Evaluation of input parameterization for batch process optimization (original) (raw)

Validation of a solution model for the optimization of a binary batch distillation column

2005

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 strongly on the ability to formulate an appropriate solution model, i.e. an approximate parameterization of the optimal inputs with a precise link to the necessary conditions of optimality. Hence, the capability of a solution model to optimize an uncertain process needs to be assessed. This paper introduces an optimality measure that can be used to verify the conjecture that the solution model derived from a simplified process model can be applied to a more rigorous process model with negligible performance penalty. This conjecture is tested in a simulation of the dynamic optimization of a batch distillation column.

Dynamic Parameter Estimation and Optimization for Batch Distillation

This work reviews a well-known methodology for batch distillation modeling, estimation, and optimization but adds a new case study with experimental validation. Use of nonlinear statistics and a sensitivity analysis provides valuable insight for model validation and optimization veri cation for batch columns. The application is a simple, batch column with a binary methanol-ethanol mixture. Dynamic parameter estimation with an L1-norm error, nonlinear confi dence intervals, ranking of observable parameters, and efficient sensitivity analysis are used to refine the model and fi nd the best parameter estimates for dynamic optimization implementation. The statistical and sensitivity analyses indicated there are only a subset of parameters that are observable. For the batch column, the optimized production rate increases by 14% while maintaining product purity requirements.

Dynamic optimization in the batch chemical industry

2002

Dynamic optimization of batch processes has attracted more attention in recent years since, in the face of growing competition, it is a natural choice for reducing production costs, improving product quality, and meeting safety requirements and environmental regulations. Since the models currently available in industry are poor and carry a large amount of uncertainty, standard model-based optimization techniques are by and large ineffective, and the optimization methods need to rely more on measurements.

A Tutorial on the Optimization of Batch Processes: II. Handling Uncertainty Using Measurements

Computers & Chemical Engineering

The main bottleneck in using optimization in industry is the way uncertainty is handled, which forms the subject of this series of two papers. The first part dealt with the characteriza- tion of the nominal solution and proposed an approach to separate the constraint-seeking and the compromise-seeking components of the inputs. This second part reviews various strategies for optimization under uncertainty, namely the robust and measurement-based optimization schemes. A novel scheme, labeled invariant-based optimization, is proposed, where optimal- ity is achieved by tracking references that remain invariant under uncertainty. The different approaches are compared via the simulation of a bioreactor for penicillin production.

Optimization of batch reactor operation under parametric uncertainty — computational aspects

Journal of Process Control, 1995

The paper describes a method for optimizing batch reactors when the models at hand are characterized by parametric uncertainty. A discrete (or discretized) probability distribution of the uncertain parameters is assumed. This leads to a differential/algebraic optimization problem (DAOP) including several model descriptions, each corresponding to a grid point in parameter space. The DAOP is then transformed to an algebraic optimization problem (AOP) using a time parameterization based on the method of orthogonal collocation. This allows the user to (i) easily include additional algebraic path or endpoint constraints, and (ii) use a computationally-attractive simultaneous solution and optimization approach. Successive linear programming is used for solving the large and sparse AOP. The proposed solution strategy is illustrated on two optimization studies of a simulated batch reactor.

Dynamic optimization of batch processes

Computers & Chemical Engineering, 2003

The optimization of batch processes has attracted attention in recent years because, in the face of growing competition, it is a natural choice for reducing production costs, improving product quality, meeting safety requirements and environmental regulations. This paper starts with a brief overview of the analytical and numerical tools that are available to analyze and compute the optimal solution. The originality of the overview lies in the classification of the various methods. The interpretation of the optimal solution represents the novel element of the paper: the optimal solution is interpreted in terms of constraints and compromises on the one hand, and in terms of path and terminal objectives on the other. This characterization is key to the utilization of measurements in an optimization framework, which will be the subject of the companion paper. #

Performance analysis of on-line batch optimization systems

Computers & Chemical Engineering, 1997

In this paper, the on-line optimization of batch reactors under parametric uncertainty is considered. A method is presented that estimates the likely economic performance of the on-line optimizer. The nmthod of t)rthogonal collocation is employed to convert the differential algebraic optimization problem (DAOP) of the dynamic optinfization into a nonlinear t)rogram (NLP) and determine the nominal optimum. Based on the resulting NLP, the optimization steps are approximated by neighbouring extremal problems and the average deviation from tile true process optimum is determined dependent on the measurement error and the parametric uncertainty. A back off from the active path and endpoint constraints is determined at each optimization step which ensures the feasible operation of the process. The method of the average deviation from optimum is developed for time optimal problems. The theory is demonstrated on an example.

End-point optimization of batch chemical processes

42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475), 2003

Batch and semi-batch processes are of considerable importance in the batch chemical industry. In the face of increased competition, process optimization provides a unified framework for reducing production costs, meeting safety requirements and environmental regulations, improving product quality, reducing product variability, and ease of scaleup [Bonvin, 1988]. In this paper, a numerical optimization approach is developed that utilizes a dynamic model to determine analytically, the structure of the optimal solution. This approach is tested via simulation of end-point optimization problems in two semi-batch chemical reactors.