1,721,051 research outputs found
Modelling activated sludge wastewater treatment plants using artificial intelligence techniques (fuzzy logic and neural networks)
Full text linkActivated 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
Studying the impact of construction dewatering discharges to the urban storm drainage network(s) of Doha city using infoworks integrated catchment modeling (ICM)
No full textThe discharge of construction dewatering flows to the storm drainage network for disposal is a common activity in Qatar. The Dupuit empirical approach was utilized to establish various hypothetical dewatering scenarios on the basis of site classifications, which were modeled on 4 Case Study Areas of Doha’s Existing Surface Drainage Network in order to study the impact of dewatering discharge against an established baseline. The simulations were undertaken using InfoWorks Integrated Catchment Modeling (ICM) software for critical and non-critical rainfall events. The results indicated significant localized flooding in excess of the baseline conditions for scenarios exceeding 0.5 m3/sec flows, while individual catchments demonstrated variations and sensitivities on the basis of catchment properties and rainfall events. It is evident that dewatering discharge under unpredictable rainfall events poses various levels of risk to the city’s infrastructure, which is further exacerbated due to the massive scale of construction activity in the country and the rising ground water table in Greater Doha Area basin
Rabee's ten-step guide to becoming an Advance HE Senior Fellow
No full textI was introduced to Advance HE (then the Higher Education Authority) in 2015 when I completed a postgraduate certificate in academic practice and, with it, was awarded a Fellowship.Achieving professional recognition motivated me to keep thinking about my teaching role and apply to be a Senior Fellow: to reflect on my growing leadership responsibilities and the opportunities I have to influence other colleagues in Teaching and Learning practice.I knew the application process for Senior Fellowship would require time and effort in reflection and gathering evidence; nevertheless, I was determined and motivated to do it. Obviously, I was very familiar with the Professional Standards Framework (PSF) – which underpins the fellowship awards –exploring it in detail in the context of my 20 years of academic experience and role; I was confident that I had the evidence of practice and leadership for a successful application
Modelling activated sludge wastewater treatment plants using artificial intelligence techniques (fuzzy logic and neural networks)
No full textActivated 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.EThOS - Electronic Theses Online ServiceDamascus UniversityGBUnited Kingdo
Rabee's ten-step guide to becoming an Advance HE Senior Fellow
No full textI was introduced to Advance HE (then the Higher Education Authority) in 2015 when I completed a postgraduate certificate in academic practice and, with it, was awarded a Fellowship.Achieving professional recognition motivated me to keep thinking about my teaching role and apply to be a Senior Fellow: to reflect on my growing leadership responsibilities and the opportunities I have to influence other colleagues in Teaching and Learning practice.I knew the application process for Senior Fellowship would require time and effort in reflection and gathering evidence; nevertheless, I was determined and motivated to do it. Obviously, I was very familiar with the Professional Standards Framework (PSF) – which underpins the fellowship awards –exploring it in detail in the context of my 20 years of academic experience and role; I was confident that I had the evidence of practice and leadership for a successful application
Features extraction from primary clarifier data using unsupervised neural networks (Kohonen Self Organising Map)
No full textGoing Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Knowledge discovery from activated sludge process using unsupervised neural networks (Kohonen Self-Organizing Map)
No full textModelling Activated Sludge Wastewater Treatment Plants Using Artificial Intelligence Techniques (Fuzzy Logic and Neural Networks)
No full textActivated 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
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