ChromOps.jl: High-order simulation and discrete forward sensitivity analysis for chromatography models
Abstract
Mechanistic chromatography models are valuable for process development, but gradient-based parameter estimation and optimization can be hindered by computational cost and the effort of deriving objective-function gradients. To address this concern, a fully differentiable Julia chromatography solver, ChromOps.jl, is presented that combines high-order spatial discretization with discrete forward sensitivity analysis (DFSA). Two high-order spatial discretizations, finite difference summation-by-parts (FD-SBP) and the discontinuous Galerkin spectral element method (DG-SEM), are compared on a 6-component ion-exchange chromatography problem with steric mass action kinetics. Both attain their theoretical convergence rates on manufactured problems, while FD-SBP shows favourable work-precision performance for both forward (primal) and dual-valued simulations and is conceptually simpler to implement. DFSA scales as tDFSA approx 1.4(1+Np)tfwd for up to Np = 24 parameters propagated in a single ForwardDiff.jl chunk, i.e. about 1.4 forward solves per additional parameter. Because the primal and dual-valued solves share the same code path, DFSA provides accurate gradients of user-defined objectives without any hand-derived chain-rule code, enabling accessible gradient-based parameter estimation and optimization.
Versions
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BibTeX
@misc{meyer2026chromopsjl,
title = {{ChromOps.jl: High-order simulation and discrete forward sensitivity analysis for chromatography models}},
author = {Kristian Meyer and Maksym Ratajczyk and Christopher Rackauckas},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2608.06131},
eprint = {2608.06131},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.06131},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - ChromOps.jl: High-order simulation and discrete forward sensitivity analysis for chromatography models
AU - Kristian Meyer
AU - Maksym Ratajczyk
AU - Christopher Rackauckas
PY - 2026
DA - 2026/10/13
PB - arXiv
DO - 10.48550/arxiv.2608.06131
UR - https://arxiv.org/abs/2608.06131
AB - Mechanistic chromatography models are valuable for process development, but gradient-based parameter estimation and optimization can be hindered by computational cost and the effort of deriving objective-function gradients. To address this concern, a fully differentiable Julia chromatography solver, ChromOps.jl, is presented that combines high-order spatial discretization with discrete forward sensitivity analysis (DFSA). Two high-order spatial discretizations, finite difference summation-by-parts (FD-SBP) and the discontinuous Galerkin spectral element method (DG-SEM), are compared on a 6-component ion-exchange chromatography problem with steric mass action kinetics. Both attain their theoretical convergence rates on manufactured problems, while FD-SBP shows favourable work-precision performance for both forward (primal) and dual-valued simulations and is conceptually simpler to implement. DFSA scales as tDFSA approx 1.4(1+Np)tfwd for up to Np = 24 parameters propagated in a single ForwardDiff.jl chunk, i.e. about 1.4 forward solves per additional parameter. Because the primal and dual-valued solves share the same code path, DFSA provides accurate gradients of user-defined objectives without any hand-derived chain-rule code, enabling accessible gradient-based parameter estimation and optimization.
ER -