Experimental evaluation and application of genetic programming to develop predictive correlations for hydrochar higher heating value and yield to optimize the energy content

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@Article{MARZBAN:2022:jece,

• author = "Nader Marzban and Judy A. Libra and Seyyed Hossein Hosseini and Marcus G. Fischer and Vera Susanne Rotter",
• title = "Experimental evaluation and application of genetic programming to develop predictive correlations for hydrochar higher heating value and yield to optimize the energy content",
• journal = "Journal of Environmental Chemical Engineering",
• volume = "10",
• number = "6",
• pages = "108880",
• year = "2022",
• ISSN = "2213-3437",
• DOI = "doi:10.1016/j.jece.2022.108880",
• URL = "https://www.sciencedirect.com/science/article/pii/S2213343722017535",
• keywords = "genetic algorithms, genetic programming, Hydrothermal carbonization, HHV, Solid yield, Energy yield, Optimization",
• abstract = "The hydrothermal carbonization (HTC) process has been found to consistently improve biomass fuel characteristics by raising the higher heating value (HHV) of the hydrochar as process severity is increased. However, this is usually associated with a decrease in the solid yield (SY) of hydrochar, making it difficult to determine the optimal operating conditions to obtain the highest energy yield (EY), which combines the two parameters. In this study, a graph-based genetic programming (GP) method was used for developing correlations to predict HHV, SY, and EY for hydrochars based on published values from 42 biomasses and a broad range of HTC experimental systems and operating conditions, i.e., 5 lteq holding time (min) lteq 2208, 120 lteq temperature (degreeC) lteq 300, and 0096 lteq biomass to water ratio lteq 0.5. In addition, experiments were carried out with 5 pomaces at 4 temperatures and two reactor scales, 1 L and 18.75 L. The correlations were evaluated using this experimental data set in order to estimate prediction errors in similar experimental systems. The use of the correlations to predict HTC conditions to achieve the maximum EY is demonstrated for three common feedstocks, wheat straw, sewage sludge, and a fruit pomace. The prediction was confirmed experimentally with pomace at the optimized HTC conditions; we observed 6.9 percent error between the measured and predicted EY percent. The results show that the correlations can be used to predict the optimal operating conditions to produce hydrochar with the desired fuel characteristics with a minimum of actual HTC runs",

}

Genetic Programming entries for Nader Marzban Judy A Libra Seyyed Hossein Hosseini Marcus G Fischer Vera Susanne Rotter

Citations