Papers for

process engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Bayesian optimization speeds up industrial process design with fewer simulations

Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models

Abstract: Optimizing industrial process flowsheets is often computationally prohibitive due to the high cost of rigorous simulations and the curse of dimensionality inherent in complex design spaces. To address these challenges, we present a reduced-space multi-fidelity Bayesian optimization (RS-MFBO) framework designed for high-dimensional, expensive black-box functions. The approach integrates Global Sensitivity Analysis (GSA) for dimensionality reduction with a fidelity-augmented Gaussian process that captures correlations between low-cost approximations and expensive high-fidelity evaluations. A cost-aware acquisition strategy, augmented with cooldown and promotion mechanisms, adaptively guides the allocation of samples across fidelities. The framework is validated on two distinct industrial process simulators: a plasmid DNA bioprocess in SuperPro Designer and a green fuel synthesis plant in Aspen HYSYS. Results across diverse economic and physical objectives demonstrate that the proposed method substantially reduces the number of high-fidelity simulator evaluations while maintaining competitive optimization performance compared to single-fidelity baselines. These results highlight RS-MFBO as a scalable, simulator-agnostic approach for cost-constrained black-box optimization.

Tue 15 SeptMachine Learning
The gist
Improving complex industrial processes often requires running many slow and expensive simulations, which can take a lot of time and computing power. To tackle this, the authors created a new method that learns which parts of the process matter most and uses cheaper, simpler models to guide the search for the best design. This approach balances detailed and approximate simulations to find efficient solutions faster. They tested it on two real-world industrial examples and showed it reduces the need for costly simulations while still finding good designs.
Open → 2609.17440v1

Separating reasoning from serialization eases engineering design tasks

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

Abstract: 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 (DEXPI) serialization changes where representation and engineering failures occur in constrained greenfield surface-process design for underground gas storage. We compare Direct DEXPI generation with generation of a lightweight Engineering Intermediate Representation (IR) on three cases: single-pressure injection, withdrawal and export, and dual-pressure injection. All six conditions use one fixed model snapshot, qwen3.8-max-0902, with one completed hosted generation per condition. Direct outputs are XSD-valid in 2 of 3 cases, while all 3 Engineering IR outputs are structurally valid under a minimal validator. Direct prompt inputs contain approximately 121.8k-121.9k tokens, compared with 617-699 tokens for the Engineering IR prompts. Engineering feasibility does not uniformly favor the IR: one IR output is rejected for an explicit cooling-state contradiction. The cases also reveal two distinct Direct DEXPI failure modes: engineering inconsistency and standards-level serialization failure. The observed comparison shows that, in these evaluated method bundles, deferring DEXPI serialization substantially reduces representation burden and helps isolate serialization failure, but reducing representation burden alone does not eliminate engineering inconsistencies.

Fri 11 SeptSoftware EngineeringArtificial Intelligence
The gist
Designing underground gas storage involves creating detailed engineering descriptions and computer-readable files that follow strict standards. This paper shows that splitting the design process into two parts—first making a simple engineering outline, then converting it into the full standard format—can reduce errors related to the format itself. The authors found that while this separation makes it easier to spot where problems happen, it does not completely fix mistakes made in the engineering design. They tested this idea on three example cases involving different gas storage scenarios.
Open → 2609.12656v1