Process Optimization and Integration

Neueste Paper

• Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering

  Michael Baldea, Linda J. Broadbelt, Marianthi G. Ierapetritou, Akhilesh Jain, Ankur Kumar, Thomas A. Kwan, F\`elix Llovell, Andrew J. Medford, Ilias Mitrai, Joel Paulson, Junyi Qiao, Matthew P. Rivera, Kirti C. Sahu, Lev Sarkisov, Zachary P. Smith, Calvin Tsay, Ching-Mei Wen, Victor M. Zavala, Huacheng Zhang, Dan Zhao · 2. Oktober 2026

  The rapid maturation of artificial intelligence (AI) and machine learning (ML) has catalyzed a profound shift in how chemical engineering problems are formulated, analyzed, and solved. Advances in computing, data availability, and learning algorithms have enabled AI/ML methods to impact applications…

• Separating Engineering Reasoning from DEXPI Serialization in LLM-Based Greenfield Surface-Process Design: A Three-Case Study for Underground Gas Storage

  Qingchuan Zhu, Shuyue Tong, Pengju Ren · 14. September 2026

  Large language models can produce engineering descriptions and structured process representations, but standards-level serialization can substantially increase the generation burden. This diagnostic study examines whether separating engineering reasoning from Data Exchange in the Process Industry (D…

• Differentiable Hybrid Modelling for Learning and Optimising Chemical Transport Processes from Experimental Data

  Arthur Jessop, Mohammed Alsubeihi, Ben Moseley, Ashwin Kumar Rajagopalan · 4. September 2026

  Reliable transport models are essential when modelling and optimising many chemical engineering processes, yet, most models assume hand-picked constitutive laws which may not reflect reality, and often assume initial conditions are known exactly. Both restrictions can significantly bias model predic…

• A tale of perfect fit and phantom optima: how data-driven models can fail in real-time optimization

  Prithvi Dake, Rahul Bindlish, James B. Rawlings · 26. August 2026

  Real-time optimization (RTO) relies on process models to locate economically optimal operating conditions. Because developing first-principles models requires significant process knowledge, data-driven alternatives are increasingly attractive. Modern machine-learning models can fit historical plant …

• Continual Distillation Learning for Rehearsal-Free Class-Incremental Learning via Decoupled Prompting

  Qifan Zhang, Yunhui Guo, Yu Xiang · 14. August 2026

  Prompt-based continual learning has shown strong performance in rehearsal-free class-incremental learning by adapting learnable prompts while freezing a pre-trained Vision Transformer (ViT) backbone. However, the effect of backbone scale remains underexplored. We observe that larger ViT backbones co…

• LLMs in Process Diagram Engineering: From Optimal PFDs to Validated P&IDs

  Timur Zakarin, Sergei Voitov, Sergei Shumilin, Evgeny Burnaev · 13. August 2026

  Nowadays, the creation of a process flow diagram (PFD) and its subsequent transformation into a piping and instrumentation diagram (P&ID) is predominantly performed manually. Applying artificial intelligence in the task could potentially lead not only to process automation and time savings, but also…

• CRAFTS: Collaborative Role-Adaptive Fine-Tuning of LLM Agents for Chemical Process Simulation

  Ziyun Zhang, Yuxin Lin, Eldin Wee Chuan Lim, Xinghao Ding · 4. August 2026

  Constructing an executable chemical-process model remains manually intensive. Chemical engineers translate underspecified requests into coupled decisions about unit operations, thermodynamics, streams, specifications, degrees of freedom (DoF), initialization, solver repair, and optimization; one err…

• A Physics-Informed Framework for PID Tuning of Chemical Processes Using Large Language Model Agents

  Zhoupeng Shou, Xiaodong Hong, Congjing Ren, Jingdai Wang, Yongrong Yang, Zuwei Liao · 30. Juli 2026

  PID tuning for chemical processes commonly relies on identified process models, whereas plant engineers often retune loops iteratively by observing responses, diagnosing deficiencies, adjusting gains, and validating the result. This work formalizes this engineer-like workflow in a language-model-ass…

• Deep reinforcement learning for process design: Review and perspective

  Qinghe Gao, Artur M. Schweidtmann · 9. Juni 2026

  The transformation towards renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches. Recently, breakthroughs in artificial intelligence offer opportunities to accelerate this transition. Specifically, deep reinforcement learning, a subclass of …

• Bayesian Optimization of a Multi-Product Chemical Reactor Using Composite Models and Partial Physics Knowledge

  Liqiu Dong, Marta Zag\'orowska, Mehmet Mercang\"oz · 9. Juni 2026

  We study data-driven real-time economic optimization of a multi-product chemical reactor when no reliable first-principles model is available beyond a steady-state energy balance. Instead of learning the economic objective directly as a black-box function, we use a composite formulation in which Gau…

• From Data to Action: Accelerating Refinery Optimization with AI

  D\'aniel Pfeifer, \'Abrah\'am Papp, Tibor Bern\'ath, Tam\'as Zolt\'an Varga, M\'ark Czifra, Botond Szil\'agyi, Edith Alice Kov\'acs · 15. Mai 2026

  Nowadays refinery optimization utilizes sheer amounts of data, which can be handled with modern Linear Programming (LP) software, but the interpreting and applying the results remains challenging. Large petrochemical companies use massive models, with hundreds of thousands of input matrix elements. …

• Physics-Informed Neural Network Digital Twin for Dynamic Tray-Wise Modeling of Distillation Columns under Transient Operating Conditions

  Debadutta Patra, Ayush Bardhan Tripathy, Soumya Ranjan Sahu, Sucheta Panda · 27. März 2026

  Digital twin technology, when combined with physics-informed machine learning with simulation results of Aspen, offers transformative capabilities for industrial process monitoring, control, and optimization. In this work, the proposed model presents a Physics-Informed Neural Network (PINN) digital …

• Context is all you need: Towards autonomous model-based process design using agentic AI in flowsheet simulations

  Pascal Sch\"afer, Lukas J. Krinke, Martin Wlotzka, Norbert Asprion · 16. März 2026

  Agentic AI systems integrating large language models (LLMs) with reasoning and tooluse capabilities are transforming various domains - in particular, software development. In contrast, their application in chemical process flowsheet modelling remains largely unexplored. In this work, we present an a…

• Complexity Bounds for Smooth Multiobjective Optimization

  Phillipe R. Sampaio · 17. Februar 2026

  We study the oracle complexity of finding $\varepsilon$-Pareto stationary points in smooth multiobjective optimization with $m$ objectives. Progress is measured by the Pareto stationarity gap $\mathcal{G}(x)$, the norm of the best convex combination of objective gradients. Our analysis relies on a n…

• Data-Driven Conditional Flexibility Index

  Moritz Wedemeyer, Eike Cramer, Alexander Mitsos, Manuel Dahmen · 23. Januar 2026

  With the increasing flexibilization of processes, determining robust scheduling decisions has become an important goal. Traditionally, the flexibility index has been used to identify safe operating schedules by approximating the admissible uncertainty region using simple admissible uncertainty sets,…

• DiffRatio: Training One-Step Diffusion Models Without Teacher Supervision

  Wenlin Chen, Mingtian Zhang, Jiajun He, Zijing Ou, Jos\'e Miguel Hern\'andez-Lobato, Bernhard Sch\"olkopf, David Barber · 21. Januar 2026

  Score-based distillation methods (e.g., variational score distillation) train one-step diffusion models by first pre-training a teacher score model and then distilling it into a one-step student model. However, the gradient estimator in the distillation stage usually suffers from two sources of bias…

• From Text to Simulation: A Multi-Agent LLM Workflow for Automated Chemical Process Design

  Xufei Tian, Wenli Du, Shaoyi Yang, Han Hu, Hui Xin, Shifeng Qu, Ke Ye · 13. Januar 2026

  Process simulation is a critical cornerstone of chemical engineering design. Current automated chemical design methodologies focus mainly on various representations of process flow diagrams. However, transforming these diagrams into executable simulation flowsheets remains a time-consuming and labor…

• Optimizing Operation Recipes with Reinforcement Learning for Safe and Interpretable Control of Chemical Processes

  Dean Brandner, Sergio Lucia · 21. November 2025

  Optimal operation of chemical processes is vital for energy, resource, and cost savings in chemical engineering. The problem of optimal operation can be tackled with reinforcement learning, but traditional reinforcement learning methods face challenges due to hard constraints related to quality and …