Enhancing high-density microalgae cultivation via exogenous supplementation of biostimulant derived from onion peel waste for sustainable biodiesel production.

Microalgae demonstrate significant potential as a source of liquid-based biofuels. However, increasing biomass productivity in existing cultivation systems is a critical prerequisite for their successful integration into large-scale operations. Thus, the current work aimed to accelerate the growth of C. vulgaris via exogenous supplementation of biostimulant derived from onion peel waste. Under the optimal growth conditions, which entailed a biostimulant dosage of 37.5% v/v, a pH of 3, an air flow rate of 0.4 L/min, and a 2% v/v inoculum harvested during the mid-log phase, yielded a maximum biomass concentration of 1.865 g/L. Under the arbitrarily optimized parameters, a comparable growth pattern was evident in the upscaled cultivation of C. vulgaris, underscoring the potential commercial viability of the biostimulant. The biostimulant, characterized through gas chromatography-mass spectrometry (GC-MS) analysis, revealed a composition rich in polyphenolic and organo-sulphur compounds, notably including allyl trisulfide (28.13%), methyl allyl trisulfide (23.04%), and allyl disulfide (20.78%), showcasing potent antioxidant properties. Additionally, microalgae treated with the biostimulant consistently retained their lipid content at 18.44% without any significant reduction. Furthermore, a significant rise in saturated fatty acid (SFA) content was observed, with C16:0 and C18:1 dominating both bench-scale (44.08% and 14.01%) and upscaled (51.12% and 13.07%) microalgae cultures, in contrast to the control group where C18:2 was prevalent. Consequently, SFA contents reached 54.35% and 65.43% in bench-scale and upscaled samples respectively, compared to 33.73% in the control culture. These compositional characteristics align well with the requirements for producing high-quality crude biodiesel.

Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction.

In this study, Machine learning (ML) models integrated with genetic algorithm (GA) and particle swarm optimization (PSO) have been developed to predict, evaluate, and analyze biochar yield using biomass properties and process operating conditions. Comparative study of different ML algorithms integrated with GA and PSO were performed to improve the ML models architecture and parameters selection. The results proposed that Ensembled Learning Tree (ELT-PSO) model outperformed all other models and is favored for biochar yield prediction (R2 = 0.99, RMSE = 2.33). The partial dependence plots (PDPs) analysis shows the potential effects of each influencing parameter impact on the biochar yield and as well as shows that how these factors will interact during the pyrolysis process. A user-friendly software was developed based on the ELT-PSO model to avoid extensive and expensive experimentations without requiring considerable ML understanding. Difference recorded by GUI was less than 2% with experimental yield.

Artificial neural networks for the prediction of biochar yield: A comparative study of metaheuristic algorithms.

In this study, an integrated framework of artificial neural networks (ANNs) and metaheuristic algorithms have been developed for the prediction of biochar yield using biomass characteristics and pyrolysis process conditions. Comparative analysis of six different metaheuristic algorithms was performed to optimize the ANN architecture and select important features. The results suggested that the ANN model coupled with the Rao-2 algorithm outperformed (R2 âˆ¼ 0.93, RMSE âˆ¼ 1.74%) all other models. Furthermore, the detailed information behind the models was acquired, identifying the most influencing factors as follows: pyrolysis temperature (56%), residence time (23%), and heating rate (8%). The partial dependence plot analysis revealed how each influencing factor affected the target variable. Finally, an easy-to-use software tool for predicting biochar yield was built using the ANN-Rao-2 model. This study demonstrates huge potential that machine learning presents in predictive modelling of complex pyrolysis processes, and reduces the time-consuming and expensive experimental work for estimating the biochar yield.

Machine learning prediction of pyrolytic gas yield and compositions with feature reduction methods: Effects of pyrolysis conditions and biomass characteristics.

This study aimed to utilize machine learning algorithems combined with feature reduction for predicting pyrolytic gas yield and compositions based on pyrolysis conditions and biomass characteristics. To this end, random forest (RF) and support vector machine (SVM) was introduced and compared. The results suggested that six features were adequate to accurately forecast (R2 > 0.85, RMSE < 5.7%) the yield while the compositions only required three. Moreover, the profound information behind the models was extracted. The relative contribution of pyrolysis conditions was higher than that of biomass characteristics for yield (55%), CO2 (73%), and H2 (81%), which was inverse for CO (12%) and CH4 (38%). Furthermore, partial dependence analysis quantified the effects of both reduced features and their interactions exerted on pyrolysis process. This study provided references for pyrolytic gas production and upgrading in a more convenient manner with fewer features and extended the knowledge into the biomass pyrolysis process.

A comparative study of machine learning methods for bio-oil yield prediction - A genetic algorithm-based features selection.

A novel genetic algorithm-based feature selection approach is incorporated and based on these features, four different ML methods were investigated. According to the findings, ML models could reliably predict bio-oil yield. The results showed that Random forest (RF) is preferred for bio-oil yield prediction (R2 ~ 0.98) and highly recommended when dealing with the complex correlation between variables and target. Multi-Linear regression model showed relatively poor generalization performance (R2 ~ 0.75). The partial dependence analysis was done for ML models to show the influence of each input variable on the target variable. Lastly, an easy-to-use software package was developed based on the RF model for the prediction of bio-oil yield. The current study offered new insights into the pyrolysis process of biomass and to improve bio-oil yield. It is an attempt to reduce the time-consuming and expensive experimental work for estimating the bio-oil yield of biomass during pyrolysis.