Dynamic optimization in the batch chemical industry (original) (raw)

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.

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.

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. #

An industrial approach for efficient modeling and advanced control of chemical batch processes

8th International Symposium on Dynamics and Control of Process Systems (2007), 2007

Chemical batch processes offer an attractive way of producing a variety of specialty products in a highly flexible manner. However, such processes are hard to control due to the absence of the notion of a steady state operationnecessitating the use of nonlinear models-and the fact that product qualities are only measured at the end of each batch. This paper proposes a new industrial modeling and control strategy that allows significant batch time reductions to be obtained, taking physical, safety and quality constraints into account. Application to an industrial reactor shows significant improvements over classical control strategies.

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.

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.

Optimal operation of batch processes via the tracking of active constraints

ISA Transactions, 2003

This paper presents a new measurement-based optimization framework for batch processes, whereby optimal operation is achieved via the tracking of active constraints. It is shown that, under mild assumptions and to a first-order approximation, tracking the necessary conditions of optimality is equivalent to tracking active constraints (both during the batch and at the end of the batch). Thus, the optimal input trajectories can be adjusted using measurements without the use of a model of the process. When only batchend measurements are available, the proposed method leads itself to an efficient batch-to-batch optimization scheme. The approach is illustrated via the simulation of a semi-batch reactor under uncertainty.

Cascade closed-loop optimization and control of batch reactors

Chemical Engineering Science, 2004

In this paper, a cascade closed-loop optimization and control strategy for batch reactors is proposed. Based on the reduction of a physical conservation model a cascade system is developed, which can effectively combine optimization and control to achieve good on-line optimization and tracking performance under the common condition where incomplete knowledge of the reaction system exists. A two-tier estimation scheme

State feedback synthesis for on-line optimization of batch reactors with multiple manipulated inputs

Computers & Chemical Engineering, 1998

In this paper, nonlinear state feedback laws are developed for on-line optimization of batch/semi-batch processes with multiple manipulated inputs. The first-order necessary conditions are cast in terms of the system Lie brackets. Optimal state feedback laws are derived which are independent of the adjoint states. It is shown that the feedback laws are, in general, dynamic in nature and provide an alternative to solving a two point boundary value problem on-line. This methodology is illustrated via simulation of several end-point optimization problems in batch chemical reactors.

Optimizing the Initial Conditions To Improve the Dynamic Flexibility of Batch Processes

Industrial & Engineering Chemistry Research, 2009

It is shown in this paper that by changing the initial operation condition of batch processes, the dynamic performance of the system can be varied largely. The initial operation conditions are often ignored in design of batch processes for flexibility against disturbances or parameter variations. When the initial conditions are not rigid, as in the case of a batch reactor where the initial reaction temperature is quite arbitrary, optimization can also be applied to determine the "best" initial condition to be used. Problems for dynamic flexibility analysis, including initial conditions and process operation, can be formulated as dynamic optimization problems. If the initial conditions are considered, the conditions can be transferred into control variables in the first step of optimization. The solution of the dynamic optimization is based on the Runge-Kutta integration algorithm and decomposition search algorithm. This method, as illustrated and tested with two highly nonlinear chemical engineering problems, enables the optimal solution to be determined.