Multi-objective operation optimization of spent coffee ground torrefaction for carbon-neutral biochar production

Many energy-intensive processes are employed to enhance biomass fuel properties to overcome the difficulties in utilizing biomass as fuel. Therefore, energy conservation during these processes is crucial for realizing a circular bioeconomy. This study develops a newly devised method to evaluate SCG biochars' higher heating value (HHV) and predict moisture content from power consumption. It is found that the increasing rates of HHV immediately follow decreases in power consumption, which could be used to determine the pretreatment time for energy conservation. The non-dominated sorting genetic algorithm II (NSGA-II) maximizes SCG biochar's HHV while minimizing energy consumption. The results show that producing SCG biochar with 23.98 MJ∙kg^-1 HHV requires 20.042 MJ∙kg^-1, using a torrefaction temperature of 244 °C and torrefaction time of 27 min and 43 sec. Every kilogram of biochar with an energy yield of 85.93 % is estimated to cost NT$ 12.21.

Machine learning and statistical analysis for biomass torrefaction: A review

Torrefaction is a remarkable technology in biomass-to-energy. However, biomass has several disadvantages, including hydrophilic properties, higher moisture, lower heating value, and heterogeneous properties. Many conventional approaches, such as kinetic analysis, process modeling, and computational fluid dynamics, have been used to explain torrefaction performance and characteristics. However, they may be insufficient in actual applications because of providing only some specific solutions. Machine learning (ML) and statistical approaches are powerful tools for analyzing and predicting torrefaction outcomes and even optimizing the thermal process for its utilization. This state-of-the-art review aims to present ML-assisted torrefaction. Artificial neural networks, multivariate adaptive regression splines, decision tree, support vector machine, and other methods in the literature are discussed. Statistical approaches (SAs) for torrefaction, including Taguchi, response surface methodology, and analysis of variance, are also reviewed. Overall, this review has provided valuable insights into torrefaction optimization, which is conducive to biomass upgrading for achieving net zero.

Dual pretreatment of mixing H2O2 followed by torrefaction to upgrade spent coffee grounds for fuel production and upgrade level identification of H2O2 pretreatment

Biochar is a carbon-neutral solid fuel and has emerged as a potential candidate to replace coal. Meanwhile, spent coffee grounds (SCGs) are an abundant and promising biomass waste that could be used for biochar production. This study develops a biochar valorization strategy by mixing SCGs with hydrogen peroxide (H₂O₂) at a weight ratio of 1:0.75 to upgrade SCG biochar. In this dual pretreatment method, the H₂O₂ oxidative ability at a pretreatment temperature of 105 °C contributes to an increase in the higher heating value (HHV) and carbon content of the SCG biochars. The HHV and carbon content of biochar increase by about 6.5% and 7.8%, respectively, when compared to the unpretreated one under the same conditions. Maximized biochar's HHV derived via the Taguchi method is 30.33 MJkg^-1, a 46.9% increase compared to the raw SCG, and a 6.5% increase compared to the unpretreated SCG biochar. The H₂O₂ concentration is 18% for the maximized HHV. A quantitative identification index of intensity of difference (IOD) is adopted to evaluate the contributive level of H₂O₂ pretreatment in terms of the HHV and carbon content. IOD increases with increasing H₂O₂ pretreatment temperature. Before torrefaction, SCGs' IOD pretreated at 50 °C is 1.94%, while that pretreated at 105 °C is 8.06%. This is because, before torrefaction, H₂O₂ pretreatment sufficiently weakens SCGs' molecular structure, resulting in a higher IOD value. The IOD value of torrefied SCGs (TSCG) pretreated at 105 °C is 10.71%, accounting for a 4.59% increase compared to that pretreated at 50 °C. This implies that TSCG pretreated by H₂O₂ at 105 °C has better thermal stability. For every 1% increase in IOD of TSCG, the carbon content of the biochar increases 0.726%, and the HHV increases 0.529%. Overall, it is demonstrated that H₂O₂ is a green and promising pretreatment additive for upgrading SCG biochar's calorific value, and torrefied SCGs can be used as a potential solid fuel to approach carbon neutrality.

Predictions of biochar production and torrefaction performance from sugarcane bagasse using interpolation and regression analysis

This study focuses on the biochar formation and torrefaction performance of sugarcane bagasse, and they are predicted using the bilinear interpolation (BLI), inverse distance weighting (IDW) interpolation, and regression analysis. It is found that the biomass torrefied at 275°C for 60min or at 300°C for 30min or longer is appropriate to produce biochar as alternative fuel to coal with low carbon footprint, but the energy yield from the torrefaction at 300°C is too low. From the biochar yield, enhancement factor of HHV, and energy yield, the results suggest that the three methods are all feasible for predicting the performance, especially for the enhancement factor. The power parameter of unity in the IDW method provides the best predictions and the error is below 5%. The second order in regression analysis gives a more reasonable approach than the first order, and is recommended for the predictions.