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- ItemExperimental evaluation and application of genetic programming to develop predictive correlations for hydrochar higher heating value and yield to optimize the energy content(Amsterdam [u.a.] : Elsevier, 2022) Marzban, Nader; Libra, Judy A.; Hosseini, Seyyed Hossein; Fischer, Marcus G.; Rotter, Vera SusanneThe 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 ≤ holding time (min) ≤ 2208, 120 ≤ temperature (°C) ≤ 300, and 0. 0096 ≤ biomass to water ratio ≤ 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 % error between the measured and predicted EY %. 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.
- ItemEvaluation of Sonocatalytic and Photocatalytic Processes Efficiency for Degradation of Humic Compounds Using Synthesized Transition-Metal-Doped ZnO Nanoparticles in Aqueous Solution(New York, NY [u.a.] : Hindawi, 2021) Maleki, Afshin; Seifi, Mehran; Marzban, NaderThe existence of a humic substance in water causes the growth of microorganisms and reduces the quality of water; therefore, the removal of these materials is crucial. Here, the ZnO nanoparticles doped using transition metals, copper (Cu) and manganese (Mn), were used as an effective catalyst for photocatalytic removal of humic substances in an aqueous environment under ultraviolet, visible light, and light-emitting diode irradiations. Also, we study the effect of the sonocatalytic method. A solvothermal procedure is used for doping, and the Cu- and Mn-doped ZnO nanocatalyst were characterized by means of FTIR, XRD, AFM, SEM, and EDAX analyses. We investigate the effect of operational variables, including doping ratio, initial pH, catalyst dose, initial HS content, and illuminance on the removal efficiency of the processes. The findings of the analyses used for the characterization of the nanoparticles illustrate the appropriate synthesis of the Cu- and Mn-doped ZnO nanocatalysts. We observe the highest removal efficiency rate under acidic conditions and the process efficiency decreased with increasing solution pH, when we tested it in the range of 3–7. Photocatalytic decomposition of HS increases with a rise in catalyst dose, but an increase in initial HS content results in decreasing the removal efficiency. We observe the highest photocatalytic degradation of humic acid while using the visible light, and the highest removal efficiency is obtained using Cu.ZnO. The Cu.ZnO also shows better performance under ultraviolet irradiation compared to other agents.