Inferential model-based control of multicomponent batch distillation

An optimization module for batch distillation with intermediate cuts

Patterns of composition change in multicomponent batch distillation

Integrating product and process design with optimal control: A case study of solvent recycling

Simulation of multicomponent batch distillation processes

Dynamic rate‐based approach for modeling and simulating batch distillation is presented. To predict separation efficiencies, mass transfer was considered explicitly using the Maxwell‐Stefan. Process dynamics were modeled by considering all relevant dynamic changes in the system, including the column periphery. The model, implemented at the large‐scale ABACUSS modeling environment, addresses implications of the dynamic and multicomponent nature of the process for its mathematical representation, the use of mass‐transfer coefficients, and the need for further experimental correlations. To validate the model, a series of separations of the highly nonideal quaternary system of methyl acetate, methanol, acetic acid, and water in a pilot‐plant batch‐distillation column with structured packings were conducted. The results show that a rate‐based approach can predict the column operation within the experimental error over the entire operation time without fitting of any kind. For the first time the use of a rate‐based approach on the ground of the Maxwell‐Stefan equations for a multicomponent batch‐distillation process was validated experimentally. Comparison of predictions of the rate‐based approach with simulations based on an equally tailored equilibrium‐stage model shows that the largest differences between the two approaches occur in short periods of very significant changes in the column profile. The additional effort of rate‐based modeling can be justified for the design of operating policies of multicomponent batch‐distillation processes.

RBDOPT: a general-purpose object-oriented module for distributed campaign optimization of reactive batch distillation

The aim of this work is to compare several of the commercial dynamic models for batch distillation available worldwide. In this context, BATCHFRAC™, CHEMCAD™ BATCH, and HYSYS.Plant® software performances are compared to experimental data. The software can be used as soft sensors, playing the roll of ad-hoc observers or estimators for control objectives. Rigorous models were used as an alternative to predict the concentration profile and to specify the optimal switching time from products to slop cuts. The performance of a nonlinear model obtained using a novel identification algorithm was also studied. In addition, the strategy for continuous separation was revised with residue curve map analysis using Aspen SPLIT™.

A method is reported to estimate the batch sizes, operating times, equipment sizes, utility loads, and costs for the batch distillation of nonideal multicomponent mixtures. The method provides an estimate for both the design and the operating policy for the reflux or reboil ratio without the need to integrate the full column model numerically. This operating policy approximates the constant product composition policy for high purities and high fractional recoveries and shows a substantial cost savings over the constant reflux or reboil policies, primarily on account of a substantial reduction in the vapor rate. The approach provides a good approximation to the results of a detailed optimization in an example for a binary mixture. A more interesting application is illustrated for the separation of a quaternary azeotropic mixture arising from the transesterification of ethyl acetate with methanol to produce ethanol and methyl acetate.

Multi-objective optimization of vapor recompressed distillation column in batch processing: Improving energy and cost savings

La strategie consiste a recycler les deux coupes de residus dans la section suivante. Le facteur de capacite, developpe par Luyben permet de determiner le nombre de plateaux optimal ainsi que le reflux optimal

A multiple-input and multiple-output (MIMO) model, namely Recursive Orthogonal Least Square (ROLS) based radial basis function (RBF) is developed to estimate product compositions in a batch distillation process from temperature measurements. The process data is generated by simulating the differential equations of the batch distillation process, changing the initial feed composition and boiluprate from batch to batch. Moreover, the reflux ratio is also randomly varied within each batch to represent the exact dynamics of the batch distillation. Temperature and distillate composition is correlated by the RBF trained by ROLS algorithm. A Single RBF network estimate the quality of products in real-time. The results show that ROLS based estimator give correct composition estimations for a batch distillation process. The robustness of the ROLS algorithm and low computational requirement makes the estimator attractive for on-line use.

Optimisation of design and operation policies of binary batch distillation with fixed product demand

Nonlinear Model Predictive Control of Multicomponent Distillation Columns Using Wave Models

Soft-sensor for on-line estimation of ethanol concentrations in wine stills

ALAMBIC - A SOFTWARE PACKAGE FOR OPTIMISING DESIGN AND OPERATION OF BATCH DISTILLATION COLUMNS