14,806 research outputs found
Data validation and missing data reconstruction using self-organizing map for water treatment
Applications in the water treatment domain
generally rely on complex sensors located at remote sites.
The processing of the corresponding measurements for
generating higher-level information such as optimization of
coagulation dosing must therefore account for possible
sensor failures and imperfect input data. In this paper, selforganizing
map (SOM)-based methods are applied to
multiparameter data validation and missing data reconstruction
in a drinking water treatment. The SOM is a
special kind of artificial neural networks that can be used
for analysis and visualization of large high-dimensional
data sets. It performs both in a nonlinear mapping from a
high-dimensional data space to a low-dimensional space
aiming to preserve the most important topological and
metric relationships of the original data elements and, thus,
inherently clusters the data. Combining the SOM results
with those obtained by a fuzzy technique that uses marginal
adequacy concept to identify the functional states (normal
or abnormal), the SOM performances of validation and
reconstruction process are tested successfully on the
experimental data stemming from a coagulation process
involved in drinking water treatment
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation
Faulty field sensors cause unreliability in the observed data that needed to calibrate and assess hydrology models. However, it is illogical to ignore abnormal or missing values if there are limited data available. This study addressed this problem by applying data imputation to replace incorrect values and recover missing streamflow information in the dataset of the Samho gauging station at Taehwa River (TR), Korea from 2004 to 2006. Soil and Water Assessment Tool (SWAT) and two machine learning techniques, Artificial Neural Network (ANN) and Self Organizing Map (SOM), were employed to estimate streamflow using reasonable flow datasets of Samho station from 2004 to 2009. The machine learning models were generally better at capturing high flows, while SWAT was better at simulating low flows.open
Multi-tier framework for the inferential measurement and data-driven modeling
A framework for the inferential measurement and data-driven modeling has been proposed and assessed in several real-world application domains. The architecture of the framework has been structured in multiple tiers to facilitate extensibility and the integration of new components. Each of the proposed four tiers has been assessed in an uncoupled way to verify their suitability. The first tier, dealing with exploratory data analysis, has been assessed with the characterization of the chemical space related to the biodegradation of organic chemicals. This analysis has established relationships between physicochemical variables and biodegradation rates that have been used for model development. At the preprocessing level, a novel method for feature selection based on dissimilarity measures between Self-Organizing maps (SOM) has been developed and assessed. The proposed method selected more features than others published in literature but leads to models with improved predictive power. Single and multiple data imputation techniques based on the SOM have also been used to recover missing data in a Waste Water Treatment Plant benchmark. A new dynamic method to adjust the centers and widths of in Radial basis Function networks has been proposed to predict water quality. The proposed method outperformed other neural networks. The proposed modeling components have also been assessed in the development of prediction and classification models for biodegradation rates in different media. The results obtained proved the suitability of this approach to develop data-driven models when the complex dynamics of the process prevents the formulation of mechanistic models. The use of rule generation algorithms and Bayesian dependency models has been preliminary screened to provide the framework with interpretation capabilities. Preliminary results obtained from the classification of Modes of Toxic Action (MOA) indicate that this could be a promising approach to use MOAs as proxy indicators of human health effects of chemicals.Finally, the complete framework has been applied to three different modeling scenarios. A virtual sensor system, capable of inferring product quality indices from primary process variables has been developed and assessed. The system was integrated with the control system in a real chemical plant outperforming multi-linear correlation models usually adopted by chemical manufacturers. A model to predict carcinogenicity from molecular structure for a set of aromatic compounds has been developed and tested. Results obtained after the application of the SOM-dissimilarity feature selection method yielded better results than models published in the literature. Finally, the framework has been used to facilitate a new approach for environmental modeling and risk management within geographical information systems (GIS). The SOM has been successfully used to characterize exposure scenarios and to provide estimations of missing data through geographic interpolation. The combination of SOM and Gaussian Mixture models facilitated the formulation of a new probabilistic risk assessment approach.Aquesta tesi proposa i avalua en diverses aplicacions reals, un marc general de treball per al desenvolupament de sistemes de mesurament inferencial i de modelat basats en dades. L'arquitectura d'aquest marc de treball s'organitza en diverses capes que faciliten la seva extensibilitat així com la integració de nous components. Cadascun dels quatre nivells en que s'estructura la proposta de marc de treball ha estat avaluat de forma independent per a verificar la seva funcionalitat. El primer que nivell s'ocupa de l'anàlisi exploratòria de dades ha esta avaluat a partir de la caracterització de l'espai químic corresponent a la biodegradació de certs compostos orgànics. Fruit d'aquest anàlisi s'han establert relacions entre diverses variables físico-químiques que han estat emprades posteriorment per al desenvolupament de models de biodegradació. A nivell del preprocés de les dades s'ha desenvolupat i avaluat una nova metodologia per a la selecció de variables basada en l'ús del Mapes Autoorganitzats (SOM). Tot i que el mètode proposat selecciona, en general, un major nombre de variables que altres mètodes proposats a la literatura, els models resultants mostren una millor capacitat predictiva. S'han avaluat també tot un conjunt de tècniques d'imputació de dades basades en el SOM amb un conjunt de dades estàndard corresponent als paràmetres d'operació d'una planta de tractament d'aigües residuals. Es proposa i avalua en un problema de predicció de qualitat en aigua un nou model dinàmic per a ajustar el centre i la dispersió en xarxes de funcions de base radial. El mètode proposat millora els resultats obtinguts amb altres arquitectures neuronals. Els components de modelat proposat s'han aplicat també al desenvolupament de models predictius i de classificació de les velocitats de biodegradació de compostos orgànics en diferents medis. Els resultats obtinguts demostren la viabilitat d'aquesta aproximació per a desenvolupar models basats en dades en aquells casos en els que la complexitat de dinàmica del procés impedeix formular models mecanicistes. S'ha dut a terme un estudi preliminar de l'ús de algorismes de generació de regles i de grafs de dependència bayesiana per a introduir una nova capa que faciliti la interpretació dels models. Els resultats preliminars obtinguts a partir de la classificació dels Modes d'acció Tòxica (MOA) apunten a que l'ús dels MOA com a indicadors intermediaris dels efectes dels compostos químics en la salut és una aproximació factible.Finalment, el marc de treball proposat s'ha aplicat en tres escenaris de modelat diferents. En primer lloc, s'ha desenvolupat i avaluat un sensor virtual capaç d'inferir índexs de qualitat a partir de variables primàries de procés. El sensor resultant ha estat implementat en una planta química real millorant els resultats de les correlacions multilineals emprades habitualment. S'ha desenvolupat i avaluat un model per a predir els efectes carcinògens d'un grup de compostos aromàtics a partir de la seva estructura molecular. Els resultats obtinguts desprès d'aplicar el mètode de selecció de variables basat en el SOM milloren els resultats prèviament publicats. Aquest marc de treball s'ha usat també per a proporcionar una nova aproximació al modelat ambiental i l'anàlisi de risc amb sistemes d'informació geogràfica (GIS). S'ha usat el SOM per a caracteritzar escenaris d'exposició i per a desenvolupar un nou mètode d'interpolació geogràfica. La combinació del SOM amb els models de mescla de gaussianes dona una nova formulació al problema de l'anàlisi de risc des d'un punt de vista probabilístic
Application of Computational Intelligence Techniques to Process Industry Problems
In the last two decades there has been a large progress in the computational
intelligence research field. The fruits of the effort spent on the research in the discussed
field are powerful techniques for pattern recognition, data mining, data modelling, etc.
These techniques achieve high performance on traditional data sets like the UCI
machine learning database. Unfortunately, this kind of data sources usually represent
clean data without any problems like data outliers, missing values, feature co-linearity,
etc. common to real-life industrial data. The presence of faulty data samples can have
very harmful effects on the models, for example if presented during the training of the
models, it can either cause sub-optimal performance of the trained model or in the worst
case destroy the so far learnt knowledge of the model. For these reasons the application
of present modelling techniques to industrial problems has developed into a research
field on its own. Based on the discussion of the properties and issues of the data and the
state-of-the-art modelling techniques in the process industry, in this paper a novel
unified approach to the development of predictive models in the process industry is
presented
A Survey of Deep Learning Methods for WTP Control and Monitoring
Drinking water is vital for everyday life. We are dependent on water for everything from cooking to sanitation. Without water, it is estimated that the average, healthy human won’t live more than 3–5 days. The water is therefore essential for the productivity of our community. The water treatment process (WTP) may vary slightly at different locations, depending on the technology of the plant and the water it needs to process, but the basic principles are largely the same. As the WTP is complex, traditional laboratory methods and mathematical models have limitations to optimize this type of operations. These pose challenges for water-sanitation services and research community. To overcome this matter, deep learning is used as an alternative to provide various solutions in WTP optimization. Compared to traditional machine learning methods and because of its practicability, deep learning has a strong learning ability to better use data sets for data mining and knowledge extraction. The aim of this survey is to review the existing advanced approaches of deep learning and their applications in WTP especially in coagulation control and monitoring. Besides, we also discuss the limitations and prospects of deep learning
Modelling activated sludge wastewater treatment plants using artificial intelligence techniques (fuzzy logic and neural networks)
Activated sludge process (ASP) is the most commonly used biological wastewater
treatment system. Mathematical modelling of this process is important for improving its
treatment efficiency and thus the quality of the effluent released into the receiving water
body. This is because the models can help the operator to predict the performance of the
plant in order to take cost-effective and timely remedial actions that would ensure
consistent treatment efficiency and meeting discharge consents. However, due to the
highly complex and non-linear characteristics of this biological system, traditional
mathematical modelling of this treatment process has remained a challenge.
This thesis presents the applications of Artificial Intelligence (AI) techniques for
modelling the ASP. These include the Kohonen Self Organising Map (KSOM),
backpropagation artificial neural networks (BPANN), and adaptive network based fuzzy
inference system (ANFIS). A comparison between these techniques has been made and
the possibility of the hybrids between them was also investigated and tested.
The study demonstrated that AI techniques offer viable, flexible and effective modelling
methodology alternative for the activated sludge system. The KSOM was found to be
an attractive tool for data preparation because it can easily accommodate missing data
and outliers and because of its power in extracting salient features from raw data. As a
consequence of the latter, the KSOM offers an excellent tool for the visualisation of
high dimensional data. In addition, the KSOM was used to develop a software sensor to
predict biological oxygen demand. This soft-sensor represents a significant advance in
real-time BOD operational control by offering a very fast estimation of this important
wastewater parameter when compared to the traditional 5-days bio-essay BOD test
procedure. Furthermore, hybrids of KSOM-ANN and KSOM-ANFIS were shown to
result much more improved model performance than using the respective modelling
paradigms on their own.Damascus Universit
Contemporary disaster management framework quantification of flood risk in rural Lower Shire Valley, Malawi
Despite floods and droughts accounting for 80% and 70% disaster related deaths and
economic loss respectively in Sub-Saharan Africa (SSA), there have been very few
attempts in SSA to quantify flood-related vulnerability and risk, especially as they relate
to the rural poor. This thesis quantifies and profiles the flood risk of rural communities
in SSA focusing on the Lower Shire Valley, Malawi. Given the challenge of hydrometeorological
data quality in SSA to support quantitative flood risk assessments, the
work first reconstructs and extends hydro-meteorological data using Artificial Neural
Networks (ANNs). These data then formed the input to a coupled IPCC-Sustainable
Development Frameworks for quantifying flood vulnerability and risk. Flood risk was
obtained by integrating hazard and vulnerability. Flood hazard was characterised in
terms of flood depth and inundation area obtained through hydraulic modelling of the
catchment with Lisflood-FP, while the vulnerability was indexed through analysis of
exposure, susceptibility and capacity and linked to social, economic, environmental and
physical perspectives. Data on these were collected through structured interviews
carried out with the communities and stakeholders in the valley and later analysed. The
implementation of the entire analysis within a GIS environment enabled the
visualisation of spatial variability in flood risk in the valley. The results show
predominantly medium levels in hazardousness, vulnerability and risk. The
vulnerability is dominated by a high to very high susceptibility component largely
because of the high to very high socio-economic and environmental vulnerability.
Economic and physical capacities tend to be predominantly low but social capacity is
significantly high, resulting in overall medium levels of capacity-induced vulnerability.
Exposure manifests as medium. Both the vulnerability and risk showed marginal spatial
variability. Given all this, the thesis argues for the need to mainstream disaster reduction
in the rather plethoric conventional socio-economic developmental programmes in SSA.
Additionally, the low spatial variability in both the risk and vulnerability in the valley
suggests that any such interventions need to be valley-wide to be effective
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