Novel nanocomposite membranes for osmotically driven processes : fabrication and application

University of Technology Sydney. Faculty of Engineering and Information Technology.For osmotically driven membrane processes, including forward osmosis (FO) and pressure retarded osmosis (PRO), water permeate was selectively induced across a semi-permeable polymeric membrane by the osmotic pressure generated from the salinity gradient. Although both FO and PRO processes are mainly driven by the osmotic pressure difference increased by the more concentrated draw solution on the permeate site of the membrane, membrane orientations for the processes are mainly confirmed as the active layer facing the feed solution (AL-FS) for FO, and the active layer facing the draw solution (AL-DS) for PRO, respectively. Although the AL-FS orientation for FO is beneficial for controlling membrane fouling on the dense active layer, diluted internal concentration polarisation (ICP) inside a FO membrane would be a major obstacle to maintaining the osmotic driving force under FO operation. These processes have been widely used for a variety of water treatments and hybrid systems as a low-energy process. However, the processes still have some critical challenges for membrane development, which are related to the following aspects: water permeability, reverse solute diffusion, concentration polarisation, membrane fouling and membrane stability. Although many earlier studies developed various kinds of polymeric membranes at laboratory scale to produce outstanding performances for overcoming existing challenges in FO and PRO processes, most of them never produced their own scaled-up membrane modules for commercial applications. This study, therefore, initially targeted novel nanocomposite membrane development for FO and PRO processes using the dual-blade casting technique and two hydrophilic nanomaterials (graphene oxide and halloysite nanotubes). Subsequently, we selected the best strategy in our activities, and the selected one was further investigated for its commercial viability

Multichannel convolution neural network for gas mixture classification

Concomitant with people beginning to understand their legal rights or entitlement to complain, complaints of offensive odors and smell pollution have increased significantly. Consequently, monitoring gases and identifying their types and causes in real time has become a critical issue in the modern world. In particular, toxic gases that may be generated at industrial sites or odors in daily life consist of hybrid gases made up of various chemicals. Understanding the types and characteristics of these mixed gases is an important issue in many areas. However, mixed gas classification is challenging because the gas sensor arrays for mixed gases must process complex nonlinear high-dimensional data. In addition, obtaining sufficient training data is expensive. To overcome these challenges, this paper proposes a novel method for mixed gas classification based on analogous image representations with multiple sensor-specific channels and a convolutional neural network (CNN) classifier. The proposed method maps a gas sensor array into a multichannel image with data augmentation, and then utilizes a CNN for feature extraction from such images. The proposed method was validated using public mixture gas data from the UCI machine learning repository and real laboratory experiments. The experimental results indicate that it outperforms the existing classification approaches in terms of the balanced accuracy and weighted F1 scores. Additionally, we evaluated the performance of the proposed method in various experimental settings in terms of data representation, data augmentation, and parameter initialization, so that practitioners can easily apply it to artificial olfactory systems

Generation of Synthetic Density Log Data Using Deep Learning Algorithm at the Golden Field in Alberta, Canada

This study proposes a deep neural network- (DNN-) based prediction model for creating synthetic log. Unlike previous studies, it focuses on building a reliable prediction model based on two criteria: fit-for-purpose of a target field (the Golden field in Alberta) and compliance with domain knowledge. First, in the target field, the density log has advantages over the sonic log for porosity analysis because of the carbonate depositional environment. Considering the correlation between the density and sonic logs, we determine the sonic log as input and the density log as output for the DNN. Although only five wells have a pair of training data in the field (i.e., sonic and density logs), we obtain, based on geological knowledge, 29 additional wells sharing the same depositional setting in the Slave Point Formation. After securing the data, 5 wells among the 29 wells are excluded from dataset during preprocessing procedures (elimination of abnormal data and min–max normalisation) to improve the prediction model. Two cases are designed according to usage of the well information at the target field. Case 1 uses only 23 of the surrounding wells to train the prediction model, and another surrounding well is used for model testing. In Case 1, the Levenberg–Marquardt algorithm shows a fast and reliable performance and the numbers of neurons in the two hidden layers are of 45 and 14, respectively. In Case 2, the 24 surrounding wells and four wells from the target field are used to train the DNN with the optimised parameters from Case 1. The synthetic density logs from Case 2 mitigate an underestimation problem in Case 1 and follow the overall trend of the true density logs. The developed prediction model utilises the sonic log for generating the synthetic density log, and a reliable porosity model will be created by combining the given and the synthetic density logs

Chemistry-informed machine learning: Using chemical property features to improve gas classification performance

Chemical recognition using machine learning based on detection by gas sensors relies on the accuracy and sensitivity of the sensors at capturing the key features of target classes. In some cases, however, the electronic signal transduced from the detection of analytes does not completely represent the key attributes, resulting in inaccurate classification results when trained from signal data alone. To overcome this shortcoming, we propose a novel ???chemistry-informed??? machine learning framework composed of two modules. From available sensor response data, Module 1 identifies and predicts the chemical properties of the analytes that give rise to the sensitivity and selectivity of the sensors, and Module 2 performs final classifications using the dataset concatenating predicted chemical properties and raw sensor responses. To evaluate the performance and generalizability of our methodology, we conducted experiments with three gas sensor array datasets for gas detection. In all the cases, the performance of gas species classification was improved when the raw features were combined with the predicted chemical property features. The main contribution of our framework is that it bridges the gap between the gas sensor signals and the target analytes, thereby improving classification performance beyond that of models trained exclusively on sensor response data

Protective effect of Korean Red Ginseng extract against Helicobacter pylori-induced gastric inflammation in Mongolian gerbils

Helicobacter pylori-induced gastric inflammation includes induction of inflammatory mediators interleukin (IL)-8 and inducible nitric oxide synthase (iNOS), which are mediated by oxidant-sensitive transcription factor NF-κB. High levels of lipid peroxide (LPO) and increased activity of myeloperoxidase (MPO), a biomarker of neutrophil infiltration, are observed in H. pylori-infected gastric mucosa. Panax ginseng Meyer, a Korean herb medicine, is widely used in Asian countries for its biological activities including anti-inflammatory efficacy. The present study aims to investigate whether Korean Red Ginseng extract (RGE) inhibits H. pylori-induced gastric inflammation in Mongolian gerbils. One wk after intragastric inoculation with H. pylori, Mongolian gerbils were fed with either the control diet or the diet containing RGE (200 mg RGE/gerbil) for 6 wk. The following were determined in gastric mucosa: the number of viable H. pylori in stomach; MPO activity; LPO level; mRNA and protein levels of keratinocyte chemoattractant factor (KC, a rodent IL-8 homolog), IL-1β, and iNOS; protein level of phospho-IκBα (which reflects the activation of NF-κB); and histology. As a result, RGE suppressed H. pylori-induced mRNA and protein levels of KC, IL-1β, and iNOS in gastric mucosa. RGE also inhibited H. pylori-induced phosphorylation of IκBα and increases in LPO level and MPO activity of gastric mucosa. RGE did not affect viable H. pylori colonization in the stomach, but improved the histological grade of infiltration of polymorphonuclear neutrophils, intestinal metaplasia, and hyperplasia. In conclusion, RGE inhibits H. pylori-induced gastric inflammation by suppressing induction of inflammatory mediators (KC, IL-1β, iNOS), MPO activity, and LPO level in H. pylori-infected gastric mucosa

Submerged versus side-stream osmotic membrane bioreactors using an outer-selective hollow fiber osmotic membrane for desalination

This study investigated the comparative performances, fouling mitigation efficiencies, and operational costs of side-stream and submerged osmotic membrane bioreactors (OMBR) systems using an outer-selective hollow fiber thin-film composite forward osmosis (OSHF TFC FO) membrane. Generally, the submerged OMBR system exhibited the higher fouling mitigation efficiency and a much slower flux decline rate when compared with that of the side-stream system. The side-stream OMBR system demonstrated an initial water flux of 15.8 LMH using 35 g/L NaCl as the draw solution, which was 2-fold higher than that of the submerged system when at its optimal performance. However, salinity accumulation in the reactor of the side-stream system was at a higher rate than for the submerged OMBR system. Both OMBR systems showed comparably high pollutant removal efficiencies over the experimental period. Annual operating costs for the side-stream OMBR system has been estimated to be 38% higher (OPEX) than for the submerged system. Membrane replacement cost accounted for the majority of the OPEX, over 89%, while the energy consumption and cleaning costs only accounted for relatively small portions. Therefore, reducing the membrane replacement cost is critical to realizing the commercial viability of the submerged OMBR system.This work was supported by the Australian Research Council (ARC) Industrial Transformation Research Hub ( IH170100009 ), the Qatar National Research Fund (QNRF) [ NPRP 9-052-2-020 ] and the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. T21-604/19-R ).Scopu