Application of neural networks in membrane separation

2020 ◽  
Vol 36 (2) ◽  
pp. 265-310 ◽  
Author(s):  
Morteza Asghari ◽  
Amir Dashti ◽  
Mashallah Rezakazemi ◽  
Ebrahim Jokar ◽  
Hadi Halakoei
Keyword(s):  
Neural Networks ◽  
Chemical Engineering ◽  
Membrane Separation ◽  
Mechanistic Model ◽  
Computing Time ◽  
Permeate Flux ◽  
Practical Applications ◽  
Advantages And Disadvantages ◽  
Wide Range ◽  
Physical And Chemical

AbstractArtificial neural networks (ANNs) as a powerful technique for solving complicated problems in membrane separation processes have been employed in a wide range of chemical engineering applications. ANNs can be used in the modeling of different processes more easily than other modeling methods. Besides that, the computing time in the design of a membrane separation plant is shorter compared to many mass transfer models. The membrane separation field requires an alternative model that can work alone or in parallel with theoretical or numerical types, which can be quicker and, many a time, much more reliable. They are helpful in cases when scientists do not thoroughly know the physical and chemical rules that govern systems. In ANN modeling, there is no requirement for a deep knowledge of the processes and mathematical equations that govern them. Neural networks are commonly used for the estimation of membrane performance characteristics such as the permeate flux and rejection over the entire range of the process variables, such as pressure, solute concentration, temperature, superficial flow velocity, etc. This review investigates the important aspects of ANNs such as methods of development and training, and modeling strategies in correlation with different types of applications [microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), electrodialysis (ED), etc.]. It also deals with particular types of ANNs that have been confirmed to be effective in practical applications and points out the advantages and disadvantages of using them. The combination of ANN with accurate model predictions and a mechanistic model with less accurate predictions that render physical and chemical laws can provide a thorough understanding of a process.

scholarly journals Proanthocyanidins: Oligomeric Structures with Unique Biochemical Properties and Great Therapeutic Promise

2012 ◽  
Vol 7 (3) ◽  
pp. 1934578X1200700 ◽  
Author(s):  
Zhanjie Xu ◽  
Peng Du ◽  
Peter Meiser ◽  
Claus Jacob
Keyword(s):  
Protein Interactions ◽  
Biological Activities ◽  
Antioxidant Properties ◽  
Chemical Properties ◽  
Cancer Chemoprevention ◽  
Biochemical Properties ◽  
Synthetic Methods ◽  
Practical Applications ◽  
Wide Range ◽  
Physical And Chemical

Proanthocyanidins represent a unique class of oligomeric and polymeric secondary metabolites found ubiquitously and in considerable amounts in plants and some algae. These substances exhibit a range of rather surprising physical and chemical properties which, once applied to living organisms, are translated into a multitude of biological activities. The latter include antioxidant properties, cancer chemoprevention, anti-inflammatory and anti-diabetic effects as well as some exceptional, yet highly interesting activities, such as anti-nutritional and antimicrobial activity. Despite the wide range of activities and possible medical/agricultural applications of proanthocyanidins, many questions still remain, including issues related to bioavailability, metabolism and the precise biochemical, extra- and intracellular targets and mode(s) of action of these highly potent materials. Among the various physical and chemical interactions of such substances, strong binding to proteins appears to form the basis of many of their biological activities. Once easy-to-use synthetic methods to produce appropriate quantities of pure proanthocyanidins are available, it will be possible to identify the prime biological targets of these oligomers, study oligomer-protein interactions in more detail and develop possible practical applications in medicine and agriculture.

scholarly journals A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Yiming Jiang ◽  
Chenguang Yang ◽  
Jing Na ◽  
Guang Li ◽  
Yanan Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Human Robot Interaction ◽  
Learning Tools ◽  
Robot Interaction ◽  
Practical Applications ◽  
Powerful Learning ◽  
Wide Range ◽  
Complex Nonlinear Systems ◽  
And Control

As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.

scholarly journals Characteristics of Some Selected Methods of Rare Earth Elements Recovery from Coal Fly Ashes

Metals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 142
Author(s):  
Aleksandra Rybak ◽  
Aurelia Rybak
Keyword(s):  
Rare Earth ◽  
Rare Earth Elements ◽  
Membrane Separation ◽  
Process Efficiency ◽  
Fly Ashes ◽  
Acid Base ◽  
Price Forecast ◽  
Advantages And Disadvantages ◽  
Wide Range ◽  
Coal Fly Ashes

The article covers the issues related to the characteristics, application, and some methods of rare earth elements (REEs) recovery from coal fly ashes. REEs are elements with growing demand and a very wide range of application, especially when it comes to modern technologies. The conducted analysis and price forecast proved the existing upward tendency, and this confirmed the need to search for new REE sources, among industrial waste (proecological effect). The development of the REE recovery technology would involve solving several problems related to REE speciation, optimization of factors controlling their extractivity and selection of the REE separation method from obtained extraction solutions with a very extreme pH and complicated composition. The paper presented advantages and disadvantages of usually used methods of REE separation from coal fly ashes, like physical and acid–base leaching. It was also presented alternative REE recovery techniques in the form of membrane and biological methods and based on ion liquids (ILs) or chelating agents. The directions of further modifications, which will allow the efficient REE recovery were presented. The aim of this article was to propose specific solutions based on the creation of appropriate multistage method of REE recovery. It will be a combination of magnetic and size separation, acid–base leaching (including roasting in justified cases), removal of matrix elements with ILs (Al, Si, and Fe), and finally REE membrane separation, allowing one to obtain the appropriate process efficiency.

scholarly journals Brightness Invariant Deep Spectral Super-Resolution

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5789
Author(s):  
Tarek Stiebel ◽  
Dorit Merhof
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Scale Invariance ◽  
Super Resolution ◽  
Training Data ◽  
Essential Property ◽  
Practical Applications ◽  
Wide Range ◽  
Cheap Alternative ◽  
The Moment

Spectral reconstruction from RGB or spectral super-resolution (SSR) offers a cheap alternative to otherwise costly and more complex spectral imaging devices. In recent years, deep learning based methods consistently achieved the best reconstruction quality in terms of spectral error metrics. However, there are important properties that are not maintained by deep neural networks. This work is primarily dedicated to scale invariance, also known as brightness invariance or exposure invariance. When RGB signals only differ in their absolute scale, they should lead to identical spectral reconstructions apart from the scaling factor. Scale invariance is an essential property that signal processing must guarantee for a wide range of practical applications. At the moment, scale invariance can only be achieved by relying on a diverse database during network training that covers all possibly occurring signal intensities. In contrast, we propose and evaluate a fundamental approach for deep learning based SSR that holds the property of scale invariance by design and is independent of the training data. The approach is independent of concrete network architectures and instead focuses on reevaluating what neural networks should actually predict. The key insight is that signal magnitudes are irrelevant for acquiring spectral reconstructions from camera signals and are only useful for a potential signal denoising.

scholarly journals On Explaining Random Forests with SAT

2021 ◽  
Author(s):  
Yacine Izza ◽  
Joao Marques-Silva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Random Forest ◽  
Random Forests ◽  
Experimental Results ◽  
Polynomial Algorithms ◽  
Practical Applications ◽  
Heuristic Approaches ◽  
Wide Range ◽  
Sat Solver

Random Forest (RFs) are among the most widely used Machine Learning (ML) classifiers. Even though RFs are not interpretable, there are no dedicated non-heuristic approaches for computing explanations of RFs. Moreover, there is recent work on polynomial algorithms for explaining ML models, including naive Bayes classifiers. Hence, one question is whether finding explanations of RFs can be solved in polynomial time. This paper answers this question negatively, by proving that computing one PI-explanation of an RF is D^P-hard. Furthermore, the paper proposes a propositional encoding for computing explanations of RFs, thus enabling finding PI-explanations with a SAT solver. This contrasts with earlier work on explaining boosted trees (BTs) and neural networks (NNs), which requires encodings based on SMT/MILP. Experimental results, obtained on a wide range of publicly available datasets, demonstrate that the proposed SAT-based approach scales to RFs of sizes common in practical applications. Perhaps more importantly, the experimental results demonstrate that, for the vast majority of examples considered, the SAT-based approach proposed in this paper significantly outperforms existing heuristic approaches.

A New Detector System For The HB5 Stem Instrument

1985 ◽  
Vol 43 ◽  
pp. 134-135
Author(s):  
J.M. Cowley
Keyword(s):  
Dark Field ◽  
Detector System ◽  
Electron Holography ◽  
Small Specimen ◽  
Bright Field ◽  
Multiple Images ◽  
Practical Applications ◽  
Wide Range ◽  
Diffraction Patterns ◽  
Operational Modes

The HB5 STEM instrument at ASU has been modified previously to include an efficient two-dimensional detector incorporating an optical analyser device and also a digital system for the recording of multiple images. The detector system was built to explore a wide range of possibilities including in-line electron holography, the observation and recording of diffraction patterns from very small specimen regions (having diameters as small as 3Å) and the formation of both bright field and dark field images by detection of various portions of the diffraction pattern. Experience in the use of this system has shown that sane of its capabilities are unique and valuable. For other purposes it appears that, while the principles of the operational modes may be verified, the practical applications are limited by the details of the initial design.

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

Automation of Process Control in Chemical Plant

2015 ◽  
Vol 2 (1) ◽  
pp. 6-12
Author(s):  
Agus Sugiarta ◽  
Houtman P. Siregar ◽  
Dedy Loebis
Keyword(s):  
Process Control ◽  
Pid Control ◽  
Control Algorithm ◽  
Chemical Engineering ◽  
Chemical Plant ◽  
Raw Material ◽  
Flow Rates ◽  
Material Supply ◽  
Wide Range ◽  
Inherent Problem

Automation of process control in chemical plant is an inspiring application field of mechatronicengineering. In order to understand the complexity of the automation and its application requireknowledges of chemical engineering, mechatronic and other numerous interconnected studies.The background of this paper is an inherent problem of overheating due to lack of level controlsystem. The objective of this research is to control the dynamic process of desired level more tightlywhich is able to stabilize raw material supply into the chemical plant system.The chemical plant is operated within a wide range of feed compositions and flow rates whichmake the process control become difficult. This research uses modelling for efficiency reason andanalyzes the model by PID control algorithm along with its simulations by using Matlab.

The Management of Post-Tonsillectomy Morbidity in Patients With Sleep Apnoea

2019 ◽  
Vol 70 (10) ◽  
pp. 3738-3740
Keyword(s):  
Day Surgery ◽  
Sleep Apnoea ◽  
Integrative Approach ◽  
Multicenter Studies ◽  
Therapeutic Modalities ◽  
Advantages And Disadvantages ◽  
Obstructive Sleep ◽  
Wide Range ◽  
The Impact ◽  
Surgery Center

The Tonsillectomy in children or adults is an intervention commonly encountered in the ENT (Ear Nose and Throat) and Head and Neck surgeon practice. The current tendency is to perform this type of surgery in major ambulatory surgery centers. Two objectives are thus pursued: first of all, the increase of the patient quality of life through the reintegration into the family as quickly as possible and secondly, the expenses associated with continuous hospitalization are reduced. Any tertiary (multidisciplinary) sleep center must ensure the complete diagnosis and treatment (including surgery) of sleep respiratory disorders. Under these conditions the selection of patients and especially the implementation of the specific protocols in order to control the postoperative complications it becomes essential. The present paper describes our experience of tonsillectomy as treatment for selected patients with chronic rhonchopathy (snoring) and mild to moderate obstructive sleep apnoea. It was presented the impact of antibiotics protocols in reducing the main morbid outcomes following tonsillectomy, in our day surgery center. The obtained results can also be a prerequisite for the integrative approach of the patients with sleep apnoea who were recommended surgical treatment. Considering the wide range of therapeutic modalities used in sleep apnoea, each with its specific advantages and disadvantages, more extensive and multicenter studies are needed. Keywords: post-tonsillectomy morbidity, day surgery center, sleep disorders

Application of the ceramic membrane with hydrophobic skin layer to separation of activated sludge

1997 ◽  
Vol 35 (8) ◽  
pp. 137-144 ◽  
Author(s):  
Tsuyoshi Nomura ◽  
Takao Fujii ◽  
Motoyuki Suzuki
Keyword(s):  
Activated Sludge ◽  
Pore Size ◽  
Ceramic Membrane ◽  
Membrane Separation ◽  
Porous Ceramic ◽  
Porous Membrane ◽  
Skin Layer ◽  
Gas Permeation ◽  
Wide Range ◽  
Cake Layer

Porous membrane of poly(tetrafluoroethylene) (PTFE) was formed on the surface of porous ceramic tubes by means of heat treatment of the PTFE particles deposit layer prepared by filtering PTFE microparticles emulsified in aqueous phase. By means of inert gas permeation, pore size was determined and compared with scanning electron micrograph observation. Also rejection measurement of aqueous dextran solutions of wide range of molecular weights showed consistent results regarding the pore size. Since the membrane prepared by this method is stable and has unique features derived from PTFE, it is expected that the membrane has interesting applications in the field of water treatment. Membrane separation of activated sludge by this composite membrane and original ceramics membrane showed that the PTFE membrane gives better detachability of the cake layer formed on the membrane. This might be due to the hydrophobic nature of the PTFE skin layer.

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