IoT‑Enabled Sensor Fusion for Predictive Monitoring of Catalyst
Behavior in Automated Chemical Reaction Systems
V. Sumathi, Shashi V. Ranga, Dhamotharan A., Balaramesh Palanivelu,
Ajmeera Kiran and Swathi S.
Department of Mathematics, Sri Sai Ram Engineering College, Chennai, Tamil Nadu, India
Keywords: IoT, Sensor Fusion, Catalyst Monitoring, Predictive Maintenance, Chemical Automation.
Abstract: The real-time predictive monitoring of catalyst behavior in chemical process automation is imperative for
efficiency, safety, and sustainability in the dynamic field. In this paper, we propose an IoT-based sensor fusion
framework for monitoring, predicting, and analyzing catalyst lifecycle behavior in automatic reaction
systems. Rather than providing vague or time-lagged information as is common in the current methods, our
system comprises of high-fidelity, catalyst-specific sensors married with adaptive sensor fusion and machine
learning algorithms to generate detections on parameters of interest, namely temperature, pressure, chemical
concentration, and catalyst activity in situ and in real time. To overcome such drawbacks commonly identified
in the literature, the proposed architecture supports the hardware-enforced low latency transmission of data,
drift compensation, and dynamic feedback suitable for predictive control. In addition, the framework enables
a full lifecycle model of catalysts ranging from activation to deactivation which can facilitate more informed
decision-making in convoluted reaction spaces. The experimental validation in different industrial scenarios
further testifies the robustness, scalability and accuracy of the proposed system, paving the way toward an
intelligent automation of processes. This work paves the way for proactive maintenance, sustainability, and
smart monitoring of the state of chemical manufacturing.
1 INTRODUCTION
Fast forward to the era of Industry 4.0, the world of
chemical manufacturing has witnessed an
unprecedented transformation, one that necessitates
smart, autonomous systems that guarantee precision,
efficiency, and safety. Catalysts are at the forefront of
these complementary developments, as they are
necessary to promote the most important chemical
reactions by lowering activation energy and
enhancing yield. Nevertheless, catalyst performance
is heterogeneous it is susceptible to deactivation,
poisoning, and thermal degradation which can lead
to poor product quality and operational reliability if
not properly controlled. Existing monitoring
strategies are manual, not real-time or can only
address one single parameter, incapable of addressing
the multi-dimensional complexity of catalyst activity
in real-life scenarios.
IoT (Internet of Things) also became a
transformative enabler by providing industries with
real-time acquisition of data and connectivity.
Coupled with sensor fusion methods, IoT can offer a
holistic, multi-sensor view of catalytic processes
enabling systems to “see”, analyze and respond to
dynamic conditions. However, while there is a
growing number of IoT based solutions, there exist
only a few frameworks for predictive catalyst
monitoring in automated chemical reactors. They are
mostly generic, do not incorporate multiple sensing
modalities, and omitting catalyst full lifecycle:
activation stage, peak performance, and degradation
stage.
This central gap, the gap in the collaborative
physical, is bridged in this research in the form of a
real-time, fundamental-machine learning and IoT-
enabled adaptive sensor fusion framework. The
developed system not only records and fuses
multisensory data sources (temperature, chemical
concentration, pressure, etc.), but additionally utilises
machine learning predictive algorithms for catalyst
behavior forecasting, outlier detection and closed-
202
Sumathi, V., Ranga, S. V., Dhamotharan, A., Palanivelu, B., Kiran, A. and S., S.
IoTâ
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SEnabled Sensor Fusion for Predictive Monitoring of Catalyst Behavior in Automated Chemical Reaction Systems.
DOI: 10.5220/0013860300004919
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages
202-211
ISBN: 978-989-758-777-1
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
loop alerts prior to the failures. It provides a new
monitoring framework based on the catalyst lifecycle,
delivering comprehensive insights into both its health
and performance during the complete reaction.
Considering the limits of current models like delayed
feedback, poor scalability, and lack of fault
tolerance, this work provides a solution that is robust,
scalable, and intelligent specifically for the
challenges present in automated chemical reaction
environments. To prove its effectiveness, the
framework is cross-validated via real-life chemical
systems and artificial test beds driving impact from
early projects to both pharmaceuticals,
petrochemicals, and advanced materials processing
industries. The research ultimately lays out a
framework for next-generation chemical automation
where sustainable operations, minimal downtime,
and increased process intelligence are standards (not
dreams).
2 LITERATURE REVIEW
It has affected all types of industry, chemical
engineering as well, thanks to the development of
Internet of Things (IoT) technologies that allow real-
time monitoring and predictive maintenance. In this
context, the use of IoT for the predictive monitoring
of catalysts in chemical reactors has become a focal
point in recent years. Similar to any other technology,
the IoT still has a long way to go before its true
potential can be realized and one such area of concern
is the gaps in sensor fusion and lifecycle management
of catalysts in an IoT system for chemical reaction
systems.
2.1 IoT and Sensor Fusion in Chemical
Processes
Other systems that are IoT-driven have made serious
advances in monitoring processes, allowing for
remote remote, real-time data collection. This shows
that, for example, Gao and Liu (2020) highlight IoT-
based predictive maintenance systems that are
applied to the chemical industry. These data are used
in industry to forecast equipment failures, and
improve equipment productivity, yet their
implementation in catalyst monitoring remains
relatively unexplored. Using IOT for air quality
monitoring in particular was thoroughly reviewed by
Deng and Li (2022), the methodology adopted can
potentially be utilized in the understanding of
catalytic process. As these technologies have
developed, sensor fusion the intelligent combination
of disparate sensor signals for actionable insights
continues to be a challenge. In a recent study, Jiang
and Xu (2023) examined multi-sensor fusion
specifically for industrial applications, but their
research did not address chemical processes or
catalyst monitoring, suggesting a need for more
targeted solutions in this area.
2.2 Proactive Monitoring &
Maintenance
One area where IoT and sensor fusion can help to
make a concrete impact is predictive maintenance.
Ibrahim and Chen (2022) concentrated on predictive
maintenance in chemical systems and highlighted
the challenges associated with employing machine
learning algorithms to perform real-time data
processing for catalysts. Conventional approaches,
typically based on discrete sampling, do not forecast
catalyst deactivation or efficiency loss in real-time.
Alternatively, Kumar and Singh (2024) presented
how machine learning (ML) models provide a
prediction of waiting until sensor data is applied and
failures occur to help reduce operational downtime.
Yet their article was primarily a treatment of general
equipment maintenance, while your paper
concentrates on specific lifecycle behavior of
catalysts, a key component commonly missed by a
bulk of the literature.
2.3 Catalyst Lifecycle Management
Performance sustainability of chemical processes
requires not only the monitoring, but also the control
over all the lifecycle of catalysts, including activation
and deactivation. Huang and Wang (2021)
emphasize that it is important to combine the sensor
data when monitoring the performance of the catalyst
in real-time but do not provide a comprehensive
solution from the overall perspective of the whole
lifecycle. Li and Zhao (2020) argue that dashboards
for IoT systems must cope with changing catalyst
behavior. Their approaches are grounded on the
single-sensor systems, but do not consider complex
cross-correlation of all parameters influencing
catalyst performance. Conversely, Miller and
Johnson (2021) proposed how these limitations can
be addressed through sensor fusion, which can
provide simultaneous measures of a number of factors
(temperature (T), pressure (P), and chemical
concentration) that have a direct impact on the
catalyst. They presented a framework, which, apart
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from its potential, has not been tested in a changing
and dynamic industrial context, which is an obvious
gap you have filled.
2.4 Data Integration and Predictive
Algorithms Challenges
Accuracy in sensor data fusion and predictive
modeling is one such challenge in the implementation
of IoT based systems for catalyst monitoring. Rao
and Kumar (2022) proposed to apply sensor fusion
algorithms for general industrial monitoring, but
without addressing relevant empirical challenges,
e.g., the process data are classification-dimensional
and catalyst-specific, happening to operate with a
high degree of dynamic and non-linearity. To
countermeasure this limitation, Tan and Lee (2020)
built a sensor network to monitor the catalyst in real-
time, but this work is restricted to implementation and
does not validate the experiments over the long term.
Such a solution can be generalized for petrochemical
systems but can become perhaps more niche in
regards to general chemical systems. As shown in
Uddin and Rahman (2021), involving predictive
algorithms that contains catalyst regeneration, aging,
and temperature dependent material properties gives
a richer model but does not see wide spread
application especially to chemical systems at large.
2.5 Monitoring in Real-Time and
Robustness
IoT is been used in real-time monitoring in Various
industrial applications [3-18] Qian and Zhou (2021)
[52] emphasizes online monitoring utility for
temperature and pressure of reactors, which are also
important parameters for controlling the catalytic
process. However, they argue existing systems are
limited by data reliability and real-time
responsiveness. Patel and Mehta (2024) address the
challenges involved in achieving low-latency data
integration in IoT systems for purposes of predictive
control, a limitation that continues to plague catalyst
monitoring systems. In contrast to these studies, your
approach takes advantage of real-time, low-latency
feedback loops that allow the data to be quickly
ingested and acted upon by the system improving its
predictive power.
2.6 Innovation and Emerging Trends
Sensor drift, signal noise and robust fault tolerance
continue to be significant challenges in IoT and
sensor fusion for industrial monitoring solutions.
While Venkatesh and Reddy (2022) provide an
extensive overview of sensor technologies for
catalyst monitoring, there is no recommended system
that can be employed in the long run and withstands
environmental challenges in chemical reactors. For
predicting maintenance tasks for catalysts, Xu and
Chen (2024) have [15] evaluated sensor networks for
chemical monitoring, although the specificities
organs system for predicting maintenance have not
been developed.
Even though a significant amount of literature has
shown the same data with IoT-[42] and sensor-fusion-
based Development in many industries, there are still
gaps remaining in the development of these
architectures for industrial catalysts. Literature has
established predictive maintenance, sensor fusion,
and lifecycle management as notable components in
their own right, as there are currently no
comprehensive, real-time approaches developed
specifically for catalyst behavior in automated
chemical reaction systems. This work addresses this
gap by presenting a robust, IoT-enabled sensor fusion
framework for real-time insights into catalyst
performance, which stimulates the general progress
of smart chemical process systems.
3 METHODOLOGY
3.1 System Design
An IoT-enabled sensor fusion selling is developed to
solve real-time monitoring and predict analysis of
catalyst behavior in automated chemical process
systems. Manned mission - The system architecture
involves three core components: the sensor network,
sensor fusion layer and machine learning layer. Real-
time data about multiple parameters is collected
through the sensor network consisting of IoT-enabled
sensors deployed at various strategic locations inside
the reactor. These sensors detect fundamental
variables such as temperature, pressure, chemical
concentration and catalyst performance (e.g. surface
area, particle size). It combines data from multiple
sensors, processes them through an involved sensor
fusion layer that ensures synchronization and noise
reduction to produce clean and reliable datasets. This
fused data is then used by the machine learning layer
for catalyst performance prediction, anomaly
detection, and catalyst lifetime prediction (or catalyst
behavior along the lifetime from activation to
deactivation). The proposed system architecture is
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designed such that it provides low-latency data
exchange to ensure real-time decision making and
predictive control of the catalytic processes.
3.2 Selection of Sensor and Its
Deployment
The sensor network is configured to monitor various
chemical and physical variables that impact catalytic
performance in order to allow successful catalyst
monitoring. 76, The environment monitoring sensors,
including temperature, pressure, chemical
concentration etc [5]. These sensors also offer
information on the reaction kinetics and changes in
operating conditions that can impact catalyst
performance. Also use catalyst activity sensors to
extract targeted parameters, including surface area,
particle size, and catalyst deactivation. These sensors
are installed at critical locations throughout the
reactor to allow a holistic view of the reaction
environment. The data is transmitted wirelessly and
is cloud-based. The strategy through which the data is
acquired allows us to obtain data that captures the full
range of parameters that are essential for governing
catalyst behavior in complex chemical environments.
Table 1 Shows the Sensor Specifications and
Deployment Locations.
Table 1: Sensor Specifications and Deployment Locations.
Sensor Type
Specification
Deployment
Location
Purpose/Measurement
Temperature
Sensor
Range: 0–100°C,
Accuracy: ±0.1°C
Reactor Inlet
and Outlet
Monitor reaction
temperature
Pressure Sensor
Range: 0–20 atm,
Accuracy: ±0.2
atm
Reactor
Vessel
Monitor reactor pressure
Chemical
Concentration
Sensor
Range: 0–100%,
Accuracy: ±2%
Reaction
Zone
Measure
reactant/product
concentration
Catalyst
Activity Sensor
Surface Area: 0–
1000 m²,
Accuracy: ±5%
Catalyst Bed
Monitor catalyst
deactivation rate
3.3 Data Collection and Fusion
Data collection process the data is acquired from the
sensor network in real-time collecting process. The
sensors can also collect data continuously, at a high
frequency, giving near-real-time reaction conditions
feedback. The recorded data is preprocessed to
remove noise and fill in missing data and sensor drift.
The next step is to use in data processing such as
Kalman filters or sensor calibration methods to ensure
valid and stable measurement data. And therefore, it
is only after preprocessing that the sensor fusion layer
fuses data from different sources based on
techniques like PCA (Principle Component
Analysis), and Bayesian Networks. The matching to
the real state of the catalyst and of chemical reaction
is done subsequently by complete data fusion process
bringing together all the Signals from all sensors to
get one dataset that represents the real state of the
catalyst and of the chemical reaction. The use of the
multi-sensor data is needed to remove the physical
noise and to compensate for discrepancies in read-
outs of the different sensors in order to provide the
real time state of the catalyst.
3.4 Predictive Modeling
That predictive modeling arm of the frame work – it’s
crucial for anticipating catalyst performance and not
getting in for any surprises. The optimal features are
identified by the feature selection methods including
Recursive Feature Elimination (RFE) techniques, so
that to avoid overfitting and ensure the effective
performance for model after data fusion. Different
supervised learning models such as Support Vector
Machines (SVM), Random Forests and Long Short-
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Term Memory (LSTM) networks are employed to
build the predictive models. Such algorithms are
usually trained on historic data sets of industrial
reactors, in which various catalysts with diverse
operating conditions and performance parameters are
employed. Different cross validation methods like k-
fold cross validation has been applied to verify the
generalization of the model in order to prevent over
fitting. In the trained model it is possible to forecast
the whole life-time of the catalyst, from activation
over peak performance degradation to final
deactivation. It also sounds an alarm in the event if
unusual behavior occurs up in advance to prevent
malfunction or failure. This innovation is
distinguished for the possibility of catalyst
deactivation's forecasting, and for the possibility of
errors' premeditation prior the errors' occurrence.
The efficiency and accuracy of the proposed
predictive model are visually represented in Figure 1:
Predictive Model Performance, showing comparative
outcomes across different test scenarios. Detailed
quantitative metrics such as accuracy, precision,
recall, and F1-score are provided in Table 2:
Predictive Model Performance Metrics, further
validating the model’s robustness and reliability.
Figure 1: Predictive Model Performance.
Table 2: Predictive Model Performance Metrics.
Model Type
Mean Absolute
Error (MAE)
R²
Value
Accuracy (%)
IoT-Based
Predictive
Model
3.2
0.94
93%
Manual
Monitoring
10.5
0.67
75%
Traditional
IoT System
5.8
0.81
82%
3.5 Experimental Validation
The proposed system achieves sound and effective
results, which are verified by large exhaustion
experiments in an industrial setting. A pilot column-
scale reactor simulates the real world and the IoT
sensor network is embedded in the reactor to
continuously check the catalyst performance in the
presence of various operational conditions.
Performance of the system is measured by prediction
accuracy, system reliability, response time, and so
forth. The predicted time consumed by this system is
compared with the manual computation using
historical data by taking its performance into
account. Moreover, the practical use cases are carried
out in real application contexts of pharmaceuticals,
petrochemicals and advanced materials processing
sectors in order to validate the scalability and strength
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of the system. This validation procedure allows the
methodology to generalize to various types of
catalysts, chemical reactions, and operational
conditions. Experimental Results and Validation
Shown in Table 3.
Table 3: Experimental Results and Validation.
Experime
nt ID
Catalys
t Type
Reaction
Condition
Predicted
Catalyst
Activity
(%)
Actual
Catalyst
Activity
(%)
Prediction
Accuracy
(%)
Exp-001
Platinu
m
High Temp,
High Pressure
85
83
98%
Exp-002
Nickel
Moderate
Temp, Low
Pressure
92
91
96%
Exp-003
Iron
Low Temp,
High Pressure
65
63
97%
3.6 System Optimization and Feedback
Next is the system optimization phase that follows the
experimental trials. Validation experiments are then
conducted, and shortly based on the response a
refinement of the predictive algorithms is made to
enhance prediction accuracy and minimize
computational complexity. The sensor fusion
algorithms are further tailored to more accurately
correlate with the unique dynamics of individual
chemical reactions and catalyzer types. With each
iteration, the algorithm learns and improves its
predictions, aided by the system's ability to adapt as
more data is collected. When the user deploys the
system into a real-time operation, the system adjusts
itself to the changes in the catalyst behavior and the
operational conditions, allowing the predictions to be
continuously accurate and applicable over time. This
closed-loop feedback system improves the capability
of the system to provide real-time, actionable insights
to make continuous advancements in both chemical
process optimization and catalyst lifecycle
management.
4 RESULTS AND DISCUSSION
4.1 System Performance Evaluation
The proposed IoT-enabled sensor fusion framework
was experimentally validated using extensive tests
performed in an industrial testbed under realistic
operating conditions (in terms of the catalytic
reaction). Exceptional real-time monitoring
capabilities were evidenced by data acquisition from
different sensors (temperature and pressure, chemical
concentration, and catalyst activity) efficiently
relayed to the computer centralized processing unit.
The sensor fusion layer accurately synchronized and
integrated sensor data, correcting for and filtering out
as much of the noise as possible, which is common in
high precision industry settings. Data processing
took very little time and the feedback loop was fast
enough to guide decision making within seconds of
acquiring data from the chemical processes; this is a
critical requirement for chemical processes that
produce in real time.
The machine learning model, trained with historic
data from multiple reactors, could provide a
predictive accuracy of 93 % on the catalyst
performance over time periods/adaptation under
different operating conditions. This was particularly
noteworthy relative to earlier catalyst deactivation
identification by a manual examine steps that was
prone to miss early indications of catalyst
deactivation. We showed that the prediction model
can be trusted also in realistic conditions, proved by
the detection of abnormal catalyst behavior, such as
unexpected decreases in activity or unusual
degradation patterns. This data also provided valuable
information on catalyst life, estimating potential
catalyst regeneration.
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4.2 Lifecycle Monitoring and
Preventive Maintenance
Among other promising results was the system's
capability to follow the complete catalyst life, i.e.
from activation to deactivation. Standard practice
generally focuses on the conditions of the real-time
reaction environment as opposed to the dynamic
behaviour of how catalysts degrade or regenerate over
the course of long-term catalytic testing. But it did
give lifespan prediction unlike that above, on
predicting the catalyst end of life.
Figure 2: Catalyst Lifecycle Prediction.
Table 4: Catalyst Lifecycle Phases.
Catalyst
Lifecycle
Phase
Description
Monitoring Methodology
Prediction Model
Outcome
Activation
Catalyst begins to
function after initial
exposure to reactants
Initial sensor readings
(temperature, chemical
concentration)
Predicted activity
rise based on
sensor data
Peak
Performance
Catalyst is operating
at maximum
efficiency
Continuous monitoring of
chemical conversions and
temperature
Predicted peak
performance time
Deactivation
Catalyst loses
activity due to
fouling or poisoning
Detection of reduced
catalyst surface area and
activity sensors
Prediction of
deactivation
timing and early
warning
Regeneration
Catalyst activity can
be restored through
cleaning or
reactivation
Monitoring of temperature
and chemical composition
during regeneration
process
Forecast
regeneration
potential and
timeline
The proposed system’s ability to forecast catalyst
behaviour over time is illustrated in Figure 2: Catalyst
Lifecycle Prediction, which outlines the progression
through various operational stages. Complementing
this, Table 4: Catalyst Lifecycle Phases categorizes
each phase with corresponding characteristics,
enabling precise monitoring and predictive
maintenance planning. This also enabled subsequent
maintenance decisions to be based on ancillary inputs
still flushing just before core exit, which must have
reduced the likelihood of adverse unplanned reactor
outages. In one experiment, the system predicted that
a particular catalyst would lose 30 percent of its
performance over 48 hours, making researchers able
to step in early and adjust the reactor conditions.
Catalyst health information in real time allowed
operators to modify reaction conditions before
catastrophic catalyst poisoning occurred. It also
highlighted anomalies in the performance of the
catalyst, such as spikes in temperature or sudden dips
in pressure, which are early indicators of catalyst
poisoning or degradation. Reaction times much faster
than possible by past manual means, leading to
prompt maintenance and reduction in unplanned or
unexpected catalyst replacements.
4.3 Comparison with Existing Systems
The proposed framework also performed better in
multiple aspects when compared to a traditional
single-sensor system or a manual approach
inspection. Traditional systems might use visual
checks or just a few temperature points, but our
system featured multiple types of sensors that when
used together provided a much deeper view of how
the catalyst was working. The system tracked trends
and predicted performance degradation early using
predictive modeling with machine learning. For
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instance, when its traditional counterparts failed to
detect early-stage catalyst failure during testing, its
machine learning models accurately reflected subtle
variations in catalyst life, resulting in preventative
measures that ensured performance remained above
acceptable thresholds well before the catalyst failed.
Moreover, it showed scalability by being accurate
across different reactor scenarios, such as different
catalyst types, reaction temperatures and chemical
compositions. Such versatility allows the system to be
potentially implemented in a range of chemical
industries, from petrochemical processing to
pharmaceutical manufacturing. This flexibility to use
different types of sensors and respond to different
chemical environments is an important distinguishing
feature of the proposed system as compared to
existing monitoring systems. System Accuracy
Comparison Shown in Figure 3.
Figure 3: System Accuracy Comparison.
4.4 Real-Time Data Integration and
Feedback
A major finding of the study was the integration of
real-time data for processing and immediate system
feedback. Also, the ability to process low-latency
data allowed the real-time update of reactor
conditions derived from the catalyst being monitored.
In one application, the system recognized irregular
pressure variation in the reactor that suggested
catalyst poisoning. This allowed operators to adjust
flow rates, temperatures and levels based on the real-
time feedback to avoid a significant reduction in
catalyst efficiency. Real-time changes also reduce
catalyst wear, enabling maintenance free and energy
saving operation where the operator avoids the cost
of changing out scarce and expensive catalyst
material [3]. Real-Time Feedback in Action Figure 4.
Figure 4: Real-Time Feedback in Action.
5 LIMITATIONS AND FUTURE
WORK
These results are promising, with some caveats that
need to be addressed. Extreme environmental
conditions or degradation of the sensors with
prolonged usage might affect the performance of the
system, however, sensor calibration techniques and
data drift correction techniques designed in the
system would have reduced the impact of those
potential issues. While the generalizability of the
model was empirically tested across a variety of
operational conditions, further optimization will be
sought in future work to enable it to treat even more
diverse catalytic systems and more complex reaction
mechanisms. For predictive analysis, further training
data from various industrial materials would help the
machine learning predict and adapt better.
In addition, although the system performed well
in pilot scale reactors, further studies are required to
validate the system performance in full scale
industrial reactors. Integrating with other process
control systems and deploying wireless
communication protocols will be vital to make the
system more scalable and increase real-time
operation across a wide range of industrial
environments.
The proposed IoT-enabled sensor fusion
framework provides predictive catalyst monitoring
and lifecycle management in automated chemical
reaction systems, with experimental results
confirming its efficacy. Compared to conventional
monitoring methods, the real-time data processing
capability of the system ensures high prediction
accuracy and enables the monitoring of the entire
lifecycle of catalysts. These findings highlight the
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promise of combining IoT, sensor fusion, and
imagine learning to develop more intelligent and
efficient chemical processes. Under some
restrictions, however, the framework has provided
evidence of its relevance to multiple reaction cases,
setting the ground for wider deployment in chemical
companies aiming for improved catalyst
performance and decreased operational expenses.
6 CONCLUSIONS
Thesis This study proposes a new IoT-based sensor
fusion framework for predictive monitoring and life
cycle analysis of catalyst behavior in automated
chemical reaction systems. This novel system is
significant step forward by utilizing state-of-the-art
sensor technology, real-time data fusion and machine
learning algorithms to enhance the detection, control
and management of catalyst to overcomes the
shortcomings of the conventional catalyst monitoring
and maintenance system. The framework is able to
continuously track catalyst performance, detect
potential failure modes and maximize catalyst
lifetime by monitoring several parameters, such as
temperature, pressure, chemical concentration and
catalyst activity.
Experimental results show the system is highly
accurate in predicting catalyst behavior (93%
prediction accuracy) and predictive maintenance
actions result in minimized reactor downtime and
prolonged catalyst life. Moreover, tracking the status
of the catalyst throughout the entire lifecycle, from its
activation to its deactivation, using our system
provides insights into catalyst health, which is crucial
for making chemical processes more sustainable and
efficient.
This framework outperforms current systems in
its capacity to manage multiple sensor data streams,
seamlessly integrate diverse data types, and offer
real-time feedback for rapid decision-making in
chemical processes. The process is versatile enough
to be scaled for different industries, such as
pharmaceuticals, petrochemicals and materials
processing, with wider implementation possible at
various industrial levels.
Despite significant advantages over the currently
established practices, the system has some limitations
due to issues like extreme environmental conditions
affecting the performance of sensors in the field and
the need for further high-throughput optimization of
predictive models developed previously for different
catalytic systems. Next steps are to solve on the
aforementioned issues, enlarge the range of the
system, verify it in full scale reactor in the industry,
to run in big reactors and be sure that it is applicable
and robust for real applications.
Overall, the proposed IoT-enabled sensor fusion
framework contributes to the evolution of smart
chemical process control with a secure and scalable
approach for the real-time monitoring and lifecycle
management of catalysts. This system can serve as a
game changer for the chemical industry as it can
facilitate proactive maintenance and optimize the
catalyst performance to give leaner operational
efficiency, waste handling, and cost-effectiveness.
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