Statistical investigation of study the effects of different parameters on hexamine production using response surface methodology (RSM)

Authors

  • Shurooq Talib Al-Humairi University of Technology-Iraq / Chemical engineering department, Baghdad-Iraq
  • Riyadh Sadeq Al-Mukhtar University of Technology-Iraq /Chemical engineering department, Baghdad-Iraq,
  • Nasma Balasiem Ahmed University of Technology-Iraq / Chemical engineering department, Baghdad-Iraq
  • Mohammad Fadhil Abid Al-Turath University College/ Biomedical Engineering department, Baghdad-Iraq
  • Ali Hussine University of Technology-Iraq / Chemical engineering department, Baghdad-Iraq
  • Hashim Mohammad University of Technology-Iraq / Chemical engineering department, Baghdad-Iraq

DOI:

https://doi.org/10.31663/tqujes.12.1.429(2022)

Keywords:

Aqueous formaldehyde ; Central composite design ;Hexamine production; Liquid phase ;Statistical analysis.

Abstract

The current study was devoted to investigate experimentally and statistically the effects of different parameters on hexamine production in a bench-scale setup operating in a batch mode. The operating variables were varied in the range of (formaldehyde/ammonia) ratio =2 to 10; temperature = 20 to 80 oC; process time = 0 to 100 min). Experimental results revealed that a positive impact of (formaldehyde/ammonia) ratio on the production process was observed across the studied range, while temperature and time showed optimum values of 60 oC and 60 min, respectively for the optimum % yield of hexamine is 98%. Statistical analysis of the process showed that the Model F-value of 17.63 reveals the model's significance.  While there is only a 0.01% chance that a "Model   F-value" could come because of noise. Values of "Prob > F" less than 0.0500 point model codes are significant.

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Published

2022-06-01

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Section

Articles

How to Cite

Statistical investigation of study the effects of different parameters on hexamine production using response surface methodology (RSM). (2022). University of Thi-Qar Journal for Engineering Sciences, 12(1), 146-152. https://doi.org/10.31663/tqujes.12.1.429(2022)