24 papers · ranked by Valyu relevance
Nikolaos Tsiantis, Julio R. Banga
Background Optimality principles have been used to explain the structure and behavior of living matter at different levels of organization, from basic phenomena at the molecular level, up to complex dynamics in whole populations. Most of these studies have assumed a single-criteria approach. Such optimality principles…
Sandra Zarychta, Tomasz Sagan, Marek Balcerzak, Artur Dąbrowski + 2 more
'Andrzej Stefański' 'Tomasz Kapitaniak'] This paper presents a novel, Fourier series based numerical method of open-loop control optimization. Due to its flexible assumptions, it can be applied in a large variety of systems, including discontinuous ones or even "black boxes", whose equations are not fully known. This…
Selina Meister, Jürgen T. Stockburger, Rebecca Schmidt, Joachim Ankerhold
'Joachim Ankerhold'] Abstract. Standard optimal control methods perform optimization in the time domain. However, many experimental settings demand the expression of the control signal as a superposition of given waveforms, a case that cannot easily be accommodated using time-local constraints. Previous approaches [1…
Helena Sofia Rodrigues, M. Teresa T. Monteiro, Delfim F. M. Torres
Optimal Control (OC) is the process of determining control and state trajectories for a dynamic system, over a period of time, in order to optimize a given performance index. With the increasing of variables and complexity, OC problems can no longer be solved analytically and, consequently, numerical methods are…
Helen Moore
This article gives an overview of a technique called optimal control, which is used to optimize real-world quantities represented by mathematical models. I include background information about the historical development of the technique and applications in a variety of fields. The main focus here is the application to…
Jingyi Yang, Yuebao Yang, Mingtao Li
OptControl.jl1 (OptControl) implements that modeling optimal control problems with symbolic algebra system based on Julia language, and generates the corresponding numerical optimization codes to solve them with packages from Julia. OptControl does not define a data type, but generates a solution script by handling…
Wenjie Xu, Yuning Jiang, Bratislav Svetozarevic, Colin N. Jones
In this paper, the CONFIG algorithm, a simple and provably efficient constrained global optimization algorithm, is applied to optimize the closed-loop control performance of an unknown system with unmodeled constraints. Existing Gaussian process based closedloop optimization methods, either can only guarantee local…
Chuanzhi Lv, Hongdan Li, Huanshui Zhang
—This paper addresses the optimal control problem of finite-horizon discrete-time nonlinear systems under state and control constraints. A novel numerical algorithm based on optimal control theory is proposed to achieve superior computational efficiency, with the novelty lying in establishing a unified framework that…
Nikolaos Tsiantis, Julio R. Banga
We revisit the idea of explaining and predicting dynamics in biochemical pathways from first-principles. A promising approach is to exploit optimality principles that can be justified from an evolutionary perspective. In the context of the cell, several previous studies have explained the dynamics of simple metabolic…
Mariana Monteiro, Sarah Fadda, Cleo Kontoravdi
Mammalian cells produce up to 80 % of the commercially available therapeutic proteins, with Chinese Hamster Ovary (CHO) cells being the primary production host. Manufacturing involves a train of reactors, the last of which is typically run in fed-batch mode, where cells grow and produce the required protein. The…
Mengjia Zhu, Oliver Pennington, Tararag Pincam, Mohammadamin Zarei + 4 more
Bioprocesses are critical for sustainable industrial development but face challenges from their inherent uncertainties that affect efficiency and scalability. This study in-troduces a worst-case operational space design framework, integrating symbolic optimization with scenario-based validation, to preemptively…
Alexandre Domingues, Susana Vinga, João M Lemos
Background The increasing availability of models and data for metabolic networks poses new challenges in what concerns optimization for biological systems. Due to the high level of complexity and uncertainty associated to these networks the suggested models often lack detail and liability, required to determine the…
László Dobos, András Király, János Abonyi
Finding the optimal operating region of chemical processes is an inevitable step toward improving economic performance. Usually the optimal operating region is situated close to process constraints related to product quality or process safety requirements. Higher profit can be realized only by assuring a relatively low…
Benjamin Michaud, François Bailly, Eve Charbonneau, Amedeo Ceglia + 2 more
Musculoskeletal simulations are useful in biomechanics to investigate the causes of movement disorder, to estimate non-measurable physiological quantities or to study the optimality of human movement. We introduce Bioptim, an easy-to-use Python framework for biomechanical optimal control, handling musculoskeletal…
Authors not listed
We develop a control-oriented model for manipulating calcium carbonate (CaCO3) precipitation through pH and/or dissolved CO2(aq) adjustments in a semi-batch process. We present two open-loop control strategies: first, a closed-form optimal control solution derived via Pontryagin’s Minimum Principle; and second, an…
Mathias Gotsmy, Dafni Giannari, Radhakrishnan Mahadevan, Jürgen Zanghellini
Fed-batch processes are prevalent in biotechnological industries, but design of experiments often results in sub-optimal conditions due to incomplete solution space characterization. We employ a single-level dynamic control (DC) algorithm for dynamic flux balance analysis (dFBA), enhancing efficiency by reducing…
Farhan Zafar, Suheel Abdullah Malik, Tayyab Ali, Amil Daraz + 4 more
'Abdul Rahman Afzal' 'Farkhunda Bhatti' 'Irfan Ahmed Khan' 'Lalit Chandra Saikia'] In this paper, we propose two different control strategies for the position control of the ball of the ball and beam system (BBS). The first control strategy uses the proportional integral derivative-second derivative with a proportional…
Cara G. Welker, Alexandra S. Voloshina, Vincent L. Chiu, Steven H. Collins
Human-in-the-loop optimization allows for individualized device control based on measured human performance. This technique has been used to produce large reductions in energy expenditure during walking with exoskeletons but has not yet been applied to prosthetic devices. In this series of case studies, we applied…
Authors not listed
This paper introduces Optical Fiber Chemistry (OFC) as a fourth-generation catalytic paradigm, distinguished not by incremental improvements in catalyst materials but by a fundamental reconfiguration of the catalytic reaction platform. By employing optical fibers as active photonic control elements, OFC achieves gen-…
Authors not listed
The optimal control of a multicomponent batch distillation campaign with a variable reflux ratio, and, an analysis of the influence of different parameters such as total pressure at the top of the column, initial mixture composition, and tray hold-up, have been presented. This optimal control strategy is applied to the…
Authors not listed
Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
Authors not listed
This paper presents an empirical comparison of process control algorithms, with particular emphasis on classical Proportional–Integral–Derivative (PID) control, Model Predictive Control (MPC), and neural network-based methods in the context of complex industrial plants. Since industrial sectors frequently demand…
Zesheng Yao, Zhen-Hua Wan, Canjun Yang, Qingchao Xia + 1 more
Model-free deep reinforcement learning (DRL) methods suffer from poor sample efficiency. To overcome this limitation, this work introduces an adaptive reduced-order-model (ROM)-based reinforcement learning framework for active flow control. In contrast to conventional actor--critic architectures, the proposed approach…
Peter Sagmeister, Lukas Melnizky, Jason Williams, C. Oliver Kappe
In modern pharmaceutical research, the demand for expeditious development of synthetic routes to active pharmaceutical ingredients (APIs) has led to a paradigm shift towards data-rich process development. Conventional methodologies en-compass prolonged timelines for reaction and analytical model developments. Both…