Water scarcity is a global challenge, and one of the promising ways to mitigate the water
resource crisis is via wastewater reclamation. Reclaimed water can generate non-potable water
to substitute the use of drinking water for irrigation or industrial processes. Water quality
and aesthetics are the primary concerns in reclaimed water since undertreated water can pose
health risks, and the unpleasant colour is likely to induce public misgiving. Ammoniacal nitrogen
(NH
3-N) and colour substances exist in the reclaimed water and can severely affect the
reclaimed water quality in different ways. Chlorine is commonly used for reclaimed water disinfection
and requires precise dosing to satisfy endorsed quality standards. However, NH
3-N
consumes chlorine and affects the chlorine dosing. Colou...[
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Water scarcity is a global challenge, and one of the promising ways to mitigate the water
resource crisis is via wastewater reclamation. Reclaimed water can generate non-potable water
to substitute the use of drinking water for irrigation or industrial processes. Water quality
and aesthetics are the primary concerns in reclaimed water since undertreated water can pose
health risks, and the unpleasant colour is likely to induce public misgiving. Ammoniacal nitrogen
(NH
3-N) and colour substances exist in the reclaimed water and can severely affect the
reclaimed water quality in different ways. Chlorine is commonly used for reclaimed water disinfection
and requires precise dosing to satisfy endorsed quality standards. However, NH
3-N
consumes chlorine and affects the chlorine dosing. Colour substances do not consume chlorine,
but it requires additional efforts and strategies to remove them from the reclaimed water.
Therefore, the on-line monitoring of NH
3-N concentrations and colour levels are usually practised
in reclaimed water facilities to assist in the removal of both substances. However, the
conventional on-line analyzers are wet-chemistry-based, and the measurement takes time. The
limitation creates a potential issue: there may not be sufficient time for the downstream chlorine
dosing system to respond to sudden surges in colour and NH
3-N levels. To tackle this
challenge, this thesis work developed time-series models based on machine learning to forecast
the NH
3-N concentrations and colour levels in the reclaimed water three hours into the
future. For the training dataset, the NH
3-N and colour data were collected by an on-line analyzer
and a customized auto-sampling spectrophotometer, respectively. Both are installed in a reclaimed water treatment facility in Hong Kong. Baseline models for forecasting NH
3-N concentrations
and colour levels were first developed with five machine learning algorithms. Long
Short-Term Memory (LSTM) was found to be the most effective algorithm, with the lowest
MSE values of 0.0405 and 0.0148 for NH
3-N and colour forecasting models, respectively. In
the training processes, novel data pre-processing methods and feature engineering techniques
were implemented to enhance forecasting model performance. The data pre-processing methods
were proved to enhance the quality of training datasets and improve the performance of
NH
3-N and colour forecasting models by reducing the MSE values by 4.2% and 8.1%. The
feature engineering results supported that the daily fluctuations in NH
3-N and colour have correlations
with the urban water consumption patterns. This finding further enhanced the NH
3-N
and colour forecasting model performance by reducing MSE by 8.9% and 28.6% compared to
baseline models. The established models can be used to assist the disinfection control strategies
based on the model predictions using traditional process control systems. This research offers
novel methods and feature engineering techniques for NH
3-N concentrations and colour levels
forecasting in reclaimed water for treatment optimization.
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