J. Jay Liu

Molecular Descriptor Subset Selection in Theoretical Peptide Quantitative Structure–Retention Relationship Model Development Using Nature-Inspired Optimization Algorithms

In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the models error were identified, thus allowing for further application of the developed methodology in proteomics.

Silver-Lactoferrin Nanocomplexes as a Potent Antimicrobial Agent

The process of silver immobilization onto and/or into bovine lactoferrin (LTF), the physicochemical properties of bovine lactoferrin and obtained silver-lactoferrin complexes, as well as antibacterial activity of silver-lactoferrin complexes were investigated in this work. Kinetic study of the silver immobilization into lactoferrin was carried out using batch sorption techniques. Spectrometric (MALDI-TOF/TOF-MS, ICP-MS), spectroscopic (FTIR, SERS), electron microscopic (TEM) and electrophoretic (I-DE) techniques, as well as zeta potential measurements, were applied for characterization of LTF and binding nature of silver in Ag-LTF complexes. On the basis of the results of the kinetics study, it was established that the silver binding to LTF is a heterogeneous process involving two main stages: (i) internal diffusion and sorption onto external surface of lactoferrin globules; and (ii) internal diffusion and binding into lactoferrin globule structure. Spectroscopic techniques combined with TEM analysis confirmed the binding process. Molecular dynamics (MD) analysis was carried out in order to simulate the mechanism of the binding process, and locate potential binding sites, as well as complement the experimental findings. Quantum mechanics (QM) simulations were performed utilizing density functional theory (DFT) in order to support the reduction mechanism of silver ions to elemental silver. Antimicrobial activity of synthesized lactoferrin complexes against selected clinical bacteria was confirmed using flow cytometry and antibiograms.

Process simulation of bioethanol production from brown algae

Abstract Steady state ethanol production from brown algae (Saccharina japonica) based on 100,000 ton/year dry feed was simulated using Aspen Plus V7.3 software. Different process units including saccharification, fermentation and purification were modeled based on experimental works obtained from literature. Acid thermal hydrolysis using H 2 SO 4 and simultaneous saccharification and fermentation (SSF) were used and modeled in this simulation. Distillation columns along with molecular sieves were used to recover ethanol from the raw fermentation broth to produce 99.5% ethanol. This simulation is the first attempt in literature to evaluate large-scale production of ethanol from macroalgae and allows its economic analysis.

Design and analysis of a diesel processing unit for a molten carbonate fuel cell for auxiliary power unit applications

Fuel cell-based auxiliary power units (APUs) are a promising technology for meeting global energy needs in an environmentally friendly way. This study uses diesel containing sulfur components such as dibenzothiophene (DBT) as a feed. The sulfur tolerance of molten carbonate fuel cell (MCFC) modules is not more than 0.5 ppm, as sulfur can poison the fuel cell and degrade the performance of the fuel cell module. The raw diesel feed in this study contains 10 ppm DBT, and its sulfur concentration should be reduced to 0.1 ppm. After desulfurization, the feed goes through several unit operations, including steam reforming, water-gas shift, and gas purification. Finally, hydrogen is fed to the fuel cell module, where it generates 500 kW of electrical energy. The entire process, with 52% and 89% fuel cell and overall system efficiencies, respectively, is rigorously simulated using Aspen HYSYS, and the results are input into a techno-economic analysis to calculate the minimum electricity selling price (MESP). The electricity cost for this MCFC-based APU was calculated as 1.57

Techno-economic analysis of production and recovery of volatile fatty acids from brown algae using membrane distillation

/kWh. According to predictions, the cost reductions for fuel cell stacks will afford electricity selling prices of 1.51

Techno-economic analysis of ethanol production from marine biomass

/kWh in 2020 and 1.495

Industrial-scale bioethanol production from brown algae and economic comparison of two pretreatment designs

/kWh in 2030. Based on a sensitivity analysis, the diesel price and capital cost were found to have the strongest impact on the MESP.

Industrial-scale bioethanol production from brown algae: Effects of pretreatment processes on plant economics

Abstract Volatile fatty acids (VFA) are promising biofuel precursors that can be processed to produce mixed alcohols or other biofuels. This study evaluates the economy of production and separation of VFAs as products of anaerobic digestion (AD) of brown algae. Membrane distillation (MD) was integrated to product recovery unit to increase the VFA concentration from 3% to 10% in fermentation broth. The process is simulated in Aspen Plus v8.4 and a techno-economic model were developed to calculate minimum VFA selling price. The results showed profitability of using membrane distillation to lower the utility and operation costs of VFA recovery. A minimum VFA selling price of 384

Economic, energy, and environmental impacts of alcohol dehydration technology on biofuel production from brown algae

/t were calculated for base case. A sensitivity analysis on permeate flux and cost were performed to cover uncertainties in MD unit. The lower cost obtained for VFA production in this study makes brown algae a reliable candidate for VFA and subsequent biofuel production processes.

Impact of volatile fatty acid recovery on economics of ethanol production from brown algae via mixed alcohol synthesis

Abstract The purpose of this study is techno-economic analysis of ethanol production from marine biomass (macroalgae), based on 10-year time frame for plant operation. This study is different from previous similar studies considering follo wing facts: (1) Biomass considered in this study is macroalgae or seaweed, to which technologies available for conversion of biomass to fuels have been applied in limited ways. (2) This study does not provide production cost of ethanol, but target biomass cost for macroalgae production through large-scale cultivation that is a key factor for success of macroalgae as a biomass feedstock. Currently high seaweeds price greatly limits the applicability of seaweeds as feedstocks for bioethanol production plant. To solve this issue a reduction of feedstock price seems the only option. This study develops a techno-economic model to analyze the economy of an ethanol production plant processing 100,000 MT/year (dry basis) brown algae. This study effectively defined maximum dry seaweed price (MDSP) that must be covered by large-scale seaweed production to reach a Return-On-Investment (ROI) break-even point after 10 years plant operation. The MDSP will act as target biomass cost for large-scale cultivation of macroalgae. Plant scale ups were also performed to examine the effects of plant capacity on MDSP.