Chemical Engineering and Process Systems Engineering (CE&PSE)

Overview

Generative Artificial Intelligence (GenAI), driven by advancements in machine learning models such as transformers and diffusion models, has emerged as a transformative technology across multiple domains. In the fields of Chemical Engineering (CE) and Process Systems Engineering (PSE), GenAI has shown remarkable potential for automating traditionally complex tasks, optimizing workflows, and innovating processes, products, and materials. This literature review synthesizes recent research and applications to outline the impact, challenges, and opportunities of GenAI in these domains.

The Applications of GenAI in Chemical Engineering

Generative AI (GenAI) is increasingly being applied in chemical engineering to address challenges in molecular design, process optimization, and catalyst development. We will review:

Molecular and Material Design

GenAI is revolutionizing molecular and material design by enabling the creation of novel molecules and materials with specific desired properties. One of the most impactful applications of GenAI in chemical engineering is its role in molecular and material design. Generative models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), have been extensively utilized to generate novel molecular structures with desired properties. These models, trained on chemical databases like PubChem or ChEMBL, can design molecules that optimize trade-offs between competing properties such as reactivity, stability, and environmental impact.

Example:

A study utilizing transformer-based models like MolGPT demonstrated the generation of drug-like molecules with superior pharmacokinetics. By integrating multi-objective optimization frameworks, researchers have also generated molecules aimed at decarbonization, such as CO2-absorbing materials.

Key advancements include:

Process Optimization

GenAI is being applied to optimize chemical engineering processes, improving efficiency and reducing costs:

Catalyst Development

Catalyst design is another area where GenAI is making significant contributions:

Examples of Industry Adoption

How GenAI is Transforming Chemical Engineering

This section describes how GenAI enhances chemical processes from real-time optimization to energy efficiency and multiscale applications.

Real-Time Process Optimization

Process Flow Design and Simulation

Predictive Maintenance

Energy Efficiency and Sustainability

Autonomous Experimental Platforms

Multiscale Optimization

Key Tools and Frameworks

Generative AI is reshaping chemical engineering by enabling real-time optimization, automating complex workflows, enhancing sustainability, and fostering innovation through autonomous experimentation. Its ability to integrate data-driven insights with simulation environments positions it as a transformative tool for achieving efficiency and innovation in chemical processes.

Case Studies of AI in Chemical Process Optimization

There are several successful case studies of AI optimizing chemical processes in the industry. These examples demonstrate how AI technologies have been applied to improve efficiency, reduce costs, enhance product quality, and accelerate innovation in chemical production.

Mesalazine Synthesis Optimization

AI was used to optimize the multistep synthesis of mesalazine, a pharmaceutical compound. Techniques like partial least squares (PLS) regression and artificial neural networks (ANNs) were employed to monitor and control reaction conditions in real-time. This approach improved the precision of intermediate and impurity quantification, enabling better reaction control and higher yields. Additionally, simulated training spectra accelerated the training process for AI models, making advanced data processing more accessible and cost-effective[19].

Catalyst Design at Johnson Matthey

Johnson Matthey utilized the Alchemite™ AI platform to optimize catalyst design and related processes. This led to a 4% increase in yield for a key process and significant cost and energy savings during scale-up. The company also achieved efficiency improvements of 50-80% in experimental programs, showcasing the potential of AI in accelerating R&D while reducing resource consumption[22].

Distillation Column Optimization

AI was applied to optimize distillation columns by adjusting parameters such as reflux ratio, feed temperature, and pressure. This resulted in improved benzene purity by 0.5% without increasing energy consumption. The system continuously adjusted these variables in real-time, ensuring optimal performance and product quality[11].

Ink Formulation at Domino Printing Sciences

Domino Printing Sciences used AI to guide testing and optimize ink formulations. The application of machine learning reduced time-to-market, identified new candidate formulations, and enabled reformulation in response to market or regulatory changes. This approach significantly streamlined the development process[22].

Predictive Maintenance and Factory Throughput

A major chemical company leveraged AI for predictive maintenance, which improved factory throughput by nearly 10%. By analyzing historical data, AI models predicted equipment failures before they occurred, reducing downtime and enhancing operational efficiency[23].

Glass Production Quality Control

AI was employed to control temperature profiles along forehearths used in glass production. Machine learning models dynamically adjusted heating zones to maintain uniform viscosity in molten glass, reducing defects such as bubbles by 3% and ensuring higher-quality products[11].

Reaction Condition Optimization

AI-driven models have been used to identify optimal reaction conditions (e.g., temperature and pressure) for various chemical processes. These models reduced reaction times and improved yields while minimizing resource use[20].

Lubricant Property Prediction

In a study on lubricants, sparse experimental data was combined with molecular dynamics simulations using Alchemite™ software. AI exploited property-property correlations to predict physical properties of known and new alkanes, aiding in material discovery with fewer experiments[22].

Key Benefits of AI in Chemical Industry Applications

• Efficiency Gains: Real-time monitoring and dynamic adjustments improve process efficiency.
• Cost Reduction: Optimized resource use reduces waste and energy consumption.
• Innovation Acceleration: Faster discovery of new materials or formulations.
• Improved Quality: Enhanced product consistency through precise control.
• Sustainability: Reduced environmental impact through waste minimization.

These case studies highlight how AI is transforming the chemical industry by addressing complex challenges with innovative solutions across research, manufacturing, and quality control processes.

AI and Precision Solution Chemistry

AI plays a transformative role in the customization of chemical products by enabling precision, efficiency, and innovation across various stages of product development and manufacturing.

Customization of Chemical Products

Efficiency and Speed in Customization

Sustainability and Cost-Effectiveness

Market Responsiveness

AI's integration into the chemical industry has revolutionized how companies approach product customization. By combining advanced analytics with machine learning models, businesses can deliver innovative solutions tailored to specific customer requirements while achieving efficiency, sustainability, and cost-effectiveness. This positions AI as a cornerstone for competitive advantage in the chemical sector.

GenAI in Continuous Industrial Process Engineering

The application of Artificial Intelligence (AI) in continuous industrial processes—such as cement production, steelmaking, sugar refining, and bioreactor/fermentation systems—has led to remarkable advancements. These technologies are driving operational efficiency, sustainability, and innovation across industries. We will review:

1. How GenAI is Revolutionizing Continuous Industrial Processes
2. How Generative AI Optimizes Bioreactors and Fermentation Systems

Revolutionizing Continuous Industrial Processes with AI

Cement Industry: Driving Efficiency and Sustainability
AI has become a cornerstone in modernizing the cement industry, addressing challenges like energy consumption, emissions reduction, and process inefficiencies.

These advancements are enabling the cement industry to transition toward smarter, more sustainable operations while meeting global decarbonization goals.

Steel and Foundry Industries: Enhancing Process Control
The steelmaking process involves complex operations such as melting, casting, and refining. AI is addressing variability in these processes to improve efficiency and product quality.

Sugar Refining: Real-Time Optimization
Sugar refining involves dynamic processes that require precise control for optimal performance. AI has introduced transformative solutions for this industry:

These innovations are helping sugar producers achieve higher efficiency and profitability while addressing sustainability concerns.

Bioreactors and Fermentation Systems

Bioreactor Optimization through AI:
Bioreactors play a critical role in industries such as pharmaceuticals, bioengineering, food production, and more. AI is revolutionizing fermentation processes by optimizing environmental conditions for microorganisms:

These innovations are enabling bioreactors to achieve higher productivity while maintaining consistency across large-scale operations.

Addressing Challenges in Fermentation:
AI also addresses common challenges faced by fermentation systems:

By leveraging these capabilities, bioreactor systems are achieving unprecedented levels of efficiency while reducing operational risks. AI has emerged as a transformative force across non-manufacturing industrial continuous processes. From optimizing cement kilns to streamlining sugar refining operations and enhancing bioreactor performance, these technologies are setting new benchmarks for efficiency, sustainability, and innovation. With ongoing advancements in machine learning and data analytics, the potential for further breakthroughs remains vast—paving the way for smarter industries that align with global sustainability goals.

GenAI for Fault Detection and Diagnosis of Industrial Process Systems

Fault detection is a critical aspect of maintaining safe and efficient operations in continuous process systems. Generative AI (GenAI) is transforming fault detection and diagnosis (FDD) in industrial process systems by leveraging advanced machine learning techniques to identify, predict, and explain faults in real time. This technology addresses challenges such as limited fault data, high-dimensional datasets, and evolving system behaviors, making it a game-changer for industries reliant on continuous processes.

We will review:

1. Anomaly detection
2. Predictive maintenance
3. Enhanced Fault Diagnosis with Explainability
4. Industry Use Cases

Anomaly Detection

Generative Adversarial Networks (GANs) are used to detect anomalies in complex systems like rotating machinery or power grids. These models can generate synthetic data to train diagnostic algorithms, improving accuracy even with limited real-world fault data[50][51].

GenAI-powered models like ACGAN-SDAE have demonstrated robust performance across varying load conditions and noisy environments, making them highly adaptable for diverse industrial applications[50][55].

Predictive Maintenance

By simulating failure scenarios and generating synthetic datasets, GenAI enables predictive maintenance strategies that preempt equipment failures. This reduces downtime and operational costs while enhancing system reliability[56].

Enhanced Fault Diagnosis with Explainability

Explainable AI (XAI) techniques integrated with GenAI improve the interpretability of fault diagnosis models:

Industry Use Cases

Generative AI is revolutionizing fault detection and diagnosis by addressing long-standing challenges in process systems engineering. From creating synthetic datasets to enabling real-time anomaly detection and predictive maintenance, GenAI enhances operational efficiency, safety, and reliability across industries. As adoption grows, its ability to integrate explainable models and adaptive solutions will further solidify its role as a cornerstone technology in industrial automation.

Challenges and Limitations

Generative AI (GenAI) applications in Chemical Engineering and Industrial Process System Engineering face several challenges and limitations across four key domains: Data Quality and Availability, Interpretability, Computational Costs, Ethical Considerations and examples from the industry.

Data Quality and Availability

High-quality data is foundational for training effective GenAI models. However, in CE and IPSE, acquiring such data poses significant challenges:

Solution paths:

Interpretability

The "black-box" nature of GenAI models limits their adoption in safety-critical fields like Industrial process engineering:

Innovation paths:

Computational Costs

GenAI models are computationally intensive, making their deployment challenging:

Mitigation Strategies:

Ethical Considerations

The ethical implications of GenAI applications in this engineering domain include:

Robust ethical frameworks emphasizing transparency, accountability, and sustainability are essential for responsible deployment[62][65]. Regular audits of AI systems can help identify biases or unintended consequences early on[80].

Examples from Industry

In conclusion, while GenAI offers transformative potential in Chemical Engineering and Industrial process engineering, addressing these challenges through collaborative efforts, technological innovation, and ethical governance is critical for its successful integration into these fields.

Future Directions for Gen AI in Chemical and Process Engineering

Four directions shape the future of GenAI in CE and PSE: Hybrid Models, Self-Supervised Learning (SSL), Decarbonization and Green Chemistry, and Model Interpretability.

Hybrid Models

Hybrid models improve the reliability of simulations in chemical engineering by combining the strengths of physics-based (mechanistic) models and data-driven machine learning (ML) approaches. This synergy addresses the limitations of each method individually, leading to more accurate, efficient, and robust simulations. Here's how hybrid models enhance reliability:

While hybrid models offer significant advantages, challenges such as computational complexity, integration of diverse data sources, and interpretability remain. Future research is focused on:

• Developing standardized frameworks for hybrid modeling.
• Enhancing interpretability through explainable AI techniques.
• Expanding applications in decarbonization and sustainable process design.

By addressing these challenges, hybrid modeling will continue to play a pivotal role in advancing chemical engineering simulations and process systems engineering.

Self-Supervised Learning

Self-supervised learning (SSL) helps overcome data scarcity in chemical engineering (CE) and process systems engineering (PSE) by leveraging large quantities of unlabeled data to pre-train models, which can then be fine-tuned for specific tasks using limited labeled data. This approach addresses the challenges posed by the high cost and time required to generate labeled datasets.

Leveraging Unlabeled Data:
SSL uses unlabeled data, which is typically abundant in industrial processes (e.g., sensor readings, operational logs), to learn meaningful representations of the underlying system. For example, SSL has been applied to fatigue damage prognostics by pre-training models on synthetic strain gauge data, enabling accurate predictions of remaining useful life (RUL) with minimal labeled examples[87][90]. In materials science, SSL frameworks like Crystal Twins have been used to predict material properties from large databases of unlabeled structural and compositional data, bypassing the need for expensive simulations or experiments[89].

Pretext Tasks for Representation Learning:
SSL employs "pretext tasks"—auxiliary learning tasks designed to extract useful features from unlabeled data. These tasks might include predicting missing sensor values, identifying anomalies, or reconstructing process states. For instance, in chemical process optimization, SSL could train a model to predict missing operational parameters or reconstruct reaction pathways, creating robust feature representations that can be fine-tuned for downstream tasks like fault detection or yield prediction.

Reducing Labeling Costs:
Traditional supervised learning requires extensive labeled datasets, which are costly and labor-intensive to generate in CE and PSE due to the need for domain expertise (e.g., annotating experimental results or process failures). SSL minimizes this dependency by generating pseudo-labels from unlabeled data[88][89]. This reduction in labeling requirements makes it feasible to apply advanced machine learning techniques even in resource-constrained environments.

Enhancing Model Generalization:
By pre-training on diverse and unlabeled datasets, SSL models learn generalizable features that can adapt to various tasks with minimal additional training. This is particularly valuable in CE and PSE, where processes often involve complex dynamics and variability across systems. For example, pre-trained SSL models can be fine-tuned for specific applications like optimizing distillation columns or predicting catalyst performance with limited task-specific data.

Applications in CE and PSE:

Computational Efficiency:
Pre-trained SSL models often require less computational effort during fine-tuning compared to training a supervised model from scratch. This efficiency is critical in CE and PSE applications where computational resources may be constrained[87].

While SSL offers significant advantages for addressing data scarcity, challenges such as model interpretability, computational complexity during pre-training, and ensuring high-quality unlabeled datasets remain. Future research should focus on:

By addressing these challenges, SSL can further enable robust AI-driven solutions even in data-scarce scenarios.

Decarbonization and Green Chemistry

GenAI can play a pivotal role in advancing sustainable practices in CE and PSE.
Applications in Decarbonization:

Startups like Entalpic use GenAI to design catalysts for energy-efficient chemical processes, contributing to decarbonization efforts in sectors like fertilizers, energy storage, and pollution control[92].

Machine learning models are being developed to optimize multiscale systems for sustainable pathways, such as reducing greenhouse gas emissions in petrochemical processes[93][94].

GenAI models can simulate chemical reactions and optimize process parameters to improve energy efficiency and reduce emissions. By identifying optimal configurations, it minimizes waste and energy use during production[95][96].

Applications in Green Chemistry:

Generative AI plays a transformative role in advancing decarbonization and green chemistry initiatives by enabling more efficient, sustainable, and innovative practices across process industries. Its ability to analyze, simulate, and optimize processes at various scales makes it a powerful tool for reducing environmental impact while driving technological progress. AI-driven tools are being used to design eco-friendly solvents, catalysts, and reaction pathways that align with green chemistry principles[97].

Designing Sustainable Chemicals and Materials:

GenAI accelerates the discovery of environmentally friendly chemicals by predicting molecular structures with desired properties. It proposes alternatives that reduce toxicity, improve biodegradability, and lower carbon footprints[98][27].

For instance, generative models have been used to design catalysts that enhance reaction rates while minimizing environmental impact[100][27].

Lifecycle Analysis:

Predictive models powered by GenAI evaluate the environmental impact of chemical processes throughout their lifecycle—from raw material extraction to disposal—helping industries adopt greener practices[100].

Optimizing Reaction Pathways:

GenAI simulates reaction conditions to identify the most sustainable methods for chemical production. This reduces reliance on hazardous solvents and reagents while maximizing yield[96][100].

A notable example is the use of AI-driven models to optimize catalytic processes for converting biomass into biofuels, enhancing yield while reducing waste[100].

Autonomous Experimental Platforms:

GenAI integrates with robotic systems to automate chemical synthesis tasks. By planning and executing synthesis routes with minimal human intervention, it accelerates the development of sustainable materials while reducing resource consumption[13][27].

Future Opportunities:

GenAI could help industries transition to circular economies by optimizing waste recycling processes or designing biodegradable materials. In the chemical industry, it supports scaling lab-scale processes to industrial levels while maintaining sustainability goals[103]. It can also assist policymakers by modeling the long-term environmental impacts of various industrial strategies.

Model Interpretability

Explainable AI (XAI) is a transformative tool for CE and PSE, addressing the "black-box" nature of traditional AI models while enhancing trust among stakeholders. By fostering transparency, reliability, and collaboration, XAI can drive the adoption of advanced AI-driven workflows in these critical industries.

Explainable AI (XAI) Techniques:

XAI techniques are essential for making AI models more transparent and interpretable, particularly in critical industrial applications such as fault detection, optimization, and process control. XAI is being used to optimize chemical processes by integrating machine learning with optimization algorithms[104].

For instance, hybrid models combining neural networks with genetic algorithms have reduced computational time significantly while achieving high accuracy in process optimization tasks like distillation. In product design, XAI enhances the prediction of physical properties like critical temperature or pressure using deep learning models. This helps in designing safer and more sustainable materials. Advanced XAI techniques are being applied to simulate complex chemical processes, enabling engineers to understand model predictions better, especially for nonlinear systems with multiple variables. Companies like NVIDIA and Alphabet are leveraging AI to optimize industrial processes while maintaining transparency through explainable frameworks[107].

The complexity of chemical processes often requires domain-specific adaptations of general XAI techniques[105][106].

Startups and Projects Utilizing Explainable AI (XAI) in Chemical Engineering (CE) and Process Systems Engineering (PSE)

Discusses innovative startups and projects that leverage XAI for chemical and process systems engineering.

AiChemist by CHELONIA

AiChemist leverages XAI for molecular design and optimization in chemical R&D :

The project integrates advanced machine learning methods with XAI to make AI outputs interpretable, aiding both researchers and regulatory agencies.

CHAI Project by imec

Developing hybrid, explainable AI algorithms to optimize chemical processes :

The project emphasizes dynamic visualization and feedback loops to continuously improve decision-making in process optimization[109].

Basetwo

A low-code platform for process engineers to build digital twins of manufacturing processes :

Basetwo's platform simplifies the integration of XAI into PSE workflows, enabling engineers to simulate, monitor, and optimize processes effectively[110].

XenonStack

Explainable AI in manufacturing, including applications relevant to PSE :

XenonStack uses XAI to provide transparent justifications for AI predictions, fostering trust among users while improving operational efficiency[108].

SchNet4AIM

SchNet4AIM is a SchNet-based architecture designed for Explainable Chemical Artificial Intelligence (XCAI):

Unlike traditional black-box models, SchNet4AIM inherently integrates explainability by linking predictions to physically rigorous atomic or pairwise terms. This approach has enabled researchers to interpret complex chemical simulations, accelerating material discovery and improving the reliability of AI-driven insights[111].

Autonomous Simulation Agents

These agents are used for real-time data analysis and predictive modeling in polymer science:

• Suggesting new polymer structures with specific properties like flexibility or strength.
• Optimizing synthesis processes dynamically based on ongoing results.

By integrating XAI, these agents provide clear explanations of their predictions, helping researchers understand the rationale behind suggested designs or process adjustments. The use of XAI has improved material design workflows, reducing development time and enhancing safety in polymer applications[112].

These startups and projects demonstrate how XAI is being applied to enhance transparency, trust, and efficiency in CE and PSE workflows. They focus on bridging the gap between complex AI models and human interpretability, ensuring safer and more effective industrial operations.

Conclusion

Generative AI represents a paradigm shift in chemical engineering and process systems engineering. By automating complex tasks, optimizing processes, and driving innovation, it has the potential to redefine industry standards. However, challenges such as data limitations, interpretability, and computational costs must be addressed to fully harness its capabilities. As research advances, GenAI is poised to play a central role in the evolution of CE&PSE, particularly in the context of sustainability and decarbonization.