Artificial Intelligence (AI) in hydrogen process optimization

 Corresponding author: Rawail Saeed Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Artificial Intelligence (AI) in hydrogen process optimization Rawail Saeed 1, * and Fnu Fahadullah 2 1 Department of Chemical Engineering, Ned University of Engineering and Technology Karachi, Sindh, Pakistan. 2 Department of Telecommunication, University of Engineering and Technology Peshawar Pakistan. World Journal of Advanced Research and Reviews, 2025, 28(02), 2605-2619 Publication history: Received 15 October 2025; revised on 25 November 2025; accepted on 28 November 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.28.2.3944 Abstract Hydrogen production as a clean energy source has gained a lot of interest due to the rising demand of clean energy solutions. Nevertheless, certain problems like inefficiencies in production processes and optimization of maintenance are currently the obstacles to the popularization of hydrogen as the viable carrier of energy. In this paper, we will discuss how Artificial Intelligence (AI) and machine learning can be used in the optimization of hydrogen production processes. With the capacity to combine predictive control with the need to improve process efficiency and optimize maintenance schedules, AI is a possibility that can change the way in which hydrogen production plants can operate. Artificial intelligence, machine learning (ML) methods, such as deep learning algorithms and reinforcement learning are implemented to model and control complex systems in the hydrogen production industry, specifically in water electrolysis and fuels cells technologies. These models are able to foresee operational practices, enhance energy use as well as increase the life time of vital parts of the plant. Also, predictive maintenance assisted by AI will help decrease the amount of downtime, making sure that everything operates and that failures will not happen unexpectedly. The present paper discusses the latest developments in AI technologies that have already been applied to the hydrogen production and some of the key results are identified in terms of the energy savings, the minimization of the operational costs, and the increased system reliability. The results indicate that AI-based optimization can also help to achieve high efficiency and sustainability of hydrogen production. Nonetheless, issues like quality of data, integration of models, and computational cost are some of the obstacles to be overcome through further research and development. In a sum up, the use of AI in hydrogen production facilities is the potential direction of making production of hydrogen more efficient, sustainable, and reliable. Since the energy environment in the world is moving towards decarbonization, AI-based technologies have a great potential in enhancing the hydrogen economy and helping to switch to renewable and less damaging energy sources. Keywords: AI-Driven Optimization; Machine Learning; Hydrogen Production; Process Efficiency; Predictive Control; Maintenance Optimization 1. Introduction Renewable energy sources play a crucial role in the transition to a low-carbon and sustainable energy future and hydrogen is expected to be one of the important contributors in decarbonization efforts. Hydrogen is a clean carrier of energy that can transform all industries including transportation, manufacturing, and storage of energy. There are however challenges to the large scale exploitation of hydrogen as a source of energy based on the inefficiencies and complexities of its production especially by electrolysis and fuels cell technologies. The combination of Artificial Intelligence (AI) and machine learning (ML) methods to the hydrogen production processes has a vast opportunity of World Journal of Advanced Research and Reviews, 2025, 28(02), 2605-2619 2606 mitigating these issues in the form of process efficiency, predictive control, and optimization of the maintenance process. This introduction presents the importance of AI in hydrogen production, the most important challenges and the purpose of the study. 1.1. Background Hydrogen generation by water electrolysis, and fuel cells is one of the most promising ways of producing clean and renewable energy. One of the technologies that play a pivotal role in the generation of green hydrogen that is essential to the realization of a sustainable hydrogen economy is water electrolysis, in which electricity is consumed to divide water into hydrogen and oxygen (Shash et al., 2025). On the same note, it is also seen that fuel cells, which can change hydrogen into electricity and use water as the only by-product, are slowly finding application in various uses and some of these applications include transportation and stationary power generation. Although these technologies have potential, there are inefficiencies in hydrogen production and utilization systems, such as high energy usage, downtimes, and this may be brought down by high maintenance costs. Hydrogen production processes optimization has thus become a concern of the researchers and leaders of the industry. The conventional tools used in process optimization are constrained by their incompetence to manage non-linear systems and their use of manual interventions that add costs to the operations and diminishment in the reliability on the systems. The solution to these challenges can be offered by using the tools of Artificial Intelligence (AI), especially machine learning (ML), that allows predictive control, real-time optimization of the process, and predictive maintenance. The machine learning models have a potential to process large volumes of data on hydrogen production systems so that the operators make data-driven decisions that improve the efficiency of the process, decrease the energy usage, and anticipate a failure before it happens (Fayyazi et al., 2023). 1.2. Problem Statement Although AI and machine learning have proven to be promising in enhancing the energy systems, their application in hydrogen production processes is immature. The current literature is more inclined to specific aspects of hydrogen production, including making the process of water electrolysis more efficient or fuel cell more efficiently, yet no extensive research has been done on the application of AI in the optimization of the overall production chain. The application of AI in predictive maintenance and process control is also at an early stage, and the difficulty of integrating data, model accuracy, and the cost of computation has impeded its application at a large scale (Sethi et al., 2025). 1.3. Objectives of the Study The core aim of the paper is to discuss the use of AI, specifically machine learning, in optimization of hydrogen production process. This study aims to: • Investigate the use of AI models in predictive control in the production of hydrogen in terms of energy optimization and process parameters. • Assess AI in predictive maintenance that would decrease down-time and increase the hydrogen production system life. • Talk about the application of digital twins, as well as machine learning to optimize and simulate hydrogen production processes in real-time. • Determine obstacles and opportunities in the use of AI in optimization of hydrogen production such as data quality, scalability, and model integration. 1.4. Significance of the Study Implementation of AI in the production of hydrogen is important in a number of ways. First, AI has the opportunity to make hydrogen production systems more energy-efficient, which is essential in making the entire hydrogen cost more affordable and competitive with other energy sources. Second, machine learning-based predictive maintenance models can be used to reveal faults prior to their occurrence and cause system failures thereby lowering maintenance costs and minimizing downtime. Third, AI-based control of processes will allow optimization of many parameters in real-time and increase the overall efficiency of hydrogen production facilities. Finally, this study can be used to develop improved AI models that can address the specifics of hydrogen production and contribute to the shift to an economy based on hydrogen. World Journal of Advanced Research and Reviews, 2025, 28(02), 2605-2619 2607 1.5. Overview of the Paper The paper is organized in the following way: the literature review will include an overview of the recent development and implementation of AI and machine learning in the hydrogen production industry, along with the major studies and advancements. The research design will be described in the methodology section and it will adopt the machine learning methods applied to predictive control and maintenance optimization in hydrogen production plants. The findings will be represented in the results and discussion section, which will provide the results of applying AI models to the hydrogen production processes in terms of their efficiency and costs associated with maintaining the hydrogen production process. Lastly, the conclusion will be a summary of the main findings, implications on the future research, and a recommendation to further introduce AI in producing hydrogen on a larger scale. Table 1 Key Applications of AI in Hydrogen Production AI Application Objective Impact on Hydrogen Production References Predictive Control Optimize energy consumption and process parameters Increases operational efficiency and reduces energy costs Fayyazi et al., 2023; Shash et al., 2025 Predictive Maintenance Forecast maintenance needs and equipment failures Reduces downtime, extends equipment lifespan Sethi et al., 2025; Ahmed et al., 2024 Process Optimization Improve hydrogen production rates and efficiency Enhances system performance and reduces waste Feng et al., 2025; Bhuiyan et al., 2025 Digital Twins and Simulation Simulate real-time operational scenarios for optimization Allows for real-time decisionmaking and process adjustments Johnrose et al., 2026; Wei et al., 2025 Figure 1 Conceptual Framework for AI in Hydrogen Process Optimization 1.6. Structure of the Paper The following parts of the paper will present an in-depth discussion of the solutions and issues of the introduction of AI in hydrogen systems of production. The literature review will be the foundation to summarize the current situation in the sphere of AI in hydrogen production including the application of machine learning in predictive control and maintenance. This paper will indicate the research design, AI models, and methods of evaluation in the methodology section. We are going to compare the effectiveness of AI-based optimization and predictive maintenance in enhancing the efficiency of the processes and minimizing the cost in the results and discussion. Lastly, a conclusion will be given in which the findings of the study will be summarized and recommendations given on how to conduct further research. World Journal of Advanced Research and Reviews, 2025, 28(02), 2605-2619 2608 2. Literature review Artificial Intelligence (AI) and machine learning (ML) applied to the processes of hydrogen production hold great potential due to the possibility of ensuring high production efficiency and lower costs and enhancing the reliability of the systems used. With the hydrogen taking a critical role in the global shift to renewable energy, AI-driven optimization is being regarded as a key to the improvement of the efficiency of hydrogen production technologies, particularly, water electrolysis, and fuel cells. This review paper summarizes existing literature on the use of AI in hydrogen production, outlines the different types of AI applied, difficulties encountered, and the current progress that has seen AI become a useful tool in optimization of the process and predictive maintenance. 2.1. Hydrogen Production through AI: Overview There are two common ways to produce hydrogen: water electrolysis and steam methane reforming (SMR) and the first one water electrolysis is the most promising way to produce clean and green hydrogen. Water undergoes electrolysis to yield hydrogen and oxygen in water electrolysis on the use of electricity and react to give out a reaction of electricity in fuel cells and the only by-product in the process is water. Both the systems need optimization in order to enhance efficiency and minimization of costs. These processes can undergo a revolution with the help of AI and machine learning. The AI will be able to both improve predictive control and optimization of operational parameters and conduct real-time corrections to reduce energy use and maximize the performance of the entire hydrogen production system. Besides, AI is able to enhance the maintenance procedures to predict equipment breakdown and plan repairs more effectively to facilitate a smoother workflow and lessen the downtime (Shash et al., 2025). 2.2. Hydrogen production using machine learning methods There are a number of machine learning algorithms that are used to optimize the production of hydrogen. These include: • Supervised Learning: Supervised learning involves the training of AI models with known inputs, which enables it to make predictions based on the known inputs. The method is common in predicting the hydrogen production rates, energy consumption, and estimating equipment life (Fayyazi et al., 2023). • Reinforcement Learning: The reinforcement learning (RL) is a form of machine learning in which an agent learns to make decisions by interacting with the environment. This method has found application to control the system of hydrogen production in a more optimized way by learning the optimal operation parameters to work most efficiently (Sethi et al., 2025). • Deep Learning: Deep learning models are based on the use of more than one layer of neural networks and are especially applicable to the analysis of non-linear, complex data in the hydrogen production systems. All these models can detect the patterns in vast data sets, including operational data, and they can be utilized to optimize hydrogen production and enhance the process control (Feng et al., 2025). • Unsupervised Learning: Unsupervised learning algorithms are those algorithms that seek to determine patterns or groupings in data that is not previously labeled. It can be helpful when it is necessary to monitor the anomalies in the production data, e.g. when the trends of equipment failures have to be identified, or the unusual behavior of the process has to be detected (Wei et al., 2025). 2.3. AI in Hydrogen Production Predictive Control One of the most important applications of AI to hydrogen production is predictive control, which presupposes the utilization of previous experience and on-site observation to adjust operational conditions to provide better performance. Predicting the future behaviors, machine learning models are involved to adjust process parameters. Indicatively, during water electrolysis, AI may be used to optimize the current and voltage utilised in the electrolyzer minimising the use of energy and still producing hydrogen rates (Ahmed et al., 2024). The application of AI to predictive control is also applicable to fuel cell systems, whereby AI can streamline the power output and make the system more reliable. Intelligence models are used to predict the performance of fuel cells in various conditions to enable operators to make decisions about fuel cells that ultimately result in the greatest efficiency and increase the lifetime of the entire system (Johnrose et al., 2026). World Journal of Advanced Research and Reviews, 2025, 28(02), 2605-2619 2609 Table 2 AI Applications in Hydrogen Production AI Technique Optimization Focus Outcome Reference Example Machine Learning (ML) Predictive control of electrolyzer parameters Improves hydrogen yield and energy efficiency Wang et al., 2024 Reinforcement Learning (RL) Adaptive tuning under variable load conditions Enhances stability and performance Zhang et al., 2023 Deep Neural Networks (DNNs) Process modeling and fault detection Enables real 2.4. AI and Predictive Maintenance Maintenance optimization is one of the issues that relate to hydrogen production plants. Conventional maintenance plans are more often than not proactive, meaning they only solve a problem when it becomes one. Nonetheless, such a strategy may result in unwarranted downtime and rising costs of operation. Predictive maintenance, which is an AIdriven process, however, leverages machine learning algorithms to anticipate equipment failure and preempt the maintenance process. Predictive maintenance AI models use sensor data of hydrogen production systems to predict the presence of warning signals of mechanical failure, including vibrations, temperature variations, and pressure changes. This allows operators to predict when equipment will fail and can plan the maintenance of equipment, preventing failures and minimizing system downtimes and enhancing its overall efficiency (Abiola et al., 2023). Figure 2 Hydrogen Production Efficiency Before vs. After AI Optimization 2.5. Digital twins are proposed to be integrated with AI in The company that has recently made significant progress in the field of AI usage in hydrogen production is the use of digital twins in combination with machine learning models. A digital twin is a simulation of a physical system which may be simulated and predicted in real time. The digital twins are applied in the context of hydrogen production to simulate the behavior of electrolyzers and fuel cells so that operators can simulate various operational scenarios and optimise the system without interfering with the real production. Hydrogen production systems can be optimized dynamically by combining digital twins with machine learning models in order to maximize efficiency by changing various variables like temperature, pressure and energy input. Such an integration enables a more precise and dynamic optimisation process that results in major enhancements in the energy consumption and system performance (Feng et al., 2025). World Journal of Advanced Research and Reviews, 2025, 28(02), 2605-2619 2610 Table 3 Key AI Applications in Hydrogen Production AI Technique Application Impact on Hydrogen Production References Supervised Learning Forecasting hydrogen production rates Predicts energy consumption and optimizes efficiency Fayyazi et al., 2023 Reinforcement Learning Optimizing operational parameters Improves energy efficiency and reduces operational costs Sethi et al., 2025 Deep Learning Identifying patterns in operational data Enhances control of electrolysis and fuel cell systems Feng et al., 2025 Unsupervised Learning Detecting anomalies in data Identifies potential issues early, optimizing maintenance schedules Wei et al., 2025 Digital Twins Simulating hydrogen production systems Real-time optimization of hydrogen production systems Johnrose et al., 2026 Figure 3 Concept of Digital Twin Integration for Hydrogen Process Optimization 2.6. Problems and Opportunities. Although the combination of AI in the production of hydrogen has enormous potential, there are still a number of challenges. These include: • Data Quality and Availability: To ensure that machine learning models work well, they require good quality data. The performance of AI models might also be affected in the case of sparse, incomplete, or noisy data in hydrogen production plants. The successful AI implementation requires the data collection to be reliable and continuous (Shanmugasundaram et al., 2025). • Scalability: AI solutions may be difficult to scale to large hydrogen production plants because of the complexity of the systems and the amount of computational power that is necessary to optimize them in real-time. Nevertheless, the increasing computational technologies and cloud-related solutions are solving these issues, which makes AI more available to large-scale applications (Ghosh et al., 2025) • Interaction with Existing Systems: The AI models used in this case should be integrated with existing hydrogen production systems, but it is important to consider system compatibility and their ability to interact. The integration should make sure that AI models can be used in the workflow and control mechanisms without any issues. World Journal of Advanced Research and Reviews, 2025, 28(02), 2605-2619 2611 Even with these limitations, the potential of AI in hydrogen manufacturing is high. Operational costs can be lowered, energy use can be made more efficient and reliability of the system can be enhanced by AI and all these aspects are important to make hydrogen use as a clean energy source widespread. 3. Methodology This section presents the experimental setup, machine learning, and the data collection and assessment standards applied to streamline the production of hydrogen with the help of Artificial Intelligence (AI). The research is expected to implement AI models and predictive control, process efficiency, and maintenance optimization, including machine learning algorithms and digital twins in hydrogen production plants. The approach is aimed at the combination of AI to optimize the performance of water electrolysis and fuel cell systems and guarantee optimal productivity and reduced downtime and energy utilization. 3.1. Research Design The study was performed in two key steps: the collection of data and model training of AI-based optimization, and that of the outcomes and their comparison with the traditional optimization methods. Data Collection: To acquire real-time operational data of hydrogen production plants, the initial task was to collect all important variables of the manufacturing process including temperature, pressure, hydrogen production rate, energy use, and maintenance log. This information was obtained using several sensors and control systems in the plant. Data collection was ongoing to allow enough information to be available to train the model and optimize the model in realtime. AI Model Selection: A number of machine learning algorithms were chosen in various tasks in the process of hydrogen production optimization: • Prediction of hydrogen production rates and energy consumption with Supervised Learning (Regression Models). • Reinforcement Learning (RL) to control dynamic processes, i.e. adjusting operational parameters, depending on real-time information, to maximize energy efficiency. • Deep Learning (Neural Networks) to recognize intricate structures of huge datasets and to optimize the work of fuel cells. • Digital Twins to simulate real-time working situations and predict the behavior of the system in various conditions. Table 4 Comparison of Traditional vs. AI-Optimized Hydrogen Production Processes Parameter Traditional Process AI-Optimized Process Process Control Fixed settings; manual tuning required Dynamic predictive control using ML algorithms Energy Efficiency Moderate (55–70%) Improved (75–85%) through adaptive optimization System Monitoring Periodic, reactive maintenance Real-time condition monitoring and anomaly detection Maintenance Approach Scheduled maintenance; higher downtime Predictive maintenance with reduced downtime Operational Cost Higher due to inefficiencies and manual adjustments Lower due to automation and process optimization Data Utilization Limited data use; manual logging Extensive data-driven decision-making via AI models World Journal of Advanced Research and Reviews, 2025, 28(02), 2605-2619 2612 Figure 4 Predictive Maintenance Impact on System Downtim 3.2. Data Preprocessing and Collection. The sampling location was a hydrogen production facility involving the use of the water electrolysis process and fuel cells. Machine learning models in the optimization of processes in the plant were trained with the data on its operations. The variables used in the dataset were: • Temperature (degC) • Pressure (Bar) • Daily Hydrogen rate (Nm3/h) • Energy Consumption (kWh) • Voltage at electrolyzer (V) and Current at electrolyzer (A). • Fuel Cell Output (kW) In order to present the information to machine learning, preprocessing measures were taken, and they comprised: • Data Cleaning: Eliminating missing or inaccurate values of the dataset. • Normalization: The data was scaled in order to be sure that the significance of each variable in the analysis was equal. • Engineering of features: New features can be engineered, e.g. moving averages, interaction terms, etc. to enhance the accuracy of the model. 3.3. Training and Testing of AI Model. In the process of model training, the dataset was divided into training and test set where 80 percent was taken as training and 20 percent as test set. Each AI model was undertaken as follows: 3.3.1. The student will have to undergo supervised learning (Regression Models): A regression model was developed to estimate the rates of hydrogen production based on the input variables that included temperature, pressure and energy consumption. The model was tested in terms of Mean Absolute Error (MAE) and the R-squared values. 3.3.2. Reinforcement Learning: Dynamic process control was done using an RL algorithm. The environment (hydrogen production system) gave feedback to the model and learnt to modify operational parameters (voltage and current) to the maximum energy consumption with a high rate of hydrogen production. The cumulative rewards and energy consumption reduction were used as indicators of the performance of the RL agent. 3.3.3. Neural Networks (Deep Learning): A deep learning algorithm was used to learn complicated patterns and achieve fuel cell optimization. The neural network was trained to estimate the fuel cell output depending on the past and the parameters of operation. Accuracy World Journal of Advanced Research and Reviews, 2025, 28(02), 2605-2619 2613 of the model was determined on the basis of Mean Squared Error (MSE) and performance based on prediction of energy output. 3.3.4. Digital Twin Integration: The simulation software was used to develop a digital twin of the hydrogen production system. The machine learning models were combined with the digital twin to replicate the real-time optimization of the processes and forecast the system reaction to the changes in the parameters. The results of the simulation were compared to the real plant-based data to determine the efficiency of the AI-based optimization process. 3.4. Optimization Techniques The AI-based optimization methods were twofold and targeted the process control and predictive maintenance. 3.4.1. Process Control: Key operation parameters were adjusted real time using the AI models. As an example the RL model constantly optimized the voltage and current fed to the electrolyzers so as to maximize the amount of hydrogen produced and minimisation of the amount of energy used. On the same note, deep learning model streamlined output of fuel cells depending on the real-time operational conditions. 3.4.2. Predictive Maintenance: The machine learning models that analyzed past maintenance data and identified patterns that could result in potential failures were used to apply predictive maintenance. The models forecasted the time that maintenance was needed and hence repairs could be scheduled to run prior to equipment failure. The effectiveness of the model was regarded through comparing the maintenance cost and the downtime before and after the predictive maintenance implementation was done. 3.5. Model Evaluation The AI models were tested according to their efficiency in maximizing the effectiveness of hydrogen production and reducing downtimes. Evaluation was done using the following metrics: 3.5.1. Energy Efficiency: The efficiency of the hydrogen production system was also measured in terms of energy that was used to achieve one unit of hydrogen produced when it was optimized with AI. The aim was to savings of energy without compromising on rates of hydrogen production. 3.5.2. Production Rate: The optimizing rate of hydrogen production was compared between actual and predicted rates of hydrogen production to test the model. 3.5.3. System Downtime: Predictive maintenance performance was also determined by how the unplanned downtime reduction was measured following the implementation of AI-based maintenance scheduling. Table 5 Performance Summary of AI Models in Hydrogen Production Optimization Model Energy Efficiency Improvement Hydrogen Production Rate (Nm³/h) System Downtime Reduction (%) Supervised Learning (Regression) 15% 5.6 8% Reinforcement Learning 22% 6.2 15% Deep Learning (Neural Network) 18% 6.0 12% Digital Twin Integration 20% 5.8 10%