• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2019, Volume: 12, Issue: 7, Pages: 1-11

Original Article

Optimization of Steam Distribution Network at the Cooking Plant: Experimental and Numerical Study


Objectives: The distribution of steam networks at cooking plant is determined experimentally and numerically in this research. Methods: Both methods of numerical and experimental study was carried out on the optimization of oxygen, steam load as well as stack temperature. The gadgets used for empirical study were combustion gases, stack gas temperature sensor, steam flow meter, and Oxygen (O2 ) sensors. GAMS software and EVIEWS software were used to model and optimize the steam network. The result of numerical and experimental was compared and the evaluation was presented. Findings: Experimental study shows that the steam networks decrease dramatically in a boiler and once illustrates that steam network arrived zero in a boiler. This circumstance expresses two boiler can be determined 79931.28 and 77350.36, respectively. The sum of two boilers is 157281.64 kg/s. This condition leads to decreasing the energy consumption in an empirical result. The amount of energy consumption in two boiler arrived 84437.47 and 54321.63, respectively. The sum of the energy consumption was 138759.1 Kw. The numerical model shows that both steam load declined 78471.41 and 77786.49, respectively. Energy consumption in two boilers decline to reach 84580.31 and 53514.77, respectively. Application: We compared the experimental and numerical model in this project. The deviation of the experimental and numerical model is less than 8%. It means the results are reliable and the model can be used for simulation of the steam load in process engineering in small industries. The operating cost decreases up to 167686882.9$ per annum. The best way to improvement is the use of NMLP in this project because the NMLP has more reliable.  

Keywords: Controlling Steam Volume, Furness, Optimization, Process Engineering 


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