Ligang Lin

Sulfur removal from fuel using zeolites/polyimide mixed matrix membrane adsorbents.

A novel membrane adsorption process was proposed for the sulfur removal from fuels. The mixed matrix membranes (MMMs) adsorbents composed of polyimide (PI) and various Y zeolites were prepared. By the detailed characterization of FT-IR, morphology, thermal and mechanical properties of MMMs adsorbents, combining the adsorption and desorption behavior research, the process-structure-function relationship was discussed. Field-emission scanning electron microscope (FESEM) images show that the functional particles are incorporated into the three-dimensional network structure. MMMs adsorbents with 40% of zeolites content possess better physical properties, which was confirmed by mechanical strength and thermo stability analysis. Influence factors including post-treatment, content of incorporated zeolites, adsorption time, temperature, initial sulfur concentration as well as sulfur species on the adsorption performance of MMMs adsorbents have been evaluated. At 4 wt.% zeolites content, adsorption capacity for NaY/PI, AgY/PI and CeY/PI MMMs adsorbents come to 2.0, 7.5 and 7.9 mg S/g, respectively. And the regeneration results suggest that the corresponding spent membranes can recover about 98%, 90% and 70% of the desulfurization capacity, respectively. The distinct adsorption and desorption behavior of MMMs adsorbents with various functional zeolites was markedly related with their various binding force and binding mode with sulfur compounds.

Membrane adsorber with metal organic frameworks for sulphur removal

Sulphur removal is significant for fuels used as hydrogen sources for modern fuel cell applications. A high performance membrane adsorber with metal organic frameworks (MOFs) has been fabricated. The architecture of the three-dimensional membrane channels provided multiple functional sites and an efficient method of incorporating [Cu3(BTC)2] particles into a polyimide (PI) matrix. The properties of the MOF-based membrane adsorber, including the adsorption contribution, regeneration behavior and effect of fuel species, have been investigated. In all cases, the inlet fuel can be desulfurized to below 0.1 mg L−1, which means that the outlet fuel can be used as a sulphur-free hydrogen source for fuel cell applications. The related discussions can propose some general suggestions for the separation of organic–organic mixtures involved in current energy and environmental problems. The desulfurization capabilities of the membrane adsorber are likely to play an influential role in the reduction of motor vehicle pollution and the development of fuel cells.

The effect of polymer concentration and additives of cast solution on performance of polyethersulfone/sulfonated polysulfone blend nanofiltration membranes

AbstractThe effect of polymer concentration of polyethersulfone (PES) plus sulfonated polysulfone (SPSF) and additives of cast solution on performance of PES/SPSF blend nanofiltration (NF) membranes was investigated. The field emission scanning electron microscopy and X-ray photoelectron spectroscopy were used to analyze characteristic of PES/SPSF blend NF membranes. The water flux of PES/SPSF blend membranes decreased dramatically with an increase in polymer concentration of PES plus SPSF. The rejection of polyethylene glycol (PEG) and salts increased with increasing polymer concentration of PES plus SPSF. When acetone was used as an additive, the water flux declined with increasing mass concentration of acetone, but the rejection of PEGs and salts increased. The PES/SPSF blend NF membranes with mimimum rejection of sodium chloride indicated that it could separate monovalent salts from multivalent salts effectively, which would be potential application in softening water for drinking water resource.

High and low frequency unfolded partial least squares regression based on empirical mode decomposition for quantitative analysis of fuel oil samples.

Accurate prediction of the model is fundamental to the successful analysis of complex samples. To utilize abundant information embedded over frequency and time domains, a novel regression model is presented for quantitative analysis of hydrocarbon contents in the fuel oil samples. The proposed method named as high and low frequency unfolded PLSR (HLUPLSR), which integrates empirical mode decomposition (EMD) and unfolded strategy with partial least squares regression (PLSR). In the proposed method, the original signals are firstly decomposed into a finite number of intrinsic mode functions (IMFs) and a residue by EMD. Secondly, the former high frequency IMFs are summed as a high frequency matrix and the latter IMFs and residue are summed as a low frequency matrix. Finally, the two matrices are unfolded to an extended matrix in variable dimension, and then the PLSR model is built between the extended matrix and the target values. Coupled with Ultraviolet (UV) spectroscopy, HLUPLSR has been applied to determine hydrocarbon contents of light gas oil and diesel fuels samples. Comparing with single PLSR and other signal processing techniques, the proposed method shows superiority in prediction ability and better model interpretation. Therefore, HLUPLSR method provides a promising tool for quantitative analysis of complex samples.

A boosting extreme learning machine for near-infrared spectral quantitative analysis of diesel fuel and edible blend oil samples

Extreme learning machines (ELMs) have drawn increasing attention due to their characteristics of simple structure, high learning speed and excellent performance. However, a single ELM tends to low predictive accuracy and instability in dealing with quantitative analysis of complex samples. To further improve the predictive accuracy and stability of ELMs, a new quantitative model, called the boosting ELM is proposed. In this approach, a large number of ELM sub-models are sequentially built by selecting a certain number of samples from the original training set according to the distribution of the sampling weights, and then their predictions are aggregated using the weighted median. The activation function and the number of hidden nodes of ELM sub-models are determined simultaneously by the ratio of mean value and standard deviation of correlation coefficients (MSR). The performance of the proposed method is tested with diesel fuel and blended edible oil samples. Compared with partial least squares (PLS) and ELMs, our results demonstrate that the boosting ELM is an efficient ensemble model and has obvious superiorities in predictive accuracy and stability. Therefore, the proposed method may be an alternative for near-infrared (NIR) spectral quantitative analysis of complex samples.

Protein adsorption and desorption behavior of a pH-responsive membrane based on ethylene vinyl alcohol copolymer

Protein adsorption and desorption behavior was investigated for a pH-responsive ethylene vinyl alcohol copolymer (EVAL) membrane with an interconnected porous structure. The transition of electrostatic behavior and conformation change of the poly(dimethylaminoethyl methacrylate) (poly(DMAEMA)) chain contributed to the pH-responsive protein adsorption and desorption. Protein adsorption was conducted under acidic and neutral conditions. Protein desorption was conducted under alkaline conditions. The protonated poly(DMAEMA) chain was positively charged and extended into the BSA solution below its pKa, providing a three-dimensional space for BSA adsorption. The maximum static protein adsorption capacity was obtained at pH 6.4. The dynamic adsorption capacities of membrane EVAL10 at 10% and 50% breakthrough were 45 and 99 mg BSA per g of membrane, respectively. The Q50% of membrane EVAL10 was equivalent to 22.6 mg BSA per mL of membrane, almost 95% of the static adsorption capacity. BSA was quickly desorbed from the membrane and 94% recovery of BSA was observed at pH 9.0 in the dynamic desorption process, due to a deprotonated and collapsed conformation of the poly(DMAEMA) chains. The dynamic adsorption capacity of the membrane did not change significantly after four sequential cycles.

Novel affinity membranes with macrocyclic spacer arms synthesized via click chemistry for lysozyme binding

Affinity membrane has great potential for applications in bioseparation and purification. Disclosed herein is the design of a novel affinity membrane with macrocyclic spacer arms for lysozyme binding. The clickable azide-cyclodextrin (CD) arms and clickable alkyne ethylene-vinyl alcohol (EVAL) chains are designed and prepared. By the azide-alkyne click reaction, the EVAL-CD-ligands affinity membranes with CD spacer arms in three-dimensional micro channels have been successfully fabricated. The FT-IR, XPS, NMR, SEM and SEM-EDS results give detailed information of structure evolution. The abundant pores in membrane matrix provide efficient working channels, and the introduced CD arms with ligands (affinity sites) provide supramolecular atmosphere. Compared with that of raw EVAL membrane, the adsorption capacity of EVAL-CD-ligands membrane (26.24mg/g) show a triple increase. The study indicates that three effects (inducing effect, arm effect, site effect) from CD arms render the enhanced performance. The click reaction happened in membrane matrix in bulk. The effective lysozyme binding and higher adsorption performance of affinity membranes described herein compared with other reported membranes are markedly related with the proposed strategy involving macrocyclic spacer arms and supramolecular working channels.

Removal of Heavy Metal in Drinking Water Resource with Cation-Exchange Resins (Type 110-H) Mixed PES Membrane Adsorbents

AbstractThe removal of heavy metal in drinking water was carried out using a new type of cation-exchange resin mixed polyethersulfone (PES) membrane adsorbent. Cation-exchange resins (Type 110-H) were incorporated into the PES porous matrix by the immersion phase separation method. The membrane adsorbents were characterized by the morphology and adsorption capacity. High values of heavy metal ion adsorption capacities of 361.79  mg Hg2+/g, 264.33  mg Pb2+/g, and 94.75  mg Cu2+/g were obtained by the static adsorption test. The dynamic capacity experiment was performed by flowing the Cu2+ solution through a stack of three pieces of flat-sheet membranes. The Cu2+ mass adsorbed per unit of membrane bed was calculated at a breakthrough concentration (20% of the feed concentration). The dynamic Cu2+ binding capacity was 6.25  mg/g membrane, and the desorption rate of the membrane adsorbent was up to 97.6%. The adsorption isotherm was fitted with the Langmuir model with a maximum adsorption capacity of 161.29  ...

Robust boosting neural networks with random weights for multivariate calibration of complex samples

Neural networks with random weights (NNRW) has been used for regression due to its excellent performance. However, NNRW is sensitive to outliers and unstable to some extent in dealing with the real-world complex samples. To overcome these drawbacks, a new method called robust boosting NNRW (RBNNRW) is proposed by integrating a robust version of boosting with NNRW. The method builds a large number of NNRW sub-models sequentially by robustly reweighted sampling from the original training set and then aggregates these predictions by weighted median. The performance of RBNNRW is tested with three spectral datasets of wheat, light gas oil and diesel fuel samples. As comparisons to RBNNRW, the conventional PLS, NNRW and boosting NNRW (BNNRW) have also been investigated. The results demonstrate that the introduction of robust boosting greatly enhances the stability and accuracy of NNRW. Moreover, RBNNRW is superior to BNNRW particularly when outliers exist.

One-step fabrication of recyclable polyimide nanofiltration membranes with high selectivity and performance stability by a phase inversion-based process

A novel one-step strategy is proposed for the recyclable polyimide nanofiltration (PI-NF) membranes. By designing the chain structure, the soluble polyimide polymer with coexisted imide rings and flexible pendant groups are fabricated. With the polyimide polymer as matrix, the NF membranes with integrally skinned asymmetric architecture were obtained by a phase inversion method. The structure evolution was demonstrated by scanning electron microscope and atomic force microscope analyses. The rejections of prepared PI-NF membrane for Rose Bengal, PEG 1000 and typical salts under optimized conditions were 92, 91.78, 98.68%, respectively. The rejections of NF membranes for different salts followed the order: Na2SO4 > MgSO4 > NaCl > MgCl2. Furthermore, the fabricated polyimide-based NF membranes show a stable performance during long-time filtration process and exhibit a confirmed excellent recyclable property.