Abstract
This paper deals with the performance enhancement for crystallization unit of a sugar plant using genetic algorithm. The crystallization unit of a sugar industry has three main subsystems arranged in series. Considering exponential distribution for the probable failures and repairs, the mathematical formulation of the problem is done using probabilistic approach, and differential equations are developed on the basis of Markov birth-death process. These equations are then solved using normalizing conditions so as to determine the steady-state availability of the crystallization unit. The performance of each subsystem of crystallization unit in a sugar plant has also been optimized using genetic algorithm. Thus, the findings of the present paper will be highly useful to the plant management for the timely execution of proper maintenance decisions and, hence, to enhance the system performance.
Highlights
The sugar industry comprises of large complex engineering systems arranged in series, parallel, or a combination of both
The juice mixture consisting of yellowish sugar crystals is suspended in a semi solid mass
These yellowish sugar crystals are treated chemically to yield white crystals, whereas crystal-free magma is recycled through sulphitors for more recovery
Summary
The sugar industry comprises of large complex engineering systems arranged in series, parallel, or a combination of both Some of these systems are feeding, crushing, refining, steam generation, evaporation, crystallization, etc. The juice mixture consisting of yellowish sugar crystals is suspended in a semi solid mass (molasses or magma). This mixture is processed in centrifuges to separate the sugar crystals from magma. Literature review The available literature reflects that several approaches have been used to analyze the system performance in terms of reliability and availability These include reliability block diagram, Monte Carlo simulation, Markov modeling, failure mode and effect analysis, fault tree analysis, and Petri nets (Misra and Weber 1989; Singer 1990; Bradley and Dawson 1998; Modarres et al 1999; Gandhi et al 2003; Adamyan and Dravid 2004; Panja and Ray 2007; Bhamare et al 2008). Gupta et al (2005) have evaluated the reliability parameters of butter manufacturing system in a dairy plant considering
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