environment and a secure manner, aiming to
analyze their effectiveness without compromising the actual production
environment. We can apply these techniques with artificial intelligence
models to perform risk analysis of increasingly complex production sys-
tems. Machine learning models can be trained using historical data, cap-
turing the relationships between variables and the characteristics of the
context in which the equipment is inserted. Thus, these models can help
prevent unwanted events through real-time inferences, providing infor-
mation to operators to support decision-making. The present work aims
to present a technique for risk assessment through time series. Through a
case study of a knockout drum, we built a digital twin and trained a neu-
ral network to infer the behavior of the liquid level in this drum. In this
way, operators can make preventive decisions to prevent the fluid level
from reaching the maximum allowed and prevent events that compromise
the safety of the operation. Our technique demonstrated satisfactory re-
sults, avoiding unscheduled stops in the natural gas flow system in the
cases analyzed in our tests and showing robustness in scenarios where
the sensors present noise in their readings.
Keywords: Digital Twins · Risk Analysis · Time Series.
1 Introduction
Industries, in general, may operate equipment in high-risk environments, requir-
ing a high level of safety and process maturity. Failure to use this equipment can
lead to severe accidents, impact worker safety, and cause financial losses to the
organization.
In this context, Digital Twins (DTs) are powerful tools for risk mitigation
and operational performance optimization. By creating a virtual model similar
to real-world equipment, DTs allow for the simulation of different operational
scenarios and analysis of equipment behavior in real-time.
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Through simulations of risk situations, it is possible to use DTs to collect data
on the behavior of system components during the occurrence of some undesired
event, even if it has never occurred in the real-world production environment of
that organization. Machine learning models can be used to analyze this infor-
mation, identifying patterns that indicate potential failures at early stages and
enabling decision-making to prevent the event from occurring.
In this work, we propose using DTs in conjunction with machine learning
models for monitoring and risk analysis of knockout drums in the oil and gas
industry context. A knockout drum is used to efficiently separate a gas stream’s
liquid and gas phases before boilers use it. When liquid accumulation occurs in
the drum, a trip is adopted as a mitigation barrier, which aims to temporarily
deactivate the gas supply to subsequent processes so that the risk of explosion is
mitigated until the liquid level is normalized. In the context of an oil refinery, a
knockout drum can be used before boilers that generate steam. Therefore, liquid
contaminants in the generator’s burner can cause explosions with significant
impacts [10].
Our work’s main objective is to propose a risk analysis technique using DTs
and a machine learning model capable of predicting liquid accumulation in this
equipment. This approach seeks to provide important information to operators
to assist in the decision-making process, avoiding the need to activate safety
systems that can cause setbacks in production and ensuring the continuity of
operation safely.
2 Related works
We searched the literature for studies that propose using algorithms for risk
analysis in industrial equipment. We grouped these works into two categories:
those that apply probabilistic models to estimate the occurrence of events and
those that use artificial neural networks to identify patterns and build inference
models.
2.1 Probabilistic models
Probabilistic models are often used during the risk analysis stage. These models
are essential for quantifying and understanding the various factors that influ-
ence the safety and reliability of industrial equipment. By focusing on applying
Bayesian networks, specifically, probabilistic models prove to be effective in rep-
resenting the conditional dependency relationships between observed variables.
The ability of Bayesian networks to handle uncertainties makes them a promis-
ing choice for risk assessment, in addition to their graphical nature, which allows
for an intuitive visualization of the connections between system elements, facil-
itating the identification of critical points and the decision-making process.
Developing a Bayesian network aimed at risk analysis requires identifying all
possible risks associated with the equipment and the relevant variables that may
exert influence. This process requires extensive knowledge about the operation
Real-time risk assessment using time series 3
of the equipment under analysis; however, it is possible to find some works in
the literature that seek to assist in this mapping process. In [8], the authors
present a mapping technique of a bow-tie diagram that allows its conversion
into a Bayesian network, helping to identify which safety barriers are critical
and possible accident scenarios.
In the work [1], the authors explore creating a Bayesian network for the event
of liquid overflow in an offshore burning system. Knowing the risk factors of the
system, the authors schematized the bow-tie diagram of the process, identifying
the causes, safety barriers, consequences, and their impacts, with liquid overflow
considered the top event. The probabilities of the primary events that served as
a basis for calculating the probabilities of safety barriers were extracted from the
works [7,5]. This work demonstrates that Bayesian networks are robust tools for
probability calculation in risk analysis. Developing a Bayesian network aimed at
risk analysis requires identifying all possible risks associated with the equipment
and the relevant variables that may exert influence.
With the advancement of the digitalization process in industries, probabilis-
tic risk assessment techniques tend to become increasingly dynamic. In [2], a
real-time probabilistic risk assessment method for the petrochemical industry is
proposed based on data monitoring. The technique combines probabilistic risk
analysis with a Dynamic Bayesian Network (DBN) to integrate prior knowledge
and online data in estimating the risk of basic events and accidents. The method
does not require historical event data or expert knowledge to define the condi-
tional relationship between data monitoring and prior risk. Experiments showed
that this method could capture real-time variations in the risk of primary events.
The solutions developed with DBN are robust and flexible, as they can handle
different types of events and data.
2.2 Artificial Neural Network
Techniques that use artificial neural networks represent a robust approach to
artificial intelligence. These models are designed to learn complex patterns and
hierarchical representations from large datasets. The generalization capability of
these models is an essential feature for risk assessment in industrial processes, as
it allows for the dealing of unprecedented scenarios based on previous behaviors.
However, the effectiveness of these models is directly dependent on the quality
and quantity of training data and the use of appropriate optimization and reg-
ularization techniques, making the development and learning process of these
modelschallenging.
Strategies incorporating historical analysis through time series stand out as
effective options for real-time risk assessment. They enable understanding of
variations and trends in historical data, allowing the projection of future scenar-
ios based on various factors. Thus, leveraging previous information recorded by
sensors makes it feasible to estimate operational conditions for a specific future
period.
In [9], a model for predicting oil-walled equipment failures using the Feed
Forward Neural Network is presented. These pieces of equipment can experience
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problems due to sand accumulation, corrosion, pressure variation, and other fac-
tors. Therefore, to ensure the performance of the equipment and avoid costs asso-
ciated with losses, it is essential to identify the origin of these failures early. The
proposed model proved effective in predictions, surpassing the results achieved
by models such as Random Forest and Decision Tree. These results indicate
that FFNN-type neural networks can represent a practical choice for the failure
prediction problem.
Some types of recurrent neural networks have also gained prominence due
to their efficiency in dealing with time series. In [11], a method for predicting
equipment operating status at a power station through historical data using
LSTM cells is presented. This method achieves a lower RMSE value than the
ARIMA model, proving the capability of LSTM networks in handling time series
analysis tasks.
Depending on the challenge requirements and the nature of the data, net-
works that use LSTM layers can also be combined with other layers, such as
GRUs. This strategy can increase the model’s generalization capability, making
more robust predictions. Thus, in [4], a method for predicting abnormal operat-
ing conditions is presented, combining physical knowledge in a neural network
model, meaning the model only receives physically essential features for solving
that problem. An LSTM-GRU-based model for multivariate time series was con-
structed to perform the prediction, integrating two RNNs to predict the future
trend of the critical variable data. The proposed method is applied to a case study
involving an abnormal condition of crude oil with water in a catalytic cracking
unit, showing superior prediction performance compared to other models.
3 Time series based risk assessment
Operational or maintenance failures can prevent the proper flow of liquid, re-
sulting in its accumulation inside the drum. This scenario represents a risk to
the stability of the process, as the accumulation can result in the activation of
the trip as a safety barrier. The trip is an action taken to interrupt the gas flow,
preventing the liquid from exceeding the maximum allowed level and entering
the pipes, extinguishing the flames of subsequent equipment fed by the clean gas
from the drum. If the flames are extinguished, gas can accumulate inside the
equipment, which could cause an explosion if there is a conducive environment.
Although necessary, activating this mitigation barrier causes a temporary halt
in production, as the gas supply to the equipment is interrupted for a certain
period.
For the development of this study, we created a DT of a knockout drum
in AVEVA Dynamic Simulation, which allows us to simulate various behaviors
to collect the necessary data for training and evaluate the model and analysis
technique. The simulator enables sending sensor reading values to external soft-
ware and receiving external commands from other software to control system
components. This communication was carried out through the OPC standard.
Real-time risk assessment using time series 5
Figure 1 presents a diagram that illustrates the modules developed for col-
lecting the training dataset. These modules communicate with the DT through
an OPC server, transmitting data to the simulation and storing the results. The
goal is to generate random situations, risky or not, to capture information about
the vessel’s behavior during these situations. This information is later used in
the training of the neural network model.
Fig. 1. Training data generation architecture diagram
The solution’s architecture can be visualized for real-time risk prediction in
the diagram presented in Figure 2. Through an OPC server, the digital twin sends
the values collected by the sensors, which go through a preprocessing stage aimed
at normalizing the values and selecting only the variables expected by the model.
Then, these values are directed to the predictive model, which makes predictions
with the goal of displaying the expected future behavior. These predictions are
presented to the operator, who can follow them and decide on the automatic
control of the gas source that feeds the vessel.
Fig. 2. Prediction architecture diagram
This can deactivate the flow of contaminated gas from the refinery and ac-
tivate the flow of gas from the external company, providing contaminant-free
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gas as soon as the risk of a trip is detected. Thus, the activation of the trip is
avoided, as the liquid present in the vessel stabilizes while the gas continues to
feed the subsequent processes.
This modularization allows improvements and updates to be implemented
with less effort, optimizing the continuous enhancement process. Furthermore,
asynchronous communication between components is essential for applications
in real-world industrial environments. This feature is due to the large amount of
data constantly generated in these environments, making it essential for efficient
data management.
3.1 Digital Twin of a knockout drum
In the petrochemical industry, dynamic simulators are essential. They replicate
the behavior of systems and processes, allowing for accurate analyses and reli-
able predictions in different scenarios. Through these simulations, it is possible
to assess the impact of variables, test different configurations, and optimize op-
erations, resulting in cost and risk reduction as well as increased efficiency and
productivity.
Through a risk analysis of a knockout drum, we identified some scenarios
that could lead the vessel to an undesired state. Based on the Bow-Tie diagram
developed in the work [1], we constructed an analysis to trace possible threats
that could result in excess liquid in the vessel. The identified threats were leak-
age in the drum, which can disturb the balance of internal pressure; the high
flow entering through the gas pipeline, where liquid accumulation can occur if
the drainage system is not prepared for sufficient flow; failures in level transmit-
ters, this information being essential in decision-making processes; operational
dysfunction in processes prior to the vessel, which can cause gas contamination.
We used AVEVA Dynamic Simulation software to implement a detailed
model of a knockout drum, including the necessary instrumentation for observ-
ability and process control. In the simulated scenario, the drum receives a flow of
natural gas from other refinery processes. After removing impurities in the gas,
the drum feeds the boilers for thermoelectric power generation, which supplies
steam and electrical energy to the entire refinery. Figure 3 presents the model of
the drum developed in the simulator. The model was based on the work [1] and
adapted to meet the risk scenarios identified in our analysis.
The knockout drum V1 has a diameter of 2.5 meters and a height of 3 me-
ters. SRC1 is the source of natural gas from the refinery. Table 1 presents the
chemical compounds present in this gas. For simulation purposes, contamination
is performed by the presence of water in its composition, which can be variable,
allowing us to evaluate the system’s behavior in high-liquid flow scenarios. The
gas has a temperature of 40◦C and a pressure of 3.5kg/cm
2
.
The SRC_CONCESSIONÁRIA and VALVE6 control the flow of gas from
the external company. This gas has the same composition as the refinery gas
but is free of contaminants,as the external company performs a prior treatment
to eliminate them. Activating the flow to use the clean gas from the external
company ensures the temporary continuity of natural gas supply to the drum
Real-time risk assessment using time series 7
Fig. 3. Knockout Drum model in AVEVA Dynamic Simulation
Table 1. Composition of natural gas
Component kg-mol
Nitrogen 0.0079
Carbon dioxide 0.0006
Methane 0.9782
Ethane 0.0095
Propane 0.0026
Butane 0.0013
Water Variable
until the liquid level is normalized, preventing an unplanned process shutdown.
The transmitters TR1, TR2, and TR3, operating redundantly, measure the liquid
level inside the drum. A voting system was implemented to ensure measurement
reliability. The liquid height readings are carried out simultaneously, and any
divergent values are discarded. The resulting final value is a reference for opening
valve VALVE4, which allows the internal liquid to flow. The valve VALVE5 is
used to simulate leakage scenarios of the drum’s content. This valve does not
require control systems as it serves only as an artifact to assist in the validations.
3.2 Dataset generation
We simulated some scenarios to observe the DT’s behavior and collect data from
the drums’ sensors to create our training and testing set. The evaluated scenar-
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ios were the risk scenarios described in subsection 3.1. For this, we developed a
module that communicates with the virtual model, assigning values to the con-
trol variables according to the failure scenarios to be simulated. The controlled
variables at this stage were leakage valve opening rate, noise insertion in the
readings of level transmitters TR1, TR2, and TR3, and control of the amount
of liquid in the natural gas flow.
This module consists of an input generation algorithm that generates random
values for each simulation variable. The entry of liquid through the gas pipeline
is simulated through random numbers between 0.0 and 10.0 that are generated
at each data reading, simulating the dynamic variation of the volume of liquid
present in the gas. The definition of the values in question aims to simulate an
intense flow of liquid in the vessel. This choice is justified by the fact that the
liquid will represent about 91% of the final product after normalizing the gas
composition by the simulator. Randomness is also applied to the simulation of
noise in level transmitters and in the opening of the leakage valve, with a 5%
probability for each event.
The probability values were defined through tests; the algorithm was run
with different probability configurations, and the results obtained were analyzed.
These values aim to ensure the representativeness of the data from each risk
scenario in the final training and evaluation set. Table 2 describes the tested
probability values and the results achieved. These values were applied in a routine
until a set of approximately 135,000 records was generated.
Table 2. Comparison between the probability values of the DT inputs
Transmitter noises Leakage
Prob. Quantity Percentage Quantity Percentage
0.01 3036 2,23% 985 0,72%
0.02 6023 4,43% 2037 1,50%
0.05 13984 10,30% 4955 3,65%
0.1 26984 19,88% 10034 7,39%
0.4 78343 57,72% 39840 29,35%
0.5 87511 64,48% 49941 36,80%
After analyzing the results obtained, the value of 0.05 was chosen because it
presents a balance between the number of occurrences and fidelity to the real
behavior of the system. The values 0.01 and 0.02 resulted in an insufficient num-
ber of occurrences for robust analysis, which could compromise the validation
of the simulated scenarios due to a lack of sufficient examples. On the other
hand, probability values higher than 0.05 generated an excessive number of oc-
currences, distorting the natural dynamics of the system. This distortion could
lead to the creation of an artificial model, compromising the reliability of the re-
sults. Therefore, the choice of 0.05 as the probability value is based on its ability
to generate sufficient occurrences for reliable analysis without compromising the
model fidelity.
Real-time risk assessment using time series 9
We built a module that reads the states of the simulation variables and stores
them in a structured format, recording the behaviors of the virtual model dur-
ing the algorithm execution. This structure facilitates the processes of treatment,
training, and evaluation of the model, allowing a deeper analysis of the simu-
lation results. The simulation was run for 65 hours, generating a dataset with
12 trip events distributed among 135,000 records. Each record is composed of
56 attributes. This information allows for a better understanding of the drum’s
behavior in different scenarios and operating conditions, supporting validation,
optimization, and model improvement.
3.3 Risk prediction model
The process of defining the structure of the model is a fundamental step in the
development of a model based on artificial intelligence. Therefore, it is necessary
to understand the characteristics of the problem and what answers are desired.
The problem presented in this work consists of analyzing the behavior of the
liquid level in the knockout drum over time, considering adverse situations such
as sensor failures, high liquid flow, and leaks. Thus, analyzing time series to
identify patterns and trends is a basis for predicting future behaviors.
LSTM layers stand out for their ability to handle long-term dependencies,
a key feature for dealing with time series. In [3], the authors present a study
introducing the use of the Seq-2-Seq technique in the industrial context for energy
consumption modeling. This technique follows an encoder-decoder model and
uses LSTM layers to map the input sequence into a fixed-dimension vector.
Then, another LSTM layer is employed to decode the target sequence from this
vector [6]. The results demonstrated this approach’s effectiveness in sequence
analysis, allowing for the detection of patterns and precise information inference.
Thus, the neural network model proposed in this study implements the Seq-2-
Seq technique, using LSTM layers as encoder and decoder. Figure 4 illustrates
the architecture of the proposed model.
Initially, the input sequence’s values were normalized using the MinMax tech-
nique. In the encoding stage, the LSTM layers process the sequence, capturing
the temporal characteristics and dependencies of the data. The result of this en-
coding is a vector representing the model’s internal state. In the decoding stage,
this vector is used as input for another LSTM layer, which generates an output
sequence. Finally, this sequence is processed by two fully connected layers, which
are responsible for generating the final output vector with the dimension of the
prediction horizon. Dropout layers are used to prevent the model from being
overfitted.
3.4 Model training
To evaluate the model’s generalization capability and estimate the ideal values
of its hyperparameters, we conducted a series of training sessions to verify the
model’s behavior in different scenarios and identify the best configuration. We
used the ADAM optimizer with a learning rate of 0.001 to train our model. Each
10 XXXXXXXXXX et al.
Fig. 4. Model architecture
experiment was run for 100 epochs. To maintain test consistency, we set the seed
value to 0. We used the TimeSeriesSplit technique for cross-validation.
As input, the model received a time series extracted from the simulation.
Each series comprised 30 sets of values obtained from sensor readings, followed
by the next 15 values of the dependent variable. In this process, we adopted the
strategy of reducing the number of features sent to the model, selecting only the
variables influencing the liquid level.
Table 3 presents the evaluated hyperparameters, the tested values, and the
best results obtained. From this analysis, it was possible to identify the ideal
configuration of hyperparameters for the proposed model, optimizing its perfor-
mance for the problem at hand.Table 3. Analysis of model hyperparameters
Hyperparameter Best value Tested values
LSTM units 64 [16, 32, 64, 128]
Dropout rate 0.2 [0.0, 0.2, 0.4]
FC units 16 [8, 16, 32]
Batch size 128 [32, 64, 128]
The Seq-2-Seq model receives as input a time series formed by the features
of a specific period and a set containing the values of the dependent variable,
where the definition of its dimensions directly influences the assertiveness of
the model’s predictions. To determine the best dimensions, we conducted some
experiments, which are presented in Table 4.
Real-time risk assessment using time series 11
Table 4. Analysis of the dimensionality of time series
Input Output RMSE
15 15 0.0491
30 15 0.0327
30 30 0.0739
When the input is too short, relevant information can be lost, compromising
the model’s ability to capture complex temporal patterns. This feature is espe-
cially problematic in time series that exhibit long-term dependencies. Similarly,
when dealing with very long outputs, the model may face difficulties in maintain-
ing relevant contexts over time. Moreover, very small outputs may not contain
enough details, resulting in simplified and less accurate predictions. As indicated
by our experiments, the model that uses a time series composed of data from the
last 30 readings and makes predictions for the following 15 units of time showed
the best performance, demonstrating a balanced relationship between input and
output dimensions.
We evaluated using hyperbolic tangent and ReLU as activation functions
for the LSTM layers. The hyperbolic tangent is a function that incorporates
non-linearity, allowing the network to learn more complex relationships between
input and output data. The ReLU function has better computational efficiency
and avoids the problem of gradient vanishing. We conducted two experiments to
assess the performance of the two activation functions on our training set. The
results are presented in Table 5.
Table 5. Results of the activation function analyses in the LSTM layers.
Activation function RMSE
ReLU 0.0623
Hyperbolic tangent 0.0327
Based on the collected results, the hyperbolic tangent was chosen as the ac-
tivation function in the LSTM layers. This choice was made due to its better
performance than ReLU, demonstrating greater flexibility in capturing corre-
lations between variables. This feature, in turn, increases the neural network’s
ability to learn more sophisticated representations.
4 Experiments and results
We conducted two evaluations to analyze the functioning of the technique pro-
posed in this work. Our objectives were to understand the efficacy of our model
and ensure that the proposed solution is applicable in various situations to pre-
vent liquid accumulation in the drum.
12 XXXXXXXXXX et al.
The first evaluation focused on collecting information about the model’s per-
formance in preventing liquid accumulation. For this, we compared the operation
of the knockout vessel in two distinct scenarios: with and without the use of the
technique presented in this work. To conduct the evaluation, both simulations
were run for a period of 24 hours, and we used the same generation module
described in subsection 3.2 to generate the inputs for our simulation.
During the experiment that did not incorporate our technique, four trips were
identified, resulting in the interruption of boiler operations due to the temporary
suspension of the gas supply. In the experiment where our technique was used,
no trips were observed, thus ensuring stability in the gas supply to the boilers.
Fig. 5. Liquid level behavior during trip prevention tests
In the graphs presented in Figure 5, the blue line indicates the real-time
liquid level reading, and the green line indicates the prediction generated by
our model. Figure 5 (a) shows the simulation’s behavior without applying the
proposed technique. We can observe that the liquid level reached the maximum
limit, and the trip was triggered to interrupt the gas supply to the boilers. In
Figure 5 (b), with the technique in operation, it can be seen that, as it approaches
the maximum level, the refinery’s gas source is automatically switched to the
treated gas from the external company. This situation ensures a continuous gas
supply to the boilers and provides more time for liquid drainage.
In our second evaluation, we explored the performance of the model’s pre-
dictions in the presence of noise, transmitter failures, and leaks. This analysis
aimed to understand how the proposed model reacts to adverse situations and
how different types of failures influence its estimates. To assess the accuracy
of the inferences, we ran each simulation for 5 minutes and stored the average
difference between the actual liquid level value and the value estimated by the
proposed model.
Figure 6 (a) demonstrates the excellent accuracy rate of the proposed model
in predictions, even with the presence of leakage. The lines in the graph remain
Real-time risk assessment using time series 13
Fig. 6. Liquid level behavior under evaluation scenarios
close, indicating a minimal discrepancy between the actual and inferred values.
The average error in this scenario was approximately 0.04.
The impact of transmitter failures on inference accuracy can be seen in Figure
6 (b). A slight difference between actual and inferred values is observed, as all
transmitters provide incorrect information. This failure interferes with the voting
system, leading the model to infer values with less precision. Tests revealed that
the predicted values were always slightly higher than the actual values, where the
average error was approximately 0.09. This indicates that the proposed technique
still showed promising results in preventing trips because even with transmitter
failures, the switch of gas source occurred before the liquid reached its maximum
level.
The insertion of noise did not significantly impact the accuracy of the in-
ferred values, as demonstrated in Figure 6 (c). The average error value in this
scenario was 0.008. This is possible because the model receives input information
from different components of the simulation. Thus, the importance of the input
attributes is distributed, and if some value is incorrect, the impact on inference
is smaller.
However, if many pieces of information are incorrect, the error rate may
increase, as shown in the scenario of Figure 6 (d). In this scenario, failures in
level transmitters and noise in sensors were simulated. Throughout the execution
of the simulation, a gradual increase in the disparity between the actual values
14 XXXXXXXXXX et al.
and the predicted values were observed, indicating that our technique did not
achieve satisfactory results in this scenario.
The model’s ability to maintain its accuracy in most of the presented risk
scenarios ensures the stability of the proposed technique in the production pro-
cess. This indicates that even in abnormal situations, the model is still capable
of providing good estimates, allowing corrective measures to be taken to avoid
the need to trigger the trip.
5 Conclusion
This work presented a real-time risk assessment technique for preventing liquid
accumulation in a knockout drum. A DT was constructed to collect training data
and evaluate the risk scenarios identified in our Bow-Tie analysis. We propose
a set of modules with an algorithm for generating the training and testing data
sets. To perform inference, we present a recurrent neural network architecture
using LSTM layers utilizing the Seq-2-Seq technique to extract patterns from
time series and make inferences about liquid-level behavior.
This research represents a significant advancement in risk analysis associated
with equipment and processes in the oil and gas industry. Artificial neural net-
works offer an approach that eliminates the need for experts and allows real-time
inferences. The presented model stood out for its ability to produce consistent
and reliable results, even when the data providedby sensors are noisy. Further-
more, this real-time risk assessment technique can be used in other industrial
contexts if necessary modifications are made. The source code and the dataset
are available at: https://anonymous.4open.science/r/analise-risco-7BD6
6 Future works
The technique proposed in this work has great potential to enhance process
safety. However, future research is necessary to understand its capabilities better
and maximize the results obtained. A promising area of investigation would be to
explore the impact of using a more extensive set of variables on the technique’s
effectiveness and precision. Moreover, incorporating other variables inferred by
different machine learning models may increase the robustness of the results.
Disclosure of Interests. The authors have no competing interests to declare that
are relevant to the content of this article.
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	Real-time risk assessment using time series: A case study of a knockout drum