cs.AIMar 2, 2026
SaveCeProAgents: A Hierarchical Agents System for Automated Chemical Process Development
Organizations: State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China · Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
Abstract
The development of chemical processes, a cornerstone of chemical engineering, presents formidable challenges due to its multi-faceted nature, integrating specialized knowledge, conceptual design, and parametric simulation. Capitalizing on this, we propose CeProAgents, a hierarchical multi-agent system designed to automate the development of chemical process through collaborative division of labor. Our architecture comprises three specialized agent cohorts focused on knowledge, concept, and parameter respectively. To effectively adapt to the inherent complexity of chemical tasks, each cohort employs a novel hybrid architecture that integrates dynamic agent chatgroups with structured agentic workflows. To rigorously evaluate the system, we establish CeProBench, a multi-dimensional benchmark structured around three core pillars of chemical engineering. We design six distinct types of tasks across these dimensions to holistically assess the comprehensive capabilities of the system in chemical process development. The results not only confirm the effectiveness and superiority of our proposed approach but also reveal the transformative potential as well as the current boundaries of Large Language Models (LLMs) for industrial chemical engineering.
Figures & tables
Figure 1: Overview of CeProAgents and CeProBench . a, The system processes heterogeneous chemical engineering inputs, including scientific documents, PFD imagery defining equipment and connections, and parameter files for simulation. b, The CeProAgents architecture comprises three specialized cohorts: the Knowledge Cohort retrieves chemical engineering knowledge from heterogeneous sources; the Concept Cohort bridges text captions, structural graphs, and PFD imagery; and the Parameter Cohort optimizes operational configurations for yield and economic targets. c, Representative multi-modal engineering results span three domains: retrieved kinetics and property data; designed and parsed PFD schematics; and optimized industrial parameters for yield and cost. d, The CeProBench suite probes six atomic tasks across the dimensions of Knowledge (Extract and Augment), Concept (Parse, Complete, and Design), and Parameter (Optimize).
Figure 2: Chemical Knowledge Enhancement and Structured Extraction via CeProAgents . a, Quantitative assessment of knowledge enhancement performance across four dimensions: Correctness, Rationality, Clarity, and Completeness. b, Ablation studies demonstrating the impact of multi-source integration; scores are compared across Full configuration and versions excluding the Knowledge Base (w/o KB), Knowledge Graph (w/o KG), and Web Search (w/o Web). c, Case study of multi-agent collaborative retrieval for industrial production queries, illustrating the interplay between Web, KB, KG, and Report Agents. d, Statistical distribution of knowledge extraction performance, showing Entity Metrics (accuracy, recall, F1) and Graph Metrics (MEC, MED). e, Representative workflow of structured knowledge extraction, transitioning from unstructured academic text to a merged, disambiguated semantic graph.
Figure 3: Process Concept Parsing, Completion, and Design via CeProAgents . a, Quantitative assessment of semantic parsing performance, showing accuracy and recall metrics for equipment and connection identification across different model backbones. b, Evaluation of generative completion capabilities using Top-K accuracy metrics, highlighting the system’s proficiency in inferring missing process components compared to baseline models. c, Representative case study illustrating the complete workflow from semantic parsing of raw PFDs to the logical completion of underspecified topologies. d, Evaluation of generative design performance comparing the Full configuration against an ablation without the Correction Agent (w/o). e, Detailed case study of iterative generative design, demonstrating how the system refines initial proposals through a multi-turn correction loop to satisfy complex engineering constraints.
Figure 4: Industrial Process Parameter Optimization via CeProAgents . a, Quantitative assessment of multi-objective optimization performance. The radar chart illustrates the normalized scores across five critical dimensions including Cost, Yield, Purity, and Iteration efficiency. b, Comparative analysis of optimization efficacy stratified by distinct unit operation types (Separation, Synthesis, and Combined). c, Evaluation of convergence efficiency illustrating the distribution of total optimization iterations and the specific iteration steps where the global optimum was identified. The box plots coupled with scatter points highlight the search speed and decision-making decisiveness of different backbones. d-f, Case studies of autonomous parameter optimization across distinct chemical processes. d, Separation process for aromatics purification involving a multi-column distillation sequence. e, Synthesis process for MMA production. f, Combined process for butadiene hydrocyanation to produce 3-PN.
Figure 5: Overall performance assessment of CeProAgents on CeProBench . a, The Knowledge dimension evaluation comparing epistemic fidelity and structural extraction capabilities. The radar chart aggregates metrics for reasoning quality (Correctness, Rationality, Clarity, Completeness) and graph construction integrity (Accuracy, Recall, F1, MEC, MED). b, The Concept dimension evaluation assessing multimodal topological reasoning across three distinct tasks: semantic parsing (Equipment/Connection Accuracy and Recall), topological completion (Top-K Accuracy), and generative design (Valid Rate and Correct Rate). c, The Parameter dimension evaluation quantifying parameter optimization efficacy. The chart contrasts optimization outcomes (Yield, Purity, Effective, Cost, Overall) with computational efficiency (Total vs. Best Iterations) and the aggregate Effective Score.
Appendix figures & tables35 assets
Supplementary material from the paper’s appendix.
Appendix
Figure S1: Extended experimental analysis of knowledge enhancement and extraction performance. a, Stratified evaluation of the Knowledge Augment task across four specific chemical engineering sub-domains: Process Route Selection, Catalyst & Condition Selection, Production Method & Process Flow, and Separation Method Selection, displaying performance scores for Correctness, Rationality, Clarity, and Completeness across five foundation models. b, Statistical distribution of Knowledge Extraction metrics comparing different efficient backbone models, illustrating the density and variance for Predicted Entity Counts (left), Mapping-based Edge Distance (MED) Coverage (center), and Average Path Length of the constructed graphs (right).
Figure S2: Extended experimental analysis of concept parsing, completion, and generative design. a, Distribution of matched edges versus matched nodes for concept parsing tasks across individual samples. b, Average count of successfully matched nodes and edges for each model. c, Variation of parsing precision and recall metrics as a function of the similarity threshold ( ). d, Parsing results for masked graph components in the topology completion task. e, Comparative distribution of topological complexity (connections versus equipment) between the initial generation step and the final iteration. f, Number of iterations required for the generative design process across distinct sample tasks.
Figure S3: Extended quantitative statistics for parameter optimization across distinct chemical process types. a-c, Stratified analysis of optimization performance and search efficiency: a, Combined processes; b, Separation processes; and c, Synthesis processes. In each panel, the left bar charts display the normalized performance scores across five key metrics, while the right scatter plots correlate the iteration step where the global optimum was identified versus the total iterations performed. d, Line graphs tracking the absolute performance trajectories for primary objectives, including Purity (Separation), Yield (Synthesis), and Effective Score (Combined), across individual benchmark cases. e, Longitudinal analysis of economic optimization displaying the raw Operating Cost values recorded across 20 distinct test cases for different model backbones.
Figure S4: Comprehensive statistical profiling of the CeProBench . a, Statistical characteristics of the Knowledge dimension corpus across industrial sub-domains, illustrating the distribution of document length, the density of entities and triples, and their correlation, validating semantic consistency. b, Composition of the Q&A dataset, detailing the distribution of task types and difficulty scores, highlighting the prevalence of high-complexity reasoning tasks in specific categories. c, Topological complexity of the PFD dataset, quantified by the count of equipment nodes and connection edges per sample. d, Frequency distribution of unit operations, contrasting the overall equipment distribution with the subset targeted for topology completion tasks, reflecting a focus on critical process units. e, Difficulty stratification of the generative text-to-design scenarios. f, Dimensionality analysis of the Simulation scenarios, showing the distribution of optimization parameters and objectives. g, Detailed breakdown of optimization parameters and objectives, emphasizing the benchmark’s focus on separation efficiency and economic optimization.
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