4 edition of Application of neural networks to the modeling of water treatment particulate removal processes found in the catalog.
Application of neural networks to the modeling of water treatment particulate removal processes
Thesis (M.Sc.) -- University of Toronto, 1998.
|Series||Canadian theses = -- Thèses canadiennes|
|The Physical Object|
|Pagination||3 microfiches : negative. --|
Processes (ISSN ; CODEN: PROCCO) is an international peer-reviewed open access journal on processes in chemistry, biochemistry, biology, materials, and related process/systems engineering research fields. The Systems and Control Division of the Canadian Society for Chemical Engineering (CSChE S&C Division) and the Brazilian Association of Chemical . The Application of Neural Network Models on the Estimation of Critical Performances of the Water-Alternating-Gas Injection Process. Si Le Van, Bo Hyun Chon* Department of Energy Resources Engineering, Inha University, Inharo, Nam-gu, Incheon , Korea. *Corresponding author. Abstract. Applying hybrid smart tools to predict uncertain.
A Hybrid Neural Network-First Principles Approach to Process Modeling Dimitris C. Psichogios and Lyle H. Ungar Dept. of Chemical Engineering, University of Pennsylvania, Philadelphia, PA A hybrid neural network-first principles modeling scheme is developed and used to model a fedbatch bioreactor. C. Aydiner, I. Demir, E. Yildiz, Modeling of flux decline in crossflow microfiltration using neural networks: the case of phosphate removal, J. Membr. Sci., () 53– S. Curcio, V. Calabrò, G. Iorio, Reduction and control of flux decline in cross-flow membrane processes modeled by artificial neural networks and hybrid systems.
aspects of importance when Modelling Biofilm Processes. Although the different processes are related and in practice normally combined within a wastewater treatment plant, I believe that this division will make it easier for the reader to locate areas related to his/her special interests. Wu et al., Application of artificial neural networks to forecasting water quality 1. INTRODUCTION A critical goal of all water utilities is to provide safe drinking water to consumers. This goal is often achieved (in part) by using disinfectants to remove harmful micro-organisms contained in drinking water.
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Linear modeling, classiﬁcation, association, control, and others —all of which ﬁnd application in hydrology today. A signiﬁ-cant growth in the interest of this computational mechanism has occurred since Rumelhart et al.
() developed a math-ematically rigorous theoretical framework for neural networks. Artificial Neural Network Modeling of Water and Wastewater Treatment Processes (Computer Science, Technology and Applications) [Khataee, Ali R., Kasiri, Masoud B.] on *FREE* shipping on qualifying offers. Artificial Neural Network Modeling of Water and Wastewater Treatment Processes (Computer Science, Technology and Applications)Cited by: 7.
The examples of ANN modeling of photocatalytic water and wastewater treatment processes are summarized in Table cial neural networks have been used for modeling of TiO 2 photocatalytic degradation of 2,4-dihydroxybenzoic acid, chosen as a model water contaminant, as a function of the concentrations of substrate and catalyst.
The Cited by: modeling, classiﬁcation, association, and control. Although the idea of artiﬁcial neural networks was proposed by McCulloch and Pitts () over ﬁfty years ago, the development of ANN techniques has experienced a renaissance only in the last de-cade due to Hopﬁeld’s effort (Hopﬁeld ) in iterative auto-associable neural networks.
In this study, an Artificial Neural Networks (ANN) based approach for modeling and predicting wastewater treatment plant reliability using Activated sludge (AS) was proposed. In the past few years, artificial neural networks (ANNs) have been used in describing and modelling wastewater treatment processes.
Artificial neural network models can be. A schematic view of Neural Network and its constituent layers Modeling dehydration of organic compounds by use of Neural Network (volumetric flow, pressure and temperature) as well as the flux characteristics (the fluxes are the network output) on the efficiency of dehydration process.
One ANN was designed for analysis of the flux parameter. The modelling of water treatment processes is challenging because of its complexity, nonlinearity, and numerous contributory variables, but it is of particular importance since water of low quality causes health-related and economic problems which have a considerable impact on people’s daily lives.
Linear and nonlinear modelling methods are used here to model residual aluminium Cited by: Complexation-microfiltration process for removal of heavy metal ions such as lead, cadmium and zinc from water had been investigated.
Two soluble derivates of cellulose was selected as complexing agents. The dependence of the removal efficiency from the operating parameters (pH value, pressure, concentration of metal ion, concentration of complexing agent Cited by: 3.
The application of artificial intelligence techniques to the operation of water and wastewater treatment plants in recent years is reviewed. The expert system approach is the most prevalent, but difficulties in acquiring and representing knowledge of the complex phenomena in these plants have led to the search for additional by: OPTIMIZATION OF DRINKING WATER TREATMENT PROCESSES USING ARTIFICIAL NEURAL NETWORK Ogwueleka, T.C.
1 and Ogwueleka, F.N. 2 1 Department of Civil Engineering, University of Abuja 2 Department of Mathematics, Statistics & Computer Science, University of Abuja. ABSTRACT Drinking water treatment is the process of removing File Size: KB.
An artificial neural network (ANN) is a flexible mathematical structure which is capable of identifying complex nonlinear relationships between input and output data sets. ANN models have been found useful and efficient, particularly in problems for which the characteristics of the processes are difficult to describe using physical by: NEURAL NETWORK IDENTIFICATION OF N-REMOVAL PROCESS IN WASTE WATER TREATMENT PLANTS.
Alejandro Goldar, Silvana R. Revollar, Rosalba Lamanna, Pastora Vega1 2 1 2. 1Processes and Systems Department, Universiry of Simón Bolívar, Caracas, Venezuela. [email protected] A. Jain and S. Indurthy, Comparative analysis of event-based rainfall-runoff modeling techniques-deterministic, statistical, and artificial neural networks, Journal of Hydrologic Engineering, ASCE, 8(2), 93–98 ().Cited by: 5.
Neural Networks for Hydrological Modeling - CRC Press Book A new approach to the fast-developing world of neural hydrological modelling, this book is essential reading for academics and researchers in the fields of water sciences, civil.
modeling of hot rolling processes using this technique, frequently getting good results. However, practical applications of this technique in the field of hot rolling are very scarce, mainly due to the lack of confidence about its performance.
This distrust on. This paper presents the use of artificial neural networks (ANNs) as a viable means of forecasting water quality parameters. A review of ANNs is given, and a case study is presented in which ANN methods are used to forecast salinity in the River Murray at Murray Bridge (South Australia) 14 days in by: Simulation of the hydrology catchment of an arid watershed using artificial neural networks.
Lake water quality monitoring using traditional water sampling and laboratory analyses is very expensive and time consuming. Application of neural networks to predict water quality using satellite imagery data has a potential to make the water qual-ity determination process cost-effective, quick, and feasible.
Recurrent High Order Neural Network Modeling for Wastewater Treatment Process system . The most commonly used type of networks in the wastewater treatment process of modelling and prediction is the feed-forward neural networks.
For neural networks theory in detail, and improve the training. on the concept of artificial neural networks (ANNs) (or simply neural networks), a widely used application of artificial intelligence that has shown quite a promise in a variety of applications in engineering, pattern recognition, and financial market analysis.
Artificial Neural Network Technology The ANNs are mathematical modeling tools that.The book presents the application of neural networks to the modelling and fault diagnosis of industrial processes. The ﬁrst two chapters focus on the funda-mental issues such as the basic deﬁnitions and fault diagnosis schemes as well as a survey on ways of using neural networks in diﬀerent fault diagnosis strategies.A new approach to the fast-developing world of neural hydrological modelling, this book is essential reading for academics and researchers in the fields of water sciences, civil engineering, hydrology and physical geography.
Each chapter has been written by one or more eminent experts working in various fields of hydrological : Hardcover.