Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2509.10826

Highly Efficient Optimal Control for Lyophilization via Simulation of Discrete/Continuous Mixed-index Differential-algebraic Equations

Prakitr Srisuma·Richard D. Braatz
View original source ↗Read source document ↗Export metadata ↓

Abstract

This article presents a highly efficient optimal control algorithm and policies for lyophilization (also known as freeze drying). The optimal solutions and control policies are derived using an extended version of the simulation-based algorithm, which reformulates the optimal control problem as a hybrid discrete/continuous system of mixed-index differential-algebraic equations and subsequently calculates the optimal control vector via simulation of the resulting DAEs. Our algorithm and control policies are demonstrated via a number of case studies that encompass various lyophilization and optimal control strategies. All the case studies can be solved within less than a second on a normal laptop, regardless of their complexity. The method is several orders of magnitude faster than the traditional optimization-based techniques while giving similar/better accuracy. The proposed algorithm offers an efficient and reliable framework for optimal control of lyophilization, which can also be extended to other similar systems with phase transitions.

Explore related subjects

math.OCcs.SYeess.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Prakitr Srisuma, Richard D. Braatz. 2025-09-13. Highly Efficient Optimal Control for Lyophilization via Simulation of Discrete/Continuous Mixed-index Differential-algebraic Equations. https://arxiv.org/abs/2509.10826

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The Riemannian Convex Bundle Method

We introduce the convex bundle method to solve convex, non-smooth optimization problems on Riemannian manifolds of bounded sectional curvature. Each step of our method is based on a model that involves the convex hull of previously collected subgradients, parallelly transported into the current serious iterate. This approach generalizes the dual form of classical bundle subproblems in Euclidean space. We prove that, under mild conditions, the convex bundle method converges to a minimizer. Several numerical examples implemented using Manopt$.$jl illustrate the performance of the proposed method and compare it to the subgradient method, the cyclic proximal point algorithm, as well as the proximal bundle method.

math.OC↗
arXiv2026

Omega-Limit Sets and Input-to-State Stability in Power Grids With Switching Equilibria

This paper studies a power transmission system with both conventional generators (CGs) and distributed energy assets (DEAs) providing frequency control. We consider an operating condition with demand aggregating two dynamic components: one that switches between different values on a finite set, and one that varies smoothly over time. Such dynamic operating conditions may result from protection scheme activations, external cyber-attacks, or due to the integration of dynamic loads, such as data centers. Mathematically, the dynamics of the resulting system are captured by a system that switches between a finite number of vector fields -- or modes--, with each mode having a distinct equilibrium point induced by the demand aggregation. To analyze the stability properties of the resulting switching system, we leverage tools from hybrid dynamic inclusions and the concept of $Ω$-limit sets from sets. Specifically, we characterize a compact set that is semi-globally practically asymptotically stable under the assumption that the switching frequency and load variation rate are sufficiently slow. For arbitrarily fast variations of the load, we use a level-set argument with multiple Lyapunov functions to establish input-to-state stability of a larger set and with respect to the rate of change of the loads. The theoretical results are illustrated via numerical simulations on the IEEE 39-bus test system.

math.OC↗
arXiv2026

Cellular flow control design for mixing based on the least action principle

We consider a novel approach for the enhancement of fluid mixing via pure stirring strategies building upon the Least Action Principle (LAP) for incompressible flows. The LAP is formally analogous to the Benamou--Brenier formulation of optimal transport, but imposes an incompressibility constraint. Our objective is to find a velocity field, generated by Hamiltonian flows, that minimizes the kinetic energy while ensuring that the initial scalar distribution reaches a prescribed degree of mixedness by a finite time. This formulation leads to a ``point-to-set" type of optimization problem which relaxes the requirement on controllability of the system compared to the classic LAP framework. In particular, we assume that the velocity field is induced by a finite set of cellular flows that can be controlled in time. To establish finite time feasibility, we introduce an operator-theoretic switching argument that combines the long-time cellular flow mixing result with the von Neumann alternating-projection theorem. We then leverage the direct method to establish the existence of an optimal solution. Finally, we derive the corresponding optimality conditions for the time-dependent control problem and conduct numerical experiments demonstrating the effectiveness of the proposed control design.

math.OC↗