Publications by Dominique Bonvin

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Concentrations measured during the course of reactions are typically corrupted by random noise. Data reconciliation techniques improve the accuracy of measurements by using redundancies in the material and energy balances expressed as relationships between ...

The subject of real-time, steady-state optimization under significant uncertainty is addressed in this paper. Specifically, the use of constraint-adaptation schemes is reviewed, and it is shown that, in general, such schemes cannot guarantee process feasib ...

This paper discusses the use of parsimonious input parameterization for the dynamic optimization of reaction systems. This parameterization is able to represent the optimal inputs with only a few parameters. In the context of batch, semibatch, and continuo ...

The material presented in this document is intended as a comprehensive, implementation-oriented supplement to the experimental optimization framework presented in [Bunin, G.A., Francois, G., Bonvin, D.: Feasible-side global convergence in experimental optimization. SIAM J. Optim. (submitted) (2014)]. The issues of physical degradation, unknown Lipschitz constants, measurement/estimation noise, gradient estimation, sufficient excitation, and the handling of soft constraints and/or a numerical cost function a

Calibration is the first step in the prediction of concentrations from spectral measurements of chemical reaction systems. It is a well-known fact that the species in the calibration set must include those in the new set. Typically, the calibration set is constructed from nonreacting mixtures of known concentrations. In this paper, it is proposed instead to use the calibration data from reacting mixtures, thereby avoiding the independent variation of possibly highly-reactive intermediates. However, for the

This paper investigates the optimal load-sharing problem of serial gas compressors in the presence of plant-model mismatch. The problem is formulated as a static real-time optimization task that is solved via modifier adaptation for interconnected systems. ...

Recently, measurement-based optimization schemes have been developed to deal with uncertainty and process variations. One of the methods therein, labeled NCO tracking, relies on appropriate parameterization of the input profiles and adjusts the correspondi ...

Model-structure identification is important for the optimization and design of biokinetic processes. Standard Monod and Tessier functions are often used by default to describe bacterial growth with respect to a substrate, leading to significant optimizatio ...

Multivariate curve resolution via alternating least squares (ALS) is used to resolve the concentration profiles C and the pure component spectra E of S species from the multivariate absorbance data A, assuming the bilinear model ...

We discuss the design of sampled-data economic nonlinear model predictive control schemes for continuous-time systems. We present novel sufficient convergence conditions that do not require any kind of terminal constraints nor terminal penalties. Instead, ...

Real-time optimization (RTO) is a class of methods that use measurements to reject the effect of uncertainty on optimal performance. This article compares six implicit RTO schemes, that is, schemes that implement optimality not through numerical optimizati ...

The identification of kinetic models for multiphase reaction systems is complex due to the simultaneous effect of chemical reactions and mass transfers. The extentbased incremental approach simplifies the modeling task by transforming the reaction system i ...

This work deals with a control strategy to regulate homogeneous reaction systems in reactors equipped with a cooling jacket. The reaction system is expressed in terms of vessel extents and viewed as a single-input (the inlet coolant temperature), two-outpu ...

This paper proposes a data-driven test for closed-loop stability. The test is based on a non-conservative stability condition that can be veriﬁed without having to actually implement the controller. It uses a set of measurements from the plant but does not ...

Dynamic optimization provides a unified framework for improving process operations while taking operational constraints into account. In the presence of uncertainty, measurements can be incorporated into the optimization framework for tracking the optimum. ...

Models of chemical reaction systems can be quite complex as they typically include information regarding the reactions, the various transfers of heat and mass, as well as the effects of the inlet and outlet flows. It is well known that a linear transformat ...

With an annual worldwide production well in excess of 100 million metric tons, synthetic polymers constitute a significant part of the modern chemical process industry. Polymer reactors - operated in continuous, batch, or semibatch mode - are therefore important processing units, but there are unique problems associated with controlling them effectively. The most significant characteristics of polymer reactors that make them one of the most challenging units to model, control, and optimize, are discussed in

Real-time optimization (RTO) methods use measurements to offset the effect of uncertainty and drive the plant to optimality. Explicit RTO schemes, which are characterized by solving a static optimization problem repeatedly, typically require multiple itera ...

The computation and modeling of extents has been proposed to handle the complexity of large-scale model identification tasks. Unfortunately, the existing extent-based framework only applies when certain conditions apply. Most typically, it is required that ...

In principal component regression (PCR) and partial least-squares regression (PLSR), the use of unlabeled data, in addition to labeled data, helps stabilize the latent subspaces in the calibration step, typically leading to a lower prediction error. A non- ...

Dominique Bonvin

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Data Reconciliation for Reaction Systems Using Extents and Shape Constraints

Input Filter Design for Feasibility in Constraint-Adaptation Schemes

Dynamic Optimization of Reaction Systems via Exact Parsimonious Input Parameterization

Implementation techniques for the SCFO experimental optimization framework

Calibration of Spectral Reaction Data

Optimal Load Sharing for Serial Compressors via Modifier Adaptation

Measurement-based Run-to-run Optimization of a Batch Reaction-distillation system

Biokinetic process model diagnosis with shape-constrained spline functions

ALS Scheme using Extent-based Constraints for the Analysis of Chemical Reaction Systems

On the Design of Economic NMPC Based on an Exact Turnpike Property

Comparison of Six Implicit Real-Time Optimization Schemes

Identification of Multiphase Reaction Systems with Instantaneous Equilibria

Output regulation via trajectory tracking for chemical reactors with jacket dynamics

Data-driven controller validation

Neighboring-Extremal Control for Singular Dynamic Optimization Problems. II- Multiple-Input Systems

Concept and Applications of Extents in Chemical Reaction Systems

Control of Polymerization Processes

Use of Transient Measurements for Real-Time Optimization via Modifier Adaptation

Incremental Parameter Estimation under Rank-Deficient Measurement Conditions

On multivariate calibration with unlabeled data