Balasubrahmanya Srinivasan

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This paper considers the minimization of batch time for an industrial inverse-emulsion polymerization reactor in the presence of uncertainty. A first optimization study resulted in the current industrial practice of a semi-adiabatic profile (isothermal operation followed by an adiabatic one), where the switching time between the two modes of operation is updated to meet the terminal constraint on reactor temperature, as well as a constraint on residual raw material levels. However, such a procedure requires

A powerful approach for dynamic optimization in the presence of uncertainty is to incorporate measurements into the optimization framework so as to track the optimum. For nonsingular control problems, this can be done by tracking active constraints along b ...

Industrial filamentous fungal fermentations are typically operated in fed- batch mode. Oxygen control represents an important operational challenge due to the varying biomass concentration. In this study, oxygen control is implemented by manipulating the s ...

The operation of dynamic processes can be optimized using models that predict the system behavior well, in particular its optimality features. In practice, however, process models are often structurally inaccurate, and on-line adaptation is typically requi ...

In this paper, it is shown that dynamic optimization problems of first-order systems can be transformed into a static paramertic programming problem, where the state plays the role of the parameter. Thus, an optimal feedback law is obtained. This concept is applied to the die-sinking Electrical Discharge Machining, a highly time varying industrial process which necessitates adapation of machining settings during operation. It is shown that the minimum-time operation of this process is equivalent to choosing

For the optimization of dynamic systems, it is customary to use measurements to combat the effect of uncertainty. In this context, an approach that consists of tracking the necessary conditions of optimality is gaining in popularity. The approach relies st ...

C. Welz, B. Srinivasan, D. Bonvin Laboratoire d’Automatique Ecole Polytechnique Fédérale de Lausanne CH-1015 Lausanne, Schweiz O. Naef Département de Chimie Ecole d’Ingénieurs de Fribourg CH-1705 Fribourg, Schweiz Optimierung einer Batch-Destillationskolonne unter Unsicherheiten basierend auf Messungen Das Ziel der dynamischen Optimierung von binären Batch-Destillationskolonnen ist die Maximierung der Destillatmenge am Ende einer Charge bei gleichzeitiger Erfüllung der Endpunktbedingung für die Reinheit des

The problem of steering a dynamical system toward optimal steady-state performance is considered. For this purpose, a static optimization problem can be formulated and solved. However, because of uncertainty, the optimal steady-state inputs can rarely be a ...

Challenges in real-time process optimization mainly arise from the inability to build and adapt accurate models for complex physico-chemical processes. This paper surveys different ways of using measurements to compensate for model uncertainty in the conte ...

Dynamic optimization can be used to determine optimal input profiles for dynamic processes. Due to plant-model mismatch and disturbances, the optimal inputs determined through model-based optimization will, in general, not be optimal for the plant. Modifie ...

The performance of a predictive controller is typically poor when the true plant evolution deviates significantly from that predicted by the model. A robust control approach that considers model uncertainty explicitly is then needed. However, it is often d ...

In the framework of process optimization, the use of measurements to compensate the effect of uncertainty has become an active area of research. One of the ideas therein is to enforce optimality by tracking the necessary conditions of optimality (NCO track ...

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 ...

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

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 ...

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 problem of controlling nonlinear nonminimum-phase systems is considered, where standard input-output feedback linearization leads to unstable internal dynamics. This problem is handled here by using the observability normal form in conjunction with input-output linearization. The system is feedback linearized upon neglecting a part of the system dynamics, with the neglected part being considered as a perturbation. A linear controller is designed to accommodate the perturbation resulting from the approxi

The performance of predictive controller is typically poor when the true plant evolution deviates significantly from that predicted by the model. A robust approach that considers model uncertainty explicitly is then needed. However, it is often difficult to find a single input profile that works for the range of uncertainty considered. Thus, multiple input profiles, i.e. one for each realization of the uncertainty, need to be determined, which is computationally extremely expensive. This paper proposes an a

This paper describes the optimization of batch processes in the presence of uncertainty and constraints. The optimal solution consists of keeping certain path and terminal constraints active and driving the sensitivities to zero. The case where the terminal constraints have a larger bearing on the cost than the sensitivities is considered, for which a two-time-scale methodology is proposed. The problem of meeting the active terminal constraints is addressed on-line using trajectory tracking, whilst pushing

A direct adaptive control methodology for the rejection of unmeasured non-vanishing disturbances is proposed. The approach uses the framework of polynomial RST controllers and relies on the internal model principle with additional degrees of freedom provid ...

Balasubrahmanya Srinivasan

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Trajectory Following for the Optimization of a Batch Polymerization Reactor

Neighboring-Extremal Control for Singular Dynamic Optimization Problems. I-Single-Input Systems

Oxygen Control for an Industrial Pilot-Scale Fed-Batch Filamentous Fungal Fermentation

On the Use of Models for Real-Time Optimization

Dynamic Optimization of First-Order Systems via Static Parametric Programming: Application to Electrical Discharge Machining

Evaluation of Input Parameterization for Batch Process Optimization

Optimierung einer Batch-Destillationskolonne unter Unsicherheiten basierend auf Messungen

Equivalence between Neighboring-Extremal Control and Self-Optimizing Control for the Steady-State Optimization of Dynamical Systems

Adaptation Strategies for Real-time Optimization

Modifier Adaptation for Run-to-Run Optimization of Transient Processes

Robust Predictive Control Based on Neighboring Extremals

Tracking the Necessary Conditions of Optimality with Changing Set of Active Constraints using a Barrier-Penalty Function

Input Filter Design for Feasibility in Constraint-Adaptation Schemes

Calibration of Spectral Reaction Data

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

Comparison of Six Implicit Real-Time Optimization Schemes

Approximate Input-Output Linearization of Nonlinear Systems Using the Observability Normal Form

Robust Predictive Control Based on Neighboring Extremals

Combined On-Line and Run-to-Run Optimization of Batch Processes with Terminal Constraints

Asymptotic Rejection of Nonvanishing Disturbances Despite Plant-Model Mismatch