[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84146-en":3,"doc-seo-84146-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84146,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","VIBES-A Two-Stage Scalable Bayesian Uncertainty Quantification Framework for Biomass Valorization","VIBES-A proposes a two-stage, scalable Bayesian uncertainty quantification framework combining Sobol global sensitivity analysis with dimensionality reduction and variational inference for posterior estimation. Dominant uncertain variables are screened in the first stage, then Bayesian calibration is performed only in the reduced space to lower computational cost while preserving inference quality. The method targets kinetic, design/operational, and economic parameters and demonstrates >80% reduction in predictive uncertainty bounds for a biomass valorization route producing bioadhesive via lignin depolymerization and crosslinking. A Python–Aspen interface enables automated simulation, stochastic-gradient optimization, and automatic differentiation.","VIBES-A Two-Stage Scalable Bayesian Uncertainty Quantification Framework: Application to a Biomass Valorization Process  \nPoulomi Das 1, Angan Mukherjee 2, and Debangsu Bhattacharyya 1*  \n1Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506, USA 2Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA  \nAbstract  \nThis paper proposes Variational Inference-based Bayesian Estimation with Sobol screening (VIBES)– a twostage scalable framework for Bayesian uncertainty quantification (UQ) . The proposed approach combines Sobol global sensitivity analysis (GSA) for screening and dimensionality reduction , followed by variational inference (VI) for UQ of kinetic, design/operational, and economic parameters. In the first stage, Sobol GSAis performed to identify dominant variables and parameters governing uncertainty in process outputs. In the second stage, Bayesian inference is performed only on the reduced dimensional space using VI, thus reducing computational burden and enhancing scalability. The framework is demonstrated on a process for bioadhesive production through base-catalyzed depolymerization of kraft lignin and subsequent crosslinking with isolated soy protein. A Python-Aspen interface is developed for automated simulation and parameter estimation, enabling Bayesian calibration through stochastic gradient-based optimization and automatic-differentiation. The methodology is generic and readily generalizable to other biomass conversion pathways. The results show that application of VIBES consistently reduces predictive uncertainty bounds across all model outputs by more than 80%, even when only the reduced-space input variables and parameters are optimized during Bayesian estimation . The framework can be potentially applied for scalable, uncertainty-aware decision-making in high-dimensional, complex chemical process systems.  \nKeywords: Bayesian uncertainty quantification, biomass valorization, Sobol sensitivity, variational inference, sustainability  \n*Corresponding author. Tel: +1-3042939335, Fax: +1-3042934139 E-mail address: [Debangsu.Bhattacharyya@mail.wvu.edu](Debangsu.Bhattacharyya@mail.wvu.edu)  \n1. Introduction  \nHigh-dimensional process models of chemical process systems can be used for process design, techno-economic analysis, sustainability analysis, and many other objectives. Such models are developed using mass, energy, and momentum conservation equations that might require submodels for reaction kinetics, thermodynamics, physical property, heat and mass transport, capital and operating cost estimates, etc. However, there are often uncertainties in the parameters and form of these sub-models that can lead to uncertainties in outputs from these models. Key outputs of interest often include both performance measures such as conversion, product yield, selectivity, utility consumption, emissions, etc. , and economic metrics like net present value, minimum selling price, levelized cost of production, etc. (Monteiro and Bhattacharyya, 2026; Morgan et al. , 2015) . Therefore, uncertainty quantification (UQ) is desired beyond nominal deterministic simulations (Kennedy and O’Hagan, 2001; Morgan et al. , 2015) , especially when process models are used for design, analysis, and decision-making.  \nUQ is particularly challenging when multiple sources of uncertainty interact across scales and process subsystems. For example, parameter uncertainty can arise from limited kinetic, thermodynamic, and mass/heat transfer data availability, whereas model-form uncertainty can result from simplified mechanisms, empirical correlations, incomplete physical descriptions, or assumptions introduced during model development (Mukherjee et al., 2025b; Ostace et al., 2020) . In addition, uncertainty in operating conditions, feed composition, experimental measurements, and boundary conditions can affect both model calibration and process-level pre","cbCaiqSasz7scr3B","https://ap.wps.com/l/cbCaiqSasz7scr3B","pdf",2897794,1,51,"English","en",105,"# Introduction\n## Uncertainty quantification challenges in high-dimensional chemical process models\n## Sources of uncertainty across model form, parameters, and operating conditions\n## Computational scalability limitations of sampling-based UQ methods\n# VIBES-A two-stage scalable Bayesian UQ approach\n## Stage 1: Sobol screening and dimensionality reduction\n## Stage 2: Variational inference in reduced space for calibration\n# Application to biomass valorization process","[{\"question\":\"What is the core idea behind VIBES-A?\",\"answer\":\"VIBES-A uses Sobol global sensitivity analysis to screen dominant uncertain variables and reduce dimensionality, then applies variational inference to perform Bayesian 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