MULTICRITERIA OPTIMIZATION OF MULTIPRODUCT BATCH CHEMICAL PROCESS USING GENETIC ALGORITHM
Optimal design problems are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is first formulated as a multiobjective optimization problem, to be solved using the well-suited nondominating sorting genetic algorithm (NSGA-II). The NSGA-II has the capability to achieve fine tuning of variables in determining a set of nondominating solutions distributed along the Pareto front in a single run of the algorithm. The NSGA-II ability to identify a set of optimal solutions provides the decision maker with a complete picture of the optimal solution space to gain better and appropriate choices. The effectiveness of NSGA-II method with multiobjective optimization problem is illustrated through a carefully referenced example. PRACTICAL APPLICATIONS
The inherent dynamic nature of batch processes allows for their ability to handle variations in feedstock and product specifications, and provides the flexibility required for multiproduct or multipurpose facilities. They are thus best suited for the manufacture of low-volume, high-value products, such as specialty chemicals, pharmaceuticals, agricultural, food and consumer products, and most recently the constantly growing spectrum of biotechnology-enabled products. Batch processing in the food industry has some unit operations, such as panning, whipping (emulsifying), and the pulling of taffy, which are not too often used in other industries, as well as other operations,such as blending, tabletting (including granulation) and lyophilization, which are common in other industries. Reduced time to market, lower production costs and improved flexibility are all critical success factors for batch processes.
Document Type: Research Article
Publication date: December 1, 2010