Experimental and artificial intelligence modelling study of oil palm trunk sap fermentation

© 2021 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/en14082137Five major operations for the conversion of lignocellulosic biomasses into bioethanol are pre-treatment, detoxification, hydrolysis, fermentation, and distillation. The fermentation process is a significant biological step to transform lignocellulose into biofuel. The interactions of biochemical networks and their uncertainty and nonlinearity that occur during fermentation processes are major problems for experts developing accurate bioprocess models. In this study, mechanical processing and pre-treatment on the palm trunk were done before fermentation. Analysis was performed on the fresh palm sap and the fermented sap to determine the composition. The analysis for total sugar content was done using high-performance liquid chromatography (HPLC) and the percentage of alcohols by volume was determined using gas chromatography (GC). A model was also developed for the fermentation process based on the Adaptive-Network-Fuzzy Inference System (ANFIS) combined with particle swarm optimization (PSO) to predict bioethanol production in biomass fermentation of oil palm trunk sap. The model was used to find the best experimental conditions to achieve the maximum bioethanol concentration. Graphical sensitivity analysis techniques were also used to identify the most effective parameters in the bioethanol process.This work supported by the Ministry of Education Malaysia through a Research University Grant of the University Technology Malaysia (UTM) (Award Number: Rk430000.7743.4J010).Published versio

Gas capture processes

Copyright © 2020 by the authors. The increasing trends in gas emissions have had direct adverse impacts on human health and ecological habitats in the world. A variety of technologies have been deployed to mitigate the release of such gases, including CO2, CO, SO2, H2S, NOx and H2. This special issue on gas-capture processes collects 25 review and research papers on the applications of novel techniques, processes, and theories in gas capture and removal

Mixture of piperazine and potassium carbonate to absorb CO<inf>2</inf> in the packed column: Modelling study

Supplementary data are available online at https://www.sciencedirect.com/science/article/pii/S0016236121019098?via%3Dihub#s0135 .A rate-based non-equilibrium model is developed for CO2 absorption with the mixture of piperazine and potassium carbonate solution. The model is based on the mass and heat transfer between the liquid and the gas phases on each packed column segment. The thermodynamic equilibrium assumption (physical equilibrium) is considered only at the gas–liquid interface and chemical equilibrium is assumed in the liquid phase bulk. The calculated mass transfer coefficient from available correlations is corrected by the enhancement factor to account for the chemical reactions in the system. The Extended-UNIQUAC model is used to calculate the non-idealities related to the liquid phase, and the Soave-Redlich-Kwong (SRK) equation of state is used for the gas phase calculations. The thermodynamic analysis is also performed in this study. The enhancement factor is used to represent the effect of chemical reactions of the piperazine promoted potassium carbonate solution, which has not been considered given the rigorous electrolyte thermodynamics in the absorber. The developed model showed good agreement with the experimental data and similar studies in the literature

Swapping the N- and C-terminal domains of human apolipoprotein E3 and AI reveals insights into their structure/activity relationship

Apolipoprotein (apo) E3 and apoAI are exchangeable apolipoproteins that play a dominant role in regulating plasma lipoprotein metabolism. ApoE3 (299 residues) is composed of an N-terminal (NT) domain bearing a 4-helix bundle and a C-terminal (CT) domain bearing a series of amphipathic α-helices. ApoAI (243 residues) also comprises a highly helical NT domain and a less structured CT tail. The objective of this study was to understand their structural and functional role by generating domain swapped chimeras: apoE3-NT/apoAI-CT and apoAI-NT/apoE-CT. The bacterially overexpressed chimeras were purified by affinity chromatography and their identity confirmed by immunoblotting and mass spectrometry. Their α-helical content was comparable to that of the parent proteins. ApoE3-NT/apoAI-CT retained the denaturation profile of apoE3 NT domain, with apoAI CT tail eliciting a relatively unstructured state; its lipid binding ability improved dramatically compared to apoE3 indicative of a significant role of apoAI CT tail in lipid binding interaction. The LDL receptor interaction and ability to promote ABCA1-mediated cholesterol efflux of apoE3-NT/apoAI-CT was comparable to that of apoE3. In contrast, apoAI-NT/apoE-CT elicited an unfolding pattern and lipid binding ability that were similar to that of apoAI. As expected, DMPC/apoAI-NT/apoE-CT discoidal particles did not elicit LDLr binding ability, and promoted SR-B1 mediated cellular uptake of lipids to a limited extent. However, apoAI-NT/apoE-CT displayed an enhanced ability to promote cholesterol efflux compared to apoAI, indicative of a significant role for apoE CT domain in mediating this function. Together, these results indicate that the functional attributes of apoAI and apoE3 can be conferred on each other and that NT-CT domain interactions significantly modulate their structure and function

Clinical Applications of Spectral CT

Dual-energy computed tomography (DECT) has evolved from a research tool to an established clinical imaging modality since its first commercial introduction in the mid-2000s. The possibility to characterize the composition of different human tissues and the quantification of certain materials like iodine, calcium, or fat have shown clinical benefit for various body regions. Virtual monoenergetic imaging (VMI) and multi-material decomposition (MMD) imaging (see Chap. 12) are the most popular and investigated applications of DECT that can be used to improve detection and conspicuity of disease as well as objective and subjective image quality. Furthermore, virtual non-contrast (VNC) imaging can reduce the radiation exposure to the patient by omitting the need for a conventional non-contrast CT scan. In this chapter we review clinically established applications of DECT for the main body regions from head to toe. Moreover, we highlight interesting experimental and preclinical research topics that may become clinically available in the future. Concluding this chapter, we discuss the potential pitfalls associated with DECT