Computers and Chemical Engineering (original) (raw)
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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.
On-line optimization of constrained multivariable chemical processes
AIChE Journal, 1987
A two-phase approach to the control and operation of complex chemical processes at their optimum operating conditions is presented. The first phase consists of on-line parameter identification and state estimation of approximate nonlinear dynamic process models using on-line and off-line measurements. In the second phase, the optimum operating strategy is determined by integrating and optimizing this identified process model over a selected time horizon into the future. The method is particularly suited to those processes that exhibit slow dynamic responses and are subject to disturbances that have a significant economic impact. Examples include batch chemical reactors, large distillation towers, and processes with significant holdup times such as large fluidized-bed reactors.
Dynamic optimization of bioprocesses: Efficient and robust numerical strategies
Journal of Biotechnology, 2005
The dynamic optimization (open loop optimal control) of non-linear bioprocesses is considered in this contribution. These processes can be described by sets of non-linear differential and algebraic equations (DAEs), usually subject to constraints in the state and control variables. A review of the available solution techniques for this class of problems is presented, highlighting the numerical difficulties arising from the non-linear, constrained and often discontinuous nature of these systems.
Journal of Intelligent Learning Systems and Applications, 2012
This paper proposes the design and a comparative study of two nonlinear systems modeling techniques. These two approaches are developed to address a class of nonlinear systems with time-varying parameter. The first is a Radial Basis Function (RBF) neural networks and the second is a Multi Layer Perceptron (MLP). The MLP model consists of an input layer, an output layer and usually one or more hidden layers. However, training MLP network based on back propagation learning is computationally expensive. In this paper, an RBF network is called. The parameters of the RBF model are optimized by two methods: the Gradient Descent (GD) method and Genetic Algorithms (GA). However, the MLP model is optimized by the Gradient Descent method. The performance of both models are evaluated first by using a numerical simulation and second by handling a chemical process known as the Continuous Stirred Tank Reactor CSTR. It has been shown that in both validation operations the results were successful. The optimized RBF model by Genetic Algorithms gave the best results.
Implementation of adaptive optimal operation for a semi-batch reaction system
Computers & Chemical Engineering, 1998
This paper presents the results of on-line optimization of the acetoacetylation of pyrrole with diketene in a laboratory-scale reactor. In addition to the desired reaction of pyrrole to 2-acetoacetyl pyrrole, there are several undesired side reactions. The selectivity can be controlled by adjusting the feed rate of diketene to a given solution of pyrrole. Variable amounts of impurities in the crude pyrrole imply different rate constants for each batch. Consequently, on-line estimation of some rate constants and subsequent adjustment of the feeding strategy through dynamic optimization are necessary to reach a desired objective.
Surrogate Modeling Optimization of a Nonlinear Batch Reactor by Polynomial Chaos Expansion
Chemical Engineering Research Bulletin, 2021
The paper presents a computationally efficient approach to develop a nonlinear data driven input/output model between the finite-time control trajectories and the quality index at the end of the batch. Polynomial chaos expansion (PCE) was applied to produce the approximate representation of the full process model of a nonlinear batch reactor with the reaction scheme .A-->k1B--> k2C A surrogate model was developed to estimate the dependence of intermediate product (B) concentration at the end of the batch on the temperature trajectory applied during the reaction. The surrogate model was then validated for its performance. Later, the surrogate model was used to determine the optimal temperature profile needed to maximize the concentration of intermediate product at the end of the batch. The validation and optimization results prove that the experimental data based PCE can provide a very good approximation of the desired outputs, providing a generally applicable approach for rapi...
Data-Driven Modeling and Optimization of Semibatch Reactors Using Artificial Neural Networks †
Industrial & Engineering Chemistry Research, 2004
In this study, a new data-driven approach has been proposed for modeling and trajectory optimization of a batch or a semibatch process. The approach is based on parametrization of input and output trajectories as finite-dimensional vectors using orthonormal polynomials (i.e., Fourier coefficients). Using input/output trajectory information available in historical databases, an artificial neural network (ANN) based model has been developed for capturing the dynamics of semibatch processes operated over a fixed interval of time. The parametrized input trajectories, initial states, and process parameters are considered as inputs to the ANN-based model, which predicts output trajectories in terms of Fourier coefficients. Single-rate as well as multirate systems can be modeled by this approach with equal ease. The resulting algebraic model is further used to formulate an optimal control problem, which can be solved using conventional nonlinear programming techniques to generate open-loop optimal input policies or optimal setpoint trajectories. The effectiveness of the proposed ANN-based modeling and trajectory optimization scheme is demonstrated using simulation studies on a benchmark multiple-input multiple-output semibatch process reported in the literature. Analysis of the simulation results reveals that the proposed ANN-based modeling approach is capable of capturing the nonlinear as well as the time-varying behavior inherent in the semibatch system fairly accurately. In addition, it also captures batch-to-batch variations in initial conditions and other process parameters. The results of operating trajectory optimization based on the proposed single-rate as well as the multirate ANN model are comparable to the results of trajectory optimization obtained using the exact first principles model.
Nonlinear Control of Chemical Processes: A Review
Recent developments in nonlinear systems theory combined with advances in control system hardware and software make the practical application of nonlinear process control strategies a reality. This Recent research efforts have concentrated on providing control system design techniques to handle many of the characteristics shown in . Adaptive control ) was promoted as a technique to solve the nonlinear problem by 'relinearizing" the process model as the process moved into different "linear" operating regions, as well as to estimate time-varying parameters (generally linear system based). Robust control system design techniques (Doyle and Stein, 1981; Doyle, 1982) were developed to account for model uncertainty. Internal model control (IMC) (Garcia and Morari, 1982) was developed to provide a transparent framework for process control system design and to explicitly handle manipulated variable constraints. Holt and Morari (1985) analyzed the effect of process deadtime in multivariable systems. Morari (1987) reviewed the three critiques (Fose, 1973; Lee and Weekman, 1976; Kestenbaum et al., 1976) and con-variables