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.
The increase in worldwide demand for energy is driven by the rapid increase in population and exponential economic development. This resulted in the fast depletion of fossil fuel supplies and unprecedented levels of greenhouse gas in the atmosphere. To valorize biomass into different bioproducts, one of the popular and carbon-neutral alternatives is biorefineries. This system is an appropriate technology in the circular economy model. Various research highlighted the role of biorefineries as a centerpiece in the carbon-neutral ecosystem of technologies of the circular economy model. To fully realize this, various improvements and challenges need to be addressed. This paper presents a critical and timely review of the challenges and future direction of biorefineries as an alternative carbon-neutral energy source.
This study presented a novel methodology to predict microalgae chlorophyll content from colour models using linear regression and artificial neural network. The analysis was performed using SPSS software. Type of extractant solvents and image indexes were used as the input data for the artificial neural network calculation. The findings revealed that the regression model was highly significant, with high R2 of 0.58 and RSME of 3.16, making it a useful tool for predicting the chlorophyll concentration. Simultaneously, artificial neural network model with R2 of 0.66 and low RMSE of 2.36 proved to be more accurate than regression model. The model which fitted to the experimental data indicated that acetone was a suitable extraction solvent. In comparison to the cyan-magenta-yellow-black model in image analysis, the red-greenblue model offered a better correlation. In short, the estimation of chlorophyll concentration using prediction models are rapid, more efficient, and less expensive.
This study aimed to investigate the transport mechanisms of ions during forward-osmosis-driven (FO-driven) dewatering of microalgae using molecular dynamics (MD) simulations. The dynamical and structural properties of ions in FO systems of varying NaCl or MgCl2 draw solution (DS) concentrations were calculated and correlated. Results indicate that FO systems with higher DS concentration caused ions to have lower hydration numbers and higher coordination numbers leading to lower diffusion coefficients. The higher hydration number of Mg2+ ions resulted in significantly lower ionic permeability as compared to Na+ ions at all concentrations (p = 0.002). The simulations also revealed that higher DS concentrations led to higher accumulation of ions in the membrane. This study provides insights on the proper selection of DS for FO systems.