BATCH-DIST - A COMPREHENSIVE PACKAGE FOR SIMULATION, DESIGN, OPTIMIZATION AND OPTIMAL-CONTROL OF MULTICOMPONENT, MULTIFRACTION BATCH DISTILLATION-COLUMNS

BATCH-DIST is a general-purpose simulation package for the design, simulation and optimization of multicomponent, multifraction batch distillation columns operating under different modes (constant reflux, variable reflux and optimal reflux policy). The package includes simulation models of varying degrees of complexity and rigor; efficient but simplified models (based on short-cut methods) for preliminary design and rapid analysis of column behavior, and rigorous models (based on solution of transient heat and mass balance differential equations) for verification and detailed column design. Besides simulation and design, BATCH-DIST can also accomplish optimization and optimal control of columns. Coded in Fortran 77, the package is flexible and user-friendly. BATCH-DIST has been extensively tested with benchmark cases involving binary and multicomponent systems, with nonideal behavior and in columns with appreciable holdup effects. Such test cases have clearly demonstrated that predictions of the simplified models in the package compare well with those of the rigorous models. This powerful and comprehensive package is expected to be computationally more efficient than existing packages

MULTICOMPONENT BATCH DISTILLATION COLUMN DESIGN

Batch distillation is characterized as a system that is difficult to design because compositions are changing continuously with time. The models reported in the literature and used in the simulators like PROCESS and BATCHFRAC, etc., are too complex to use for obtaining optimal design because of high computational time and large memory requirement. In this work we are presenting a short-cut method for design of multicomponent batch distillation columns, operating under variable reflux and constant reflux conditions. The method is essentially a modification of the short-cut method widely used in the design of continuous multicomponent columns. The technque has been tested for both binary and multicomponent systems, and the results compare favorably with the rigorous methods of design. The method is very efficient and can be used for preliminary design and analysis of batch columns. The model used in this method has a number of tuning parameters that can be used for model adaptation. The main features of the short-cut method include lower computational time (which is independent of the number of plates in the column), lower memory requirements, and its adaptability to design

Optimizing spatiotemporal sensors placement for nutrient monitoring: a stochastic optimization framework

Nutrient monitoring is very important for the area of food–energy–water nexus. The sensor network for nutrient monitoring requires dynamic sensing where the positions of the sensors change with time. In this work, we have proposed a methodology to optimize a dynamic sensor network which can address the spatiotemporal aspect of nutrient movement in a watershed. This is a first paper in the series where an algorithmic and methodological framework for spatiotemporal sensor placement problem is proposed. Dynamic sensing is widely used in wireless sensors, and the current approaches to solving this problem are data intensive. This is the first time we are introducing a stochastic optimization approach to dynamic sensing which is efficient. This framework is based on a novel stochastic optimization algorithm called Better Optimization of Nonlinear Uncertain Systems (BONUS). A small case study of the dynamic sensor placement problem is presented to illustrate the approach. In the second paper of this series, we will present a detailed case study of nutrient monitoring in a watershed