Life cycle assessment: Sustainability of biodiesel production from black soldier fly larvae feeding on thermally pre-treated sewage sludge under a tropical country setting.

More energy is needed nowadays due to global population growth. Concurrently, sewage sludge generation has also increased steadily stemming from the inevitable urbanization. As such, black soldier fly larvae (BSFL) can be potentially deployed to solve both issues. This paper investigates the environmental sustainability of biodiesel production derived from sludge-fed BSFL feedstock. A cradle-to-gate life cycle assessment (LCA) was performed through SimaPro software utilizing the ReCiPe 2016 Midpoint (H) and Endpoint (H) methods. The entire LCA covered 3 main stages, including the thermal pre-treatment of sludge, BSFL rearing and processing, and lastly lipid extraction and biodiesel production. LCA showed that the sludge pre-treatment stage had the highest environmental impact, while BSFL rearing and processing had the least due to the suitable geographical climate. Electricity usage during the pre-treatment stage was the main contributing component, followed by chemical usage during biodiesel production. After normalizing, it was observed that land occupation, marine ecotoxicity, freshwater ecotoxicity and freshwater eutrophication were more impactful than the commonly studied global warming potential (GWP). Lipid content and biodiesel conversion efficiency were determined as the sensitive factors which could influence the LCA outcome. In comparison with other types of biodiesel, BSFL biodiesel had a milder impact in terms of climate change, land occupation, terrestrial acidification, marine and freshwater eutrophication. Furthermore, this biological reduction of sludge through BSFL valorization avoided sludge landfilling, which reduced up to 100 times GWP. Therefore, sludge-fed BSFL biodiesel production is an environmentally-sound and highly potential solution that should be investigated comprehensively.

Current perspectives, future challenges and key technologies of biohydrogen production for building a carbon-neutral future: A review.

The ever-increasing quantity of greenhouse gases in the atmosphere can be attributed to the rapid increase in the world population as well as the expansion of globalization. Hence, achieving carbon neutrality by 2050 stands as a challenging task to accomplish. Global industrialization had necessitated the need to enhance the current production systems to reduce greenhouse gases emission, whilst promoting the capture of carbon dioxide from atmosphere. Hydrogen is often touted as the fuel of future via substituting fossil-based fuels. In this regard, renewable hydrogen happens to be a niche sector of novel technologies in achieving carbon neutrality. Microalgae-based biohydrogen technologies could be a sustainable and economical approach to produce hydrogen from a renewable source, while simultaneously promoting the absorption of carbon dioxide. This review highlights the current perspectives of biohydrogen production as an alternate source of energy. In addition, future challenges associated with biohydrogen production at large-scale application, storage and transportation are included. Key technologies in producing biohydrogen are finally described in building a carbon-neutral future.

Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: A multi-criteria modeling approach.

The thermochemical processes such as gasification and co-gasification of biomass and coal are promising route for producing hydrogen-rich syngas. However, the process is characterized with complex reactions that pose a tremendous challenge in terms of controlling the process variables. This challenge can be overcome using appropriate machine learning algorithm to model the nonlinear complex relationship between the predictors and the targeted response. Hence, this study aimed to employ various machine learning algorithms such as regression models, support vector machine regression (SVM), gaussian processing regression (GPR), and artificial neural networks (ANN) for modeling hydrogen-rich syngas production by gasification and co-gasification of biomass and coal. A total of 12 machine learning algorithms which comprises the regression models, SVM, GPR, and ANN were configured, trained using 124 datasets. The performances of the algorithms were evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). In all cases, the ANN algorithms offer superior performances and displayed robust predictions of the hydrogen-rich syngas from the co-gasification processes. The R2 of both the Levenberg-Marquardt- and Bayesian Regularization-trained ANN obtained from the prediction of the hydrogen-rich syngas was found to be within 0.857-0.998 with low prediction errors. The sensitivity analysis to determine the effect of the process parameters on the model output revealed that all the parameters showed a varying level of influence. In most of the processes, the gasification temperature was found to have the most significant influence on the model output.