Comparative Study of Deep Reinforcement Learning Algorithm for

Optimization of Hydrodynamic Characteristics in Multiphase

Reactors

Suchita Walke and Jagdish W. Bakal

Pillai HOC College of Engineering and Technology, Mumbai University Rasayani, Maharashtra, 410207, India

Keywords: Hydrodynamic Optimization, Multiphase Reactors, Deep Reinforcement Learning, Soft Actor‑Critic

Algorithm, Proximal Policy Optimization.

Abstract: The proper design of multiphase reactors to optimize their hydrodynamic properties is still one of the most

important challenges in chemical and process engineering due to the complexity of fluid interactions and

dynamic behaviours of these systems. This challenge is addressed in this study, which presents a

comprehensive comparative study of state-of the-art Deep Reinforcement Learning (DRL) algorithms, namely

Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC). Diligently

designed virtual simulation environment mimics the complex functionalities of multiphase reactors for an

accurate assessment of gas holdup, liquid velocity profiles, and bubble size distribution, which are significant

parameters in terms of reactor performance. This involves developing a custom reward function, that weights

these against energy consumption, to allow the reactor to perform ideally. Experimental results indicate that

SAC converges faster to solutions, as well as is more accurate in the optimization of hydrodynamic parameters

and energy casting. DQN is limited by its discrete action space preventing it from being applied to continue

reactors and PPO has a relatively moderate performance. This approach not only highlights the promise of

DRL for optimizing reactor dynamics, but also offers tangible guidance on algorithm selection for practical

engineering implementations. The results open doors to implementing state-of-the-art DRL algorithms in

industrial environments, greatly improving energy-efficient management of industrial systems. Future studies

will be targeted at implementing in the real world and hybrid DRL-CFD frameworks for multiphase reactor

systems.

1 INTRODUCTION

Multiphase reactors are ubiquitous in the chemical

industry, playing a central role in processes like

chemical synthesis, petrochemical refining, and

biochemical manufacturing. The non-ideal flow and

the mixing efficiency of gas, liquid, and solid phases

is of great importance to these types of reactors and

controls product quality. For hydrodynamic

optimization yielding optimal performance, fine-

tuning parameters like gas holdup, liquid velocity

distribution, and bubble size is key. However,

traditional optimisation methods such as; empirical

models and computational fluid dynamics (CFD) are

usually very computationally expensive, and they

cannot respond in real-time (

Ranade, V. V., &

Chaudhari, R. V. 2014). CFD models are widely

utilized but often prove to be limited due to the

enormous amount of computational power needed to

interpret multiphase reactors in hydrodynamic terms

(

Versteeg, H. K., & Malalasekera, W.2007). Moreover,

these simulations are highly scenario-specific and do

not adapt to changes in reactor state over time. Such

empirical models are computationally cheap, but

reliant on experimental data, which means that they

can have a limited generalisability to other reactor

geometries and operating conditions (

Krishna, R., &

Van Baten, J. M. 2001)

. CHALLENGES AHEAD ·

Getting past these issues will necessitate novel tools

that can dynamically optimize the performance of

the reactor while minimizing computational

overhead. Machine learning (ML), now regarded as a

disruptive technology for optimizing processes and

the ability to address high-dimensional, non-linear

dynamics challenges, has recently risen to

prominence. In the portfolio of ML approaches,

reinforcement learning (RL) has shown tremendous

362

Walke, S. and Bakal, J. W.

Comparative Study of Deep Reinforcement Learning Algorithm for Optimization of Hydrodynamic Characteristics in Multiphase Reactors.

DOI: 10.5220/0013930000004919

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 5, pages

362-368

ISBN: 978-989-758-777-1

promise in process control utilizations. (

Sutton, R. S.,

& Barto, A. G. 2018).

RL algs: learn by trial and error

interacting with an environment, reward signals

would give useful feedback to use for decision

making However, traditional reinforcement learning

(RL) methods are not designed for complex

continuous systems, such as multiphase reactors,

since they are based on discrete action spaces that

limit their scaling (

Kober, J., Bagnell, J. A., & Peters, J.

2013).

To overcome these limitations, deep

reinforcement learning (DRL)—a combination of RL

and deep neural networks—utilizes the ability of

deep learning to represent complex high-dimensional

environments. Deep reinforcement learning (DRL)

has shown remarkable success in complex decision-

making tasks including robotics, gaming, and

autonomous vehicles

Mnih, V., et al. (2015). Process

engineering, and dynamic systems like multiphase

reactors in particular, are a relatively underexplored

domain of its potential. Deep Reinforcement

Learning (DRL) can be a viable solution for this

problem because of its real-time optimization of

hydrodynamic parameters. The present study intends

to examine the performance of four advanced DRL

algorithms, namely, DQNs, PPO, and SAC, to

improve hydrodynamic behaviours in multiphase

reactors. DQN, one of the early DRL algorithms is

based on Q-learning and uses a neural network to

approximate the value function and can be used for

discrete action spaces

Van Hasselt, H., Guez, A., &

Silver, D. (2016). PPO algorithm is a policy gradient

family algorithm that enhances stabilization and

exploration, which are common challenges in

conventional RL

Schulman, J., et al. (2017). One such optimized

method is SAC, a state-of-the-art algorithm that adds

entropy regularization to the underlying objective

function, leading to better exploration-exploitation

tradeoff, thereby making it suitable for a continuous

action space (

Haarnoja, T., et al., 2018). The study

utilizes a simulated multiphase reactor environment

via OpenFOAM, an open-source computational fluid

dynamics (CFD) platform. We implement an efficient

simulator that includes the interactions between gas

and liquid, bubble motion, and energy consumption

metrics to the realism of the simulation environment.

The reward function is bespoke for increasing the

holdup of gas and uniformity of liquid velocity whilst

minimizing energy penalty. Comparative analysis of

the performance of each algorithm from the

perspective of convergence speed, optimization

accuracy, and computational efficiency is reported.

SAC demonstrated the highest optimization

accuracy and convergence speed, compared to DQN

and PPO, in preliminary results, and it is well suited

to high dimensional, continuous environments such

as multiphase reactors. PPO tastes competitive

performance but fails to adapt under rapidly changing

conditions. DQN is a good approach to use when

seeking good policies in a discrete environment (such

as a game) and we would like to achieve similar

results in continuous systems, however due to the

nature of the algorithm we cannot. These findings

highlight the promising role of DRL in optimizing

multiphase reactor hydrodynamic characteristics

toward improved operation and sustainability. These

results form a solid basis for upcoming studies, such

as the deployment of open-source implementations

and the coupling of hybrid DRL-CFD frameworks to

further enhance reactor optimization.

2 LITERATURE SURVEY

The general optimization of multiphase reactors has

been studied widely in the literature over both

classical and modern computational approaches.

Early studies mainly centred on using computational

fluid dynamics (CFD) as an assessment and

optimization tool for the hydrodynamic properties of

multiphase reactors. Detailed knowledge of the gas–

liquid interactions, bubble dynamics, and flow

patterns could be obtained through studies based on

computational fluid dynamics (CFD). Such as

Krishna and Van Batten, who performed CFD

simulations on bubble column reactors and created

models for predicting bubble size distribution and

gas holdup. Despite being highly informative, these

techniques were limited by their computational

intensity and lack of real-time adaptability. Due to the

complexity and unstructured nature of CFD

simulations, empirical modelling approaches were

developed based on experimental data to predict

reactor performance over a range of operating

conditions. Studies by Degaleesan et al.

demonstrated that operating parameters, including

gas flow rate and liquid viscosity, have a key role in

determining the hydrodynamic behaviour of bubble

columns. However, these models were specific to

hard limit systems and were not generalizable across

different reactor designs. Machine learning (ML)

with nonlinear mapping/pattern recognition

capabilities is another trend in optimizations, and

researchers started to apply data-driven methods to

multiphasic reactor systems. They used experimental

or simulated datasets to train ML models to predict

Comparative Study of Deep Reinforcement Learning Algorithm for Optimization of Hydrodynamic Characteristics in Multiphase Reactors

363

hydrodynamic parameters. Zhou et al. In a previous

study, used ANNs for predicting gas holdup and

bubble velocity in bubble columns. Although the

predictive accuracy provided benefits over empirical

methods, such models were incapable of self-

governed decision-making with operating timers for

optimization as not developed at that time.

To no one’s surprise, RL grew in a new direction

as a new flavour of optimization that has much

potential compared to classical optimizers.

Reinforcement learning (RL) enables an agent to

explore an environment and obtain feedback as a

reward for its actions, which it learns optimal control

strategies using trial-and-error. Wang et al. They

optimized flow patterns in stirred tanks by 30% more

energy-efficient using basic RL algorithms. But these

early implementations of RL were limited only to the

simplest of control problems and hand-crafted rules

and did not leverage recent developments in deep

reinforcement learning (DRL). Deep Reinforcement

Learning (DRL) is the combination of the high-

dimensional non-linearity that a deep neural network

can represent and the advantages of fuzzy reward-

based learning of Reinforcement Learning (RL).

Dynamic Programming Methods DRL (Deep

Reinforcement Learning) DRL uses reinforcement

learning principles in deep architectures, which work

have been filtered for big fields like robotics or self-

automobiles, where designing a decentralized

dynamic decision process is a great challenge in an

ever-changing environment. Research on its possible

use for process optimization in chemical engineering

is already emerging as well. For instance, Li et al. (a)

(

Santosh. W. and Sathe, V. 2012)

is to learn an optimal

reaction conditions of chemical processes using

DRL, and this outperforms traditional methods. Deep

Q-Networks (DQN) were one of the first successful

deep-reinforcement learning algorithms that could

approximate Q-value functions environments with

discrete actions. Guo et al. In a recent study, DQN was

used to control gas flow rates in multiphase systems

with moderate success

Li, X., Zhang, Q., & Chen, G.

(2020)

. However, because DQN is discrete-content, a

direct application to systems that require continuous

action for control, e.g., multiphase reactors are

difficult

Van Hasselt

PPO Schulman et al, overcame the limitations of

these previous approaches, via stabilizing training

after clipped updates, which upended the landscape of

reinforcement learning. PPO has been used in several

engineering applications, and we showed that it

performs well inside of a process optimizer. Gao et al.

for in situ bioreactor optimization, which yielded

considerable advancements in yield and efficiency

through PPO. Entropy Regularization in DRL Soft

Actor-Critic (SAC), a well-established DRL

algorithm, proposed regularization of the entropy in

order to facilitate exploration and to enhance learning

efficiency in continuous action spaces. Haarnoja et al.

Haarnoja, T., et al. (2018).

originally proposed SAC,

and it has been adopted in various optimization tasks.

Li et al. applied SAC to improve hydrodynamic

features in simulated multiphase reactors, resulting in

better performance compared to conventional RL

and DRL algorithms. Despite the advancement, there

are still gaps in the application of DRL for multiphase

reactors. Most existing studies either focus on

specific individual hydrodynamic parameters

whereas such variables must be optimized holistically

at once due to interactions between gas holdup, liquid

velocity, and energy consumption. Moreover, the

combination of DRL with computational fluid

dynamics (CFD) simulations is still in its infancy, and

future work can explore the development of hybrid

frameworks to capitalize on both platforms.

3 PROPOSED SYSTEM

Figure 1: Block Diagram of DRL Framework for

Hydrodynamic Optimization in Multiphase Reactors.

The present work is focused on developing and

improving the hydrodynamic features in multiphase

reactors via advanced Deep Reinforcement Learning

(DRL) algorithms. The study compares the

effectiveness of three different DRL algorithms:

Deep Q-Networks (DQN), Proximal Policy

Optimization (PPO), and Soft Actor-Critic (SAC),

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

364

with the aim of optimizing gas holdup, liquid velocity

profiles, and bubble size distribution while

minimizing energy consumption. In this research, a

multiphase reactor dynamic was made through

simulation environment by way of Open FOAM. The

optimization is controlled by a reward function that

accounts for both the increase of hydrodynamic

parameters and the reduction of energy costs. PPO

and SAC cater to continuous action spaces, with SAC

using entropy regularization to balance exploration

and exploitation. Here, agents undergo iterative

training, interacting with the environment and refining

their behaviour through feedback from the reward

function.

The methods are evaluated by their convergence

time, the accuracy of the optimization, and the time

used in the optimization process, which are visualized

through gas holdup trends, energy consumption

reduction, and the stabilization of the reward. Because

SAC can effectively handle high-dimensional

continuous environments, the study expects SAC to

excel compared to its counterparts. And this study has

implications beyond the specific applications of DRL

to reactor optimization, as it offers a framework for the

potential integration of DRL methodologies into

industrial workflows, setting a foundation for future

real-time, data-driven approaches to reactor

management, as illustrated in the figure 1.

3.1 Proposed Work and Its

Implementation

Figure 2: Hydrodynamic Characteristics in Multiphase

Reactors.

We propose to optimize the hydrodynamic

features of multiphase reactors employing state-of-

the-art Deep Reinforcement Learning (DRL)

algorithms. Goal: Enhance important parameters like

gas holdup, liquid velocity profiles, and bubble size

distribution and also reduce energy used. The figure

2 shows the Hydrodynamic Characteristics in

Multiphase Reactors To do this, we implement and

evaluate three cutting-edge DRL algorithms: Deep

Q-Networks (DQN), Proximal Policy Optimization

(PPO), and Soft Actor-Critic (SAC). Aiming tackle

this aspect, the study incorporates these algorithms

into a simulation gating to train agents to

dynamically adjust and optimize the reactor's

operating bounties.

3.2 Simulation Environment

This study relies on a developed simulation

environment with OpenFOAM, an open source

computational fluid dynamics (CFD) platform. The

environment is engineered to simulate the intricate

and dynamic interaction of multiphase reactors that

create gas-liquid phase flow, turbulence, and bubble

dynamics. To represent real reactor conditions,

realistic boundary conditions, turbulence models, and

parameter variability are included. We use Python

APIs to interface this simulation onto DRL

framework to allow real-time interactions where

agents observe states, take actions and receive

rewards. The environment also adds perturbations

such as varying gas flow rates as well as liquid

viscosities to test the robustness of the DRL

algorithms.

3.3 Reward Function Design

They are trained on data until October, 2023. It is

focused on enhancing the gas holdup (H) and the

uniformity of liquid velocity (V), and reducing the

energy consumption (E). Mathematically, the reward

function is given as:

𝑅=𝛼𝐻+𝛽𝑉−𝛾𝐸 (1)

Here: R is the cumulative reward. α, β and γ are

weighting coefficients tuned to level each parameter

importance. H is theparameter for gas holdup which

is importantfor increasing themass transfer

efficiency. V ensures the uniformity of liquid velocity

profiles for optimal profiling of fluid in channels. E

was energy consumption, which the system tries to

be minimized. This definition guarantees that the

agent will be motivated to navigate through

configurations that maximise reactor performance.

3.4 DRL Algorithm Implementation

The DRL framework employs three different

algorithms that are tuned according to the

optimization problem. DQN deals with discrete

Comparative Study of Deep Reinforcement Learning Algorithm for Optimization of Hydrodynamic Characteristics in Multiphase Reactors

365

action spaces by approximating Q-values; PPO

ensures stability in continuous environments using

clipped policy updates; SAC adds an entropy term to

the loss to encourage exploration vs exploitation

balancing suitable for higher-dimensional

continuous systems. The interaction of the agent with

the environment is modeled as a Markov Decision

Process (MDP) in which the agent observes a state,

takes an action, transitions into the next state, and

receives a reward. The optimization objective is to

maximize expected cumulative reward (G):

𝐺𝑡 =

∑

𝛾𝑘𝑅𝑡 + 𝑘





(2)

where 𝛾 is the discount factor, prioritizing immediate

over long-term rewards?

3.5 Training and Policy Optimization:

For DQN, the agent updates the Q-value function

using the Bellman equation:

𝐿(𝜃) = 𝐸[



𝑅+𝛾max





𝑄

(

𝑆



,𝑎



;𝜃



)

−𝑄

(

𝑆,𝐴;𝜃

)





] (3)

Here, 𝜃 and 𝜃



represent the weights of the

primary and target Q-networks, respectively. PPO

optimizes the policy using the clipped surrogate

objective:

𝐿𝑃𝑃𝑂 = 𝐸[𝑚𝑖𝑛(𝑟𝑡(𝜃)𝐴𝑡,𝑐𝑙𝑖𝑝(𝑟𝑡(𝜃),1 − 𝜖,1 + 𝜖)𝐴𝑡)](4)

where 𝑟𝑡(𝜃) is the probability ratio between new

and old policies, and 𝐴𝑡 is the advantage function?

SAC enhances exploration and stability by

optimizing a soft Q-function:

𝐿𝑆𝐴𝐶 = 𝐸[𝑄(𝑆,𝐴) − 𝛼𝑙𝑜𝑔𝜋(𝐴 ∣ 𝑆)] (5)

where 𝛼 controls the trade-off between

exploration and exploitation.

4 PERFORMANCE

EVALUATION

The algorithms are benchmarked for their rates of

convergence, ability to minimize cost function, and

their computational efficiency. Metrics of interest

include improvements in gas holdup, even number of

liquid velocity, and reductions in energy inputs. It

outperforms PPO and DQN by converging faster and

optimizing more accurately. While PPO yields

competitive performance, it needs quite a few more

training episodes to converge, and DQN is not well

suited for continuous action spaces due to its vanilla

form. This paper lays a solid DRL-based framework

for optimizing multiphase reactors with SAC being

the most successful algorithm. These results highlight

DRL's significant potential in enhancing reactor

efficiency and scalability, opening doors for real-time

applications in industrial settings. Future work will

investigate hybrid DRL-CFD approaches which

would enhance the adaptability and efficacy of this

framework in real application scenarios.

Algorithm 1: DRL Agent Training for Hydrodynamic

Optimization

Setup the simulated reactor specs, including the instance

with reactor contrast specs, Boundry Conditions, and Flow

dynamics

Step 2: Initialize the DRL algorithm (DQN, PPO, or SAC)

with random policy parameters.

Step 3: Define the reward function that encourage gas

holdup, velocity uniformity and energy efficiency.

Step 4: Start training loop: Take note of the current

reactor status. Choose an action and take it according to the

agent policy. Move to the state and observe the reward. A

method to use the rewards obtained to update the policy of

the agent in order to choose better actions. You will

evaluate the performance of your agent constantly to

guarantee an increase in the stability of your reward and

the optimal configuration of your parameters. Keep training

till reward doesn’t increase or we reach the max number of

iterations.

Step 6: Save the policy model for deployment

Algorithm 2: Policy Adaptation for Dynamic Reactor

Conditions

In Step 1, we load trained DRL model, deploy it in

simulation or real-world environment.

Step 2: Implement dynamic changes in operating

conditions like gas flow rates or liquid viscosity.

Step 3: Observe updated reactor state, evaluate agent

behaviour using present policy If the agent has suggested

action, take that action to optimize the hydrodynamic

performance to the new conditions.

Step 5: Read the environment, measuring some

characteristics of the key parameters — the gas holdup,

energy consumption, and the overall response in the

environment.

Step 6: Optionally, Adjust the policy to enforce efficiency

statistics when environmental conditions are not static.

Step 7: Deploy the modified policy for perpetual

optimization and accommodate for future variations with

no friction.

5 EXPERIMENT RESULT AND

DISCUSSION

Herein, we present a depiction of our proposed

implementation of a Deep Reinforcement Learning

(DRL) framework to optimize hydrodynamic

attributes in multiphase reactors and the results are

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

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366

promising. Leveraging DRL such as Deep Q-

Networks (DQN), Proximal Policy Optimization

(PPO) and Soft Actor-Critic (SAC), the framework

greatly improves reactor performance indicators,

while keeping computational efficiency. In this way,

some of the numerical and experimental methods

utilized in literature can be confirmed with this study

to attain the optimization of crucial design parameters

such as gas holdup, liquid velocity profiles and

energy consumption having direct impacts on the

thorough efficiency and sustainability of the reactor.

We implemented the simulation environment using

OpenFOAM, an excellent tool for testing DRL

algorithms. Agents then began to interact with the

environment, learning which actions were optimal

based on how well the reactor performed relative to

the intended reward function. SAC turned out to be

the best performing algorithm with regards to

convergence speed and optimisation with respect to

PPO & DQN. The SAC algorithm utilized an entropy

regularization term to facilitate exploration while

maximizing the expected return, which was

particularly beneficial in high-dimensional

continuous-action environments (Yu et al., 2022).

Table 1: Performance Evaluation.

Algorith

Convergenc

e Speed

(Episodes)

Gas Holdup

Improveme

nt (%)

Energy

Consumptio

n Reduction

(%)

DQN 800 7 4

PPO 500 12 8

SAC 300 16 11

Corresponding Graph for the above Table:

Figure 3: Performance Evaluation Metrics.

The PPO also showed competitive results,

performing quite comparably in continuous action

spaces but very effectively stabilizing policy

updates. However, it needed many more training

episodes to converge than SAC. The DQN algorithm

worked well with a discrete-action environment and

showed slow convergence and sub-par results in the

reactor dynamics where the dynamics was quite

complex. The algorithms were assessed in terms of

convergence speed (in episodes), gas holdup

improvement (%) and energy consumption reduction

(%). The results are collated in the table 1. As shown

in table 1, SAC had the quickest convergence and

most optimization gains. Gas holdup improved by

16%, and energy consumption decreased by 11%–

significantly better than other algorithms. PPO

showed 12% enhanced gas holdup and 8% decreased

energy consumption, while DQN revealed moderate

improvements of 7 and 4%. The finding highlighted

the promise of DRL algorithms, especially SAC, to

enhance the hydrodynamic parameters of multiphase

reactors. In particular, SAC's ability to balance

exploration and exploitation allowed it to determine

efficient operating conditions in a short time frame.

Compared to PPO, PPO showed good results, but not

as good as this due to convergence speed. As DQN is

mostly used on discrete action spaces, its limitations

became clear in the relative continuous and dynamic

environment of multiphase reactors. The figure 3

shows the Performance Evaluation Metrics.The

reward function was important to guide the agents to

optimal solutions. The reward function allowed

agents to perform both efficiently and with high

performance, balancing gas holdup, liquid velocity

uniformity with energy consumption. Through the

use of iteration on a few real reactor interactions,

agents were able to learn to greatly outperform

traditional processes. Proposed DRL algorithms

demonstrate the ability to solve the complexities

behind multiphase reactor dynamics. SAC

outperformed the alternatives, demonstrating a

computationally efficient, suitable framework for

longer term, complex real-world scenarios. The

results demonstrate the power of DRL in

transforming reactor operations and enable our

visions of sustainable and efficient industrial

processes. Thus, future work will expand on the

hybrid DRL-CFD approach and also validate the

framework in experimental reactor configurations.

6 CONCLUSIONS

The current study highlights the application of Deep

Reinforcement Learning (DRL) approach to optimize

hydrodynamic performance of multiphase reactors.

This study showcases the latest advancements in

DRL research for reactor control by demonstrating

the performance of three distinct algorithms: Deep Q-

Networks (DQN), Proximal Policy Optimization

(PPO), and Soft Actor-Critic (SAC). This dynamic

Comparative Study of Deep Reinforcement Learning Algorithm for Optimization of Hydrodynamic Characteristics in Multiphase Reactors

367

framework demonstrates an improvement in gas

holdup, liquid velocity profiles, and energy

consumption, which allows for a scalable approach to

optimizing industrial processes. Experiment results

showed that SAC was the best algorithm with the

optimal helping accuracy and the fastest convergence.

Also, while other trained models rely exclusively on

experience replay, in this high-dimensional,

continuous-action environment, we found that

entropic regularization, which allows balancing

exploration and exploitation, allowed the trained

model to achieve better performance in the reactor.

PPO also did well, but took longer to converge.

Although, DQN works fast on discrete environments

and does not work able to work with continuous

action spaces, which in turn makes it less suitable for

the dynamic needs of the reactor. These findings

highlight the versatility and feasibility of DRL agents

in industrial applications, thus enabling rapid and

efficient reactors management. In future efforts, we

will look forward to verifying the proposed method in

experimental setups, and merging DRL with hybrid

CFD models. This work lays the groundwork for

future improvements in smart process optimization

and offers value to sustainable and efficient industrial

operations

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