Ahmed Ragab

Marzouk Benali

Olumoye Ajao

Daria C. Boffito

2026-05-31

Artificial Intelligence Chemistry (publié)

A stratified approach for heterogeneous data fusion using polygon generation, deep learning and ensemble modeling

Mohamed Elhefnawy

Nicolas Pelletier

Jean-Martin Lussier

Mouloud Amazouz

The widespread adoption of digitalization across various industries has resulted in the accumulation of vast amounts of data from diverse so… (voir plus)urces, offering opportunities to analyze complex phenomena. However, such analysis remains challenging due to the heterogeneity in data types, structures, formats, sampling frequencies and other factors. While numerous artificial intelligence (AI) techniques exist to analyze these heterogeneous datasets, no single AI technique can effectively handle all types of heterogeneous data acquired from different sources, while accurately predicting multiple outputs. To address this challenge, this paper proposes an ensemble learning approach based on diversified regression techniques to predict multiple continuous outputs. This approach captures the data distribution of the targeted phenomena from different perspectives. A novel stratified modeling technique is introduced, in which the data are first classified by predicting stratification labels before undergoing the regression modeling. This stratification is achieved using an innovative fusion approach based on a polygon generation representation technique, effectively breaking down the primary complex discriminative problem into smaller and more manageable subproblems. The effectiveness of the proposed method is validated using a dataset collected from harvester machinery in the forest industry. The method predicts key outputs such as merchantable wood volume, the log count, and the proportion of hardwood and softwood of different sizes for predetermined forest plots. The results demonstrate that the performance of this stratified approach outperforms comparable methods from the literature, demonstrating its superior performance.

2026-05-15

Ecological Informatics (publié)

A stratified approach for heterogeneous data fusion using polygon generation, deep learning and ensemble modeling

Mohamed Elhefnawy

Nicolas Pelletier

Jean-Martin Lussier

Mouloud Amazouz

The widespread adoption of digitalization across various industries has resulted in the accumulation of vast amounts of data from diverse so… (voir plus)urces, offering opportunities to analyze complex phenomena. However, such analysis remains challenging due to the heterogeneity in data types, structures, formats, sampling frequencies and other factors. While numerous artificial intelligence (AI) techniques exist to analyze these heterogeneous datasets, no single AI technique can effectively handle all types of heterogeneous data acquired from different sources, while accurately predicting multiple outputs. To address this challenge, this paper proposes an ensemble learning approach based on diversified regression techniques to predict multiple continuous outputs. This approach captures the data distribution of the targeted phenomena from different perspectives. A novel stratified modeling technique is introduced, in which the data are first classified by predicting stratification labels before undergoing the regression modeling. This stratification is achieved using an innovative fusion approach based on a polygon generation representation technique, effectively breaking down the primary complex discriminative problem into smaller and more manageable subproblems. The effectiveness of the proposed method is validated using a dataset collected from harvester machinery in the forest industry. The method predicts key outputs such as merchantable wood volume, the log count, and the proportion of hardwood and softwood of different sizes for predetermined forest plots. The results demonstrate that the performance of this stratified approach outperforms comparable methods from the literature, demonstrating its superior performance.

2026-05-15

Ecological Informatics (publié)

Multi agent deep reinforcement learning for supervising local controllers in energy-intensive industrial processes

Karim Nadim

Hakim Ghezzaz

Industrial plants are equipped with several local controllers with a high degree of interaction. Controllers in complex systems tend to oper… (voir plus)ate in a competitive way to achieve their own objective, which can negatively impact other controllers and consequently the global KPI. In addition, the rapid changes in process dynamics, the variations, and fluctuations in the process conditions and production targets introduce major challenges in optimizing the whole process. As a result, operators struggle to adjust the controllers’ setpoints to optimize the process operation. Therefore, there is a clear need for an approach that captures the controllers’ interdependence and optimizes the setpoints in real-time to ensure energy-efficient operations. This paper proposes an intelligent decentralized supervisory control approach based on multi-agent deep reinforcement learning (MADRL) to recommend the optimal combinations of controllers’ setpoints that maintain desired process operation. Multiple agents are developed based on the deep deterministic policy gradient algorithm to collaborate and control different interconnected subsystems. The agents are trained via interacting with a process simulation, where each agent performs actions (setpoint changes) and observes certain rewards (global KPI to be maximized) and states (measured variables) from the simulation. The approach is validated on a case study based on a heat recovery network of a thermomechanical pulp mill comprising four different subsystems. The proposed decentralized approach was compared to two centralized approaches: a baseline control set by the process expert and a single DDPG agent. The multi-agent approach was able to reduce the steam flow consumption by 6.7 % compared to the experts’ baseline and 5.3% compared to the single agent with faster convergence. Two possible strategies were proposed to implement this approach in the industry, depending on the criticality of the process and the degree of fidelity of its process simulation.

2026-04-30

Control Engineering Practice (publié)

Reliable dynamic causality analysis for efficient prescriptive maintenance of degraded industrial equipment

Karim Nadim

Hakim Ghezzaz

Mohamed-Salah Ouali

Developing efficient prescriptive maintenance strategies is essential in mitigating the performance degradation of equipment in energy-inten… (voir plus)sive process industries. This paper proposes an innovative approach that integrates data-driven causality analysis and reinforcement learning (RL) to reduce/slow-down the equipment degradation. The degradation is first modeled by constructing representative health indicators (HIs) using deep learning autoencoders. The HIs are then exploited using clustering and interpretable machine learning techniques to identify the degradation's root causes. Afterward, a dynamic causal model is discovered in the form of a Petri net (PN) using process mining techniques. The causal model incorporates the temporal information and sequential relationships between the identified root causes. Finally, an RL agent is integrated with the PN model to recommend the optimal sequence of events that diminishes the performance degradation rate. The proposed approach is tested successfully on a complex case study of a black liquor concentrator in a Kraft pulp mill that is subjected to a decline in operational performance and capacity due to the fouling degradation phenomenon. The obtained results show that the fouling rate was minimized, leading to an approximate 30 % saving in maintenance costs and a reduction of around 4.8 kt CO2/year in greenhouse gas emissions.

2026-01-19

Journal of Loss Prevention in the Process Industries (publié)

Simulate intelligently: Causal incremental reinforcement learning for streamlined industrial chemical process design optimization

Eslam G. Al-Sakkari

Mohamed Ali

Daria C. Boffito

2025-10-31

Journal of Environmental Chemical Engineering (publié)

Ensemble machine learning to accelerate industrial decarbonization: Prediction of Hansen solubility parameters for streamlined chemical solvent selection

Eslam G. Al-Sakkari

Mostafa Amer

Olumoye Ajao

Marzouk Benali

Daria C. Boffito

Mouloud Amazouz

2024-11-30

Digital Chemical Engineering (publié)

Learn-To-Design: Reinforcement Learning-Assisted Chemical Process Optimization

Eslam G. Al-Sakkari

Mohamed Ali

Daria C. Boffito

Mouloud Amazouz

This paper proposes an AI-assisted approach aimed at accelerating chemical process design through causal incremental reinforcement learning … (voir plus)(CIRL) where an intelligent agent is interacting iteratively with a process simulation environment (e.g., Aspen HYSYS, DWSIM, etc.). The proposed approach is based on an incremental learnable optimizer capable of guiding multi-objective optimization towards optimal design variable configurations, depending on several factors including the problem complexity, selected RL algorithm and hyperparameters tuning. One advantage of this approach is that the agent-simulator interaction significantly reduces the vast search space of design variables, leading to an accelerated and optimized design process. This is a generic causal approach that enables the exploration of new process configurations and provides actionable insights to designers to improve not only the process design but also the design process across various applications. The approach was validated on industrial processes including an absorption-based carbon capture, considering the economic and technological uncertainties of different capture processes, such as energy price, production cost, and storage capacity. It achieved a cost reduction of up to 5.5% for the designed capture process, after a few iterations, while also providing the designer with actionable insights. From a broader perspective, the proposed approach paves the way for accelerating the adoption of decarbonization technologies (CCUS value chains, clean fuel production, etc.) at a larger scale, thus catalyzing climate change mitigation.

2024-07-08

Systems and Control Transactions (publié)

Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities

Eslam G. Al-Sakkari

Daria C. Boffito

Mouloud Amazouz

Carbon capture, utilization, and sequestration (CCUS) is a promising solution to decarbonize the energy and industrial sector to mitigate cl… (voir plus)imate change. An integrated assessment of technological options is required for the effective deployment of CCUS large-scale infrastructure between CO2 production and utilization/sequestration nodes. However, developing cost-effective strategies from engineering and operation perspectives to implement CCUS is challenging. This is due to the diversity of upstream emitting processes located in different geographical areas, available downstream utilization technologies, storage sites capacity/location, and current/future energy/emissions/economic conditions. This paper identifies the need to achieve a robust hybrid assessment tool for CCUS modeling, simulation, and optimization based mainly on artificial intelligence (AI) combined with mechanistic methods. Thus, a critical literature review is conducted to assess CCUS technologies and their related process modeling/simulation/optimization techniques, while evaluating the needs for improvements or new developments to reduce overall CCUS systems design and operation costs. These techniques include first principles- based and data-driven ones, i.e. AI and related machine learning (ML) methods. Besides, the paper gives an overview on the role of life cycle assessment (LCA) to evaluate CCUS systems where the combined LCA-AI approach is assessed. Other advanced methods based on the AI/ML capabilities/algorithms can be developed to optimize the whole CCUS value chain. Interpretable ML combined with explainable AI can accelerate optimum materials selection by giving strong rules which accelerates the design of capture/utilization plants afterwards. Besides, deep reinforcement learning (DRL) coupled with process simulations will accelerate process design/operation optimization through considering simultaneous optimization of equipment sizing and operating conditions. Moreover, generative deep learning (GDL) is a key solution to optimum capture/utilization materials design/discovery. All of these developed methods will be generalizable where the extracted knowledge can be transferred to future works to help cutting the costs of CCUS value chain.

2024-02-29

Science of The Total Environment (publié)

Machine learning-assisted selection of adsorption-based carbon dioxide capture materials

Eslam G. Al-Sakkari

Terry M.Y. So

Marzieh Shokrollahi

Philippe Navarri

Ali Elkamel

Mouloud Amazouz

2023-07-31

Journal of Environmental Chemical Engineering (publié)