Mathematical modeling of sugar plant: a fuzzy approach

Kumar, Ashish; Saini, Monika

doi:10.1007/s41872-017-0038-0

Cited by 15 publications

(3 citation statements)

References 30 publications

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“…Ghosh et al [27] proposed a methodology to reduce life-cycle costs for a contemporary helicopter through improvement in reliability and maintainability parameters. Kumar and Saini [28] proposed a mathematical model of sugar plant to analyze the availability using fuzzy reliability approach. Kadyan and Kumar [29] studied the operational behavior of B-Pan crystallization system in sugar industry based on system availability using Markovian approach.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of evaporation system in sugar industry using RAMD analysis

Saini

Kumar

2019

J Braz. Soc. Mech. Sci. Eng.

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The main aim of this study is to analyze the application of reliability, availability, maintainability and dependability in identification of most sensitive subsystem of evaporation system in sugar plant. For this analysis, transition diagram of all subsystems is drawn and respective Chapman-Kolmogorov differential equations are derived using Markov birth-death process. All failure and repair rates of each subsystem are exponentially distributed. Various measures of system effectiveness like mean time between failures, mean time to repair and dependability ratio for all the three subsystems are also computed. Sensitivity analysis of system reliability is also performed. The reliability and maintainability are estimated at various time instants. Analysis of all the subsystems shows that sulphited syrup is highly sensitive from reliability point of view. The derived measures are shared with system designers for their active deliberation.

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Section: Introductionmentioning

confidence: 99%

Performance analysis of evaporation system in sugar industry using RAMD analysis

Saini

Kumar

2019

J Braz. Soc. Mech. Sci. Eng.

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“…Kadyan and Kumar [31] analyzed the availability based operational behavior of B-Pan crystallization system in the sugar industry. Kumar and Saini [32] developed a mathematical model of sugar plant as a whole system. Recently, Dahiya et al [33] analyzed a feeding system of sugar plant subject to coverage factor.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling and performance evaluation of A-pan crystallization system in a sugar industry

2019

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In this paper, an effort has been made to formulate a mathematical model of A-pan crystallization system of a sugar plant using fuzzy reliability approach. The sugar plant comprises eight subsystems. A-pan crystallization system is one of the most important representative among sugar plant systems. The A-pan crystallization system has four subsystems arranged in a series. The configuration of first and second subsystems is 2-out-of-2: G with two cold standby while third and fourth subsystems are in single-unit configuration. A mathematical model has been proposed by considering exponential distribution for failure and repair rates. By considering fuzzy reliability approach and Markov birth-death model differential equations have been derived. These equations are then solved by Runge-Kutta method of fourth order using MATLAB (Ode 45 function) to obtain the fuzzy availability. The results of the proposed model are beneficial for system designers.

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“…Gurunathan (2017) presented a systematic approach to estimate the availability of a regenerator subsystem) of a fluid catalytic cracking unit of a refinery using the Markov approach. Kumar and Saini (2018) developed a mathematical model of a sugar manufacturing plant. Malik and Tewari (2018) worked on performance modeling and maintenance priorities for the water flow system of a coal-based thermal power plant.…”

Section: Introductionmentioning

confidence: 99%

Stochastic modelling and availability analysis of a critical engineering system

Gupta¹

2019

IJQRM

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Purpose The purpose of this paper is to present the technique for evaluating the performance of a condensate system of a coal-based thermal power plant situated in the northern part of India. The data which used for system availability evaluation are not precise and are uncertain and, further, collected from concerned power plant history sheets and from discussion through plant personnel. Design/methodology/approach In the proposed model, traditional Markov birth-death process using a probabilistic approach is used to analyze the performance of a complex repairable condensate system of power plant up to a desired degree of accuracy. This approach has been demonstrated by breaking the condensate system into six subsystems arranged in series with two feasible states, namely, working and failed, labeled in a transition diagram and modeled as a Markov process, using Chapman–Kolmogorov equations, which are used for development of a probabilistic stochastic model for availability analysis in a more effecting manner, considering some suitable assumptions. Findings This study of analysis of reliability and availability can help in increasing the plant production and performance. The analysis is done with the help of availability matrices, which are developed using different combinations of failures and repair rates of all subsystems. To achieve the goal of maximum power generation, it is required to run the various subsystem of the concerned system of plant, failure free for a long duration. Therefore, the present approach may be a more powerful analysis tool to access the performance of all subsystems of a condensate system in terms of availability level achieved in availability matrices. The results of present study are found to be highly beneficial to the plant management for making maintenance decisions. Originality/value The present paper suggests a suitable technique for stochastic modeling and availability evaluation of an industrial system using Markovian approach and drawing a transition diagram to represent the operational behavior of the system. The present methodology includes the advantage of the ability to model and develop a more complex industrial system and helps in improving the performance and handling the uncertainties and possibilities of an industrial system.

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Mathematical modeling of sugar plant: a fuzzy approach

Cited by 15 publications

References 30 publications

Performance analysis of evaporation system in sugar industry using RAMD analysis

Performance analysis of evaporation system in sugar industry using RAMD analysis

Mathematical modeling and performance evaluation of A-pan crystallization system in a sugar industry

Stochastic modelling and availability analysis of a critical engineering system

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