Scheduling of gasoline blending and distribution using graphical genetic algorithm. (2nd February 2020)

• Record Type: Journal Article
• Authors: Bayu, Feleke; Panda, Debashish; Shaik, Munawar A.; Ramteke, Manojkumar
• Is Part Of: Computers & chemical engineering. Volume 133(2020)
• Publication Date: 2020-02-02
• Subjects: Genetic algorithm -- Gasoline blending -- Scheduling -- Multi-objective optimization; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2019.106636
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A graphical genetic algorithm is developed to optimize gasoline blending. Gasoline blending and distribution is solved for multiple objectives. Optimization of bigger size problems show better results compared to MINLP. Significant size reduction is achieved using new model. Abstract: Scheduling of gasoline blending, and distribution (SGBD) involves allocating resources and sequencing the operations to give gasoline a high economic potential without compromising its quality and the customers' demand. The existence of nonlinearity and the need for multi-objective optimization makes SGBD complex. In this study, a graphical genetic algorithm (GGA) model involving a discrete-time representation is developed for both single- and multi-objective SGBD. In the single-objective formulation, the production cost is minimized, whereas in the multi-objective formulation, the sum of the square of fluctuation in inter-period blending rate is additionally minimized. The efficacy of the proposed model is checked by solving three industrial problems which involve the production of 20, 35 and 45 orders of different gasoline grades, respectively over the time-horizon of 8 days. The proposed model gives lower production cost compared to MINLP formulation and the reduction found to be increasing with the increase in problem size. Graphical abstract: Image, graphical abstract