113 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
Teaching hydrology: A case study of teaching and learning
No full textThe aim of this paper is to highlight established hydrology teaching methods and evaluate potential teaching enhancements. The results of applying a student-centered approach to hydrology students demonstrates that it increases student's engagement with materials, critical thinking and problem solving skills. However, this approach does not improve the conceptual understanding of hydrology for all students, as some students show resistance to a student-centered approach. Furthermore, conclusions demonstrated that not all students are ready and eager to engage in this deep learning approach employed to enhance their educational experience
Studying the effects of integrating private water tanks on the performance of water distribution networks
Full text linkPrivate water tanks in Water Distribution Networks are essential in providing additional storage
to satisfy the consumer needs. These tanks, that are located at the rooftop or underground area of
the building, can affect the empirical parameters of the network, including pressure, flowrate,
pump’s power consumption, water age and chlorine concentration. However, they are often
neglected in design stages for the sake of quicker simulations and simplified calculations. This
oversight compromises the accuracy of network’s simulation by omitting crucial tank parameters,
like orifice inlet and volume capacity, which in worst cases, can cause pipe burst or insufficient
supply. Hence, this prompts to investigate the impact of adding private tanks on the network’s
performance.
This study employed a quantative research using pressure-driven analysis to integrate private
tanks in network models. This integration is carried out by incoperating mass balance models at
the nodes to mimic the filling and emptying condition of the private tank, which is extended to
the hydraulic model of the network, by modifying the energy conservation and mass conservation
equations. In total, three real-time networks from Dubai and Abu Dhabi, United Arab Emirates,
along with five sample networks, were built within hydraulic tools, EPANET and WDnetXL,
using the data from local water distribution codes. As private tanks are mandatory only in
resedential buildings, data from Supervisory Control And Data Acquisition and water meter
readings were also used to build consumption pattern to model the filling and emptying behaviour
of these tanks during simulation.
The results show that incorporating private tanks in a water system affects the pressure and flow,
where having larger tank’s inlet diameter, the flowrate in the pipe increases, which increases the
head loss in the pipes. This raises the pump’s power consumption and maintenance costs, due to
a higher risk of pipe bursts because of more flow. Ultimately, it can further result in decrease in
the pressure at the downstream nodes by 50% due to higher head losses at the upstream.
Furthermore, large tank volume would increase the overall flow from the pumps and water age
as large tanks tend to close the orifice for a longer duration, which increases the water age in pipes
and increases the risk of contamination in pipes. As such, this study advocates using the smallest
values for orifice diameter and tank volume sizes that achieves the network reliability.
Additionally, this research also improves the network performance by reducing the pump’s
running time by using water supplied from private tanks. This decreases the overall pump’s
carbon footprint by 40% along with leakage rates, when compared to networks without private
tank. Ultimately, the findings of this research would provide an accurate depiction during design,
minimising the risk of overdesign by avoiding unnecessary parameter changes by the designers
and prompting conservation strategies using private tanks
Review of Latest Advances in Nature-Inspired Algorithms for Optimization of Activated Sludge Processes
No full textThe activated sludge process (ASP) is the most widely used biological wastewater treatment system. Advances in research have led to the adoption of Artificial Intelligence (AI), in particular, Nature-Inspired Algorithm (NIA) techniques such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) to optimize treatment systems. This has aided in reducing the complexity and computational time of ASP modelling. This paper covers the latest NIAs used in ASP and discusses the advantages and limitations of each algorithm compared to more traditional algorithms that have been utilized over the last few decades. Algorithms were assessed based on whether they looked at real/ideal treatment plant (WWTP) data (and efficiency) and whether they outperformed the traditional algorithms in optimizing the ASP. While conventional algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) were found to be successfully employed in optimization techniques, newer algorithms such as Whale Optimization Algorithm (WOA), Bat Algorithm (BA), and Intensive Weed Optimization Algorithm (IWO) achieved similar results in the optimization of the ASP, while also having certain unique advantages
Analyzing the Impact of Orifice Size and Retention Time in Private Tanks on Water Quality Indicators in Distribution Networks
No full textChlorine decay in water distribution networks is significantly affected by the presence of private storage tanks, particularly due to the orifice size and retention time, which influence both hydraulic flow behavior and water residence time. This study introduces a novel simulation framework that integrates pressure-driven analysis with a first-order kinetic model for chlorine decay, implemented using the WQnetXL tool and validated through simulations in EPANET. Two schematic models, including a real-world case from Dubai, were analyzed under varying orifice sizes and retention times. Results show that larger orifices lead to higher initial chlorine concentrations during tank filling due to increased flow rates, but result in a rapid decline in chlorine levels once the tanks reach full capacity. In contrast, smaller orifices maintain more stable chlorine concentrations over time due to prolonged inflow durations. Extended retention times further delay tank filling and sustain higher chlorine levels until the system transitions to behavior typical of demand-driven analysis. A reliability assessment of the Dubai case study indicated that incorporating private tanks can result in deviations in chlorine concentration of up to 30 percent compared to conventional models. This approach addresses a key gap in conventional network modeling by quantifying the influence of decentralized storage on disinfection effectiveness and network reliability
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 text- …
